TY - JOUR
T1 - Shortening the project schedule
T2 - solving multimode chance-constrained critical chain buffer management using reinforcement learning
AU - Szwarcfiter, Claudio
AU - Herer, Yale T.
AU - Shtub, Avraham
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.
PY - 2024/6
Y1 - 2024/6
N2 - Critical chain buffer management (CCBM) has been extensively studied in recent years. This paper investigates a new formulation of CCBM, the multimode chance-constrained CCBM problem. A flow-based mixed-integer linear programming model is described and the chance constraints are tackled using a scenario approach. A reinforcement learning (RL)-based algorithm is proposed to solve the problem. A factorial experiment is conducted and the results of this study indicate that solving the chance-constrained problem produces shorter project durations than the traditional approach that inserts time buffers into a baseline schedule generated by solving the deterministic problem. This paper also demonstrates that our RL method produces competitive schedules compared to established benchmarks. The importance of solving the chance-constrained problem and obtaining a project buffer tailored to the desired probability of completing the project on schedule directly from the solution is highlighted. Because of its potential for generating shorter schedules with the same on-time probabilities as the traditional approach, this research can be a useful aid for decision makers.
AB - Critical chain buffer management (CCBM) has been extensively studied in recent years. This paper investigates a new formulation of CCBM, the multimode chance-constrained CCBM problem. A flow-based mixed-integer linear programming model is described and the chance constraints are tackled using a scenario approach. A reinforcement learning (RL)-based algorithm is proposed to solve the problem. A factorial experiment is conducted and the results of this study indicate that solving the chance-constrained problem produces shorter project durations than the traditional approach that inserts time buffers into a baseline schedule generated by solving the deterministic problem. This paper also demonstrates that our RL method produces competitive schedules compared to established benchmarks. The importance of solving the chance-constrained problem and obtaining a project buffer tailored to the desired probability of completing the project on schedule directly from the solution is highlighted. Because of its potential for generating shorter schedules with the same on-time probabilities as the traditional approach, this research can be a useful aid for decision makers.
KW - Chance constraints
KW - Critical chain buffer management
KW - Multimode project management
KW - Project scheduling
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85171561153&partnerID=8YFLogxK
U2 - 10.1007/s10479-023-05597-8
DO - 10.1007/s10479-023-05597-8
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AN - SCOPUS:85171561153
SN - 0254-5330
VL - 337
SP - 565
EP - 592
JO - Annals of Operations Research
JF - Annals of Operations Research
IS - 2
ER -