Journal of Optimization in Industrial EngineeringJournal of Optimization in Industrial Engineering
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Feed provided by Journal of Optimization in Industrial Engineering. Click to visit.An Integrated Approach of Fuzzy Quality Function Deployment and Fuzzy Multi-Objective ...
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The emergence of sustainability paradigm has influenced many research disciplines including supply chain management. It has drawn the attention of manufacturing companies’ CEOs to incorporate sustainability in their supply chain and manufacturing activities. Supplier selection problem, as one of the main problems in supply chain activities, is also combined with sustainable development where traditional procedures are now transformed to sustainable initiatives. Moreover, allocating optimal order quantities to sustainable suppliers has also attracted attention of many scholars and industrial practitioners, which has not been comprehensively addressed. Therefore, a practical model of supplier selection and order allocation based on the sustainability Triple Bottom Line (TBL) approach is presented in this research article. The proposed approach utilizes Fuzzy Analytical Hierarchy Process combined with Quality Function Deployment (FAHP-QFD) for reflecting buyer’s sustainability requirements into the preference weights that are then exerted by an efficient Fuzzy Assessment Method (FAM) to assess the suppliers to obtain their sustainability scores. Thereupon, these scores are utilized in a fuzzy multi-objective mix-integer non-linear programming model (MINLP) for allocating orders to suppliers based on the manufacturer’s sustainability preference. A real-world application of food industry is presented to show the practicality of the proposed approach.Wed, 28 Feb 2018 20:30:00 +0100Service Performance Improvement Model: The Case of Teklehaymanot General Hospital
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In service sector, there are challenges in keeping an optimum balance between customers' demand and availability of resources. This problem is going to be more intense in the health sector due to the fact that both arrival and service times are random. Therefore, designing the service environment by keeping the optimum balance between customers’ demand and available resources is becoming a series problem in Teklehaymanot General Hospital. This paper tries to develop a model that investigates the performances of Teklehaymanot General Hospital and determines the optimum number of specialist doctors based on their respective workload. To address this objective, the study develops a model using Arena Simulation Software that considers the real working environment and scenario of Teklehaymanot General Hospital. For the purpose of this research, three years’ secondary data that include the type of services and number of specialized doctors under each service channel are collected from the hospital records and fitted to the model. The findings of the study show that there are unbalanced distributions on the daily workload among specialist doctors and extended long waiting time of patients in Teklehaymanot General Hospital. It reveals that specialist doctors who are working in pre-breast center, Hematology oncology imaging, neurology, obstetrics & gynecology, ophthalmology, pulmonology, urgent care, urology and women’s imaging are relatively overloaded, whereas those who are working in ENT Allergy Audiology, gastroenterology, Nuclear Medicine, orthopedics, physical therapy, and surgery are relatively underloaded. Moreover, from the scenario analysis, the result shows thatadditional specialized doctors in the fifteen areas are required so as to reduce the waiting time of patients by 54.41%. Therefore, the hospital is recommended to have a balanced workload distribution among specialist doctors and increase the number of specialist doctors by one or two in the fifteen service areas. Wed, 28 Feb 2018 20:30:00 +0100Design of a Hybrid Genetic Algorithm for Parallel Machines Scheduling to Minimize Job Tardiness ...
http://www.qjie.ir/article_272_114444.html
This paper studies the parallel machine scheduling problem subject to machine and job deterioration in a batched delivery system. By the machine deterioration effect, we mean that each machine deteriorates over time, at a different rate. Moreover, job processing times are increasing functions of their starting times and follow a simple linear deterioration. The objective functions are minimizing total tardiness, delivery, holding and machine deteriorating costs. The problem of total tardiness on identical parallel machines is NP-hard, thus the under investigation problem, which is more complicated, is NP-hard too. In this study, a mixed-integer programming (MILP) model is presented and an efficient hybrid genetic algorithm (HGA) is proposed to solve the concerned problem. A new crossover and mutation operator and a heuristic algorithm have also been proposed depending on the type of problem. In order to evaluate the performance of the proposed model and solution procedure, a set of small to large test problems are generated and results are discussed. The related results show the effectiveness of the proposed model and GA for test problems.Wed, 28 Feb 2018 20:30:00 +0100Location-Allocation and Scheduling of Inbound and Outbound Trucks in Multiple Cross-Dockings ...
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This paper studies multiple cross-dockings where the loads are transferred from origins (suppliers) to destinations (customers) through cross-docking facilities. Products are no longer stored in intermediate depots and incoming shipments are consolidated based on customer demands and immediately delivered to them to their destinations. In this paper, each cross-docking has a covering radius that customers can be served by at least one cross-docking provided. In addition, this paper considers the breakdown of trucks. We present a two-stage model for the location of cross-docking centers and scheduling inbound and outbound trucks in multiple cross-dockings.We work on minimizing the transportation cost in a network by loading trucks in the supplier locations and route them to the customers via cross-docking facilities. The objective, in the first stage, is to minimize transportation cost of delivering products from suppliers to open cross-docks and cross-docks to the customers; in the second-stage, the objective is to minimize the makespans of open cross-dockings and the total weighted summation of completion time. Due to the difficulty of obtaining the optimum solution tomedium- and large-scale problems, we â€Śpropose four types of metaheuristic algorithms, i.e., genetic, simulated annealing, differential evolution, and hybrid algorithms.The result showed that simulated annealing is the best algorithm between the four algorithms.Wed, 28 Feb 2018 20:30:00 +0100Effects of Probability Function on the Performance of Stochastic Programming
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Stochastic programming is a valuable optimization tool where used when some or all of the design parameters of an optimization problem are defined by stochastic variables rather than by deterministic quantities. Depending on the nature of equations involved in the problem, a stochastic optimization problem is called a stochastic linear or nonlinear programming problem. In this paper,a stochastic optimization problem is transformed intoan equivalent deterministic problem,which can be solved byany known classical methods (interior penalty method is applied here).The paper mainly focuseson investigatingthe effect of applying various probability functions distributions(normal, gamma, and exponential) for design variables. The following basic required equations to solve nonlinear stochastic problems with various probability functionsfor random variables are derived and sensitivity analyses to studythe effects of distribution function typesand input parameterson the optimum solution are presented as graphs and in tables by studyingtwoconsidered test problems. It is concluded that thedifference between probabilistic and deterministic solutions toa problem, when the normal distribution ofrandom variables isused, is very different fromthe results when gamma and exponential distribution functions are used. Finally, it is shownthat the rate of solution convergence tothe normal distribution is faster than the other distributions.Wed, 28 Feb 2018 20:30:00 +0100A Bi-Objective Airport Gate Scheduling with Controllable Processing Times Using Harmony Search ...
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Optimizing gate scheduling at airports is an old, but also a broad problem. The main purpose of this problem is to find an assignment for the flights arriving at and departing from an airport, while satisfying a set of constraints.A closer look at the literature in this research line shows thatin almost all studies airport gate processing time has been considered as a fix parameter. In this research, however, we investigate a more realistic situation in which airport gate processing time is a controllable. It is also assumed that the possible compression/expansion processing time of a flight can be continuously controlled, i.e. it can be any number in a given interval.Doing sohas some positive effectswhich lead to increasing the total performance at airports’ terminals. Depending on the situation, different objectives become important.. Therefore, a model which simultaneously (1) minimize the total cost of tardiness, earliness, delay andthe compression as well as the expansion costs of job processing time, and (2) minimize passengers overcrowding on gate is presented. In this study, we first propose a mixed-integer programming model for the formulated problem. Due to complexity of problem, two multi-objective meta-heuristic algorithms, i.e. multi-objective harmony search algorithm (MOHSA) and non-dominated sorting genetic algorithm II (NSGA-II) are applied in order to generate Pareto solutions. For calibrating the parameter of the algorithms, Taguchi method is used and three optimal levels of the algorithm’s performance are selected. The algorithms are tested with real-life data from Mehrabad International Airport for nine medium size test problems. The experimental results show that NSGA-II has better convergence near the true Pareto-optimal front as compared to MOHSA; however, MOHSA finds a better spread in the entire Pareto-optimal region.Finally, it is possible to apply some practical constraints into the model and also test them with even large real-life problems instances.Wed, 28 Feb 2018 20:30:00 +0100Presentation and Solving Non-Linear Quad-Level Programming Problem Utilizing a Heuristic ...
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The multi-level programming problems are attractive for many researchers because of their application in several areas such as economic, traffic, finance, management, transportation, information technology, engineering and so on. It has been proven that even the general bi-level programming problem is an NP-hard problem, so the multi-level problems are practical and complicated problems therefore solving these problems would be significant. The literature shows several algorithms to solve different forms of the bi-level programming problems (BLPP).Not only there is no any algorithm for solving quad-level programming problem, but also it has not been studied by any researcher. The most important part of this paper is presentation and studying of a new model of non-linear multi-level problems.Then we attempt to develop an effective approach based on Taylor theorem for solving the non-linear quad-level programming problem. In this approach, by using aproposedsmoothing method the quad-level programming problem is converted to a linear single problem. Finally, the single level problem is solved using the algorithm based on Taylor algorithm. The presented approach achieves an efficient and feasible solution in an appropriate time which has been evaluated by solving test problems. Wed, 28 Feb 2018 20:30:00 +0100A Stochastic Optimization Approach to a Location-Allocation Problem of Organ Transplant Centers
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Decision-making concerning thelocation of critical resource on the geographical network is important in many industries.In the healthcare system,these decisions include location of emergency and preventive care. The decisions of location play a crucial role due to determining the travel time between supply and de//////mand points and response time in emergencies.Organs are considered as highly perishable products,whosevarietyof each product has a specific perish time. Despite the importance of this field,only a small proportion of healthcare sector is dedicated to this field. Matching and finding the best recipient for a donated organ is one of the major problems in this field, which is also crucial for the overall organ transplantation process.Balancing the demand and supply in a transplant organ supply chain in order to decrease the waiting list needs certain scheduling and management.The main contribution of this paper consists of considering recipient regionsas another component of the supply chain;in addition,importance of transportation time and waiting lists hasled us to consider a bi-objective model. In addition, uncertainty of input data has led us to consider a stochastic approach.Wed, 28 Feb 2018 20:30:00 +0100Optimizing a Fuzzy Green p-hub Centre Problem Using Opposition Biogeography Based Optimization
http://www.qjie.ir/article_535413_114444.html
Hub networks have always been acriticalissue in locating health facilities. Recently, a study has been investigated by Cocking et al. (2006)in Nouna health district in Burkina Faso, Africa, with a population of approximately 275,000 people living in 290 villages served by 23 health facilities. The travel times of the population to health services become extremely high during the rainy season, since many roads are unusable. In this regard, for many people, travelling to a health facility is a deterrent to seeking proper medical care. Furthermore, in real applications of hub networks, the travel times may vary due to traffic, climate conditions, and land or road type.To handle this challenge this paper considers the travel times are assumed to be characterized by trapezoidal fuzzy variables in order to present a fuzzy green capacitated single allocation p-hub center system (FGCSApHCP) with uncertain information. The proposed FGCSApHCP is redefined into its equivalent parametric integer nonlinear programming problem using credibility constraints. The aim is to determine the location of pcapacitated hubs and the allocation of center nodes to them in order to minimize the maximum travel time in a hub-and-center network in such uncertain environment. As the FGCSApHCP is NP-hard, a novel algorithmcalledoppositionbiogeography based optimizationis developed to solve that. This algorithm utilizes a binary oppositionbased learning mechanism to generate a diversity mechanism. At the end, both the applicability of the proposed approach and the solution methodologies are demonstrated using GAMS/BARON Software under severalkind of problems. Sensitivity analyses on the number of hubs and center nodes are conducted toprovide more insights as well. Wed, 28 Feb 2018 20:30:00 +0100Fuzzy Mathematical Model For A Lot-Sizing Problem In Closed-Loop Supply Chain
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The aim of lot sizing problems is to determine the periods where production takes place and the quantities to be produced in order to satisfy the customer demand while minimizing the total cost. Due to its importance on the efficiency of the production and inventory systems, Lot sizing problems are one of the most challenging production planning problems and have been studied for many years with different modeling features. In this paper, we propose a fuzzy mathematical model for the single-item capacitated lot-sizing problem in closed-loop supply chain. The possibility approach is chosen to convert the fuzzy mathematical model to crisp mathematical model. The obtained crisp model is in the form of mixed integer linear programming (MILP), which can be solved by existing solver in crisp environment to find optimal solution. Due to the complexity of the problems harmony search (HS) algorithm and genetic algorithm (GA) have been used to solve the model for fifteen problem. To verify the performance of the algorithm, we computationally compared the results obtained by the algorithms with the results of the branch-and-bound method. Additionally, Taguchi method was used to calibrate the parameters of the meta-heuristic algorithms. The computational results show that, the objective values obtained by HS are better from GA results for large dimensions test problems, also CPU time obtained by HS are better than GA for Large dimensions.Wed, 28 Feb 2018 20:30:00 +0100Hub Covering Location Problem Considering Queuing and Capacity Constraints
