English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Purpose: The incorporation of environmental objective into the conventional supplier selection\udpractices is crucial for corporations seeking to promote green supply chain management (GSCM).\udChallenges and risks associated with green supplier selection have been broadly recognized by\udprocurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS)\udproblem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply\udchain environment. In this empirical study, a mathematical model with fuzzy coefficients is\udconsidered for sustainable strategic supplier selection (SSSS) problem and a corresponding model\udis developed to tackle this problem.\udDesign/methodology/approach: Sustainable strategic supplier selection (SSSS) decisions are\udtypically multi-objectives in nature and it is an important part of green production and supply\udchain management for many firms. The proposed uncertain model is transferred into\uddeterministic model by applying the expected value measure (EVM) and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multiobjective\udoptimization model for minimizing lean cost, maximizing sustainable service and\udgreener product quality level. Finally, a mathematical case of textile sector is presented to\udexemplify the effectiveness of the proposed model with a sensitivity analysis.\udFindings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both\udthe theoretical and practical research related to multi-objective optimization as well as in the study\udof sustainable strategic supplier selection (SSSS) under uncertain environment. Our results\udsuggest that decision makers tend to select strategic supplier first then enhance the sustainability.\udResearch limitations/implications: Although the fuzzy expected value model (EVM) with\udfuzzy coefficients constructed in present research should be helpful for solving real world\udproblems. A detailed comparative analysis by using other algorithms is necessary for solving\udsimilar problems of agriculture, pharmaceutical, chemicals and services sectors in future.\udPractical implications: It can help the decision makers for ordering to different supplier for\udmanaging supply chain performance in efficient and effective manner. From the procurement and\udengineering perspectives, minimizing cost, sustaining the quality level and meeting production\udtime line is the main consideration for selecting the supplier. Empirically, this can facilitate\udengineers to reduce production costs and at the same time improve the product quality.\udOriginality/value: In this paper, we developed a novel multi-objective programming model\udbased on genetic algorithm to select sustainable strategic supplier (SSSS) under fuzzy\udenvironment. The algorithm was tested and applied to solve a real case of textile sector in\udPakistan. The experimental results and comparative sensitivity analysis illustrate the effectiveness\udof our proposed model.Peer Reviewe

Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment