Compressor Schedule Optimization for a Refrigerated Warehouse Using Metaheuristic Algorithms

This paper investigates the suitability of several metaheuristic algorithms for the problem of compressor schedule optimization for a refrigerated warehouse. A realistic simulator of such a warehouse is used, based on domain knowledge, and tuned to match an actual experimental cooling appliance. The problem consists in finding an on-off sequence for the adopted optimization horizon and time step that minimizes the energy cost while preserving cooling chamber temperature constraints. To enable the application of metaheuristic optimization algorithms, the problem has to be appropriately encoded. Three different encoding schemes have been designed, suited to both binary and continuous optimization methods. Several metaheuristic algorithms known from the literature are used. Most of them deliver solutions considerably better than a common-sense heuristic compressor schedule. Interestingly, the classical genetic algorithm setup, as well as a setup that was applied to a similar problem in prior research, appear not to work well. The best results are achieved for an alternative genetic algorithm configuration, determined by a series of tuning experiments. Comparable results can be also obtained by the IPOP-CMA-ES or PBIL algorithms, which do not require such extensive tuning and may be preferred by practitioners.