Proposing an Algorithm for R&Q Inventory Control Model with Stochastic Demand Influenced by Shortage

In this article, the continuous - review inventory control system has been studied. A new constraint of demand dependent on the average percent of product shortage has been added to the problem. It means that the average demand has a direct relationship with shortage in a period. This constraint, which is related to the costs of credit loss of the organization due to product shortage, has been considered in the inventory model. In this paper, the mathematical model of this problem has been presented and then, two heuristic approaches based on the genetic and simulated annealing algorithms are developed. Computational results indicate that the simulated annealing algorithm can provide better results compare to the genetic algorithm

Novel Pareto-based meta-heuristics for solving multi-objective multi-item capacitated lot-sizing problems

International audienceCapacitated production lot-sizing problems (LSPs) are challenging problems to solve due to their combinatorial nature. We consider a multi-item capacitated LSP with setup times, safety stocks, and demand shortages plus lost sales and backorder considerations for various production methods (i.e., job shop, batch flow, or continuous flow among others). We use multi-objective mathematical programming to solve this problem with three conflicting objectives including: (i) minimizing the total production costs; (ii) leveling the production volume in different production periods; and (iii) producing a solution which is as close as possible to the just-in-time level. We also consider lost sales, backorders, safety stocks, storage space limitation, and capacity constraints. We propose two novel Pareto-based multi-objective meta-heuristic algorithms: multi-objective vibration damping optimization (MOVDO) and a multi-objective harmony search algorithm (MOHSA). We compare MOVDO and MOHSA with two well-known evolutionary algorithms called the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective simulated an-nealing (MOSA) to demonstrate the efficiency and effectiveness of the proposed methods