Conference Proceeding Article
In this work, we investigate a multi-period Home HealthCare Scheduling Problem (HHCSP) under stochastic serviceand travel times. We first model the deterministic problemas an integer linear programming model that incorporatesreal-world requirements, such as time windows, continuityof care, workload fairness, inter-visit temporal dependencies.We then extend the model to cope with uncertainty in durations,by introducing chance constraints into the formulation.We propose efficient solution approaches, which providequantifiable near-optimal solutions and further handlethe uncertainties by employing a sampling-based strategy. Wedemonstrate the effectiveness of our proposed approaches oninstances synthetically generated by real-world dataset forboth deterministic and stochastic scenarios.
Artificial Intelligence and Robotics | Computer Sciences | Theory and Algorithms | Transportation
Intelligent Systems and Decision Analytics
Proceedings of the Twenty-Seventh International Conference on Automated Planning and Scheduling ICAPS 2017: Pittsburgh, June 18-23
City or Country
Menlo Park, CA
CHEN, Cen; RUBINSTEIN, Zachary; SMITH, Stephen; and LAU, Hoong Chuin.
Tackling large-scale home health care delivery problem with uncertainty. (2017). Proceedings of the Twenty-Seventh International Conference on Automated Planning and Scheduling ICAPS 2017: Pittsburgh, June 18-23. 358-366. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3864
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