Solving stochastic resource-constrained project scheduling problems by closed-loop approximate dynamic programming

Project scheduling problems with both resource constraints and uncertain task durations have applications in a variety of industries. While the existing research literature has been focusing on finding an a priori open-loop task sequence that minimizes the expected makespan, finding a dynamic and ad...

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Bibliographic Details
Published in:European journal of operational research Vol. 246; no. 1; pp. 20 - 33
Main Authors: Li, Haitao, Womer, Norman K.
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.10.2015
Elsevier Sequoia S.A
Subjects:
Algorithms
Approximate dynamic programming
Approximation
Dynamic programming
Heuristic
Project management
Resource-constrained project scheduling
Scheduling algorithms
Simulation
Stochastic models
Stochastic scheduling
Studies
Uncertain task durations
Uncertain task durations
Stochastic scheduling
Approximate dynamic programming
Resource-constrained project scheduling
Simulation
ISSN:0377-2217, 1872-6860
Online Access:Get full text
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Summary:Project scheduling problems with both resource constraints and uncertain task durations have applications in a variety of industries. While the existing research literature has been focusing on finding an a priori open-loop task sequence that minimizes the expected makespan, finding a dynamic and adaptive closed-loop policy has been regarded as being computationally intractable. In this research, we develop effective and efficient approximate dynamic programming (ADP) algorithms based on the rollout policy for this category of stochastic scheduling problems. To enhance performance of the rollout algorithm, we employ constraint programming (CP) to improve the performance of base policy offered by a priority-rule heuristic. We further devise a hybrid ADP framework that integrates both the look-back and look-ahead approximation architectures, to simultaneously achieve both the quality of a rollout (look-ahead) policy to sequentially improve a task sequence, and the efficiency of a lookup table (look-back) approach. Computational results on the benchmark instances show that our hybrid ADP algorithm is able to obtain competitive solutions with the state-of-the-art algorithms in reasonable computational time. It performs particularly well for instances with non-symmetric probability distribution of task durations. •A computationally tractable closed-loop algorithm for stochastic resource-constrained project scheduling (SRCPSP).•Theoretical results of the rollout policy for SRCPSP, including the sequential improvement property.•Enhanced base policy by embedding constraint programming in the rollout framework.•Integrated look-back and look-ahead approximation architectures to improve effectiveness and efficiency of the algorithm.
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2015.04.015