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In this paper, a hub covering location problem is considered. Hubs, which are the most congested part of a network, are modeled as M/M/C queuing system and located in placeswhere the entrance flows are more than a predetermined value.A fuzzy constraint is considered in order to limit the transportation time between all origin-destination pairs in the network.On modeling, a nonlinear mathematical program is presented.Then, the nonlinear constraints are convertedto linear ones.Due to the computational complexity of the problem,genetic algorithm (GA),particle swarm optimization (PSO)based heuristics, and improved hybrid PSO are developedto solve the problem. Since the performance of the given heuristics is affected by the corresponding parameters of each, Taguchi method is appliedin order to tune the parameters. Finally,the efficiency ofthe proposed heuristicsis studied while designing a number of test problems with different sizes.The computational results indicated the greater efficiency of the heuristic GA compared to the other methods for solving the problemWed, 28 Feb 2018 20:30:00 +0100A Multi-Objective Mixed-Model Assembly Line Sequencing Problem With Stochastic Operation Time
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In today’s competitive market, those producers who can quickly adapt themselves todiverse demands of customers are successful. Therefore, in order to satisfy these demands of market, Mixed-model assembly line (MMAL) has an increasing growth in industry. A mixed-model assembly line (MMAL) is a type of production line in which varieties of products with common base characteristics are assembled on. This paper focuses on this type of production line in a stochastic environment with three objective functions: 1) total utility work cost, 2) total idle cost, and 3) total production rate variation cost that are simultaneously considered. In real life, especially in manual assembly lines, because of some inevitable human mistakes, breakdown of machines, lack of motivation in workers and the things alike, events are notdeterministic, sowe consideroperation time as a stochastic variable independently distributed with normal distributions; for dealing with it, chance constraint optimization is used to model the problem. At first, because of NP-hard nature of the problem, multi-objective harmony search (MOHS) algorithm is proposed to solve it. Then, for evaluating the performance of the proposed algorithm, it is compared with NSGA-II that is a powerful and famous algorithm in this area. At last, numerical examples for comparing these two algorithms with some comparing metrics are presented. The results have shown that MOHS algorithm has a good performance in our proposed model.Wed, 28 Feb 2018 20:30:00 +0100Modeling and Solution Procedure for a Preemptive Multi-Objective Multi-Mode Project Scheduling ...
http://www.qjie.ir/article_535423_114444.html
In this paper, a preemptive multi-objective multi-mode project scheduling model for resource investment problem is proposed. The first objective function is to minimize the completion time of project (makespan);the second objective function is to minimize the cost of using renewable resources. Non-renewable resources are also considered as parameters in this model. The preemption of activities is allowed at any integer time units, and for each activity, the best execution mode is selected according to the duration and resource. Since this bi-objective problem is the extension of the resource-constrained project scheduling problem (RCPSP), it is NP-hard problem, and therefore, heuristic and metaheuristic methods are required to solve it. In this study, Non-dominated Sorting Genetic AlgorithmII (NSGA-II) and Non-dominated Ranking Genetic Algorithm (NRGA) are used based on results of Pareto solution set.We also present a heuristic method for two approaches of serial schedule generation scheme (S-SGS) and parallel schedule generation scheme (P-SGS) in the developed algorithm in order to optimize the scheduling of the activities.The input parameters of the algorithm are tuned with Response Surface Methodology (RSM). Finally, the algorithms are implemented on some numerical test problems, and their effectiveness is evaluated. Wed, 28 Feb 2018 20:30:00 +0100Scheduling of Multiple Autonomous Guided Vehicles for an Assembly Line Using Minimum Cost ...
http://www.qjie.ir/article_537064_114444.html
This paper proposed a parallel automated assembly line system to produce multiple products having multiple autonomous guided vehicles (AGVs). Several assembly lines are configured to produce multiple products in which the technologies of machines are shared among the assembly lines when required. The transportation between the stations in an assembly line (intra assembly line) and among stations in different assembly lines (inter assembly line) are performed using AGVs. Scheduling of AGVs to service the assembly lines and the corresponding stations are purposed. In the proposed problem the assignment of multiple AGVs to different assembly lines and the stations are performed using minimum-cost network flow (MCF). It optimizes weighted completion time of tasks for each short-term window by formulating the task and resource assignment problem as MCF problem during each short-term scheduling window. Wed, 28 Feb 2018 20:30:00 +0100A new version of earned value analysis for mega projects under interval-valued fuzzy environment
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The earned value technique is a crucial and important technique in analysis and control the performance and progress of mega projects by integrating three elements of them, i.e., time, cost and scope. This paper proposes a new version of earned value analysis (EVA) to handle uncertainty in mega projects under interval-valued fuzzy (IVF)-environment. Considering that uncertainty is very common in mega projectsâ€™ activities, the proposed IVF-EVA model is very useful and applicable in evaluating the progress of projects. In this paper, analyzing earned value indices and calculating them with linguistic terms have been discussed. They are then converted into interval-valued fuzzy numbers (IVFNs) for the evaluations. Finally, an application example from the recent literature is presented and steps of the proposed IVF-EVA are elaborated.The earned value technique is a crucial and important technique in analysis and control the performance and progress of mega projects by integrating three elements of them, i.e., time, cost and scope. This paper proposes a new version of earned value analysis (EVA) to handle uncertainty in mega projects under interval-valued fuzzy (IVF)-environment. Considering that uncertainty is very common in mega projectsâ€™ activities, the proposed IVF-EVA model is very useful and applicable in evaluating the progress of projects. In this paper, analyzing earned value indices and calculating them with linguistic terms have been discussed. They are then converted into interval-valued fuzzy numbers (IVFNs) for the evaluations. Finally, an application example from the recent literature is presented and steps of the proposed IVF-EVA are elaborated.Sun, 25 Dec 2016 20:30:00 +0100A Ratio-Based Efficiency Measurement for Ranking Multi-Stage Production Systems in DEA
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Conventional data envelopment analysis (DEA) models are used to measure efficiency score of production systems when they are considered as black boxes and their internal relationship is ignored. This paper deals with a common special case of network systems which is called multi-stage production system and can be generalized to many organizations. A multi stage production system has some stages in which the outputs of each stage are used as the inputs of the next stage to produce the final outputs of the system. Most of the approaches handling multi-stage systems in DEA evaluate efficiency measure of a production system considering the interrelationship between its stages; however, they do not present their ranking or impact of each stage in ranking of a special multi-stage system through comparison with the others. In this paper, considering the series internal structure of the multi-stage systems and their efficiency measure, we propose some new ratio-based DEA models to determine the best and the worst rank of the multi-stage systems over all sets of feasible weights. In order to improve the performance of the whole system, the proposed models are used to recognize the stages with the most important role in the system’s inefficiency. Some numerical examples are presented to illustrate the approach.Wed, 28 Feb 2018 20:30:00 +0100Hierarchical group compromise ranking methodology based on Euclideanâ€“Hausdorff distance ...
