An integrated Principal Component Analysis and multi-objective mathematical programming approach to agile supply chain network design under uncertainty

Abstract

The design of agile supply chain networks has attracted more attention in recent years according to the competitive business environment. Further, due to high degree of uncertainty in agile supply chains (SCs), developing robust and efficient decision making tools are of interest. In this study, an integrated approach based on principal component analysis (PCA) and multiobjective possibilistic mixed integer programming (MOPMIP) approaches is proposed to optimally design agile supply chain network under uncertainty. The PCA method is used for ranking and filtering the suppliers, constituting the first layer of the supply chain, based on agility criteria. The proposed MOPMIP model is employed to construct the agile supply chain network under uncertainty. In the proposed MOPMIP model, three objective functions including 1) total costs minimization, 2) total delivery time minimization and 3) maximization of flexibility are considered. An interactive fuzzy solution approach is used to solve the proposed MOPMILP model. Two numerical examples, is conducted to evaluate the performance and efficiency of the proposed integrated approach for agile supply chain network design under uncertainty.

Quality Index Evaluation Model of MPP Sold under the E-Commerce Platform

“…e maximum number of new variable generations is equal to the number of original variables minus one. e new variables are uncorrelated with each other [4]. Because no assumptions are made about variable distributions in PCA, this approach can process any dispersed data [5].…”

Evaluation of the Physical Properties of Bedding Materials for Dairy Cattle Using Fuzzy Clustering Analysis

“…According to [32], PCA is used to explain the dispersion structure with a few linear combinations of the original variables. The authors in [33] state that the PCA method generates new variables as the linear uncorrelated combination out of the original variables, where the new axes or variables are called principal components, and the value of new variables are principal component scores. The number of new variables will be equal to the number of original variables.…”

New Mathematical model of Retailer-to-Individual Customer Optimal Product Supply Strategies Under False Demand Pattern: Customer Discount Mode

“…Kim & Krishnan (2015) studied the interactive impact of product demand uncertainty and product price on online consumer purchase decisions. Moradi et al (2018) studied the optimization decision model of agile supply chain networks under uncertain demand conditions. Kisialiou, Gribkovskaia, and Laporte (2019) solve the problem of supply vessel planning in upstream offshore oil logistics under uncertain demand.…”