An Event-Based Supply Chain Partnership Integration Using a Hybrid Particle Swarm Optimization and Ant Colony Optimization Approach

Abstract

Integrating a partnership with potentially stronger suppliers is widely acknowledged as a contributor to the organizational competitiveness of a supply chain. This paper proposes an event-based model which lists the events related with all phases of cooperation with partners and puts events into a dynamic supply chain network in order to understand factors that affect supply chain partnership integration. We develop a multi-objective supply chain partnership integration problem by maximizing trustworthiness, supplier service, qualified products rate and minimizing cost, and then, apply a hybrid algorithm (PSACO) with particle swarm optimization (PSO) and ant colony optimization (ACO) that aims to efficiently solve the problem. It combines the advantages of PSO with reliable global searching capability and ACO with great evolutionary ability and positive feedback. By using the actual data from 1688.com, experimental studies are carried out. The parameter optimizing of the hybrid algorithm is firstly deployed and then we compare the problem solution results of PSACO with the original PSO, ACO. By studying the partnership integration results and implementing analysis of variance (ANOVA) analysis, it shows that the event based model with PSACO approach has validity and superiority over PSO and ACO, and can be served as a tool of decision making for supply chain coordination management in business.

Multi-objective sustainable supply chain network optimization based on chaotic particle—Ant colony algorithm

“…Soleimani et al [ 37 ] proposed a hybrid particle swarm algorithm and genetic algorithm to solve the problem based on the advantages and disadvantages of particle swarm algorithm and genetic algorithm when studying the problem of designing a large-scale network closed-loop supply chain network. Lu et al [ 38 ] combined the global search capability of PSO with the powerful evolutionary capability of ACO and a bit of positive feedback to solve the multi-objective supply chain partner integration problem.…”

Smart Elderly Care: An Intelligent e-Procurement System for Elderly Supplier Selecting

“…For the supplier selection, in order to further the study of supply chain management, Kang (2016) examined the relationship between supply chain integration and supply chain collaboration between supply network partners [12]. Aiming at the accuracy and efficiency of partner selection, Lu et al (2016) proposed PSACO, a hybrid algorithm based on particle swarm optimization and ant colony optimization [13]. Xie et al (2018) aimed to study a twoechelon closed-loop dual channel supply chain consisting of a single online direct selling platform provider and a single offline channel service provider.…”

An Investigation of Particle Swarm Optimization Topologies in Structural Damage Detection

“…The PSO algorithm is a population-based meta-heuristic that was proposed by Kennedy and Eberhart [5]. It is based on simulating the foraging behavior of bird flocking, and it has been widely used in many fields, such as benchmark function optimization [6], image processing [7], scheduling decision [8], and engineering [9]. However, it is well-known that the classical PSO algorithm yields premature convergence.…”