Solution-Guided Multi-Point Constructive Search for Job Shop Scheduling

TL;DR

This paper introduces SGMPCS, a novel constructive search method for job shop scheduling that maintains elite solutions during multiple resource-limited searches, outperforming several other constructive techniques.

Contribution

The paper presents SGMPCS, a new search technique that combines solution-guided search with multi-point constructive strategies, and demonstrates its effectiveness on job shop scheduling problems.

Findings

SGMPCS outperforms other constructive methods.

Less diverse elite solutions can lead to better performance.

SGMPCS lags behind tabu search in effectiveness.

Abstract

Solution-Guided Multi-Point Constructive Search (SGMPCS) is a novel constructive search technique that performs a series of resource-limited tree searches where each search begins either from an empty solution (as in randomized restart) or from a solution that has been encountered during the search. A small number of these "elite solutions is maintained during the search. We introduce the technique and perform three sets of experiments on the job shop scheduling problem. First, a systematic, fully crossed study of SGMPCS is carried out to evaluate the performance impact of various parameter settings. Second, we inquire into the diversity of the elite solution set, showing, contrary to expectations, that a less diverse set leads to stronger performance. Finally, we compare the best parameter setting of SGMPCS from the first two experiments to chronological backtracking, limited discrepancy search, randomized restart, and a sophisticated tabu search algorithm on a set of well-known benchmark problems. Results demonstrate that SGMPCS is significantly better than the other constructive techniques tested, though lags behind the tabu search.