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Problem space search algorithms for resource-constrained project scheduling

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Abstract

The Resource-Constrained Project Scheduling (RCPS) problem is a well known and challenging combinatorial optimization problem. It is a generalization of the Job Shop Scheduling problem and thus is NP-hard in the strong sense. Problem Space Search is a local search "metaheuristic" which has been shown to be effective for a variety of combinatorial optimization problems including Job Shop Scheduling. In this paper, we propose two problem space search heuristics for the RCPS problem. These heuristics are tested through intensive computational experiments on a 480-instance RCPS data set recently generated by Kolisch et al. [12]. Using this data set we compare our heuristics with a branch-and-bound algorithm developed by Demuelemeester and Herreolen [9]. The results produced by the heuristics are extremely encouraging, showing comparable performance to the branch-and-bound algorithm.

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Authors
  1. Kedar S. Naphade
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  2. S. David Wu
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  3. Robert H. Storer
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Naphade, K.S., David Wu, S. & Storer, R.H. Problem space search algorithms for resource-constrained project scheduling. Annals of Operations Research 70, 307–326 (1997). https://doi.org/10.1023/A:1018982423325

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