Evolutionary fuzzy intelligent system for multi-objective supply chain network designs: an agent-based optimization state of the art

Tarimoradi, Mosahar; Zarandi, M. H. Fazel; Zaman, Hosain; Turksan, I.B.

doi:10.1007/s10845-015-1170-1

Cited by 9 publications

(1 citation statement)

References 92 publications

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“…Supply chain management (SCM) involves managing a network of interconnected businesses, optimizing product flows, and reducing transportation costs [1,2]. It encompasses various activities, from material acquisition to the distribution of final products [1]. To ensure successful operations, it is crucial to measure supply chain performance and monitor financial indicators [3].…”

Section: Introductionmentioning

confidence: 99%

Scenario-Based Optimization of Supply Chain Performance under Demand Uncertainty

Gobachew

Haasis

2023

Sustainability

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This study presents a comprehensive supply chain performance optimization model that addresses the trade-off between supply chain cost and customer service level in the distribution network. The model incorporates both deterministic and scenario-based approaches, allowing for a more realistic representation of supply chain operations. The model is applied to a case company operating in the pharmaceutical supply chain in Ethiopia. The goal is to improve the company’s supply chain performance by optimizing various factors such as establishment costs, handling costs, transportation costs, and demand satisfaction. The study considers both financial measures (supply chain cost) and non-financial measures (customer service level) to evaluate the performance of the supply chain. The results of the study demonstrate the effectiveness of the proposed model in identifying the optimal trade-off between supply chain costs and customer service levels. By comparing the results of the model with the current situation of the case company, it is determined that the company can achieve significant cost reductions of up to 25.26% while still meeting customer demands. The model also takes into account the uncertainty in demand, providing more realistic recommendations for distribution center locations, transportation planning, and order fulfillment. The implications of using this optimization model include the potential for cost savings, improved decision-making, enhanced customer satisfaction, and, ultimately, a more successful supply chain. However, the study has some limitations, including a need for further research on other objectives and considerations, such as environmental impacts and disruptions, which could be addressed in future research directions.

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