Extending Systematic Local Search for Job Shop Scheduling Problems

Abstract

Hybrid search methods synthesize desirable aspects of both constructive and local search methods. Constructive methods are systematic and complete, but exhibit poor performance on large problems because bad decisions made early in the search persist for exponentially long times. In contrast, stochastic local search methods are immune to the tyranny of early mistakes. Local search methods replace systematicity with stochastic techniques for diversifying the search. However, the lack of systematicity makes remembering the history of past states problematic. Typically, hybrid methods introduce a stochastic element into a basically constructive search framework. Lynce [6] uses randomized backtracking in a complete boolean satisfiability solver which incorporates clause (nogood) learning to ensure completeness. Jussein & Lhomme [4] perform a constructive search while keeping conflict sets (nogoods) in a Tabu list and backtrack via a stochastic local search in the space of conflict sets.

Our method, called Systematic Local Search (SysLS) [3], follows the opposite approach. We incorporate systematicity within an inherently stochastic search method (like [2]). SysLS searches through a space of complete variable assignments and relaxes the requirement for maintaining feasibility. It preserves full freedom to move heuristically in the search space with maximum heuristic information available. While many local search methods easily get trapped in local optima, SysLS records local optima as nogoods in a search memory. Nogoods force the search away from these maximally consistent but unacceptable solutions. Our method is analogous to other diversification mechanisms in local search (eg-Tabu search) but is systematic and inherits the sound resolution rule for nogood learning. In this paper, we extend SysLS for optimization and, in particular, for job shop scheduling problems.