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. تفاصيل المقالة

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. تفاصيل المقالة

The supply chain network design not only assists organizations production process (e.g.,plan, control and execute a product’s flow) but also ensure what is the growing need for companies in a longterm. This paper develops a three-echelon supply chain network problem including multiple plants, multiple distributors, and multiple retailers with amulti-mode demand satisfaction policy inside of production planning and maintenance. The problem is formulated as a mixed-integer linear programming model. Because of its NP-hardness, three meta-heuristic algorithms(i.e., tabu search, harmony search and genetic algorithm) are used to solve the given problem. Also, theTaguchi method is used to choose the best levels of the parameters of the proposedmeta-heuristic algorithms. The results show that HS has abetter solution quality than two other algorithms. تفاصيل المقالة

This paper presents a fuzzy decision-making approach to deal with a clustering supplier problem in a supply chain system. During recent years, determining suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of these decisions is usually complex and unstructured. In general, many quantitative and qualitative factors, such as quality, price, and flexibility and delivery performance, must be considered to determine suitable suppliers. The aim of this study is to present a new approach using particle swarm optimization (PSO) algorithm for clustering suppliers under fuzzy environments and classifying smaller groups with similar characteristics. Our numerical analysis indicates that the proposed PSO improves the performance of the fuzzy c-means (FCM) algorithm. تفاصيل المقالة

Logistic network design is one of the most important strategic decisions in supply chain management that has recently attracted theattention of many researchers. Transportation network design is then one of the most important fields of logistic network. This study isconcerned with designing a multi-stage and multi-product logistic network. At first, a mixed integer nonlinear programming model(MINLP) is formulated that minimizes transportation and holding costs. Then, a hybrid priority-based Genetic Algorithm (pb-GA) andsimulated annealing algorithm (SA) is developed in two phases to find the optimal solution. The solution is represented by a matrix and avector. Response Surface Methodology (RSM) is also used to adjust the significant parameters of the algorithm. Finally, several testproblems are generated which show that the proposed metaheuristic algorithm can find good solutions in reasonable time spans. تفاصيل المقالة

This paper proposes a new mixed integer programming model for multi-item capacitated lot-sizing problem with setup times, safety stock and demand shortages in closed-loop supply chains. The returned products from customers can either be disposed or be remanufactured to be sold as new ones again. Due to the complexity of problem, three meta-heuristics algorithms named simulated annealing (SA) algorithm, vibration damping optimization (VDO) algorithm and harmony search (HS) algorithm have been used to solve this model. Additionally, Taguchi method is conducted to calibrate the parameter of the meta-heuristics and select the optimal levels of the algorithm’s performance influential factors. To verify and validate the efficiency of the proposed algorithms in terms of solution quality, the obtained results were compared with those obtained from Lingo 8 software for a different problem. Finally, computational results of these algorithms were compared and analyzed by producing and solving some small, medium and large-size test problems. The results confirmed the efficiency of the HS algorithm against the other methods. تفاصيل المقالة

Multi attribute decision making methods are considered as one of the most useful methods for solving ranking problems. In some decision making problems, while the alternatives for corresponding criteria are compared in a pairwise comparison manner, if the criteria are inherently fuzzy, debates will arise in ranking alternatives due to the closeness of the values of the criteria. In this research, the fuzzy PROMETHEE II is proposed as a solution in such conditions. First, using the ANP method, the criteria are weighted. Then, the ranking process is accomplished both by the fuzzy PROMETHEE II and the fuzzy TOPSIS methods. Finally, calculating Spearman correlation coefficient, the results of these two approaches are compared. Then, the most important risks are selected via the fuzzy binary goal programming and ranked again through the fuzzy PROMETHEE II and fuzzy TOPSIS methods finally, in the last step, these ranking two are compared. As a case study, highway construction risks are ranked through this method. تفاصيل المقالة

[1] Corresponding author e-mail: mehdi.foumani@monash.edu [1] Corresponding author e-mail: mehdi.foumani@monash.edu Lot-sizing problems (LSPs) belong to the class of production planning problems in which the availability quantities of the production plan are always considered as a decision variable. This paper aims at developing a new mathematical model for the multi-level capacitated LSP with setup times, safety stock deficit, shortage, and different production manners. Since the proposed linear mixed integer programming model is NP-hard, a new version of simulated annealing algorithm (SA) is developed to solve the model named revised SA algorithm (RSA). Since the performance of the meta-heuristics severely depends on their parameters, Taguchi approach is applied to tune the parameters of both SA and RSA. In order to justify the proposed mathematical model, we utilize an exact approach to compare the results. To demonstrate the efficiency of the proposed RSA, first, some test problems are generated; then, the results are statistically and graphically compared with the traditional SA algorithm. تفاصيل المقالة

Facility location-allocation models are used in a widespread variety of applications to determine the number of required facility along with the relevant allocation process. In this paper, a new mathematical model for the capacitated multi-facility location-allocation problem with probabilistic customer's locations and fuzzy customer’s demands under the Hurwicz criterion is proposed. This model is formulated as α-cost minimization model according to different criteria. Since our problem is strictly Np-hard, a new hybrid intelligent algorithm is presented to solve the stochastic-fuzzy model. The proposed algorithm is based on a vibration damping optimization (VDO) algorithm which is combined with the simplex algorithm and fuzzy simulation (SFVDO). Finally, a numerical example is presented to illustrate the capability of the proposed solving methodologies. تفاصيل المقالة