Mixed-integer evolution strategy using multiobjective selection applied to warehouse design optimization

This paper reports about the application of a new variant of multiobjective Mixed-Integer Evolution Strategy to a warehouse design optimization problem. The algorithm is able to deal with real-valued, integer, and discrete nominal input variables in a multiobjective problem, and utilizes a multiobjective selection procedure based on either crowding distance or hypervolume contribution (also called S metric). The warehouse design optimization problem investigated in this study is represented by a warehouse simulator (provided by our collaboration partner) which calculates four warehouse performance measures: total handling time, crate fill rate, order latency, and investment cost. Two of those are treated as objectives, while the other two are represented as constraints. As demonstrated by the results, the algorithm generates solutions which cover the whole Pareto front, as opposed to the expert-generated solutions. Moreover, the algorithm is able to find solutions which improve on the expert-generated solutions with respect to both objectives.