Involvement of smart technologies in an advanced supply chain management to solve unreliability under distribution robust approach

Soumya Kanti Hota, Santanu Kumar Ghosh, Biswajit Sarkar

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


The proposed study described the application of innovative technology to solve the issues in a supply chain model due to the players’ unreliability. The unreliable manufacturer delivers a percentage of the ordered quantity to the retailer, which causes shortages. At the same time, the retailer provides wrong information regarding the amount of the sales of the product. Besides intelligent technology, a single setup multiple unequal increasing delivery transportation policy is applied in this study to reduce the holding cost of the retailer. A consumed fuel and electricity-dependent carbon emission cost are used for environmental sustainability. Since the industries face problems with smooth functioning in each of its steps for unreliable players, the study is proposed to solve the unpredictable player problem in the supply chain. The robust distribution approach is utilized to overcome the situation of unknown lead time demand. Two metaheuristic optimization techniques, genetic algorithm (GA) and particle swarm optimization (PSO) are used to optimize the total cost. From the numerical section, it is clear the PSO is 0.32 % more beneficial than GA to obtain the minimum total cost of the supply chain. The discussed case studies show that the applied single-setup-multi-unequal-increasing delivery policy is 0.62 % beneficial compared to the single-setup-single-delivery policy and 0.35 % beneficial compared to the single-setup-multi-delivery policy. The sensitivity analysis with graphical representation is provided to explain the result clearly.

Original languageEnglish
Pages (from-to)461-492
Number of pages32
JournalAIMS Environmental Science
Issue number4
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 the Author(s), licensee AIMS Press.

All Science Journal Classification (ASJC) codes

  • Environmental Science(all)


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