A hybrid genetic algorithm for two-stage multi-item inventory system with stochastic demand

Neural Computing and Applications

Volumn 21, Issue 6, 2012, Pages 1087-1098

  Zhang, Yuli ^a   Song, Shiji ^a   Zhang, Heming ^a   Wu, Cheng ^a   Yin, Wenjun ^b  

^a TSINGHUA UNIVERSITY   (China)

^b IBM CHINA RESEARCH LAB   (China)

Author keywords

Heuristic search; Hybrid genetic algorithm; Monte Carlo method; Multi echelon inventory; Stochastic demand

Indexed keywords

EFFECTIVE ALGORITHMS; HEURISTIC SEARCH; HEURISTIC SEARCH TECHNIQUE; HYBRID GENETIC ALGORITHMS; INITIAL SOLUTION; INVENTORY COSTS; INVENTORY SYSTEMS; LONG-RUN AVERAGE COST; MULTI-ECHELON INVENTORY; MULTI-ITEM; MULTIECHELON; NUMERICAL EXPERIMENTS; PROCESS ANALYSIS; SETUP COSTS; STOCHASTIC DEMAND; STOCHASTIC INVENTORY; STOCHASTIC OPTIMIZATION MODEL; SUPPLY-DEMAND;

GENETIC ALGORITHMS; HEURISTIC ALGORITHMS; INFORMATION ANALYSIS; INVENTORY CONTROL; MODULAR ROBOTS; MONTE CARLO METHODS; STOCHASTIC SYSTEMS;

COST BENEFIT ANALYSIS;

EID: 84865652319     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-011-0658-7     Document Type: Article

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