Computational solution of capacity planning models under uncertainty
Parallel Computing - Special issue on parallel computing in economics, finance and decision-making
A multiple-depot, multiple-vehicle, location-routing problem with stochastically processed demands
Computers and Operations Research
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Industrial Engineering - Supply chain management
Design of supply-chain logistics system considering service level
Computers and Industrial Engineering - Supply chain management
New stochastic models for capacitated location-allocation problem
Computers and Industrial Engineering
Multicriterion genetic optimization for due date assigned distribution network problems
Decision Support Systems - Special issue: Collaborative work and knowledge management
An approximation algorithm for a facility location problem with stochastic demands and inventories
Operations Research Letters
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This paper presents a new mathematical model for designing distribution networks in a supply chain system considering service level constraint optimizing strategic decisions (location), tactical decisions (inventory), and assigning decisions. In real-world cases, demand, traveling time or any parameters in classical models may change over the period of time. So, considering uncertainty yields more flexibility for the results and the proposed model. In our study, environmental uncertainty is described by discrete scenarios. In this model, we have service level constraint in order to prevent inventory lost in distribution centers (DCs). Also, we assume that customer's demand is stochastic with Poisson distribution function and DCs have coverage radius constraints thus any DC cannot service all the customers. In this model, location of DCs is selected and optimized and the best flow of products from supplier to DCs also from DCs to customers is determined. In this way, the customers' demand should be satisfied at least service level. To solve this nonlinear integer programming model we first present a new and robust solution based on a genetic search framework and then based on genetic algorithm results and some optimizer rules we propose a new heuristic method. Finally, some numerical examples are presented to illustrate the effectiveness of the proposed algorithms.