Fundamentals of queueing theory (2nd ed.).
Fundamentals of queueing theory (2nd ed.).
Constraint-Based Scheduling
A New Approach to Computing Optimal Schedules for the Job-Shop Scheduling Problem
Proceedings of the 5th International IPCO Conference on Integer Programming and Combinatorial Optimization
Constraint-directed search: a case study of job-shop scheduling
Constraint-directed search: a case study of job-shop scheduling
A queueing control model for retail services having back room operations and cross-trained workers
Computers and Operations Research
INFORMS Journal on Computing
Staffing Multiskill Call Centers via Linear Programming and Simulation
Management Science
Beyond Singleton Arc Consistency
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Dual modelling of permutation and injection problems
Journal of Artificial Intelligence Research
Proactive algorithms for job shop scheduling with probabilistic durations
Journal of Artificial Intelligence Research
CSCLP'05 Proceedings of the 2005 Joint ERCIM/CoLogNET international conference on Constraint Solving and Constraint Logic Programming
Flow-Based combinatorial chance constraints
CPAIOR'12 Proceedings of the 9th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
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In a facility with front room and back room operations, it is useful to switch workers between the rooms in order to cope with changing customer demand. Assuming stochastic customer arrival and service times, we seek a policy for switching workers such that the expected customer waiting time is minimized while the expected back room staffing is sufficient to perform all work. Three novel constraint programming models and several shaving procedures for these models are presented. Experimental results show that a model based on closed-form expressions together with a combination of shaving procedures is the most efficient. This model is able to find and prove optimal solutions for many problem instances within a reasonable run-time. Previously, the only available approach was a heuristic algorithm. Furthermore, a hybrid method combining the heuristic and the best constraint programming method is shown to perform as well as the heuristic in terms of solution quality over time, while achieving the same performance in terms of proving optimality as the pure constraint programming model. This is the first work of which we are aware that solves such queueing-based problems with constraint programming.