Improving Energy Consumption and Order Tardiness in Picker-to-Part Warehouses with Electric Forklifts: A Comparison of Four Evolutionary Algorithms

Improving energy consumption (EC) and order tardiness (OT) for a warehouse picker-to-parts system is challenging task since these two objectives are interrelated in complex way with forklift activities. Thus, this research aims to minimize EC OT multi-objective mixed-integer mathematical model by considering electric operations. The proposed addresses lack of studies controlling (i) batching, (ii) batch assignment, (iii) sequencing, (iv) routing, (v) battery charging schedule. feasibility the presented validated solving small-sized examples. To solve medium- large-sized case studies, we also propose compare four evolutionary algorithms (MOEAs). In illustrative examples, study identifies number charging, orders, forklifts as significant parameters affecting OT. Our analysis provides regression models connecting from Pareto-optimal frontiers, results can help industrial practitioners academic researchers find investigate relationship between making relevant decisions warehouses served forklifts. Among MOEAs developed, show that NSGA-II non-dominated sorting variable neighborhood search dynamic learning strategy (NSGA-VNS-DLS) outperforms other accuracy, diversity, CPU time.