A compact reformulation of the two-stage robust resource-constrained project scheduling problem

•We consider a two-stage robust resource-constrained project scheduling problem.•Activity durations are uncertain and lie in a budgeted uncertainty set.•A new compact mixed-integer programming formulation for the problem is derived.•In computational experiments we compare against the current best me...

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Vydáno v:Computers & operations research Ročník 130; s. 105232
Hlavní autoři: Bold, Matthew, Goerigk, Marc
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.06.2021
Pergamon Press Inc
Témata:
Algorithms
Budgeted uncertainty
Decision making
Job shops
Operations research
Optimization
Project management
Project scheduling
Resource constraints
Resource scheduling
Robust optimisation
Uncertainty
Resource constraints
Project scheduling
Budgeted uncertainty
Robust optimisation
ISSN:0305-0548, 1873-765X, 0305-0548
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Shrnutí:•We consider a two-stage robust resource-constrained project scheduling problem.•Activity durations are uncertain and lie in a budgeted uncertainty set.•A new compact mixed-integer programming formulation for the problem is derived.•In computational experiments we compare against the current best method.•Our approach is easy to apply and solve 49% more instances given the same time. This paper considers the resource-constrained project scheduling problem with uncertain activity durations. We assume that activity durations lie in a budgeted uncertainty set, and follow a robust two-stage approach, where a decision maker must resolve resource conflicts subject to the problem uncertainty, but can determine activity start times after the uncertain activity durations become known. We introduce a new reformulation of the second-stage problem, which enables us to derive a compact robust counterpart to the full two-stage adjustable robust optimisation problem. Computational experiments show that this compact robust counterpart can be solved using standard optimisation software significantly faster than the current state-of-the-art algorithm for solving this problem, reaching optimality for almost 50% more instances on the same benchmark set.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2021.105232