Resource allocation by genetic algorithm with fuzzy inference
Expert Systems with Applications: An International Journal
Lower and upper bounds for a two-level hierarchical location problem in computer networks
Computers and Operations Research
The capacity and distance constrained plant location problem
Computers and Operations Research
A fuzzy-knowledge resource-allocation model of the semiconductor final test industry
Robotics and Computer-Integrated Manufacturing
Expert Systems with Applications: An International Journal
Monte Carlo bounding techniques for determining solution quality in stochastic programs
Operations Research Letters
Survey: Facility location dynamics: An overview of classifications and applications
Computers and Industrial Engineering
Hi-index | 12.05 |
This study addresses a facility location and task allocation problem of a two-echelon supply chain against stochastic demand. Decisions include locating a number of factories among a finite set of potential sites and allocating task assignment between factories and marketplaces to maximize profit. The study represents the addressed location-allocation problem by bi-level stochastic programming and develops a genetic algorithm with efficient greedy heuristics to solve the problem. The contribution of the study pivots on a formal representation of system configuration design and operations optimization for a two-echelon supply chain. The proposed solution algorithm can find near optimal solution while consuming less computational time for large-size problems as compared to an optimization-based tool. In addition, this study investigates the industrial-cluster effect in a two-echelon supply chain by using the proposed algorithm. Experiments reveal that the proposed algorithm can efficiently yield nearly optimal solutions against stochastic demands.