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.A Multi-objective Mixed Model Two-sided Assembly Line Sequencing Problem in a Make –To- Order ...
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Mixed model two-sided assembly lines (MM2SAL) are applied to assemble large product models, which is produced in high-volume. So, the sequence planning of products to reduce cost and increase productivity in this kind of lines is imperative. The presented problem is tackled in two steps. In step 1, a framework is developed to select and prioritize customer orders under the finite capacity of the proposed production system. So, an Analytic Network Process (ANP) procedure is applied to sort customers’ order based on 11 assessment criteria. In step 2, a mathematical model is formulated to determine the best sequence of products to minimize the total utility work cost, total idle cost, tardiness/earliness cost, and total operator error cost. After validation of the presented model using GAMS software, according to the NP-hard nature of this problem, a genetic algorithm (GA) and particle swarm optimization (PSO) are used. The performance of these algorithms are evaluated using some different test problems. The results show that the GA algorithm is better than PSO algorithm. Finally, a sign test for the two metaheuristics and GAMS is designed to display the main statistical differences among them. The results of the sign test reveal GAMS is an appropriate software for solving small-sized problems. Also, GA is better than PSO algorithm for large sized problems in terms of objective function and run time.Sun, 30 Jun 2019 19:30:00 +0100Production Planning and Control Strategies Used as A Gear Train for The Death and Birth of ...
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This study is conducted to developed innovative production planning and control strategies to manufacturing industries so as to improve production performance and competitiveness of basic metal sectors Though the study was conducted through field observation and questioner used as primary data and literature review on research articles, books, and electronic-sources which used as secondary data. While the questioner and filed observation data collection were done from two selected Ethiopian basic metal industries. Since the collected data were employed by both using descriptive and empirical analysis. Waste in the production process, poor plant layout systems, defective products, improper material requirement planning, deficiency on control and monitoring systems, insufficient inventory control, poor workflow strategies, null warehouse management systems, problems in information systems and information management strategies were investigated as the main challenges of developing the nation basic metal industries. As a result of these challenges, the performance and global competitiveness of local basic metal industries are poor and weak. As well the literature’ finding endorse that production planning and controls have gradual advancement in developed manufacturing industries but it is found to be at its infant stage in developing manufacturing industries. Due to these challenges and weak performances on the developing firms, the entire production process on the industries was declining, and then they approach to die. Though the new product planning and controlling strategies can bridge the gap and birth will begin within proper implementations of the model to basic metal industries.Sun, 30 Jun 2019 19:30:00 +0100Fixture Design and Work Piece Deformation Optimization Using the Iterative Simplex Algorithm
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Presents article is deal with optimization of the fixture for end milling process, the most important objective being the minimization of work piece deformation by changing the layout of fixture elements and the clamping forces. The main objective of this work has been the optimization of the fixtures Work piece deformation subjected to clamping forces for End milling operation. The present analysis is used in hollow rectangular isotropic material work piece for FEA analysis and its optimization A linear programing (L.P) simplex model optimization activity has been performed both on fixture-work piece systems modeled with FEM and on fixture-work piece systems modeled with 3-D solid elements. The optimization constraints is selected as W/P deformation in x,y,z direction for various clamping forces, in order to provide a new design of fixture. The MATLAB code has been developed for L.P. model optimization purpose. Present MATLAB code is validated by using available literature. This paper deals with application of the L.P. model for w/p deformation optimization for a accommodating work piece. A simplex iterative algorithm that minimizes the work piece elastic deformation for the entire clamping force is proposed. It is shown via an example of milling fixture design that this algorithm yields a design that is superior to the result obtained from either fixture layout or w/p deformation optimization alone.Sun, 30 Jun 2019 19:30:00 +0100Investigating Water Supply System Electro-Mechanical Equipments Problems: A Case Study of Ethiopia
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Water is an essential element of life. The government of Ethiopia in collaboration with development allies’ attempts to increase pure water supply. Even though the coverage boosted dramatically still there is critical challenges in maximizing equipment’s reliability, improving service quality, maximizing capacity utilization, minimizing life cycle costs of water production machinery and reducing water waste. The objective of this study was to identify installation, operation, maintenance and related challenges, to evaluate the performance of pump station and to investigate the root causes so as improvements can be made deliberately. In this regard 20 town water supply stations were selected in Amhara region of Ethiopia and initially on site visit carried out and various existing situational surveys regarding the existing installation, operation and maintenance practice have been conducted. Next information collected using questionnaires, interview and focus group discussions. Then imperative performance indicating measurements taken and the data organized and important performance indicating parameters were analyzed using quantitative techniques. The study proved that all the pump stations run under the minimum performance requirements and the problems are deep enough to challenge the service quality and service cost of the pump stations. Unless the problems will be solved soon systematically the problems may be even mature to the nastiest situation that cannot handled.Sun, 30 Jun 2019 19:30:00 +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 +0100Classifying the Customers of Telecommunication Company in order to Identify Profitable ...
