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.Integrated 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.Thu, 28 Feb 2019 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.Thu, 28 Feb 2019 20:30:00 +0100Application of Failure Mode Effect Analysis (FMEA) for Efficient and Cost-effective ...
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The aim of this paper is to show the application of Failure Mode Effect Analysis (FMEA) for efficient and cost-effective manufacturing. Companies need better economic gains from enhanced production, but downtime affects this paradigm. Bair Dar Textile Share Company (BDTSC) is no exception. The looming section of the case company faces on average 38.69% of downtime from the total production time which highly affects its production performance, and thus profitability. The research tries to show the economic gain from the reduced high downtime in the case company by taking the advantages of Failure Mode Effect Analysis (FMEA). As a result FMEA, failure modes, cause, and their effects on the specific section of the company were identified and prioritized using their Risk Priority Numbers (RPN). By taking the FMEA on the looming process machines and focusing on the vital few 20% causes of the identified failure modes, the findings of the research show that the company can decrease the total downtime from its 178 loom machines by 299.04hrs/day. As a result, the company can save downtime that can produce 1,672.82 meters of fabric/cloth and enhance its performance by 4.18%. This downtime reduction in turn results in a daily profit of 38,220.56 ETB (Ethiopian Birr) or 11,466,168.00 ETB annually.Thu, 28 Feb 2019 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 .Thu, 28 Feb 2019 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.Thu, 28 Feb 2019 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.Thu, 28 Feb 2019 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 +0100Classification of Streaming Fuzzy DEA Using Self-Organizing Map
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The classification of fuzzy data is considered as the most challenging areas of data analysis and the complexity of the procedures has been obstacle to the development of new methods for fuzzy data analysis. However, there are significant advances in modeling systems in which fuzzy data are available in the field of mathematical programming. In order to exploit the results of the researches on fuzzy mathematical programming, in this study, a new fuzzy data classification method based on data envelopment analysis (DEA) is provided when fuzzy data are imported as a stream. The proposed method can classify data that changes are created in their behavioral pattern over time using updating the criteria of predicting fuzzy data class. To reduce computational time, fuzzy self-organizing map (SOM) is used to compress incoming data. The new method was tested by simulated data and the results indicated the feasibility of this technique in the face of uncertain and variable conditions.Thu, 28 Feb 2019 20:30:00 +0100Modelling and solving the job shop scheduling Problem followed by an assembly stage considering ...
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This paper considers job shop scheduling problem followed by an assembly stage and Lot Streaming (LS). It is supposed here that a number of products have been ordered to be produced. Each product is assembled with a set of several parts. The production system includes two stages. The first stage is a job shop to produce parts. Each machine can process only one part at the same time. The second stage is an assembly shop that contains several parallel machines. Maintenance operations and access restrictions to machines in the first stage are also considered. The objective function is to minimize the completion time of all products (makespan). At first, this problem is described and modelled as a mixed integer linear programming, and GAMS software is applied to solve small-sized problems. Since this problem has been proved to be strongly NP-hard, two new algorithms based on GA and SA are developed to solve the medium- and large-sized problems. In order to verify the effectiveness of the proposed algorithms, a statistical analysis is used along with Relative Percentage Deviation (RPD) factor and well-known criterion. IMP. Various problems are solved by the proposed algorithms. Computational results reveal that both of the two proposed algorithms have good performance. However, the method based on the genetic algorithm performs better than the other proposed algorithm with respect to the objective functiThu, 28 Feb 2019 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.Thu, 28 Feb 2019 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 Regret Minimization Approach in Product Portfolio Management with respect to Customers’ ...
