فهرست مطالب

Journal of Industrial and Systems Engineering
Volume:12 Issue: 4, Autumn 2019

  • تاریخ انتشار: 1398/08/10
  • تعداد عناوین: 15
  • Fatemeh Rakhshan *, MohammadReza Alirezaee Pages 1-17

    Productivity growth and efficiency improvements are the major sources of economic development. Pure efficiency, scale efficiency, and technology are basic factors, and rules and regulations and balance are recently known factors affecting the Malmquist productivity index. The index is the most common productivity growth index that uses data envelopment analysis models over multiple time periods. In this paper, we focus on the effect of the ethics factor in the decomposition of Malmquist productivity change index at the bank branch level by first developing an ethics model using some ethical codes and then calculating the ethics factor of decision making units. The ethics model uses weight restrictions for the constant returns to scale technologies to increase discrimination power of basic data envelopment analysis models. Then, the proposed ethics model is applied to a sample of 41 commercial bank branches and the results for both traditional and extended Malmquist index are analyzed.

    Keywords: Data Envelopment Analysis, Malmquist productivity index, Ethical codes, Weight restrictions, bank branches
  • Hossein Salehi, Reza Tavakkoli Moghaddam *, AtaAllah Taleizadeh, Ashkan Hafezalkotob Pages 18-26

    This paper proposes a modified non-dominated sorting genetic algorithm (NSGA-II) for a bi-objective location-allocation model. The purpose is to define the best places and capacity of the distribution centers as well as to allocate consumers, in such a way that uncertain consumers demands are satisfied. The objectives of the mixed-integer non-linear programming (MINLP) model are to (1) minimize the total cost of the network and (2) maximize the utilization of distribution centers. To solve the problem, a fuzzy modified NSGA-II with local search is proposed. To illustrate the results, computational experiments are generated and solved. The experimental results demonstrate that the performance metrics of the fuzzy modified NSGA-II is better than the original NSGA-II.

    Keywords: location-allocation, fuzzy rule base, multi-objective evolutionary algorithm
  • Neda Manavizadeh *, Mahnaz Shaabani, Soroush Aghamohamadi Pages 27-56

    This study introduces a green location, routing and inventory problem with customer satisfaction, backup distribution centers and risk of routes in the form of a non-linear mixed integer programming model. In this regard, time window is considered to increase the customer satisfaction of the model and transportation risks is taken into account for the reliability of the system. In addition, different factors are detected as the major factors affecting the risk of routs and a fuzzy TOPSIS method is applied to rank the related risk factors. Next, due to the complexity of the investigated model, two algorithms including multi-objective gray wolf optimization algorithms (MOGWO) and Non-Dominated Sorting Genetic algorithm (NSGA-II) are applied to solve the large-sized instances. The results prove the superiority of MOGWO in dealing with large-sized instances. In the next step, some sensitivity analysis is implemented on the model based on a case study andthe related results of case study are reported as well.

    Keywords: Location routing inventory, Green supply chain, backup strategy, Customer Satisfaction, Fuzzy TOPSIS, multi objective gray wolf optimization algorithm
  • Hassan Gharoun, Mahdi Hamid *, SeyedHossein Iranmanesh, Reza Yazdanparast Pages 57-75

    Two-sided assembly lines have been extensively studied due to their application in various auto industries. This paper investigates balancing problem type-II, which serves to minimize cycle time and consider learning effect based on a predefined workstation and costs pertaining to the assignment of operators with various skills. To this end, an integrated approach based on discrete event simulation (DES), artificial neural network (ANN), and data envelopment analysis (DEA) is utilized to optimize the performance of two-sided assembly line balancing (2S-ALB) problem type-II. The developed approach is applied to a real case study. Since many scenarios (suggestions for production line improvement) are needed for the simulation, the 2k Factorial design of experiment (DOE) is used to reduce their number. ANN and DEA were then used to select the best scenarios. It has been shown that incorporating learning effect and multi-skilled operators can improve the performance of 2S-ALB problem type-II better than does the conventional approach.