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Effective knowledge and awareness of customers require the market segmentation, through which the customers who have the same needs and purchasing patterns as well as the same response to marketing plans are identified. The selection of a proper variable is a requirement, among other, for a successful market segmentation. In today' world, on one hand, the consumers are bombarded with new goods and new services, and on the other hand, they face the varying qualities of the goods and services. Consequently, such uncertainties will lead to more vague decisions and cumulative data. The timely and accurate analysis of these cumulative data can bring about competitive advantages to the enterprises. Furthermore, thanks to new technology and global competition, the majority of organizations have focused on Customer Relationship Management (CRM), with the goal of better serving the customers. The customer relationship planning entails the facilitation and creation of interfaces related to market segmentation, which is considered as a requirement for predicting behavior of the prospective customers in the future. Market segmentation refers to the process of dividing the customers into some segments based on their common characteristics while different groups have the least similarity to each other. This is followed by the formulation of plans for new product production, advertisement and marketing in accordance with the characteristics of each group of customers. Current study aims at identifying the profitable customers of a telecom System, based on their first transaction, using binary tree. The customers of System 780 participated in this case study. The dependent variable and independent variable of the study were identified through mining the data of customers, registered in the databases of System 780. The results showed the acceptable calculation error in distinguishing the profitable customers from other customers.Sun, 30 Jun 2019 19:30:00 +0100Using the Hybrid Model for Credit Scoring (Case Study: Credit Clients of microloans, Bank ...
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In any country, commercial banks lay the groundwork for economic growth by collecting national resources and capitals and allocating them to different economic sectors. Optimal allocation of resources is especially important in achieving this goal. Banks with an effective and dynamic system of customer assessment can efficiently allocate their resources to customers regardless of their geographic area. Following[M1] a linear programming optimization approach, this research employs the UTilités Additives DIScriminantes (UTADIS) model for credit scoring of bank customers. The advantages of the proposed technique are high flexibility, mutual interaction with decision makers, and the ability to update under various macroeconomic conditions. The chosen environment is a branch of Bank Refah Kargaran, one of the popular banks in Iran. According to the experimental results, the proposed technique demonstrates high effectiveness. Also, the results indicate that the initial credit score and age of the applicants are the most influential factors for credit scoring of customers.Sun, 30 Jun 2019 19:30:00 +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, 30 Jun 2019 19:30:00 +0100Hierarchical Group Compromise Ranking Methodology Based on Euclidean–Hausdorff Distance ...
