The shifting bottleneck procedure for job shop scheduling
Management Science
Artificial Intelligence - Special issue on knowledge representation
A Constraint-Based Method for Project Scheduling with Time Windows
Journal of Heuristics
Iterative Flattening: A Scalable Method for Solving Multi-Capacity Scheduling Problems
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
From precedence constraint posting to partial order schedules: A CSP approach to Robust Scheduling
AI Communications - Constraint Programming for Planning and Scheduling
Scheduling a single robot in a job-shop environment through precedence constraint posting
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
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Iterative flattening search (ifs) is an iterative improvement heuristic schema for makespan minimization in scheduling problems. Given an initial solution, ifsiteratively interleaves a relaxation-step, which randomly retracts some search decisions, and an incremental solving step (or flattening-step) to recompute a new solution. The process continues until a stop condition is met and the best solution found is returned. In recent work we have created a uniform software framework to analyze component techniques that have been proposed in ifsapproaches. In this paper we combine basic components to obtain hybrid variants and perform a detailed experimental evaluation of their performance. Specifically, we examine the utility of: (1) operating with different relaxation strategies and (2) using different searching strategies to built a new solution. We present a two-step experimental evaluation: (a) an extensive explorative evaluation with a spectrum of parameter combination; (b) a time-intensive evaluation of the best ifscombinations emerged from the previous. The experimental results shed light on weaknesses and strengths of the different variants improving the current understanding of this family of meta-heuristics.