Joint decision of inventory and pricing for deteriorating items with partial backlogging and multi-constraint

Abstract

This paper studies the joint decision of inventory replenishment and pricing of multiple retailers for deteriorating items with partial backlogging and multiple constraints. In the model, deterioration rate is simulated as time varying function of Weibull distribution. Two constraints are inbuilt in the model, i.e., supply capacity of the supplier and comprehensive customer service level of retailers. This is a multi-constraint non-linear programming problem, and considering the complexity of computation using classic differentiation derivation method, in this paper, we develop three meta-heuristic algorithms to solve the model, i.e., simulated annealing (SA) algorithm, particle swarm optimisation (PSO) and quantum behaved PSO (QBPSO). Experiment shows that QBPSO is the most effective and efficient algorithm among the proposed three algorithms. It is shown that service level at about 80% (or shortage rate at 20%) can obtain best profit, and price elasticity coefficient significantly impacts the pricing and total profit, but it has little impact on inventory decisions.