Scheduling a single parallel-batching machine with non-identical job sizes and incompatible job families

TL;DR

This paper addresses the complex problem of scheduling jobs with varying sizes and incompatible families on a single machine, proposing new formulations and algorithms to optimize total weighted completion time.

Contribution

It introduces three new MILP formulations, a column generation algorithm, and a heuristic framework for this challenging scheduling problem.

Findings

SPF and B&P solve instances with up to 150 jobs optimally.

Heuristics outperform existing methods.

Proposed methods are computationally efficient for large instances.

Abstract

We study the scheduling of jobs on a single parallel-batching machine with non-identical job sizes and incompatible job families. Jobs from the same family have the same processing time and can be loaded into a batch, as long as the batch size respects the machine capacity. The objective is to minimize the total weighted completion time. The problem combines two classic combinatorial problems, namely bin packing and single machine scheduling. We develop three new mixed-integer linear-programming formulations, namely an assignment-based formulation, a time-indexed formulation (TIF), and a set-partitioning formulation (SPF). We also propose a column generation (CG) algorithm for the SPF, which is the basis for a branch-and-price (B&P) algorithm and a CG-based heuristic. We develop a preprocessing method to reduce the formulation size. A heuristic framework based on proximity search is also developed using the TIF. The SPF and B&P can solve instances with non-unit and unit job durations to optimality with up to 80 and 150 jobs within reasonable runtime limits, respectively. The proposed heuristics perform better than the methods from the literature.