Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Constraint-Based Scheduling
Optimizing with Constraints: A Case Study in Scheduling Maintenance of Electric Power Units
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods
Journal of Scheduling
Texture measurements as a basis for heuristic commitment techniques in constraint-directed scheduling
A Hybrid Method for the Planning and Scheduling
Constraints
Online Stochastic Combinatorial Optimization
Online Stochastic Combinatorial Optimization
Optimal server scheduling in nonpreemptive finite-population queueing systems
Queueing Systems: Theory and Applications
Planning and Scheduling by Logic-Based Benders Decomposition
Operations Research
A theoretic and practical framework for scheduling in a stochastic environment
Journal of Scheduling
Metaheuristics for minimizing the makespan of the dynamic shop scheduling problem
Advances in Engineering Software
Checking-up on branch-and-check
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Introduction to Stochastic Programming
Introduction to Stochastic Programming
Solving two-machine assembly scheduling problems with inventory constraints
Computers and Industrial Engineering
Reconsidering mixed integer programming and MIP-Based hybrids for scheduling
CPAIOR'12 Proceedings of the 9th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
INFORMS Journal on Computing
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We address a dynamic repair shop scheduling problem in the context of military aircraft fleet management where the goal is to maintain a full complement of aircraft over the longterm. A number of flights, each with a requirement for a specific number and type of aircraft, are already scheduled over a long horizon. We need to assign aircraft to flights and schedule repair activities while considering the flights requirements, repair capacity, and aircraft failures. The number of aircraft awaiting repair dynamically changes over time due to failures and it is therefore necessary to rebuild the repair schedule online. To solve the problem, we view the dynamic repair shop as successive static repair scheduling sub-problems over shorter time periods. We propose a complete approach based on the logic-based Benders decomposition to solve the static sub-problems, and design different rescheduling policies to schedule the dynamic repair shop. Computational experiments demonstrate that the Benders model is able to find and prove optimal solutions on average four times faster than a mixed integer programming model. The rescheduling approach having both aspects of scheduling over a longer horizon and quickly adjusting the schedule increases aircraft available in the long term by 10% compared to the approaches having either one of the aspects alone.