Optimising supplier selection under risk: a multi-method approach
Résumé
This paper presents an innovative approach to optimise supplier selection while minimising transportation costs in supply chain management. The methodology integrates mixed integer linear programming (MILP), genetic algorithm (GA), and a stochastic programming approach (MILP combined with Monte Carlo simulation, called MCLP) to address the complexities of supplier selection. The MILP model is designed to incorporate various factors such as supplier reliability, predictable demand, and risk factors, thereby providing a comprehensive evaluation framework. To efficiently solve the MILP model, the GA is utilised to explore the solution space and identify near-optimal solutions. Additionally, MCLP is integrated to account for uncertainty and variability in the supply chain environment, enhancing the robustness of the optimisation approach. This research contributes to advancing supply chain optimisation by offering a practical framework that considers multiple criteria and minimises transportation costs in supplier selection processes.
Mots clés
- supply chain supplier selection optimisation cost minimisation mixed integer linear programming MILP genetic algorithm stochastic programming approach
- supply chain
- supplier selection
- optimisation
- cost minimisation
- mixed integer linear programming
- MILP
- genetic algorithm
- stochastic programming approach
Domaines
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