Energy Consumption and Tardiness Improvement for a Flexible Job Shop and a Warehouse

Abstract

In recent years, energy consumption (EC) is studied to see its effects on monetary and non-monetary costs in manufacturing and warehousing. EC in manufacturing and warehousing, however, needs to be studied with conventional performance measures such as production/operation tardiness since there is a trade-off between EC and tardiness costs. Therefore, this research is conducted with four objectives to study EC and tardiness for a flexible job shop and a warehouse as follows. The first objective of this research is to integrate job scheduling and layout, which are interrelated in improving EC and tardiness for a flexible job shop. Thus, we propose an energy-aware optimization model in which scheduling is integrated with layout in a single-level framework. The results indicate that a hybrid ant colony optimization and simulated annealing (ACO-SA) is the most efficient algorithm to solve case studies. The second objective is to improve EC and order tardiness (OT) for a warehouse picker-to-parts system with fast charging technology. We propose a mixed-integer linear mathematical model to control the schedules of electric-forklift battery chargings, order batching, batch sequencing, batch assignment, and forklift routing, simultaneously. The results exhibit that a hybrid non-dominated sorting genetic algorithm (NSGA-II) and non-dominated sorting variable neighborhood search (NSVNS) with dynamic learning strategy (NSGA-VNS-DLS) outperforms other algorithms. The third objective of this research is to present a simulation-based experimental design concerning significant factors that affect EC and OT in a warehouse picker-to-parts system. This objective provides Pareto-optimal scenarios from two factorial designs performed for EC and OT. Finally, the fourth objective of this research is to study the interdependence of the warehouse parts-to-picker system (warehouse reserve area) and picker-to-parts system (warehouse forward area) with a simulation-based experimental design to improve EC and OT in warehousing. This objective also provides Pareto-optimal scenarios resulted from two factorial designs of EC and OT.