http://www.qjie.ir/article_270_0.html
Proposinga hierarchical group compromise method can be regarded as one of major multi-attributedecision-making tools that can be introduced to rankthe possible alternatives among conflict criteria. In complex and hesitantsituations, crisp data are inadequate to model thedecision-making problems. In this respect, decision makers’ (DMs) judgmentsare considered as imprecise or fuzzy. In the group decision making,an aggregation of the DMs’ judgments and fuzzy group compromise ranking is more capable and powerful than the classicalcompromise ranking. In this respect, the main purpose of this paper is to extend a new hierarchical group compromise rankingmethodology under a hesitant fuzzy environment to handle uncertainty. In the hesitant fuzzy environment for the margin of error, the DMs could assign the opinions toseveral membership degrees for an element. For this reason, the hesitant fuzzy set (HFS) is a very useful tool to deal with the hesitant/vague situations. The HFSis taken into accountforthe process of the proposed hierarchical groupcompromise rankingmethodology, namely HFHG-CR, and for avoiding the data loss,the DMs’ opinions with risk preferences are considered for each step separately. Hence, the DMs’ judgments are aggregated at the end of the proposed methodology. Also, the Euclidean–Hausdorff distance measure is utilizedin a new proposed index for calculating the average group score, worst group score and compromise measureregarding each DM. In addition, a new ranking index is introduced to obtain the final compromise solutiontothe evaluation.The proposed HFHG-CR methodologyis applied to a practical example for afacility location selection problem, i.e.,cross-dock location problem,to show the validation and application. Finally, the ranking of the proposed methodology is compared with the recent method from the literature.Sun, 25 Dec 2016 20:30:00 +0100Centralized Supply Chain network Ddesign: Monopoly, Duopoly and Oligopoly Competitions under ...
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This paper presents a competitive supply chain network design problem in which one, two, or three supply chains are planning to enter the price-dependent markets simultaneously in uncertain environments and decide to set the prices and shape their networks. The chains produce competitive products either identical or highly substitutable. Fuzzy multi-level mixed integer programming is used to model the competition modes, and then the models are converted into an integrated bi-level one to be solved, in which the inner part sets the prices in dynamic competition and the outer part shapes the network cooperatively.Finally, a real-world problem is investigatedto illustrate how the bi-level model works and discuss how price, market share, total income, and supply chain network behave with respect to key marketing activities such as advertising, promotions, and brand loyalty.Wed, 31 Jan 2018 20:30:00 +0100Hybrid Techniques of Multi-Criteria Decision-Making for Location of Automated Teller Machines ...
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Location is an important factor in the activity of economic enterprises. Owing to the importance, location-based sciencesought/seeks to provide the methods in order to determine and select the optimal location in the activities of enterprises. Enterprises seek to use scientific methods to maximize the services and efficiency and minimize the costs. Suitable location plays an important role in many fields such as reducing the costs and increasing the customer satisfaction. Location studies have been proposed in recent years as one of the key elements in the success and survival of industrial centers as considered at many national and international levels. This study, as an applied research, provides a new framework in location of ATMs using multi-criteria decision-making approach and fuzzy AHP and fuzzy ELECTRE III. The multi-criteria decision-making approaches were based on similar studies in other countries and viewpoints of experts and managers of Shahr Bank branches in Tehran, 1st District Municipality, and the establishment of favorable sites was identified by combining the information in order to influence the location of ATMs including competitors (0.202), price of land (0.199), access to facilities and utilities, poles and important centers of town (0.189), quality of track (0.180), security (0.120), transport and traffic (0.112), population under coverage (0.065), and regulation (0.039). At the end, the most appropriate locations of establishment of ATMs were determined to cover the demands of Tehran, 1st District Municipality using fuzzy AHP methods and ELECTRE III.Wed, 31 Jan 2018 20:30:00 +0100Optimization of Multi-period Three-echelon CitrusSupply Chain Problem
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In this paper, a new multi-objective integer non-linear programming model is developed for designing citrus three-echelon supply chain network. Short harvest period, product specifications, high perished rate, and special storing and distributing conditions make the modeling of citrus supply chain more complicated than other ones. The proposed model aims to minimize network costs including waste cost, transportation cost, and inventory holding cost, and to maximize network’s profits. To solve the model, firstly the model is converted to a linear programming model.Then three multi-objective meta-heuristic algorithms are used including MOPSO, MOICA, and NSGA-II for finding efficient solutions. The strengths and weaknesses of MOPSO, MOICA, and NSGA-II for solving the proposed model are discussed. The results of the algorithms have been compared by several criteria consisting of number of Pareto solution, maximum spread, mean ideal distance, and diversification metric.Computational results show that MOPSO algorithm finds competitive solutions in compare with NSGA-II and MOICA.Wed, 31 Jan 2018 20:30:00 +0100Solving the Fixed Charge Transportation Problem by New Heuristic Approach
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The fixed charge transportation problem (FCTP) is a deployment of the classical transportation problem in which a fixed cost is incurred, independent of the amount transported, along with a variable cost that is proportional to the amount shipped. Since the problem is considered as an NP-hard, the computational time grows exponentially as the size of the problem increases. In this paper, we propose a new heuristic along with well-known metaheuristic like Geneticalgorithm (GA), simulated annealing (SA) and recently developed one, Keshtel algorithm (KA) to solve the FCTP. Contrary to previous works, we develop a simple and strong heuristic according to the nature of the problem and compare the result with metaheuristics. In addition, since the researchers recently used the priority-based representation to encode the transportation graphs and achieved very good results, we consider this representation in metaheuristics and compare the results with the proposed heuristic. Furthermore, we apply the Taguchi experimental design method to set the proper values of algorithms in order to improve their performances. Finally, computational results of heuristic and metaheuristics with different encoding approaches, both in terms of the solution quality and computation time, are studied in different problem sizes.Wed, 31 Jan 2018 20:30:00 +0100Two-echelon Supply Chain Considering Multiple Retailers with Price and Promotional Effort ...
