A simulated annealing methodology to multiproduct capacitated facility location with stochastic demand
Jin Qin1, Hui Xiang2, Yong Ye1
1School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China.
Thescientificworldjournal
|April 3, 2015
Summary
This study introduces a combined simulated annealing (CSA) algorithm to optimize facility location and inventory policies for stochastic demands, minimizing total costs. The CSA approach proved robust for multiproduct supply chains, highlighting transportation costs and demand deviation as key decision factors.
Area of Science:
- Operations Research
- Supply Chain Management
- Optimization
Background:
- Stochastic demands in supply chains necessitate safety stock, increasing inventory costs.
- Capacitated facility location problems with multiple products are complex to solve efficiently.
Purpose of the Study:
- To develop a model and algorithm for a stochastic multiproduct capacitated facility location problem.
- To minimize total supply chain costs, including transportation, inventory, operation, and setup costs.
Main Methods:
- A nonlinear mixed-integer programming model was formulated assuming normally distributed stochastic demands.
- A combined simulated annealing (CSA) algorithm was developed, featuring nested optimization for facility location and demand allocation.
Main Results:
- The CSA algorithm demonstrated robustness and practicality for multiproduct facility location and inventory policy optimization.
- Suboptimal facility location decisions and inventory policies were generated by the CSA approach.
- Transportation costs and demand deviation were identified as the most influential factors on optimal decisions.
Conclusions:
- The developed CSA algorithm is an effective tool for addressing complex stochastic facility location problems.
- Understanding the impact of transportation costs and demand variability is crucial for optimizing supply chain decisions.
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