The Home Service Assignment, Routing, and Appointment scheduling (H-SARA) problem integrates the strategic fleet-sizing, tactical assignment, operational vehicle routing and scheduling problems at different decision levels, with a single period planning horizon and uncertainty (stochasticity) from the service duration, travel time, and customer cancellation rate. We propose a stochastic mixed-integer linear programming model for the H-SARA problem. Additionally, a reduced deterministic version is introduced which allows to solve small-scale instances to optimality with two acceleration approaches. For larger instances, we develop a tailored two-stage decision support system that provides high-quality and in-time solutions based on information revealed at different stages. Our solution method aims to reduce various costs under stochasticity, to create reasonable routes with balanced workload and team-based customer service zones, and to increase customer satisfaction by introducing a two-stage appointment notification system updated at different time stages before the actual service. Our two-stage heuristic is competitive to CPLEX’s exact solution methods in providing time and cost-effective decisions and can update previously-made decisions based on an increased level of information. Results show that our two-stage heuristic is able to tackle reasonable-size instances and provides good-quality solutions using less time compared to the deterministic and stochastic models on the same set of simulated instances.
@InProceedings{johnn_et_al:OASIcs.ATMOS.2021.4, author = {Johnn, Syu-Ning and Zhu, Yiran and Miniguano-Trujillo, Andr\'{e}s and Gupte, Akshay}, title = {{Solving the Home Service Assignment, Routing, and Appointment Scheduling (H-SARA) Problem with Uncertainties}}, booktitle = {21st Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2021)}, pages = {4:1--4:21}, series = {Open Access Series in Informatics (OASIcs)}, ISBN = {978-3-95977-213-6}, ISSN = {2190-6807}, year = {2021}, volume = {96}, editor = {M\"{u}ller-Hannemann, Matthias and Perea, Federico}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2021.4}, URN = {urn:nbn:de:0030-drops-148737}, doi = {10.4230/OASIcs.ATMOS.2021.4}, annote = {Keywords: Home Health Care, Mixed-Integer Linear Programming, Two-stage Stochastic, Uncertainties A Priori Optimisation, Adaptive Large Neighbourhood Search, Monte-Carlo Simulation} }
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