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This study deals with the effects of a supply chain (SC) with single product, multiple retailers and a manufacturer, where the manufacturer(he) produces lotsize of the product that contains a random portion of imperfect quality item. The imperfect quality products are sold in a secondary shop. The new contribution of this paper is a new non-linear demand function. Demand of the end customers varies with pricing and promotional effort of the rivalry amongst the retailers which can be used for the electronic goods, new lunched products, etc. We investigate the behavior of the supply chain under Manufacturer-Stackelberg(MS), and Retailer-Stackelberg(RS) model structures. The nature of the mentioned models provides great insights to a firm’s manager for achieving optimal strategy in a competitive marketing system. Within the framework of any bilevel decision problem, a leader's decision is influenced by the reaction of his followers. In MS model structure, following the method of replacing the lower level problem with its Kuhn-Tucker optimality condition, we transform the nonlinear bilevel programming problem into a nonlinear programming problem with the complementary slackness constraint condition. The objective of this paper is to determine the optimal selling price and promotional effort of each retailer, while the optimal wholesale price of the perfect quality products are determined by the manufacturer so that the above strategies are maximized. Finally, numerical examples with sensitivity analysis of the key parameters are illustrated to investigate the proposed model.Wed, 31 Jan 2018 20:30:00 +0100Participative Biogeography-Based Optimization
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Biogeography-Based Optimization (BBO) has recently gained interest of researchers due to its simplicity in implementation, efficiency and existence of very few parameters. The BBO algorithm is a new type of optimization technique based on biogeography concept. This population-based algorithm uses the idea of the migration strategy of animals or other species for solving optimization problems. the original BBO sometimes has not resulted in desirable outcomes. Migration, mutation and elitism are three Principal operators in BBO. The migration operator plays an important role in sharing information among candidate habitats. This paper proposes a novel migration operator in Original BBO. The proposed BBO is named as PBBO and new migration operator is examined over 12 test problems. Also, results are compared with original BBO and others Meta-heuristic algorithms. Results show that PBBO outperforms over basic BBO and other considered variants of BBO.Wed, 31 Jan 2018 20:30:00 +0100Integrated due date setting and scheduling on a single machine considering an unexpected ...
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In this paper, an integrated machine scheduling withits due date setting problem has been considered. It is assumed that the machine is subject to some kind of random unavailability. Due dates should be set in an attractive and reliable manner, implying that they should be short and possible to be met. To this end, first, long due dates are penalized in the objective function. Then, for each customer order, the probability of meeting his/her promised due dateis forced to be at least as large as his/her required service level. To handle this integrated problem, first, the optimal due date formulafor any arbitrary sequence is derived. By using this formula, the mathematical programming formulation of the problem,including a nonlinear non-convex expression, is developed. By defining a piecewise linear under-estimator, the solutions of the resultantmixed integer linear programming formulation have become the lower bounds of the problem. Dynasearch is a very efficient heuristic utilizing the dynamic programming approach to search exponential neighborhoods in the polynomial time. Aniterated dynasearch heuristic is developed for the sequencing part of the problem. Each generated sequence is evaluated by computing its optimal due datesusing the above-mentioned formula. Numerical results confirmed the high quality of the solutions found by this algorithm, as compared with the lower bound.Wed, 31 Jan 2018 20:30:00 +0100Parallel Jobs Scheduling with a Specific Due Date: Asemi-definite Relaxation-based Algorithm
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This paper considers a different version of the parallel machines scheduling problem in which the parallel jobs simultaneously requirea pre-specifiedjob-dependent number of machines when being processed.This relaxation departs from one of the classic scheduling assumptions. While the analytical conditions can be easily statedfor some simple models, a graph model approach is required when conflicts of processor usage are present. The main decisions and solving steps are as follows, respectively. (i) Converting the scheduling problem to graph model; (ii) Dividing jobs into independent sets: in this phase, we propose a semi-definite relaxation algorithm in which we use graph coloring concept; (iii) Sequencing the independent sets as a single-machine scheduling in which jobs in such a system arejob sets formed by using a semi-definite relaxation solution and determining the problem as a schedule that minimizes the sum of the tardiness of jobs. In this regard, after grouping the jobs by a semi-definite programming relaxation algorithm, we used the rounding algorithm for graph coloring. We also proposed a variable neighborhood search algorithm for sequencing the obtained job sets in order to minimize the sum of the tardiness. Experimental results show that this methodology is interesting by obtaining good results.Wed, 31 Jan 2018 20:30:00 +0100The Optimal Number of Hospital Beds Under Uncertainty: A Costs Management Approach
http://www.qjie.ir/article_538169_0.html
Equipping hospital beds uses a great deal of a hospital's resources. Therefore, it is essential to consider the hospital beds' efficiency. To increase its efficiency, a fuzzy unrestricted model for managing hospital expenses is presented in this paper. The lack of beds in hospitals leads to patients’ admission loss and consecutively profit loss. On the other hand, increasing the bed count leads to an increase in equipment expenses. Therefore, in order to determine optimal bed capacity, it is of utmost importance to consider these two costs simultaneously. In our paper, hospital admission system is modeled with a multi-server queuing system (M/M/K). Therefore, to calculate the total cost function, limiting probabilities of multi-server queueing model is used. Furthermore, due to uncertain nature of parameters, such as interest rate and hospitalization profit in various future time periods, these uncertainties are covered by fuzzy logic. Finally, to determine the optimal bed count, Lee and Li's fuzzy ranking method is used. This model is implemented ona case study. Its goal is to determine the optimal bed count for emergency unit of Razi hospital in Torbat Heydarieh. Considering the high capability of Markovian chains in modeling different circumstances and the various queueing models, the proposed model can be extended for various hospital units.Sun, 04 Feb 2018 20:30:00 +0100EMCSO: An Elitist Multi-Objective Cat Swarm Optimization
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This paper introduces a novel multi-objective evolutionary algorithm based on cat swarm optimizationalgorithm (EMCSO) and its application to solve a multi-objective knapsack problem. The multi-objective optimizers try to find the closest solutions to true Pareto front (POF) where it will be achieved by finding the less-crowded non-dominated solutions. The proposed method applies cat swarm optimization (CSO), a swarm-based algorithm with ability of exploration and exploitation, to produce offspring solutions and uses thenon-dominated sorting method to findthe solutionsas close as to POFand crowding distance technique toobtain a uniform distribution among thenon-dominated solutions. Also, the algorithm is allowedto keep the elites of population in reproduction processand use an opposition-based learning method for population initialization to enhance the convergence speed.The proposed algorithm is tested on standard test functions (zitzler’ functions: ZDT) and its performance is compared with traditional algorithms and is analyzed based onperformance measures of generational distance (GD), inverted GD, spread,and spacing. The simulation results indicate that the proposed method gets the quite satisfactory results in comparison with other optimization algorithms for functions of ZDT1 and ZDT2. Moreover, the proposed algorithm is applied to solve multi-objective knapsack problem.Sun, 04 Feb 2018 20:30:00 +0100The Preemptive Just-in-time Scheduling Problem in a Flow Shop Scheduling System
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Flow shop scheduling problem has a wide application in the manufacturing and has attracted much attention in academic fields. From other point, on time delivery of products and services is a major necessity of companies’ todays; early and tardy delivery times will result additional cost such as holding or penalty costs. In this paper, just-in-time (JIT) flow shop scheduling problem with preemption and machine idle time assumptions is considered in which objective function is minimizing the sum of weighted earliness and tardiness. A new non-linear mathematical model is formulated for this problem and due to high complexity of the problem meta-heuristic approaches have been applied to solve the problem for finding optimal solution. The parameters of algorithms are set by Taguchi method. Each parameter is tested in three levels. By implementation of many problems with different sizes these levels are determined .The proposed model is solved by three meta-heuristic algorithms: genetic algorithm (GA), imperialist competitive algorithm (ICA) and hybrid of GA and ICA. To evaluate the performance of the proposed algorithms many test problems have been designed. The Computational results indicate the superiority of the performance of hybrid approach than GA and ICA in finding thebest solution in reasonable computational time.Sun, 04 Feb 2018 20:30:00 +0100A Combined Fuzzy Logic and Analytical Hierarchy Process Method for Optimal Selection and ...
