Solving a Multi-resource Partial-ordering Flexible Variant of the Job-shop Scheduling Problem with Hybrid ASP

TL;DR

This paper introduces a novel approach to solve a complex multi-resource, flexible job-shop scheduling problem using Answer Set Programming, effectively handling partial orders and resource flexibility in real-world production scenarios.

Contribution

It models the MPF-JSS problem with ASP and Difference Logic, proposing two multi-shot solving strategies for efficient schedule optimization.

Findings

Successfully solved 87 out of 91 real-world instances.

Demonstrated the effectiveness of ASP and Difference Logic in complex scheduling.

Provided practical solutions for production activities requiring expert and resource coordination.

Abstract

Many complex activities of production cycles, such as quality control or fault analysis, require highly experienced specialists to perform various operations on (semi)finished products using different tools. In practical scenarios, the selection of a next operation is complicated, since each expert has only a local view on the total set of operations to be performed. As a result, decisions made by the specialists are suboptimal and might cause significant costs. In this paper, we consider a Multi-resource Partial-ordering Flexible Job-shop Scheduling (MPF-JSS) problem where partially-ordered sequences of operations must be scheduled on multiple required resources, such as tools and specialists. The resources are flexible and can perform one or more operations depending on their properties. The problem is modeled using Answer Set Programming (ASP) in which the time assignments are efficiently done using Difference Logic. Moreover, we suggest two multi-shot solving strategies aiming at the identification of the time bounds allowing for a solution of the schedule optimization problem. Experiments conducted on a set of instances extracted from a medium-sized semiconductor fault analysis lab indicate that our approach can find schedules for 87 out of 91 considered real-world instances.