Adaptive Large Neighborhood Search for the Just-In-Time Job-shop Scheduling Problem

Abstract

This paper focuses on the just-in-time job-shop scheduling problem with operation-wise distinct due-dates. The studied problem is known to be NP-Hard, so to solve it we present an adaptive large neighborhood search (ALNS) algorithm, that iteratively adapts its parameters as the search moves from lower to higher quality neighborhoods to focus on optimizing smaller subsets of the decision variables. The experimental results showed that this method performed at least as good as the state of the art in 63% of the studied instances, while strictly improving 14% of the same benchmark.