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In an uncertain and competitive environment, product portfolio management (PPM) becomes more challenging for manufacturers to decide what to make and establish the most beneficial product portfolio. In this paper, a novel approach in PPM is proposed in which the environment uncertainty, competitors’ behavior and customer’s satisfaction are simultaneously considered as the most important criteria in achieving a successful business plan. In terms of uncertainty, the competitors’ product portfolios are assumed as different scenarios with discrete occurrence probabilities. In order to consider various customer preferences, three different market segments are assumed in which the sensitivity of customers towards the products price are considered as high, medium and low and modeled by means of a modified utility functions. The best product portfolio with minimum risk of loss and maximum customer satisfaction is then established by means of a novel regret minimization index. The proposed index aims at finding the best product portfolio which minimizes the total possible loss and regret of the manufacturer, with respect to the other competitors of the market. To better illustrate the practicality of the approach, a numerical example is presented. The results show that the selected products in the suggested portfolio have the highest utility value in all market segments and also they are expected to achieve the highest expected payoff in each possible marketing scenario.Thu, 28 Feb 2019 20:30:00 +0100Economic Lot Sizing and Scheduling in Distributed Permutation Flow Shops
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This paper addresses a new mixed integer nonlinear and linear mathematical programming economic lot sizing and scheduling problem in distributed permutation flow shop problem with number of identical factories and machines. Different products must be distributed between the factories and then assignment of products to factories and sequencing of the products assigned to each factory has to be derived. The objective is to minimize the sum of setup costs, work-in-process inventory costs and finished products inventory costs per unit of time. Since the proposed model is NP-hard, an efficient Water Cycle Algorithm is proposed to solve the model. To justify proposed WCA, Monarch Butterfly Optimization (MBO), Genetic Algorithm (GA) and combination of GA and simplex are utilized. In order to determine the best value of algorithms parameters that result in a better solution, a fine-tuning procedure according to Response Surface Methodology is executed.Thu, 28 Feb 2019 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 +0100Healthcare Districting Optimization Using Gray Wolf Optimizer and Ant Lion Optimizer Algorithms ...
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In this paper, the problem of population districting in the health system of South Khorasan province has been investigated in the form of an optimization problem. Now that the districting problem is considered as a strategic matter, it is vital to obtain efficient solutions in order to implement in the system. Therefore in this study two meta-heuristic algorithms, Ant Lion Optimizer (ALO) and Grey Wolf Optimizer (GWO), have been applied to solve the problem in the dimensions of the real world. The objective function of the problem is to maximize the population balance in each district. Problem constraints include unique assignment as well as non-existent allocation of abnormalities. Abnormal allocation means compactness, lack of contiguous, and absence of holes in the districts. According to the obtained results, GWO has a higher level of performance than the ALO. The results of this problem can be applied as a useful scientific tool for districting in other organizations and fields of application.Thu, 28 Feb 2019 20:30:00 +0100Multi-response Optimization of Grooved Circular Tubes Filled with Polyurethane Foam as Energy ...
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The main objective of this research is to improvethe design and performance of the polyurethane foam-filled thin-walled aluminum grooved circular tubes using multi-response optimization (MRO) technique. The tubes are shaped with the inner and the outer circular grooves at different positions along the axis. For this aim, several numerical simulations using ABAQUS finite element explicit code are performed to study the energy absorption of these structures. The effects of the grooves distance, tube diameter, grooves depth, foam density, and tube thickness are investigated onthecrashworthiness parameters of grooved circular tubes. Finite-element analysis is performed along the lines defined by design of experiments (DOE) technique at different combinations of the design parameters. The MRO is carried out using the mathematical models obtained from response surface methodology (RSM) for two crashworthiness parameters termed as the specific energy absorption (SEA) and the crushing force efficiency (CFE). Finally, by analyzing all the design criteria including theabsorbed energy of tube, themass of tube, the mean crushing load, and the maximum crushing load, the optimal density of polyurethane foam and geometric parameters were obtained through both multi-objective optimization process and Pareto diagram. A comparison of the obtained results indicates the significance of grooves distance and the inner diameter of thetube as the most influential parameters.Thu, 28 Feb 2019 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 +0100An Integrated Approach for Facility Location and Supply Vessel Planning with Time Windows
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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.Thu, 28 Feb 2019 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.Thu, 28 Feb 2019 20:30:00 +0100Hierarchical group compromise ranking methodology based on Euclidean–Hausdorff distance ...
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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 +0100An Algorithm Based on Theory of Constraints and Branch and Bound for Solving Integrated ...