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Proposing a hierarchical group compromise method can be regarded as a one of major multi-attributes decision-making tool that can be introduced to rank the possible alternatives among conflict criteria. Decision makers’ (DMs’) judgments are considered as imprecise or fuzzy in complex and hesitant situations. In the group decision making, an aggregation of DMs’ judgments and fuzzy group compromise ranking is more capable and powerful than the classical compromise ranking. This research extends a new hierarchical group compromise ranking methodology under a hesitant fuzzy (HF)environment to handle uncertainty, in which for the margin of error, the DMs could assign the opinions in several membership degrees for an element. The hesitant fuzzy set (HFS)is taken into account for the process of the proposed hierarchical group compromise ranking methodology, namely HFHG-CR, and for avoiding the data loss, the DMs’ opinions with risk preferences are considered for each step separately. Also, the Euclidean–Hausdorff distance measure is utilized in a new proposed index for calculating the average group score, worst group score and compromise measure regarding each DM. A new ranking index is presented for final compromise solution for the evaluation. Proposed HFHG-CR methodology is applied to a practical example for a facility location selection problem, i.e. cross-dock location problem, to show the validation and application.Sun, 30 Jun 2019 19:30:00 +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.Sun, 30 Jun 2019 19:30:00 +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 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 in costs.Sun, 30 Jun 2019 19:30:00 +0100Three Hybrid Metaheuristic Algorithms for Stochastic Flexible Flow Shop Scheduling Problem with ...
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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.Sun, 30 Jun 2019 19:30:00 +0100Developing a New Bi-Objective Functions Model for a Hierarchical Location-Allocation Problem ...
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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.Sun, 30 Jun 2019 19:30:00 +0100An Evolutionary Algorithm Based on a Hybrid Multi-Attribute Decision Making Method for the ...
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This paper addresses the multi-mode multi-skilled resource-constrained project scheduling problem. Activities of real world projects often require more than one skill to be accomplished. Besides, in many real-world situations, the resources are multi-skilled workforces. In presence of multi-skilled resources, it is required to determine the combination of workforces assigned to each activity. Hence, in this paper, a mixed-integer formulation called the MMSRCPSP is proposed to minimize the completion time of project. Since the MMSRCPSP is strongly NP-hard, a new genetic algorithm is developed to find optimal or near-optimal solutions in a reasonable computation time. The proposed genetic algorithm (PGA) employs two new strategies to explore the solution space in order to find diverse and high-quality individuals. Furthermore, the PGA uses a hybrid multi-attribute decision making (MADM) approach consisting of the Shannon’s entropy method and the VIKOR method to select the candidate individuals for reproduction. The effectiveness of the PGA is evaluated by conducting numerical experiments on several test instances. The outputs of the proposed algorithm is compared to the results obtained by the classical genetic algorithm, harmony search algorithm, and Neurogenetic algorithm. The results show the superiority of the PGA over the other three methods. To test the efficiency of the PGA in finding optimal solutions, the make-span of small size benchmark problems are compared to the optimal solutions obtained by the GAMS software. The outputs show that the proposed genetic algorithm has obtained optimal solutions for 70% of test problems.Sun, 30 Jun 2019 19:30:00 +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 +0100Designing Tolerance of Assembled Components 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.Sun, 30 Jun 2019 19:30:00 +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 +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 +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.Sun, 30 Jun 2019 19:30:00 +0100Bi-objective Optimization of a Multi-product multi-period Fuzzy Possibilistic Capacitated Hub ...
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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 +0100A Bi-objective Model to Optimize Reliability and Cost of System for the Aggregate Production ...
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In this research, a bi-objective model is developed to deal with a supply chain including multiple suppliers, multiple manufacturers, and multiple customers, addressing a multi-site, multi-period, multi-product aggregate production planning (APP) problem. This bi-objective model aims to minimize the total cost of supply chain including inventory costs, manufacturing costs, work force costs, hiring, and firing costs, and maximize the minimum of suppliers' and producers' reliability by the considering probabilistic lead times, to improve the performance of the system and achieve a more reliable production plan. To solve the model in small sizes, a ε-constraint method is used. A numerical example utilizing the real data from a paper and wood industry is designed and the model performance is assessed. With regard to the fact that the proposed bi-objective model is NP-Hard, for large-scale problems one multi-objective harmony search algorithm is used and its results are compared with the NSGA-II algorithm. The results demonstrate the capability and efficiency of the proposed algorithm in finding Pareto solutions.Sun, 30 Dec 2018 20:30:00 +0100The Regional Performance Impacts in the Supply Chain integration: Evidence from East Africa ...
