A Scenario-Based Stochastic Optimization Model for Consolidated Product Shipment under Supply Uncertainty

Syamsul Anwar

Abstract


Abstract: Efficient logistics system planning is key to facing increasingly tight business competition. The problem of product distribution from multi-suppliers to ports is often faced with supply uncertainty and limited transportation and storage capacity, thus requiring an adaptive and optimal decision-making approach in responding to supply dynamics. This study proposes a scenario-based stochastic mixed-integer linear programming (SMILP) model to support decision-making in a multi-supplier distribution system to ports through a single hub under supply uncertainty. This model takes the case of a product delivery system with consolidation in the sago-starch supply chain, where shipping companies and suppliers are faced with supply fluctuations, limited warehouse capacity, and challenges in selecting the appropriate type of ship. This model considers supply uncertainty, limited warehouse capacity at suppliers, and the selection of large ship types as transportation modes. The optimization objective is to minimize the total logistics cost, which includes shipping, storage, and ship activation costs, while ensuring the fulfillment of minimum demand at the port each period. The implementation results show that this model is effective in adapting to supply variations, utilizing transportation and storage capacity efficiently, and consistently selecting a combination of shipping and inventory strategies that minimize costs in the face of uncertainty.

Keywords


Shipment Consolidation, Supply Uncertainty, Stochastic Mixed-Integer Linear Programming

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References


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