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One of the most important decision making problems in many production systems is identification and determination of products and their quantities according to available resources. This problem is called product-mix. However, in the real-world situations, for existing constrained resources, many companies try to provide some products from external resources to achieve more profits. In this paper, an integrated product-mix-outsourcing problem (IPMO) is considered to answer how many products should be produced inside of the system or purchased from external resources. For this purpose, an algorithm based on Theory of Constraints (TOC) and Branch and Bound (B&B) algorithm is proposed. For investigation of the proposed algorithm, a numerical example is presented. The obtained results show the optimal result by the new algorithm is as same as the results of integer linear programming.Thu, 28 Feb 2019 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 +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 +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 +0100A Mathematical Model for Multiple-Load AGVs in Tandem Layout
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Reducing cost of material handling has been a big challenge for companies. Flexible manufacturing system employed automated guided vehicles (AGV) to maintain efficiency and flexibility of manufacturing system. This paper presents a new non-linear integer mathematical programming model to group n machines into N loops, to make an efficient configuration for AGV system in Tandem layout. The model minimize both inter-loop, intra-loop flow and use balanced-loops strategy to balance workload in system simultaneously. This paper significantly considers multiple-load AGVs, which has ability to reduce fleet size and waiting time of works. A modified variable neighborhood search method is applied for large size problems, which has good accuracy for small and medium size problems. The results indicate that using multiple load AGV instead of single load AGV will reduce system penalty cost up to 44%.Thu, 28 Feb 2019 20:30:00 +0100Design and Analysis of Assembled components Tolerance Using Weibull Distribution
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Tolerancing is one of the most important tools for planning, controlling, and improving quality in the industry. Tolerancing conducted by design engineers to meet customers’ needs is a prerequisite for producing high-quality products. Engineers use handbooks to conduct tolerancing. While use of statistical methods for tolerancing is not a new concept, engineers often use known distributions, including the normal distribution. However, if the statistical distribution of the given variable is unknown, a new statistical method will be employed to design tolerance. Therefore, in this study we want to offer a proper statistical method for determining tolerance. The use of statistical methods to design tolerance is not a new concept; however, flexible use of statistical distributions can enhance its performance. In this regard, Weibull distribution is proposed. To illustrate the proposed method first technical characteristics of production parts were selected randomly, and then manufacturing parameters were determined using maximum likelihood method. Finally, the Goodness of Fit test was used to ensure the accuracy of the obtained results.Wed, 17 Oct 2018 20:30:00 +0100A Game Theoretical Approach to Optimize Policies of Government Under the Cartel of Two Green ...
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In this research, firms aim at maximizing two purposes of social welfare (environment) and profitability in the supply chain system. It is assumed that there are two supply chains, a green and an ordinary, each consists of a manufacturer and a supplier; in which the manufacturer generates profit through franchises. The green and the ordinary manufacturers form a cartel on the market of a certain product with the goal of increasing their mutual profits and maintaining a certain level of social welfare, while the government, as a leader, intervene financially using tax rates and incentives. We formulate the problem as a Stackelberg game model seeking the equilibrium solutions. A numerical example is presented and a sensitivity analysis is carried out. The results show that the investment’s encouraging tax rate in green technology has no impact on the optimal production of the green and ordinary manufacturers. Therefore, it is not an affective variable on the product market, but it is an important variable for the state utility function. Another highlight is that if tax rates are not equal for green and ordinary goods, then either the green or the ordinary producer will be withdrawn from the market. The most important result of this study is that if the government wants to maximize its utility function when the final product’s market is facing with a cartel and the price collusion between the green and ordinary producer, it should realize the equality between the ordinary and green tax rate and there is no difference between these two parameters of the government's decision. If the government is willing to keep the green producer in the market, the optimal and absolute tax rate of green chain is obtained by assuming zero profit of the green manufacturer.Wed, 17 Oct 2018 20:30:00 +0100Bottleneck Identification Using Time Study And Simulation Modeling Of Apparel Industries
http://www.qjie.ir/article_543730_0.html
This study deals with bottleneck detection with simulation techniques and time study. Modeling and simulation are potential tools for analyzing assembly lines such as clothing in a garment. Thus, the researchers attempted to experiment 160 numbers of replication in the arena tool.
