Multi-Objective Optimization for Green Dual-Channel Supply Chain Network Design Considering Transportation Mode Selection

Abstract

A well-designed supply chain network should not only meet the efficient cost but also realize the sustainable effect on environment. The purpose of this article is to develop a multi-objective model to capture the trade-off between total cost and environmental performance in the green dual-channel supply chain network. Moreover, the transportation mode has been considered as a decision variable. With regard to the complexity of such network, a new swarm intelligence algorithm known as a multi-objective particle swarm optimization (MOPSO) algorithm has been employed to tackle this problem. The effectiveness of the present model and approach is evaluated by a numerical experiment, and the results show that the added environmental performance is actually proportional with the increased cost. Additionally, the comparison between different mode decisions shows that a better trade-off between two objectives will be obtained when considering the transportation mode selection.

Decoupling Institutional Pressure on Green Supply Chain Management Efforts to Boost Organizational Performance: Moderating Impact of Big Data Analytics Capabilities

“…GSCM has received more and more attention in the literature (Lee et al, 2012;Zhang and Yang, 2018) and the studies trying to link resource-based view (RBV) theory to GSCM clarify firms' competing stance and improve performance (Barksdale and Pratt, 1980;Gavronski et al, 2011;Choi and Hwang, 2015). The RBV believes that the company's resource combination determines the company's competitive position, not the product configuration of these resources (Wernerfelt, 1995).…”

A Systematic Review of Sustainable Supply Chain Network Design: Optimization Approaches and Research Trends

“…Metaheuristic NSGA-II [21,115,116,124]; PSO [26,54,111]; SA [7,106]; PSO and NSGA-II [63,98]; GA and PSO [30,77,100]; GA and EA & PSO [69]; SA, PSO and NSGA-II [141]; hybrid swarm intelligence [17]; SPEA2 and ALNS [101]; LNS with multi-directional LS [66]; hybrid firefly algorithm and SA [76]; swarm intelligence and ABC [120]; multi-neighborhood descent traversal algorithm [125]; SA and RDA algorithms and hybrid GA-PSO [29];…”

“…Other exact solvers are also considered in the literature such as AMPL, Lingo, and Gurobi. The use of MATLAB software is considered by the authors for developing metaheuristic solution approaches [17,26,54,69,76,77,98,100,115,124], and the fuzzy c-means clustering method [122]. There are other tools for solving the optimization models.…”

“…For instance, the NSGA-II was developed to address the network design of a sustainable capacitated facility location problem that aims to minimize supply chain costs and environmental impacts [116]. Another solution method based on the particle swarm optimization (PSO) algorithm was adopted in [26,54,111]. PSO was used to solve a MILP model that minimizes cost and GHG emissions arising from location-allocation decisions of a green supply chain [111].…”

“…Supply chain configuration: A variety of supply chain configurations is considered, such as single [54] or multiple periods [55], single [56] or multiple products [57], and the number of levels/echelons involved in the network design of the supply chain [7].…”