نشریه پژوهشهای مهندسی صنایع در سیستمهای تولید
پیاپی 3 (بهار و تابستان 1393)

This article reviews and categorizes the papers written on the application of game theory in facility location problems. The objective of the problem can be minimizing the transportation costs and simultaneously maximizing the maximum covering with fixed demand، a linear relation can be assumed between cost and pairwise distance of facilities. There are some problems of location facilities in which competing companies look to maximize their market share and minimize transportation costs. These models are known as the competitive facility location. Game theory is one of the important methods to analysis these problems solving. Game theory is an appropriate method for obtaining the minimum distance and maximum market share to Reduce fixed costs and increase network utilization. Articles related to this issue have been studied from 1929 until 2013 and an analytical report is presented. This article can be used as a reference for researchers in the field of applied game theory problems for facility location، especially on location competitive problems.

In some statistical process control applications، the quality of a product is characterized by the combination of both correlated variable and attributes quality characteristics. To the best of our knowledge، there is no method in the literature available for identifying the shift magnitude in the out-of-control quality characteristics in multivariate-attribute processes. In this paper، a neural network (NN) -based method is proposed to identify the magnitude of shifts in the out-of-control quality characteristics. The proposed methodology can also determine the process state and diagnose the quality characteristic (s) responsible for out-of-control signals. The performance of the proposed NN-based method in determining the mean shifts magnitude، detecting the process change as well as diagnosing the out-of-control quality characteristic (s) is evaluated based on a numerical example through simulation studies. In addition، the performance of the proposed NN in detection and diagnosis is compared with existing methods in the literature. The results of simulation study show the satisfactory performance of the proposed NN

Cell formation، as one of the most important decision problems in designing a cellular manufacturing system، consists of grouping parts into part families and machines into cells. In a dynamic environment، the part demand/mix change is considered over a planning horizon divided into periods. Hence، the formation of cells for one period may no longer be effective for other periods and therefore، reconfiguration of cells is essential. Due to the variation of demand and the need for cells reconfiguration، virtual cell formation concept is introduced by researchers to take the advantage of cell formation without incurring reconfiguration charges. On the other hand، Simultaneous consideration of supply chain issues and cell formation results in lower distribution and procurement costs and faster response to customers. In traditional manufacturing systems، first، the supply chain is designed، the number of production facilities is determined and the facilities are assigned to support each market for each product; then، the organization of the processes (product line، process or cell formation) within factory is decided. In this paper، a new bi-objective robust optimization mathematical model is developed for integrating procurement، production and distribution planning considering various conflicting objectives simultaneously as well as the uncertainty of some critical parameters such as customer demands. The augmented ε-constraint method is utilized to solve the proposed mathematical model and to find a preferred compromise solution. Moreover، a real world industrial case is provided to justify the applicability of the proposed model.

Location-routing problem is one of the most challenging problems in supply chain management. In real world، many companies hire vehicles for servicing demands of customers، so these vehicles do not return to these companies after ending services. On the other hand، managers are constantly faced with the problem of whether serving each costumer’s demand by one vehicle will result in higher benefits or delivering their demands by more than one vehicle will lead to increased profits. Therefore، in response to this challenge and in order to get closer to the real world، a new problem which is called split delivery open location-routing problem is modeled in this study. Since the problem is a NP-Hard، Tabu search and simulated annealing algorithms are used for solving it. The mathematical model is run by cplex10. 1 software for the small size instances. In addition، in order to improve the proposed solution algorithms، a heuristic algorithm for generating suitable initial solution is presented. Finally، after generating the new experimental instances and tuning parameters of the proposed algorithms، the numerical results of the problem solving by cplex10. 1 software and the suggested algorithm are analyzed. The results show the efficiency of the two algorithms and superiority of simulated annealing algorithm over tabu search algorithm. The results also indicate that considering the assumption of split delivery lead to final cost reduction، especially when the demand variance is relatively small and the mean is greater than half the vehicle capacity and less than three quarters of the vehicle capacity.

Production planning problems are generally modeled as mixed integer programming problems; and solved through heuristic algorithms، because of their innate high computational complexity. In this paper، a mixed integer nonlinear programming (MINLP) model is designed for multi-item، multi-period production planning to replenish orders of the buyer and minimizing the supplier’s costs. It is supposed that the order quantity is constant، and ordering occurs at once. This model has been developed according to the realistic assumptions of SAPCO Company، which is a major supplier of automotive parts in Iran، and one of its partner companies. In addition، an efficient heuristic algorithm based on A* search has been proposed to solve this mathematical model. The proposed search algorithm does not need an initial solution; also، it can overcome the memory overhead through bounding the stored states in its open-list. Actually، in spite of the simplicity of the proposed algorithm، which is established based on the simple inventory management equations; it is able to generate efficiently optimal or near-optimal solutions in comparison with an exact solution method، a greedy search algorithm، and simulated annealing algorithm as a metaheuristic algorithm.

This paper proposes a model، which aims to analyze the partial trade credit financing in a supply chain by economic order quantity-based model for deteriorating items. If the order quantity is more than a specified quantity fully permissible delayed payment is possible، otherwise partial trade credit is offered. Selling and purchasing costs are not equal and interest charged in stocks is not necessarily greater than interest earned on investment. In order determine the unique and optimum solution several lemmas and theorems are defined. Finally to show validity of the proposed model and applicability of developed algorithm، experimental results are provided.

Reverse logistic has attracted a lot of attention from researchers in recent years due to government regulations، environmental problems، extension of social responsibility and customer demands. In addition، the decline in the natural resources and raw materials combined with the increase in production costs and problems of dealing with trash of the industry and consumer products makes the cycle of consumption very interesting to researchers from the production point to the last stage of recycling. This subject gave way to newer concepts like integrated، closed-loop and stable supply chain in the past decade. This paper presents a robust design for a multi-product، multi-echelon، closed-loop logistic network model in an uncertain environment. To this aim، a multi-objective mathematical programming model is developed wherein its objective functions include profit، social and environmental impacts. First، a deterministic mixed-integer linear programming model is developed for designing a closed-loop supply chain network. Then، the robust counterpart of the proposed mixed-integer linear programming model is presented by using the recent extensions in robust optimization theory. Finally، to assess the robustness of the solutions obtained by the novel robust optimization model، they are compared to those generated by the deterministic mixed-integer linear programming model in a number of realizations under different test problems.