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One of the main challenges for transportation engineers is the consideration of pedestrian safety as the most vulnerable aspect of the transport system. In many countries around the world, a large number of accidents recorded by the police are composed of accidents involving pedestrians and vehicles, for example when pedestrians may be struck by passing vehicles when crossing the street. Careful consideration of the parameters that are involved in selecting the type and optimum location of pedestrian crosswalks results in a higher pedestrian safety coefficient and a reduced accident rate at these facilities. At the start of this study, these parameters that are important in specifying the optimum type and location of pedestrian crosswalks were determined. Then the data layers of these identified parameters were defined using the ArcGIS software. These layers can subsequently be used for determination of the optimal positioning of pedestrian crosswalks. To specify the boundary changes for each parameter, fuzzy membership functions were defined for each parameter using fuzzy logic. The Analytical Hierarchy Process method (AHP) was used in order to combine these layers of information after the fuzzy membership functions were defined. Expert Choice software was used to determine the final weight resultant of the professionals' poll that was conducted. A field study sample has been carried out to determine the optimal location of pedestrian crosswalks in the city of Tehran. The final output from the ArcGIS software shows the ideal locations and the appropriate type of pedestrian crosswalks in the field study sample. The results indicate that the use of fuzzy logic in definition of membership functions of location parameters, along with using AHP for determination of the weight of data layers built in ArcGIS, is a satisfactory combined method for specifying the location of pedestrian crosswalks.Sun, 04 Feb 2018 20:30:00 +0100Presenting a joint replenishment-location model under all-units quantity discount and solving ...
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In this paper a model is proposed for distribution centers location and joint replenishment of a distribution system that is responsible for orders and product delivery to distribution centers. This distribution centers are under limitedwarehouse space and this can determine amount of requirement product by considering proposed discount.The proposed model is develop to minimize total costs consists of location, ordering, purchaseunder All-units quantity discount condition and items maintenance by adjustment Frequency of replenishment in each distribution center. To solve this model, first we solve the model with genetic algorithm by confining the time between too replenishments then by use of the Quantity Discount RAND algorithm method the upper and lower limits of the time between two replenishments will be determined. After obtaining the optimal upper and lower limits, the model will be resolved by harmony search and genetic algorithms. The results show that the presented chromosome structure is so efficient so that the statistical experiments result indicates there isn’t much difference between solution means after finding the optimal upper and lower limits. We used response surface methodology for tune proposed algorithms parameters. Efficiency of proposed algorithms is examined by diverse examples in different dimensions. Results of these experiments are compared by using of ANOVA and TOPSIS with indexes of objective function value and algorithms runtime. In both comparisons harmony search algorithm has more efficiency than genetic algorithm.Sun, 04 Feb 2018 20:30:00 +0100A Two-dimensional Warranty Model with Consideration of Customer and Manufacturer Objectives ...
