Abstract
There are several methods available for modeling sustainable supply chain and logistics (SSCL) issues. Multi-objective optimization (MOO) has been a widely used method in SSCL modeling (SSCLM), nonetheless selecting a suitable optimization technique and solution method is still of interest as model performance is highly dependent on decision-making variables of the model development process. This study provides insights from the analysis of 95 scholarly articles to identify research gaps in the MOO for SSCLM and to assist decision-makers in selecting suitable MOO techniques and solution methods. The results of the analysis indicate that economic and environmental aspects of sustainability are the main context of SSCLM, where the social aspect is still limited. More SSCLMs for sourcing, distribution, and transportation phases of the supply chain are required. Additionally, more sophisticated techniques and solution methods, including hybrid metaheuristics approaches, are needed in SSCLM.
Highlights
As the aim of this paper is to identify the research gaps in Multi-objective optimization (MOO) for sustainable supply chain and logistics modeling (SSCLM) (C) and to assist decision-makers (I) in selecting suitable MOO techniques and solution methods (O) based on their varied sustainable supply chain and logistics (SSCL) issues addressed by the literature (M), the authors developed the following research questions
This study provided a review of 95 published papers in the field of MOO for SSCLM
The review aimed to identify the research gaps in MOO for SSCLM and to assist decision-makers in selecting suitable MOO methods and solutions in developing SSCLM. These purposes were achieved through different research questions covering sustainability dimensions, indicators, supply chain phases, decision levels, optimization techniques, solution methods, and uncertainty in SSCLM
Summary
Traditional supply chain models have focused only on operational efficiency by reducing the total cost, lead time, defective items, unused capacity, and processing time [2,3,4], but novel supply chain models incorporate environmental and social objectives in addition to economic performance [5,6,7,8] This phenomenon is evident by the growing research on sustainable supply chain and logistics modeling (SSCLM) [9,10]. SSCLM is a complex process as it involves diverse stakeholders from suppliers to customers for managing products and services accounting for economic, environmental, and social impacts [11] This complexity becomes more emphasized when different phases of the supply chain (sourcing, manufacturing, warehousing, distribution, and transportation), different types of a supply chain (forward, reverse, and close loop), different levels of decision-making (strategic, tactical, and operational), and supply chain environment (certainty or uncertainty) are considered
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