Comparing Iterative Improvement Heuristics for Multi-Capacity Scheduling Problems

Iterative Flattening search is a local search schema introduced for solving scheduling problems with a makespan minimization objective. It is an iterative two-step procedure, where on each cycle of the search a subset of ordering decisions on the critical path in the current solution are randomly retracted and then recomputed to produce a new solution. Since its introduction, other variations have been explored and shown to yield substantial performance improvement over the original formulation. In this spirit, we propose and experimentally evaluate further improvements to this basic local search schema. Specifically, we examine the utility (1) of operating with a more flexible solution representation, (2) of adopting a more focused decision retraction strategy and (3) of integrating iterative-flattening search with a complementary tabu search procedure. We evaluate these extensions on large benchmark instances of the Multi-Capacity JobShop Scheduling Problem (MCJSSP) which have been used in previous studies of iterative flattening search procedures.