Proceedings of the 35th conference on Winter simulation: driving innovation
Multicriterion genetic optimization for due date assigned distribution network problems
Decision Support Systems - Special issue: Collaborative work and knowledge management
A heuristic algorithm for master planning that satisfies multiple objectives
Computers and Operations Research
A toolbox for simulation-based optimization of supply chains
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Decomposition heuristic to minimize total cost in a multi-level supply chain network
Computers and Industrial Engineering
Decentralized supply chain planning framework for third party logistics partnership
Computers and Industrial Engineering
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Unifying simulation and optimization of strategic sourcing and transportation
Proceedings of the 40th Conference on Winter Simulation
Fuzzy optimization for supply chain planning under supply, demand and process uncertainties
Fuzzy Sets and Systems
Hybrid algorithm for discrete event simulation based supply chain optimization
Expert Systems with Applications: An International Journal
Simulation of a supply chain game with multiple fuzzy goals
Fuzzy Sets and Systems
Application of fuzzy sets to manufacturing/distribution planning decisions in supply chains
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Proceedings of the Winter Simulation Conference
Journal of Intelligent Manufacturing
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Analytic models have been developed to solve the integrated production-distribution problems in supply chain management (SCM). As one of the major constraints in analytic models, operation time has mostly been known or disregarded. However, in the real systems, due to the various kinds of uncertain factors such as unexpected delays, queuing, breakdowns, operation time in the analytic model cannot correctly represent the dynamic behavior of the consumption of real operation time. To solve this problem, in this paper, we propose a hybrid approach combining the analytic and simulation model. Operation time in the analytic model is considered as a dynamic factor and adjusted by the results from independently developed simulation model, which includes general production-distribution characteristics. We obtain the more realistically optimal production-distribution plans for the integrated supply chain system reflecting stochastic natures by performing the iterative hybrid analytic-simulation procedure.