Parallel Shared-Memory Multi-Objective Stochastic Search for Competitive Facility Location
A stochastic search algorithm for local multi-objective optimization is developed and applied to solve a multi-objective competitive facility problem for firm expansion using shared-memory parallel computing systems. The performance of the developed algorithm is experimentally investigated by solving competitive facility location problems, using up to 16 shared-memory processing units. It is shown that the developed algorithm has advantages against its precursor in the sense of the precision of optimization and that it has almost linear speed-up on 16 shared-memory processing units, when solving competitive facility location problems of different scope reasonable for practical applications.
KeywordsFacility Location Multi-Objective Optimization Stochastic Search Shared Memory Parallel Computing
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