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The objective of this study was to investigate the relationship between regional firm performance and supply chain integration practices. In addition to literature survey, the primary data collection instrument used was a questionnaire which was administrated to a total sample of 200 industry experts, administrative officers, functional department heads and directors, the industry managing directors and senior staff from 21 manufacturing firms in Ethiopian were incorporated. The data were studied using the AHP, fuzzy TOPSIS, the mind map, and descriptive analysis. Further, the challenges, practices, performance, the practices of East African basic metal industry supply chain integration influences the dynamics contribution of the regional economy was fragmental. According to this study, due to weak, fragmental and non-integrated supply chain systems, the regional economic dynamic contribution of the sector is weak and poor in quality, productivity, flexibility, and global competitiveness were investigated. Moreover, these investigations and analysis of this paper have both practical and scholarly implications. The result is provided to contribute to the future supply chain integration model development and make an action to supply chain integrations for process improvements. Finally, the implications for the design on the dynamic capability of supply chain strategies are showing, and the future research agenda was presentedMon, 21 Jan 2019 20:30:00 +0100Multi-Objective Optimization for Multi-Product Multi-Period Four Echelon Supply Chain Problems ...
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The multi-objective optimization for a multi-product multi-period four-echelon supply chain network consisting of manufacturing plants, distribution centers (DCs) and retailers each with uncertain services and uncertain customer nodes are aimed in this paper. The two objectives are minimization of the total supply chain cost and maximization of the average number of products dispatched to customers. The decision variables are determined the number and the locations of reliable DCs and retailers, the optimum number of items produced by plants, the optimum quantity of transported products, the optimum inventory of products at DCs, retailers and plants, and the optimum shortage quantity of the customer nodes. The problem is first formulated into the framework of a constrained multi-objective mixed integer linear programming model. After that, the problem is solved by using meta-heuristic algorithms that are Multi-objective Genetic Algorithm (MOGA), Fast Non-dominated Sorting Genetic Algorithms (NSGA-II) and Epsilon Constraint Methods via the MATLAB software to select the best in terms of the total supply chain cost and the total expected number of products dispatched to customers simultaneously. At the end, the performance of the proposed multi-objective optimization model of multi-product multi-period four-echelon supply chain network design is validated through three realizations and an innumerable of various analyses in a real world case study of Bangladesh. The obtained outcomes and their analyses recognize the efficiency and applicability of the proposed model under uncertainty.Mon, 21 Jan 2019 20:30:00 +0100An Optimization Model for Heterogeneous Vehicle Routing and Scheduling Problem with Fixed Cost ...
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Vehicle routing problem aims to find the optimal routes that must be traveled by a fleet of vehicles to satisfy the demand of the customers. In this research, the vehicle routing and scheduling problem is developed for a heterogeneous fleet with the fixed cost of applying vehicles and earliness and tardiness costs in a green reverse logistics network. Since the complexity order of these problems is higher than that of the polynomial ones, this problem is known as NP-hard. As the problem dimensions increase, the exact solving time of the problem increases considerably. Thus, metaheuristic methods are proposed to approximately solve these problems. After developing mixed integer nonlinear model, the Genetic Algorithm (GA) is used to find the near-optimal solutions for the large-scale cases. Finally, the performance of the GA is investigated for several examples by comparing its computation time and solution quality with the computation time and exact solution of the LINGO software. According to the results, the developed GA has an acceptable performance in providing solutions with minimum error in a rational time.Mon, 21 Jan 2019 20:30:00 +0100Production Constraints Modelling: A Tactical Review Approach
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A constraint is a limitation or a restriction that poses a threat to the performance and efficiency of a system. This paper presented a tactical review approach to production constraints modelling. It discussed the theory of constraints (TOC) as a thinking process and continuous improvement strategy to curtail constraints in other to constantly increase the performance and efficiency of a system. It also x-rayed the working process of implementing the TOC concept which consists of five steps called “Process of On-Going Improvement”. Furthermore, it talked about constraints programming and constraints-based models which were explained to some details. Finally, production constraints model formulation procedures for linear programming and non-linear programming scenarios were extensively discussed with reference to published literature as instances of production constraints modelling were also cited.Sat, 08 Jun 2019 19:30:00 +0100A system Dynamics Approach to Designing aCrowdfunding Model in Technological Entrepreneurship ...