    Keywords: Two-sided assembly line, discrete event simulation, Neural Network, Data Envelopment Analysis (DEA), Learning Effect
  • MohhamadHossein Dehghani Sadrabadi, Rouzbeh Ghousi *, Ahmad Makui Pages 76-106

    The business environment, especially in the supply chain, is virtually fluctuating and is entangled with a lot of problems. Accordingly, a tailored mechanism should be adopted to deal with these problems. To do so, supply chains must take precautionary measures such as storing products and holding safety stock, etc. Given the importance of storage in supply chains, warehouses and depots should be carefully taken into account and located in such a way that their best performance is warranted. In this regard, this paper addresses a robust Multi-Objective multi-product model to design a distribution system under operational risks and disruption considerations. In the proposed model, the objective functions include minimizing the total distribution system cost, the total environmental impacts caused by supply chain along with minimizing the maximum lost sales in customer zones, while taking into consideration possible complete multiple disruptions in facilities and routes between them. Besides, a ε-constraint method is utilized to convert the Multi-Objective problem to a single objective model. In this paper, a two-stage robust possibilistic programming approach is deployed to cope with the uncertainty and disruption risks in the proposed model. Eventually, a real automotive case study is applied to the proposed model, via which the applicability and performance of the proposed model are endorsed. Results indicate that considering operational and disruption risks in the supply chain using two-stage robust optimization will require high costs but it will lead to economic savings and technical advantages in the long term.

    Keywords: Warehouse, robust optimization, Uncertainty, Fuzzy logic, Disruption, Distribution network design
  • Mostafa Zandieh *, SeyedYasser Shariat, Masoud Rabieh, Majid Tootooni Pages 107-135

    The purpose of this paper is to develop a new framework for strategic decision making in a turbulent environment via a dynamic sustainability balanced scorecard (BSC). Environmental factors are selected by fuzzy TOPSIS method and added to a dynamic model of BSC for a company. The decision-making model is proposed in three main scenarios: Optimistic (economic growth scenario), Realistic (average long term economic situation) and Pessimistic (continuity of current sanctions situation scenario) and two internal policies: Production maximization is the first internal policy and Productivity maximization is the second internal policy. The model is separately simulated in each scenario and policy, with the dynamic BSC model and every main aspect of the organization is analyzed with the majority of profit-making and its sustainability. The results show that a different policy is preferred in each scenario, which can help strategic managers for the decision-making process in uncertain and turbulent environments. Due to the increasing complexity of organizations in the competitive environment, it is necessary to propose performance evaluation models. The Balanced Scorecard (BSC) model is one of the most commonly used models for enterprise performance assessment that can be significantly adapted to environmental conditions. This research is novel because the environmental factors are added to a dynamic model of BSC for a company that has been encompassed with a turbulent economic, political and social environment within last years.

    Keywords: Decision Making, Environmental management, Measurement, Sustainability, system dynamics
  • Sepideh Khalafi, Ashkan Hafezalkotob *, Davood Mohamaditabar, MohammadKazem Sayadi Pages 136-153

    Recently, following the raise in expense pressures led to lower economic growth, an increasing number of manufacturers have begun to investigate eventuality of handling returned product in a more cost-effective and proper procedure. Significance of Reverse Logistics (RL) is becoming greater due to various governmental, societal, and environmental reasons. Number of papers present in the literature on RLs is a well index of its importance. In some industries, appropriately collected returned products could be used as raw material for another product, increasing Supply Chain (SC) profits and reducing the waste. Since, perishable goods have a limited shelf -life, they can be reusable if they are collected before they reach a critical time. Accordingly, in the present study, a Mixed Integer Linear Programming (MILP) model was introduced for a network of closed-loop SC with recycling of returned perishable goods, involving suppliers, producers, retailers, together with collection and disposal centers, in a multi-product, multi-period, and multi-level basis. To do this, a case study was performed on milk and yogurt products of a company in dairy industry. The model was solved and analyzed using GAMS software. Results obtained from assessment of the model indicated that, timely collection of perishable goods and their use in production of new products reduces total costs of perishable SC network.