The experiment monitors the assembly process resources without altering the actual production scheme. In this study, the arena software and POM dedicated to modeling and measuring the performance of the existing Ronny t-shirt sewing line. The t-shirt is formed and has 12 main parts are assembled. For each activity, the researchers took 15 sampling observations using stopwatch. All the collected data are statistically analyzed using the input analyzer in arena for statistical significance and the expression resolution to be used for the simulation model. The result shows the line is operating with a line balance efficiency of 72.56%. In the course of action using Opt Quest different types of scenarios had been developed and line balance efficiency increase to 75.3% and the company saves 518,400 birr per year that spent for salary and in addition to it the line can be produced additional 60 t-shirts from the 20 lines of the production. This arena simulation model has considered the production resources, processing time of each activity and testing different scenarios.
Sat, 20 Oct 2018 20:30:00 +0100Three Hybrid Metaheuristic Algorithms for Stochastic Flexible Flow Shop Scheduling Problem with ...
http://www.qjie.ir/article_543744_0.html
Stochastic flexible flow shop scheduling problem (SFFSSP) is one the main focus of researchers due to the complexity arises from inherent uncertainties and also the difficulty of solving such NP-hard problems. Conventionally, in such problems each machine’s job process time may encounter uncertainty due to their relevant random behaviour. In order to examine such problems more realistically, fixed interval preventive maintenance (PM) and budget constraint are considered.PM activity is a crucial task to reduce the production efficiency. In the current research we focused on a scheduling problem which a job is processed at the upstream stage and all the downstream machines get busy or alternatively PM cost is significant, consequently the job waits inside the buffers and increases the associated holding cost. This paper proposes a new more realistic mathematical model which considers both the PM and holding cost of jobs inside the buffers in the stochastic flexible flow shop scheduling problem. The holding cost is controlled in the model via the budget constraint. In order to solve the proposedmodel, three hybrid metaheuristic algorithms are introduced. They include a couple of well-known metaheuristic algorithms which have efficient quality solutions in the literature. The two algorithms of them constructed byincorporationof the particle swarm optimization algorithm (PSO) and parallel simulated annealing (PSA) methods under different random generation policies. The third one enriched based on genetic algorithm (GA) with PSA. To evaluate the performance of the proposed algorithms, different numerical examples are presented. Computational experiments revealed that the proposed algorithms embedboth desirable accuracy and CPU time. Among them, the PSO-PSAП outperforms than other algorithms in terms of makespan and CPU time especially for large size problems.Sat, 20 Oct 2018 20:30:00 +0100Developing a new bi-objective functions model for a hierarchical location-allocation problem ...
http://www.qjie.ir/article_543781_0.html
In this research, a hierarchical location-allocation problem is modeled in a queue framework. The queue model is considered as M/M/1/k, in which system capacity is finite, equals to k. This is the main contribution of the current research. Customer's enters to the system in order to find the service according to a Poisson. In this problem, the hierarchical location-allocation model is considered in two levels. Also, the model has two objective functions: maximizing the total number of demand coverage and minimizing the waiting time of customers in queues to receive services. After modeling and verifying the validity of the presented model, it is solved using NSGA II and MOPSO meta-heuristics.
Keywords: Hierarchical-allocation problem, Queue Theory, Meta-heuristic Algorithm.Sat, 20 Oct 2018 20:30:00 +0100Bi-objective Optimization of a Multi-product multi-period Fuzzy Possibilistic Capacitated Hub ...
http://www.qjie.ir/article_543806_0.html
The hub location problem is employed for many real applications, including delivery, airline and telecommunication systems and so on. This work investigates on hierarchical hub network in which a three-level network is developed. The central hubs are considered at the first level, at the second level, hubs are assumed which are allocated to central hubs and the remaining nodes are at the third level. In this research, a novel multi-product multi-objective model for capacitated hierarchical hub location problem with maximal covering under fuzzy condition first is suggested. Cost, time, hub and central hub capacities are considered as fuzzy parameters, whereas manyparameters are uncertainty and indeterministic in the real world. To solve the proposed fuzzy possibilistic multi-objective model, first, the model is converted to the equivalent auxiliary crisp model by hybrid method and then is solved by two meta-heuristic algorithms such as Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Non-Dominated Ranked Genetic Algorithm (NRGA) using MATLAB software The statistical results report that there is no significant difference between means of two algorithms exception CPU time criteria. In general, in order to show efficiency of two algorithms, we used Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the resultsclearly show that the efficiency of NRGA is better than NSGA-II and finally, figures are achieved by MATLAB software that analyze the conflicting between two objectives.Sat, 20 Oct 2018 20:30:00 +0100