فهرست مطالب zavardehi

Scheduling has become an attractive area for artificial intelligence researchers. On other hand, in today's real-world manufacturing systems, the importance of an efficient maintenance schedule program cannot be ignored because it plays an important role in the success of manufacturing facilities. A maintenance program may be considered as the heath care of manufacturing machines and equipments. It is required to effectively reduce wastes and have an efficient, continuous manufacturing operation. The cost of preventive maintenance is very small when it is compared to the cost of a major breakdown. However, most of manufacturers suffer from lack of a total maintenance plan for their crucial manufacturing systems. Hence, in this paper, we study a maintenance operations planning optimization on a realistic variant of parallel batch machines manufacturing system which considers non-identical parallel processing machines with non-identical job sizes and fixed/flexible maintenance operations. To reach an appropriate maintenance schedule, we propose solution frameworks based on an Artificial Immune Algorithm (AIA), as an intelligent decision making technique. We then introduce a new method to calculate the affinity value by using an adjustment rate. Finally, the performance of proposed methods are investigated. Computational experiments, for a wide range of test problems, are carried out in order to evaluate the performance of methods.

In this paper, we consider the step fixed-charge transportation problem (FCTP) in which a step fixed cost, sometimes called a setup cost, is incurred if another related variable assumes a nonzero value. In order to solve the problem, two metaheuristic, a spanning tree-based genetic algorithm (GA) and a spanning tree-based memetic algorithm (MA), are developed for this NP-hard problem. For comparing GA and MA, twenty eight problems with different specifics have been generated at random and then the quality of the proposed algorithms was evaluated using the relative percentage deviation (RPD) method. Finally, based on RPD method, we investigate the impact of increasing the problem size on the performance of our proposed algorithms.

Fixed charge solid transportation problem (FCSTP) is one of the main and most important problems in transportation and network research areas. To tackle such an NP-hard problem, An Electromagnetism-like algorithm (EM) is employed. To the best of our knowledge, EM has been considered for any kind of transportation problems. Due to the significant role of parameters on the algorithm’s performance, a calibration in EM is carried out with the aid of a set of experimental design. The efficiency of employed parameters is measured by the experimental design method. To evaluate the performance of the proposed EM, a computational study has been conducted and the associated results obtained by the EM are compared with simulated annealing algorithm (SA).

This paper presents a new mathematical programming model for an integrated production and air transportation in supply chain management with sequence-dependent setup times in order to design an applied procedure for the production and distribution schedule. The aim of this model is to minimize the total supply chain cost consisting of the costs of distribution, production earliness and tardiness, and delivery. Because of the complexity and NP-hardness of this problem, two meta-heuristics based on genetic algorithm (GA) and variable neighborhood search (VNS) are proposed. The parameters of these algorithms and their appropriate operators are set and determined by the use of the Taguchi experimental design. Then, the quality of the results obtained by these algorithms is compared. The computational results show that the developed VNS outperforms the proposed GA.