    Keywords: Closed-loop supply chain, forward, reverse logistics, mixed integer programming, perishable products
  • Amin Alirezaee, SeyedJafar Sadjadi * Pages 154-171

    During the past few decades, there have been tremendous efforts in cooperative advertising. In spite of many practical applications in real life, cooperation in advertising and pricing strategies in a one-manufacturer and multi-retailer supply chain is almost overlooked in the literature. Hence, this paper seeks to investigate optimum co-op advertising and pricing decisions in a B2B relationship for a supply chain consist of a manufacturer and numerous multiple retailers in Iran as a case study. This paper introduces a game theoretic model containing pricing and cooperative advertising in a one-manufacturer and multi-retailer structure. Non-cooperative and cooperative game structures are used for analyzing the proposed model. The non-cooperative game structure uses Stackelberg game among the echelons and Nash game in the retailer echelon. Motivated by a real case study including an Iranian supply chain data of one manufacturer and 150 retailers, a novel model proposed to tackle the similar condition occurred in real life. The results indicate that the manufacturer prefers to suggest higher participation rate to smaller retailers. Sensitivity analysis is presented, and some managerial insights are finally derived from the results.

    Keywords: Cooperative advertising, pricing, supply chain coordination, participation rate, game theory, retailer segmentation
  • Foroogh Ghollasi, Hassan Hosseini Nasab *, MohammadBagher Fakhrzad, Javad Tayyebi Pages 172-197

    This paper addresses a bi-objective mixed integer optimization model under uncertainty for population partitioning problem. The objective functions are to minimize the number of communications between partitions and to balance their population. The main constraints are defined for creating contiguous and compact partitions as well as assigning uniquely each basic unit to one partition. To deal with the uncertainty of parameters, a robust programming method is proposed that causes the uncertainty parameters lie between the interval of best-case (the deterministic mode) and worst-case (the highest uncertainty level for all parameters). As the suggested method is NP-Hard, three meta-heuristic algorithms NSGAII, PESA, and SPEA are developed and, to evaluate the efficiency of the algorithms, 10 small-size examples, 10 medium-size examples and, 10 large-size examples are generated and solved. According to computational results, the SPEA has the best performance. The method is examined for a real-world application, as a case study in Iran.

    Keywords: partitioning, interval uncertainty, Multi-Objective Optimization, robust programming
  • Mostafa Tavallaaee, Fatemeh Rakhshan*, Mohammad Reza Alirezaee Pages 198-207

    The role of some factors such as efficiency, rule and regulations, and balance have been already investigated in the context of productivity analysis based on data envelopment analysis models. Along with the studies that take the role of cost factors into account, this paper presents a novel four-component decomposition of Malmquist productivity growth index from a financial point of view. The cost efficiency model applied here uses assurance region weight restrictions to increase discrimination power of basic data envelopment analysis models. In the proposed decomposition, the proportion of cost efficiency changes during two time periods is determined as a quantity measure between zero and one. A real case study from banking industry including 66 branches located in east Tehran is employed to show the applicability of the proposed methods and the results were been analyzed.

    Keywords: Data Envelopment Analysis, Malmquist Index, cost efficiency, weight restrictions
  • Seyed Mohammad Taghi Fatemi Ghomi, Sajjad Rahmanzadeh*, Mohsen Sheikh Sajadieh Pages 208-226

    This paper proposes a truck scheduling model in a cross dock system under multi-period, multi-commodity condition with fixed outbound departures. In an operational truck scheduling problem, outbound trucks leave the cross dock terminals at predetermined times and delayed loads are kept as inventory that are sent at the next period (a time slot in a day). The proposed model optimizes the inbound truck scheduling problem through the minimizing cross dock operational costs. An accelerated Benders decomposition technique based on Covering Cut Bundle (CCB) strategy and a heuristic approach are developed to solve the model. Finally, numerical analysis introduces the sensitivity of the input parameters to the objective value.

