A Permutation-Based Heuristic Method for the Blocking Job Shop Scheduling Problem

Abstract

In the manufacturing of highly customized goods and the operation of automatic logistics systems, efficient schedules constitute an everyday challenge. Therefore, the job shop problem is established as a standard model in scheduling research. While classical variants are well studied, the involvement of practically relevant conditions, such as the absence of intermediate buffers and customer-oriented optimization criteria, shows a lack of theoretical understanding. This work provides a study in this research direction by examining the applicability of a scheduling-tailored heuristic search method to the blocking job shop problem with total tardiness minimization. Permutation-based encodings are used to represent a schedule. Appearing redundancy and feasibility issues are discussed. Two well-known neighborhood structures for sequencing problems are applied and an advanced repairing technique to construct feasible blocking job shop schedules is proposed. The computational results obtained by embedding the components in a simulated annealing framework highlight advantages of the heuristic solution approach against existing general-purpose methods.