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Crowdfunding, a new financing method for funding ideas or ventures through a large number of relatively small contributions from many individualshas witnessed the phenomenal development over the past decade.It involves bypassing traditional financial intermediaries and using online web-based platforms to connect users of funds with retail funders.this research first explores in greater detail the crowdfunding phenomenon, discussing its main aspects, as well as the role of the involved Stakeholders, and then introduce the A system dynamics approach to designing a crowdfunding model in technological entrepreneurship ecosystem with a focus on technology incubators centers.The present study is based on the system dynamics method and this research, in terms of the purpose is applied and in terms of the survey method. So for analysis of data, Vensim software has been used.The simulation results show that technological entrepreneurship ecosystem policy combinations can effectively promote crowdfunding, which attracts more entrepreneurs to provide their ideas. So crowdfunding could promote entrepreneurial to give a greater impact on economic, and contribute to building a more sustainable society.Tue, 18 Jun 2019 19:30:00 +0100Measuring the Performances of Medical Diagnostic Laboratories Based on Interval Efficiencies
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The classic data envelopment analysis (DEA) models have overlooked the intermediate products, internal interactions and the absence of data certainty; and deal with analyzing the network within the “Black Box” mode. This results in the loss of important information and at times a considerable modification occurs in efficiency results. In this paper, a Three-stage network model is considered with additional inputs and undesirable outputs and obtains the efficiency of the network, as interval efficiency in presence of the imprecise datum. The proposed model simulates the internal structure of a diagnostic lab (the pre-test, the test and the post-test). In this study, the criteria for evaluation are obtained by using the Fuzzy Delphi method. Due to the social, economic and environmental problems of health care organizations, the importance of sustainability criteria is evident in the case study indicators. We utilized the multiplicative DEA approach to measure the efficiency of a general system and a heuristic technique was used to convert non-linear models into linear models. Ultimately, this paper concentrates on the interval efficiency to rank the units.Sat, 13 Jul 2019 19:30:00 +0100An Efficient Economic-Statistical Design of Simple Linear Profiles Using a Hybrid Approach of ...
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Statistically constrained economic design for profiles usually refers to the selection of some parameters such as the sample size, sampling interval, smoothing constant, and control limit for minimizing the total implementation cost while the designed profiles demonstrate a proper statistical performance. In this paper, the Lorenzen-Vance function is first used to model the implementation costs. Then, this function is extended by the Taguchi loss function to involve intangible costs. Next, a multi-objective particle swarm optimization (MOPSO) method is employed to optimize the extended model. The parameters of the MOPSO are tuned using response surface methodology (RSM). In addition, data envelopment analysis (DEA) is employed to find efficient solutions among all near-optimum solutions found by MOPSO. Finally, a sensitivity analysis based on the principal parameters of the cost function is applied to evaluate the impacts of changes on the main parameters. The results show that the proposed model is robust on some parameters such as the cost of detecting and repairing an assignable cause, variable cost of sampling, and fixed cost of sampling.Sat, 13 Jul 2019 19:30:00 +0100