A hybrid metaheuristic Algorithm for competitive supply chain network design considering marketing strategies

Abstract

Purpose: In this paper, a thorough model for creating a network for a supply chain that is multi-period, multi-echelon, and multi-product inventory controlled is proposed. Under the condition of static competition, different marketing plans and guerilla marketing techniques are taken into account. The proposed model's objective is to effectively meet customer demands in the face of existing competitors and demand price inelasticities. The suggested optimization model takes into account multiple goals that simultaneously take into account the market share and overall profit of the supply chain network under consideration.

Design/ Methodology/Approach: An effective hybrid meta-heuristic algorithm is developed that combines a Taguchi-based non-dominated sorting genetic algorithm-II and a particle swarm optimization to address the proposed multi-objective mixed-integer nonlinear programming model. The proposed hybrid solution algorithm applies a variable neighborhood decomposition search to improve a local search procedure.

Findings: Computing results show how effectively the suggested model and solution algorithm handle the pressure of competition by implementing the right marketing strategies.

Contribution to policy and practice: For further research, competition conditions, including foresight and dynamic competition, as well as considering the impact of the product price on the attractiveness of a service provider in the network design is proposed. Additionally, the uncertainty of the key parameters can be considered in the network design process. Finally, an efficient solution algorithm can be developed to tackle such a complex problem.

Originality/Value: The present research may assist scholars to move beyond a simple dyadic context and toward examining complete supply networks.