On Solving Manufacturing Cell Formation via Bicluster Editing

TL;DR

This paper presents new exact methods for solving the Manufacturing Cell Formation Problem by leveraging a novel approach to the Bicluster Graph Editing Problem, including a Branch-and-Cut algorithm, preprocessing, and mathematical formulations, achieving optimal solutions for complex instances.

Contribution

It introduces the first exact methods for MCFP using the grouping efficacy measure, based on a novel connection to BGEP and advanced optimization techniques.

Findings

Accelerated convergence with the separation algorithm.

Effective preprocessing for low-density instances.

Proved several previously unknown optima.

Abstract

This work investigates the Bicluster Graph Editing Problem (BGEP) and how it can be applied to solve the Manufacturing Cell Formation Problem (MCFP). We develop an exact method for the BGEP that consists of a Branch-and-Cut approach combined with a special separation algorithm based on dynamic programming. We also describe a new preprocessing procedure for the BGEP derived from theoretical results on vertex distances in the input graph. Computational experiments performed on randomly generated instances with various levels of difficulty show that our separation algorithm accelerates the convergence speed, and our preprocessing procedure is effective for low density instances. Other contribution of this work is to reveal the similarities between the BGEP and the MCFP. We show that the BGEP and the MCFP have the same solution space. This fact leads to the proposal of two new exact approaches for the MCFP based on mathematical formulations for the BGEP. Both approaches use the grouping efficacy measure as the objective function. Up to the authors' knowledge, these are the first exact methods that employ such a measure to optimally solve instances of the MCFP. The first approach consists of iteratively running several calls to a parameterized version of the BGEP, and the second is a linearization of a new fractional-linear model for the MCFP. Computational experiments performed on instances of the MCFP found in the literature show that our exact methods for the MCFP are able to prove several previously unknown optima.