Scheduling unrelated parallel machines to minimize total weighted tardiness

Abstract

This paper addresses the batch scheduling problem of unrelated parallel machines attempting to minimize the total weighted tardiness. Identical or similar jobs are typically processed in batches to decrease setup and/or processing times. Local dispatching rules such as the earliest weighted due date, the shortest weighted processing time, and the earliest weighted due date with a process utilization spread are tailored to the batch scheduling requirements. Based on the features of batch scheduling, a two-level batch scheduling framework is suggested. Existing heuristics, which show excellent performance in terms of total weighted tardiness for the single machine scheduling, such as the modified earliest due date rule and the modified cost over time rule, are extended for the problem. The simulated annealing algorithm as a meta-heuristic is also presented to obtain near optimal solutions. The proposed heuristics are compared through computational experiments with data from the dicing process of a compound semiconductor manufacturing facility

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

“…In these cases the real processing times are not known until the job starts executing on the machine. A batch scheduling problem with the goal of minimising the total weighted tardiness is considered in [37]. In this problem, a batch is not allocated to only a single machine, but rather a set of machines that can process the jobs contained in the batch simultaneously.…”