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Warranty is a powerful implement for marketing strategy that is used by manufacturersand creates satisfaction for consumers by ensuring to compensate for incorrect operation of the product. Warranty serviceresults ina cost named warranty cost for a manufacturer.This cost is a function of warranty policy, regions, and product failures pattern. Since this service coversthe cost of uncertain failure of the product, it makes some utility for customers. In this paper, we developed a novel customer utility function that is used as a customer objective to be maximized. In addition to the manufacturer objective, minimizing the warranty costisconsidered simultaneously. There are four restrictions on warranty parameters such as time, usage, unit product price and the R&D expenditure to be considered. Finally, we will propose a novel bi-objective model that maximizesthe utility function for customers and minimizesthe warranty cost for the manufacturer. This model will be solved with an evolutionary algorithmcalled Non-Dominated Sorting Genetic Algorithm (NSGA-II) and non-dominated Pareto solutionswill be gained from this method.To give a numerical instance, for a certain usage rate’s range of costumers, different warranties are provided and compared. It is believed that the computational results can help manufacturers to determine optimal solutions for the objective functions and consequentlywarranty parameters.Mon, 05 Feb 2018 20:30:00 +0100An Efficient Hybrid Algorithm for Dynamic Facility Layout Problem using Simulation Technique and PSO
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One of the most important and effective issues of nowadays manufacturing companies, is how to arrange their facilities in the most economic manner. Different methods and approaches have been examined by researchers to address the so-called facility layout problem (FLP). As a combinatorial problem FLP is a NP-Hard one, and so classic and exact optimization methods just can be useful in small scale problems. Therefore, developing of meta-heuristic algorithms to solve and analysis of such problems is used by many researchers. In this research, a new hybrid heuristic method is developed by combination of discrete Particle Swarm Optimization (PSO) algorithm and simulation technique to address Dynamic Facility Layout Problem (DFLP) which is the main contribution of the current study. To show the efficiency of the proposed algorithm, several test problems which were taken from the literature have been examined and the results were compared to other algorithms. As the computational results show, the quality of solutions and the algorithm speed are suitable enough to be used in real world problems.Mon, 05 Feb 2018 20:30:00 +0100Investigating Knowledge Management Practices in a Successful Research and Development Organization
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In this paper after a review on the concept and literature of knowledge management, the conceptual model of a successful knowledge management system that is currently being applied in a research and development (R&D) aerospace organization is presented and discussed. The main contribution of the paper is presenting the model in its useful and practical status without becoming involved in theoretical discussions that have different shapes but similar meanings.Mon, 05 Feb 2018 20:30:00 +0100Studying and Identifying the Effective Factors on Tax Evasion by Fuzzy DEMATEL-Method
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The main goal of this research is to identify the effective factors on tax evasion by fuzzy DEMATEL-method in Iran. At the present time tax evasion is one of the economic problems in developing countries. Our country has had in this problem for several decades. In this paper, we attempted to determine effective factors in tax evasion, and the relational structure of these factors is examined by fuzzy DEMATEL-method, and meanwhile to recognize their interaction, the hierarchy of the influence of these factors should be known, too. research results showed that among the effective factors, lack of law-makers, of dominance interference institutions which are not charge and the vast exemptions have the highest impact on tax evasion.Mon, 05 Feb 2018 20:30:00 +0100Three Approaches to Time Series Forecasting of Petroleum Demand in OECD Countries
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Petroleum (crude oil) is one of the most important resources of energy and its demand and consumption is growing while it is a non-renewable energy resource. Hence forecasting of its demand is necessary to plan appropriate strategies for managing future requirements. In this paper, three types of time series methods including univariate Seasonal ARIMA, Winters forecasting and Transfer Function-noise (TF) models are used to forecast the petroleum demand in OECD countries. To do this, we use the demand data from January 2001 to September 2010 and hold out data from October 2009 to September 2010 to test the sufficiency of the forecasts. For the TF model, OECD petroleum demand is modeled as a function of their GDP. We compare the root mean square error (RMSE) of the fitted models and check what percentage of the testing data is covered by the confidence intervals (C.I.). Accordingly we conclude that Transfer Function model demonstrates a better forecasting performance.Mon, 05 Feb 2018 20:30:00 +0100Cost Analysis of a Multi-period Multi-echelon Supply Chain and Solving it by Simulated ...
http://www.qjie.ir/article_538338_0.html
In this paper, we consider a multi-echelon supply chain, including a number of suppliers, an assembler, a number of distributer and retailers, in order to obtain the corresponding cost function and to minimize it during a multi-period planning horizon. The total cost consists of purchasing cost of raw parts (materials) from suppliers by the assembler, cost of raw parts (materials) transportation from supplier to assembler, cost of assembling in the assemblerâ€™s site, cost of product transportation from assembler to distributors and transportation from distributors to retailers, and inventory system costs, including holding and shortage costs, in distribution centers. After modeling the problem, we propose a simulated annealing based heuristic in order to solve it. We then design a number of numerical small-size and big-size examples considering ten suppliers, one assembler, three distributors and eight retailers who are operating in a four-period planning horizon and solve them using the proposed algorithm.Tue, 13 Feb 2018 20:30:00 +0100An Efficient Hybrid Solution Algorithm for the Capacitated Facility Location-Allocation Problem ...
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In this study, we discuss the capacitated facility location-allocation problem under uncertainty where the uncertainty is characterized by given finite numbers of scenarios. In this model, the objective function minimizes the total expected costs of transportation and opening facilities, while relative regret in each scenario is restricted. To tackle the problem efficiently and effectively, an efficient hybrid solution algorithm based on several meta-heuristics and an exact algorithm is developed. This algorithm generates neighborhoods by combining the main concepts of variable neighborhood search, simulated annealing, and tabu search and finds the local optima by using an algorithm that uses an exact method in its framework. Finally, to test the algorithms’ performance, we apply numerical experiments on both randomly generated and standard test problems. Computational experiments show that our algorithm is more effective and efficient in term of CPU time and solutions quality in comparison with the CPLEX solver.Tue, 13 Feb 2018 20:30:00 +0100Constitution of A 4-Level Supply Chain By Minimization of Total Cost
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In this paper we describe the way a supply chain that intends to minimize the costs of the whole chain is formed. Total costs of the chain contains, buying raw material from suppliers, cost of assembling in the assembler place, cost of raw material transportation from supplier to assembler and cost of product transportation from assembler to distributor center and from distributor center to retailers and cost of product maintenance and product shortage cost in distributor centers. After modeling the noticed problem, a given numeric example of will be developed. This problem is developed with 10 suppliers, one assembler, 3 distributor centers and 8 retailers. This problem will be solved for 4 periods. Afterwards this problem is solved by Lingo software. The result of the problem designs a supply chain and some of supplier will be eliminated from chain. This result will be an alternative for decision maker for designing the supply chain.Tue, 13 Feb 2018 20:30:00 +0100An aggregated Supplier Selection method based on QFD and TOPSIS (Case Study: A Financial ...
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With daily development of information technology supply chain of service-based organizations like financial institutions and the increased value of outsourced activities, also the importance of customer satisfaction, outsourced affairs must have done by the suppliers who have the ability of accomplishing the organizational demands. To mitigate the risk of invalid supplier selections, verification and selection of the suppliers should be performed with an optimized and systematic solution. In order to help the selection of suppliers in the IT department of financial organizations, a different model by using a hybrid QFD-TOPSIS solution in MCDM methods is suggested, in this study. First goal of the provided model is finding the most related criteria and the second one is offering an optimized solution to the supplier selection problem. To begin the QFD part in the mentioned method, two categories of criteria are needed. Then, after the formation of the House of Quality, in a real case study that was performed in a private bank in Iran, the suppliers are ranked by using the proposed method. The greatest efficiencies of this method are not only finding the best supplier by measuring the nearest distance to the ideal and the farthest one to the negative-ideal solution but also closing the opinions of employers to the technical requirements (sub-criteria) of information technology supplier qualifications. Finally, a model reliability part is designed to indicate the validation of the proposed method and a sensitivity analysis is implemented to find the most sensitive sub-criteria. That is the results of ranking alter if sensitive sub-criteria change .Tue, 13 Feb 2018 20:30:00 +0100Diversified Particle Swarm Optimization for Hybrid Flowshop Scheduling
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The aim of this paper is to propose a new particle swarm optimization algorithm to solve a hybrid flowshop scheduling with sequence-dependent setup times problem, which is of great importance in the industrial context. This algorithm is called diversified particle swarm optimization algorithm which is a generalization of particle swarm optimization algorithm and inspired by an anarchic society whose members behave anarchically to improve their situations. Such anarchy lets the algorithm explore the solution space perfectly and prevent falling in the local optimum traps. Besides, for the first time, for the hybrid flowshop, we proposed eight different local search algorithms and incorporate them into the algorithm in order to improve it with the help of systematic changes of the neighborhood structure within a search for minimizing the makespan. The proposed algorithm was tested and the numerical results showe that the proposed algorithm significantly outperforms other effective heuristics recently developed.Tue, 13 Feb 2018 20:30:00 +0100Implementing Bounded Linear Programming and Analytical Network Process Fuzzy models to Motivate ...
