A Hybrid Parallel Simulated Annealing Algorithm to Optimize Store Performance
نویسنده
چکیده
Solving optimization problems in highdimensional stochastic search spaces is a difficult task. In this work, we present a hybrid parallel simulated annealing algorithm to find optimal solutions in such domains. The algorithm uses a population of several starting points initially to explore the search space in a parallel fashion and after each evaluation better members reproduce asexually while worse members are eliminated from the population. We discuss several case studies from grocery retail domain to solve store performance optimization problem since grocery retail is one of the domains where customer behavior models are probabilistic and the product space is highdimensional.
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