particle swarm algorithm

The present study presents a three-tier model with incomplete and uncertain information of supply chain needs, benefits and services. Objectives of the issue include determining the best decision to determine the optimal order amount and shortage for the manufacturer and the selling price of each player according to the shortage, discount and maintenance costs, purchase and marketing to achieve maximum revenue, minimum costs and The sum is the maximum possible profit for all the players participating in the chain. To solve the model, Gamz software and meta-heuristic algorithms have been used and finally, Given the complexity of the complexity of closed-loop supply chain problems, the problem ahead cannot be solved in a reasonable time for real-world dimensions. Therefore, to solve it, the meta-heuristic approach in the form of genetic algorithms and optimization of particle aggregation and the combination of these two algorithms have been used. The results show that the combined algorithm of genetics and particle swarming has a better situation compared to genetic and particle swarming algorithms.

In this research, we investigate the two-echelon singleperiod inventory problem, in which the raw materials and the nal product should be provided at the beginning of the selling period. Raw materials that remain unused and the nal products that remain unsold until the end of the period, can be sold at a discount at the end of the period. the use of coecient in nal products is dierent from each raw material, so the raw materials and the nal product may not have a one-to-one relationship; we have a certain limited budget for buying the raw materials and producing the nal products. Supplier will oer the discount for each raw material to reduce his inventory level, if we buy more than a certain quantity of that raw materials. Since the price of the nal product at the end of the period is so Low, we set a cumulative discount policy to encourage customers for our nal product. Consequently, one of the costumers buys more than a certain quantity, and the price of each nal product for that costumer will be less than the normal price. If the nal product is sold out during the period, depending on the demand, some of our customers will wait for transforming raw materials into the nal product during the period. The objective of this problem is to nd the order of quantities of both the raw materials and the nal product, such that the expected prot can be maximized. A mathematical model was rst developed for this problem, then a particle swarm optimization algorithm (PSO) was proposed to solve it. Finally, a numerical example was given to illustrate the application of the proposed methodology. This example was solved by the developed PSO algorithm, and to receive a better answer from PSO, we ran the algorithm 50 times.