Sustainable supply chain partner selection and order allocation: A hybrid fuzzy PL-TODIM based MCGDM approach

Abstract

Sustainability, as a trend of social development and the embodiment of corporate social responsibility, has begun to receive more attention. To achieve this goal, sustainable supplier selection (SSS) and order allocation (OA) are seen as the crucial activities in corporate management. In the process of SSS, the psychological behavior of decision-makers (DMs) could play a critical role in the evaluation results. Therefore, introducing it into the decision-making process may lead to decision in line with the actual situation. In the uncertain multi-criteria group decision-making (MCGDM) problem described by probability linguistic term sets (PLTS), the DMs can evaluate the criteria of each supplier based on his own preference and hesitation, which is useful to avoid the loss of information. For this reason, this study develops a novel multi-criteria group decision-making combined with fuzzy multi-objective optimization (MCGDM-FMOO) model for SSS/OA problems by considering the triple bottom line (TBL) in which includes economic, environmental and social factors. The proposed method includes four stages. (1) the best-worst method (BWM) and entropy weight method are utilized to assign the weights of criteria to obtain the comprehensive weight. According to the output weights, the an acronym for interactive and multi-criteria decision-making in Portugese (TODIM) approach is applied to rank the suppliers under PLTS environment; (2) a FMOO model that can effectively deal with uncertainties and dynamic nature of parameter is formulated for allocating optimal order quantities; (3) two novel approaches are utilized to solve the FMOO model in order to obtain the richer Pareto frontier; and (4) the final OA solution is achieved by technique for order preference by similarity to ideal solution (TOPSIS) method. Finally, the validity and practicability of proposed MCGDM-FMOO model are verified by an example and comparative analysis with other classical MCGDM methods.

Development of a Generic Decision Tree for the Integration of Multi-Criteria Decision-Making (MCDM) and Multi-Objective Optimization (MOO) Methods under Uncertainty to Facilitate Sustainability Assessment: A Methodical Review

“…An epsilon is defined as the threshold of acceptable limit for each of the objective functions, and considering varying epsilon, the Pareto set is identified [95]. An improved ε-constraint method named Augmented ε-constraint (AUGMECON) was also used by Hasani [96] and Zhong et al [97]. AUGMECON is more effective than the traditional ε-constraint method.…”

“…AUGMECON is more effective than the traditional ε-constraint method. It requires less computational time as it provides a weak Pareto-optimal solution [97]. It also allows the simultaneous minimization of two functions that have conflicting objectives [96].…”

“…It also allows the simultaneous minimization of two functions that have conflicting objectives [96]. Other mathematical programming methods used in the reviewed articles were Weighted Goal Programming [93,98], LP metrics [97,99,100], and Mixed Integer Linear Programming [101][102][103]. Weighted Goal Programming allows the incorporation of DMs' preferences for each objective function more than standard goal programming and converts all of the conflicting variables into a normalized weighted single-objective function [98,104].…”

R&D mode and coordination of green products in sustainable supply chain considering power structures

“…It is found that the fixed subsidy policy and ARC policy improve the level of farmers’ environmentally sustainable investment and increase the profits of farmers and companies. Zhong et al [ 55 ] constructed a fuzzy multi-objective optimization model to select suppliers of sustainable supply chains from three aspects: economy, environment and society.…”

A multi-stage group decision making approach for sustainable supplier selection based on probabilistic linguistic time-ordered incentive operator

“…Consequently, Pang proposed the concept of probabilistic linguistic term set (PLTS), which considers DMs' preferences for different linguistic terms by assigning different probabilities to them [16]. The rationality and flexibility of PLTS have attracted increasing attention from scholars, who have studied PLTS from various perspectives, including its operational laws [17,18], integration with MAGDM [19,20], and applications of probabilistic linguistic preference relations [21][22][23], etc. In addition, PLTS has found wide applications in fields such as patients prioritization assessment [24], evaluation and selection of online learning platforms [25], and risk assessment of marine ranching equity financing [26].…”