An evaluation of three DoE-guided meta-heuristic-based solution methods for a three-echelon sustainable distribution network

Abstract

This article evaluates the efficiency of three meta-heuristic optimiser (viz. MOGA-II, MOPSO and NSGA-II)-based solution methods for designing a sustainable three-echelon distribution network. The distribution network employs a bi-objective location-routing model. Due to the mathematically NP-hard nature of the model a multidisciplinary optimisation commercial platform, modeFRONTIER ® , is adopted to utilise the solution methods. The proposed Design of Experiment (DoE)-guided solution methods are of two phased that solve the NP-hard model to attain minimal total costs and total CO 2 emission from transportation. Convergence of the optimisers are tested and compared. Ranking of the realistic results are examined using Pareto frontiers and the Technique for Order Preference by Similarity to Ideal Solution approach, followed by determination of the optimal transportation routes. A case of an Irish dairy processing industry's three-echelon logistics network is considered to validate the solution methods. The results obtained through the proposed methods provide information on open/closed distribution centres (DCs), vehicle routing patterns connecting plants to DCs, open DCs to retailers and retailers to retailers, and number of trucks required in each route to transport the products. It is found that the DoE-guided NSGA-II optimiser based solution is more efficient when compared with the DoE-guided MOGA-II and MOPSO optimiser based solution methods in solving the biobjective NP-hard three-echelon sustainable model. This efficient solution method enable managers to structure the physical distribution network on the demand side of a logistics network, minimising total cost and total CO 2 emission from transportation while satisfying all operational constraints.

A Review of Heuristics and Hybrid Methods for Green Vehicle Routing Problems considering Emissions

“…For instance, Wang et al [37] employed a shared transportation fleet and minimized a multi-objective function based on distances, vehicles utilization, and carbon emissions. Validi et al [49] considered concurrent minimization of total cost and total carbon emission in a three-echelon LRP context. Besides those works, our findings show few works tackling GVRPs as a multi-objective problem or with multi-objectivebased heuristics (see MH-MO, HMH-MO percentages indicators in Figure 6).…”

“…Validi et al [49] faced a sustainable three-echelon distribution network by proposing a mathematical formulation that aims at optimizing the routing throughout the transportation network while minimizing carbon emissions and transportation operating costs. +e authors presented three metaheuristic approaches to address this problem, that is, the multi-objective genetic algorithm of type II (MOGA-II), the MOPSO, and the NSGA-II.…”

Multi-echelon agri-food supply chain network design integrating operational and strategic objectives: a case of public distribution system in India

“…This is generally achieved by eliciting their preferences and selecting the alternatives using appropriate MCDM methods. The other advanced methodologies integrated metaheuristics and MCDM methods to discard the poor solutions and further refine the solution space iteratively (Validi et al 2020(Validi et al , 2021. The other set of approaches incorporate the trade-off among the objectives by providing rank order or weight of the objectives.…”

Bi- and multi-objective location routing problems: classification and literature review

“…DoE guided MOGA-II was used in the second phase while the TOPSIS method was used to analyze the feasible optimal solutions sets from the first and second phases. The same model was considered by Validi et al (2021) which employed two GA-based and one PS-based metaheuristic, namely NSGA-II, MOGA-II, and MOPSO. These algorithms were also two-phased and DoE-guided.…”