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In this research, the factors affecting on university employeesâ€™ motivation and productivity are identified and classified in seven groups; the impact of each motivation factor on the productivity is presented by ANP fuzzy model. Eight universities in Iran were analyzed in this research work. The aim of this study is to explore the productivity of employees. This paper attempts to give new insights about designing the portfolio factors motivating employees for productivity improvement by implementing BLP and ANP fuzzy models. The research results show that there is a positive and significant relationship among reward system, motivation factors and human resources productivity. Also, among the options of reward system, the factors of internal (inherent) reward, non-financial external reward and financial external reward had the most impact on increasing motivation and productivity factors. At the next stage, a BLP model is designed according to the importance and impact of each reward system option on motivation and productivity factors and organization limitations including budget, facilities and conditions to design portfolio factors motivating employees with the aim of improving productivity. The research results show that actualizing performance evaluation, receiving the feedback from the results of doing tasks by different ways, providing an opportunity for all employees to progress, coordination between job specifications and employeesâ€™ abilities and a manager competency are very critical for improving the organization productivity.Tue, 13 Feb 2018 20:30:00 +0100An Integrated Approach for Facility Location and Supply Vessel Planning with Time Windows
http://www.qjie.ir/article_538388_0.html
This paper presents a new model of two-echelon periodic supply vessel planning problem with time windows mix of facility location (PSVPTWMFL-2E) in an offshore oil and gas industry. The new mixed-integer nonlinear programming (MINLP) modelconsists ofa fleet composition problem and a location-routing problem (LRP). The aim of the model is to determine the size and type of large vessels in the first echelon and supply vessels in the second echelon.Additionally,the location of warehouse(s),optimal voyages and related schedules in both echelons are purposed.The total cost should be kept at a minimum and the need of operation regions and offshore installationsshould be fulfilled.A two-stage exact solution method, which is common for maritime transportation problems, is presented for small and medium-sized problems. In the first stage, all voyages are generated and in the second stage, optimal fleet composition, voyages and schedules are determined. Furthermore, optimal onshore base(s) to install central warehouse(s)and optimal operation region(s) to send offshore installation’s needs are decided in the second stage.Sat, 17 Feb 2018 20:30:00 +0100An Efficient Bi-objective Genetic Algorithm for the Single Batch-Processing Machine Scheduling ...
http://www.qjie.ir/article_538503_0.html
This paper considers the problem of minimizing make-span and maximum tardiness simultaneously for scheduling jobs under non-identical job sizes, dynamic job arrivals, incompatible job families,and sequence-dependentfamily setup time on the single batch- processor, where split size of jobs is allowed between batches. At first, a new Mixed Integer Linear Programming (MILP) model is proposed for this problem; then, it is solved by -constraint method.Since this problem is NP-hard, a bi-objective genetic algorithm (BOGA) is offered for real-sized problems. The efficiency of the proposed BOGA is evaluated to be comparedwith many test problemsby -constraint method based on performance measures. The results show that the proposed BOGAis found to be more efficient and faster than the -constraint method in generating Pareto fronts in most cases.Sun, 25 Feb 2018 20:30:00 +0100A Job Shop Scheduling and Location of Battery Charging Storage for the Automated Guided ...
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When the Automated Guided Vehicles (AGVs) are transferring the parts from one machine to another in a job shop environment, it is possible that AGVsstopon their guidepaths since their batteries are discharged.Consequently, it is essential to establish at least one Battery Charging Storage (BCS) to replace full batteries with empty batteries for the stopped AGVs. Due to non-predictable routes for AGVs in the manufacturing systems, to find the best place toestablish the BCS can impact performance of the system. In this paper, anintegrated mathematical modelof job shop and AGV schedulingwith respect tothe location of a BCS is proposed. The proposed nonlinear model is transformed into a linear form to beefficiently solvedin GAMS software. Finally, several numerical examplesare presented to test the validity of the proposed mathematical model.The results show that the optimal cost and location of BCS can be obtained with respect to the number of AGVs, machines, parts, and other problem parameters. In addition, it is concluded that the increasing number of AGVs in a manufacturing systemcannot be always a suitable policy for reducing the cost because in such conditions.Further to that, the conflict of AGVs may increase leading tothe increase of the makespan. In other words, following the optimal point, increasing AGVs leads to the increase incosts.Sun, 25 Feb 2018 20:30:00 +0100Optimizing a Multi-objective Fuzzy Closed Loop Supply Chain Network Design Considering Dynamic ...
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During the last decade, reverse logistics networks received a considerable attention due to economic importance and environmental regulations and customer awareness. Integration of leading and reverse logistics networks during logistical network design is one of the most important factors in supply chain. In this research, an Integer Linear Programming model is presented to design a multi-layer reverse-leading, multi-product, and multi-period integrated logistics network by considering multi-capacity level for facilities under uncertainty condition. This model included three objectives: maximizing profit, minimizing delay of goods delivering to customer, andminimizing returned raw material from suppliers. This research gives financial incentives to encourage customers in order to return their used product. Considering that theremainingvalue ofused products is the main incentive of a company to buy second-handed goods, a dynamic pricing approach is determined to define purchase price for these types of products, and based on that, the percentage of returned products were collected by customers. In addition, in this study, parameters have uncertainty features and are vague; therefore,at first,they are converted into exact parameters and, then, because model is multi-objective, the fuzzy mathematical programming approach is used to convert multi-objective model into a single objective; finally,the model by version 8 of Lingo is run.In order to solve a large-sized model, a non-dominated sorting genetic algorithm II (NSGA-II)was applied. Computational results indicate the effect of the proposed purchase price onencourage customers to return the used products.Sun, 25 Feb 2018 20:30:00 +0100