Design of supply chain networks with multi-phased discount price and service level: formulation, complexity, and algorithm

A mixed integer programming problem is developed in this paper for the purpose of choosing supply chain members. Most of such literature work used to focus on the multiobjective programming (e.g., time and cost). However, in practice, it is not necessary to minimize both time and cost in some cases. The key point in the supply chain planning of perishable commodities is to efficiently meet customer's demands before the commodities are devaluated. Therefore, this paper proposes a supply chain network for perishable commodities with multi-phased discount price and service level. The objective is to maximize the total profit and demonstrate that the transportation problem of a single item with multiphased discount puce is NP-hard in the strong sense. This paper proposes a hybrid algorithm based on particle swarm optimization and simulated annealing as a solution to the problem, as well as a novel network encoding method. The operation of the proposed algorithm includes the mutation position selection based on the regret vector. The solution remains feasible even after the mutation execution. In the experiment and analysis, the Taguchi method is used to analyze how the parameters in the algorithm affect the solution quality. Sensitivity analysis of the parameters on the performance of the algorithm is also studied.