Computers and Operations Research
A Joint Location-Inventory Model
Transportation Science
Efficient algorithms for various supply chain problems
Efficient algorithms for various supply chain problems
Stochastic Transportation-Inventory Network Design Problem
Operations Research
Multiobjective simulation optimization using an enhanced genetic algorithm
WSC '05 Proceedings of the 37th conference on Winter simulation
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
A simulation-optimization approach for integrated sourcing and inventory decisions
Computers and Operations Research
Integration of strategic and tactical decisions for vendor selection under capacity constraints
Computers and Operations Research
Expert Systems with Applications: An International Journal
Strategic design of competing supply chain networks with foresight
Advances in Engineering Software
A multi-objective optimization for green supply chain network design
Decision Support Systems
Expert Systems with Applications: An International Journal
Journal of Global Optimization
Optimization of a stochastic remanufacturing network with an exchange option
Decision Support Systems
Expert Systems with Applications: An International Journal
Multi objective outbound logistics network design for a manufacturing supply chain
Journal of Intelligent Manufacturing
Linear location-inventory models for service parts logistics network design
Computers and Industrial Engineering
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When designing supply chains, firms are often faced with the competing demands of improved customer service and reduced cost. We extend a cost-based location-inventory model (Shen et al. 2003) to include a customer service element and develop practical methods for quick and meaningful evaluation of cost/service trade-offs. Service is measured by the fraction of all demands that are located within an exogenously specified distance of the assigned distribution center. The nonlinear model simultaneously determines distribution center locations and the assignment of demand nodes to distribution centers to optimize the cost and service objectives. We use a weighting method to find all supported points on the trade-off curve. We also propose a heuristic solution approach based on genetic algorithms that can generate optimal or close-to-optimal solutions in a much shorter time compared to the weighting method. Our results suggest that significant service improvements can be achieved relative to the minimum cost solution at a relatively small incremental cost.