Job Shop Scheduling with Lot-sizing and Batching in an Uncertain Real-world Environment

This paper proposes a novel algorithm for a real-world job shop-scheduling problem, where both lot-sizing and batching processes are considered. A fuzzy rule-based system is developed for determining lot sizes, where the input variables are workload on the shop floor, size of the job and its urgency while the output is the size of the lots. Both input and output variables are modelled as linguistic variables with imprecise values represented by using fuzzy sets. A fuzzy multi-objective genetic algorithm is developed to generate schedules of jobs whose processing times and due dates are imprecise and modelled by using fuzzy sets. A genetic algorithm takes into consideration the determined size of lots for jobs, and considers batching together jobs of similar characteristics in order to reduce the required setup time. The objectives considered are to minimize average tardiness, number of tardy jobs, setup times, idle times of machines and throughput times of jobs. The developed algorithm is tested on real-world data obtained from a printing company.