    Keywords: Cross dock, scheduling, heuristic algorithm, sensitivity analysis
  • Masoud Rabani*, Neda Manavizadeh, Abtin Boostani, soroush aghamohamadi Pages 227-241

    This paper presents a novel multi-objective location arc-routing model in order to locate disposal facilities and to design optimal routes of residential waste taking into consideration many complicated real constraints such as a heterogeneous fleet of vehicles, time windows for customers, disposal facilities and the depot, capacities for vehicles and facilities. The first objective is the minimization of transportation costs, including service costs and fuel costs of vehicles. The second one minimizes total number of utilized vehicles. And finally, the third objective function is considered for minimizing total number of established disposal centers. Moreover, to come closer to reality the service time, amount of demands, capacities and cost parameters are considered as fuzzy ones. To solve the proposed model, a credibility-based fuzzy mathematical model and its interactive solution method with three recent approaches, are used and the results are compared with each other.

    Keywords: Waste collection problem, multi-objective optimization, time windows, interactive fuzzy programming, chance constraint programming
  • atefe Banihashemi, MohammadSaber Fallahnezhad *, Amirhossein Amiri Pages 242-251

    An essential tool for examining the quality of manufactured products is acceptance sampling. This research applies the concept of minimum angle method to extend two variables sampling plans including the variables multiple dependent state (VMDS) sampling plan and the variables repetitive group sampling (VRGS) plan on the basis of the process yield index Spk. Optimal parameters of acceptance sampling plans can be determined by solving a non-linear optimization model with the following conditions: 1) The objective function of the plan is to minimize the average sample number. 2) Constraints are set in a way that the compliance rate will be satisfied with the ideal operating characteristic (OC) curve as well as the producer’s and costumer’s risks. The assessment of the proposed plans reveals that by increasing the rate of convergence to the ideal OC curve, the proposed VRGS plan performs better than the proposed VMDS plan in terms of the average sample number. A numerical example is considered to reveal the applicability of the proposed acceptance sampling plans.

    Keywords: acceptance sampling, minimum angle method, nonlinear optimization, operating characteristic curve, yield index
  • Habib Dehghani Ashkezari, Saeed Yaghoubi* Pages 252-268

    Blood plasma is a yellowish liquid component of blood that holds the blood cells (red blood cells, white blood cells, and platelets) in whole blood in suspension. Plasma is human-based so that it just makes in the body thus only donors can be the source for preparing plasma. Plasma has usage in two-part therapy and medicine. This article addresses the design of an integrated blood plasma supply chain network considering demand in two segments of therapy and medicine. To this goal, a MILP scenario-based mathematical programming model is developed which minimizes the total cost as well as the unsatisfied demand. After that, the actual data of a case study are used to illustrate the applicability also the performance of the offered model as well as validation. The obtained results show the superiority of the recovered plasma method compared to the apheresis plasma method for the blood transmission network. As well, the maximum use of the capacity instantly after the opening of each collection centers is beneficial for reducing the total cost.

    Keywords: Blood plasma supply chain, network design, health systems, scenario-based optimization
  • Seyed Mohammad Seyedhosseini*, Mohammad Baharshahi, Kamran Shahanaghi Pages 269-282

    Estimation of remaining useful life (RUL) is one of most interesting subjects in prognostic and health management. Performing an analysis of the results of such estimation can increase the reliability and the safety of the system, and reduce the unnecessary costs. In this paper, a similarity-based combination method is proposed to combine several run-to-failure historical datasets in order to directly estimate the RUL. In this method, reference datasets are clustered and the initial RUL is calculated based on the artificial neural networks trained by the reference datasets. By using the extended Dempster-Shafer, the similarity between the initial RUL and the average RUL for each dataset is obtained. The proposed methodology is tested and validated on Commercial Modular Aero-Propulsion System Simulation (C-MAPSS), test-bed developed by NASA. The results of the evaluation show that the proposed method outperforms other methods in the literature.

    Keywords: Dempster-Shafer theory, information integration, remaining useful life