An efficient computational method for large scale surgery scheduling problems with chance constraints

We propose an efficient solution method based on a decomposition of set-partitioning formulation of an integrated surgery planning and scheduling problem with chance constraints. The studied problem is characterized by a set of identical operating rooms (ORs), a set of surgeries with uncertain durat...

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Vydáno v:Computational optimization and applications Ročník 69; číslo 2; s. 535 - 561
Hlavní autoři: Noorizadegan, Mahdi, Seifi, Abbas
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.03.2018
Springer Nature B.V
Témata:
Columnar structure
Computer simulation
Convex and Discrete Geometry
Decomposition
Formulations
Job shops
Management Science
Mathematics
Mathematics and Statistics
Operations Research
Operations Research/Decision Theory
Optimization
Production scheduling
Scheduling
Search algorithms
Sensitivity analysis
Shortest-path problems
Statistics
Surgeons
Surgery
Integer programming
Integer chance constrained programming
Set-partitioning formulation
Surgery planning and scheduling
ISSN:0926-6003, 1573-2894
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Shrnutí:We propose an efficient solution method based on a decomposition of set-partitioning formulation of an integrated surgery planning and scheduling problem with chance constraints. The studied problem is characterized by a set of identical operating rooms (ORs), a set of surgeries with uncertain durations, a set of surgeons, and surgery dependent turnover times. The decision variables include the number of ORs to open, assignments of surgeries and surgeons to ORs in admissible periods, and the sequence of surgeries to be performed in a period. The objective is to minimize the cost of opening ORs and the penalties associated with turnover times.In the proposed formulation, the column generation subproblem is decomposed over ORs and time periods. The structure of the subproblem is further exploited and transformed to a shortest path problem. A search algorithm has been devised to efficiently solve the resulting subproblem, subject to some optimality and feasibility conditions. The proposed computational method outperforms the standard chance constrained model and reduces the solution time significantly. Furthermore, extensive simulation experiments have been carried out to compare the performance of three variants of the underlying formulations and evaluate the sensitivity of the decisions to the probability of exceeding a session length.
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ISSN:0926-6003
1573-2894
DOI:10.1007/s10589-017-9947-0