Design of a Mathematical Model for Logistic Network in a Multi-Stage Multi-Product Supply Chain Network and Developing a Metaheuristic Algorithm

Logistic network design is one of the most important strategic decisions in supply chain management that has recently attracted the attention of many researchers. Transportation network design is then one of the most important fields of logistic network. This study is concerned 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 a vector. Response Surface Methodology (RSM) is also used to adjust the significant parameters of the algorithm. Finally, several test problems are generated which show that the proposed metaheuristic algorithm can find good solutions in reasonable time spans

Fuzzy Particle Swarm Optimization Algorithm for a Supplier Clustering Problem

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

Three Metaheuristic Algorithms for Solving the Multi-item Capacitated Lot-sizing Problem with Product Returns and Remanufacturing

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

Preventive maintenance effect on the aggregate production planning model with tow-phase production systems: modeling and solution methods

This paper develops two mixed integer linear programming (MILP) models for an integrated aggregate production planning (APP) system with return products, breakdowns and preventive maintenance (PM). The goal is to minimize the cost of production with regard to PM costs, breakdowns, the number of laborers and inventory levels and downtimes. Due to NP-hard class of APP, we implement a harmony search (HS) algorithm and vibration damping optimization (VDO) algorithm for solving these models. Next, the Taguchi method is conducted to calibrate the parameter of the metaheuristics and select the optimal levels of factors influencing the algorithm’s performance. Computational results tested on a set of randomly generated instances show the efficiency of the vibration damping optimization algorithm against the harmony search algorithm. We find VDO algorithm to obtain best quality solutions for APP with breakdowns and PM, which could be efficient for large scale problems. Finally, the computational results show that the objective function values obtained by APP with PM are better than APP with breakdown results

A New Hybrid Algorithm to Optimize Stochastic-fuzzy Capacitated Multi-Facility Location-allocation Problem

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

Using Hybrid Fuzzy PROMETHEE II and Fuzzy Binary Goal Programming for Risk Ranking: A Case Study of Highway Construction Projects

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.</p

Electrical fuzzy C-means: A new heuristic fuzzy clustering algorithm

Many heuristic and meta-heuristic algorithms have been successfully applied in the literature to solve the clustering problems. The algorithms have been created for partitioning and classifying a set of data because of two main purposes: at first, for the most compact clusters, second, for the maximum separation between clusters. In this paper, we propose a new heuristic fuzzy clustering algorithm based on electrical rules. The laws of attraction and repulsion of electric charges in an electric field are conducted the same as the target of clustering. The electrical fuzzy C-means (FCM) algorithm proposed in this article use the electrical rules in electric fields and Coulomb’s law to obtain the better and the realest partitioning, having respect to the maximum separation of clusters and the maximum compactness within clusters. Computational results show that our proposed algorithm in comparison with FCM algorithm as a well-known fuzzy clustering algorithm have good performance