论文信息 - Multi-objective genetic local search algorithm for supply chain simulation optimisation

Multi-objective genetic local search algorithm for supply chain simulation optimisation

This paper presents a hybrid simulation optimisatio n algorithm that integrates a multi-objective genetic algorithm and response surface-based metamodelling techniques. The optimisation problem involves a sea rch in a high dimensional space with different ranges f or decision variable scales, multiple stochastic objec tiv functions and problem specific constraints. A case study demonstrates the application of a hybrid simulation optimisation algorithm to optimal cyclic planning f or a generic supply chain network.

Gaļina Merkurjeva | Liāna Napalkova

[1] Galina Merkuryeva,et al. Theoretical Framework of Multi-objective Simulation-Based Genetic Algorithm for Supply Chain Cyclic Planning and Optimisation , 2008, Tenth International Conference on Computer Modeling and Simulation (uksim 2008).

[2] Galina Merkuryeva,et al. Two-phase simulation optimisation algorithm with applications to multi-echelon cyclic planning , 2010, Int. J. Simul. Process. Model..

[3] Galina Merkuryeva. Response Surface-Based Simulation Metamodelling Methods , 2005 .

[4] Tomoyuki Hiroyasu,et al. DIVIDED RANGE GENETIC ALGORITHMS IN MULTIOBJECTIVE OPTIMIZATION PROBLEMS , 1999 .

[5] Galina Merkuryeva,et al. Supply Chain Cyclic Planning and Optimisation , 2009 .

[6] Kalyanmoy Deb,et al. A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[7] Vincent A. Mabert,et al. Cyclical schedules for capacitated lot sizing with dynamic demands , 1991 .

[8] Yuri Merkuryev,et al. SIMULATION-BASED ENVIRONMENT FOR MULTI-ECHELON CYCLIC PLANNING AND OPTIMISATION , 2007 .

[9] Galina Merkuryeva,et al. Integrating Analytical and Simulation Techniques in Multi-Echelon Cyclic Planning , 2007, First Asia International Conference on Modelling & Simulation (AMS'07).