Significance of multi-objective optimization in logistics problem for multi-product supply chain network under the intuitionistic fuzzy environment

Abstract

Determining the methods for fulfilling the continuously increasing customer expectations and maintaining competitiveness in the market while limiting controllable expenses is challenging. Our study thus identifies inefficiencies in the supply chain network (SCN). The initial goal is to obtain the best allocation order for products from various sources with different destinations in an optimal manner. This study considers two types of decision-makers (DMs) operating at two separate groups of SCN, that is, a bi-level decision-making process. The first-level DM moves first and determines the amounts of the quantity transported to distributors, and the second-level DM then rationally chooses their amounts. First-level decision-makers (FLDMs) aimed at minimizing the total costs of transportation, while second-level decision-makers (SLDM) attempt to simultaneously minimize the total delivery time of the SCN and balance the allocation order between various sources and destinations. This investigation implements fuzzy goal programming (FGP) to solve the multi-objective of SCN in an intuitionistic fuzzy environment. The FGP concept was used to define the fuzzy goals, build linear and nonlinear membership functions, and achieve the compromise solution. A real-life case study was used to illustrate the proposed work. The obtained result shows the optimal quantities transported from the various sources to the various destinations that could enable managers to detect the optimum quantity of the product when hierarchical decision-making involving two levels. A case study then illustrates the application of the proposed work.

Sustainable Supply Chain Management in the Route for a Circular Economy: An Integrative Literature Review

“…The scenario of uncertainties can change collaboration relationships and, consequently negatively interfere in the partner network and in the supply of product and raw materials. Therefore, quantitative studies such as [92] can be useful to help industry managers to validate these relationships raised in this theoretical study.…”

A Multi-Strategy Adaptive Comprehensive Learning PSO Algorithm and Its Application

“…However, MSACLPSO is still insufficient in solving large-scale parameter optimization problems, such as time complexity and easy stagnation, among others. In the future, these applications should be considered [ 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 ]. The algorithm should be deeply studied, and the parameter adaptability of MSACLPSO in different stages and scales should also be further explored in future works.…”

Enhanced Heap-Based Optimizer Algorithm for Solving Team Formation燩roblem

“…In [17], the researchers deal with the problem as a multi-objective problem, their aim is to find an ideal team who can get the job done while keeping project management and personnel costs to a minimum. Since multi-objective is an area of mathematics used in multiple criterion decision-making, it deals with optimization issues involving two or more objective functions that must be optimized at the same time [18,19]. This assumption makes the problem even more difficult, as we have multi-objective that must be dealt with.…”