Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems

TLDR

Combining assignment and scheduling decisions in production management introduces additional complexity and new problems. The study proposes two novel methods to jointly address assignment and job‑shop scheduling under total or partial flexibility. The first method, Localization (AL), constructs an ideal assignment model for resource allocation, while the second method uses an evolutionary algorithm guided by that model, employing advanced genetic operators and a robust encoding to improve solution quality. Case studies demonstrate the efficiency of both approaches.

Abstract

Traditionally, assignment and scheduling decisions are made separately at different levels of the production management framework. The combining of such decisions presents additional complexity and new problems. We present two new approaches to solve jointly the assignment and job-shop scheduling problems (with total or partial flexibility). The first one is the approach by localization (AL). It makes it possible to solve the problem of resource allocation and build an ideal assignment model (assignments schemata). The second one is an evolutionary approach controlled by the assignment model (generated by the first approach). In such an approach, we apply advanced genetic manipulations in order to enhance the solution quality. We also explain some of the practical and theoretical considerations in the construction of a more robust encoding that will enable us to solve the flexible job-shop problem by applying the genetic algorithms (GAs). Two examples are presented to show the efficiency of the two suggested methodologies.