TY - GEN
T1 - Solving the home service assignment, routing, and appointment scheduling (H-SARA) problem with uncertainties
AU - Johnn, Syu Ning
AU - Zhu, Yiran
AU - Miniguano-Trujillo, Andrés
AU - Gupte, Akshay
N1 - Publisher Copyright:
© Syu-Ning Johnn, Yiran Zhu, Andrés Miniguano-Trujillo, and Akshay Gupte.
PY - 2021/9/27
Y1 - 2021/9/27
N2 - 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 mixedinteger 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 reasonablesize instances and provides good-quality solutions using less time compared to the deterministic and stochastic models on the same set of simulated instances.
AB - 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 mixedinteger 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 reasonablesize instances and provides good-quality solutions using less time compared to the deterministic and stochastic models on the same set of simulated instances.
KW - Adaptive Large Neighbourhood Search
KW - Home Health Care
KW - Mixed-Integer Linear Programming
KW - Monte-Carlo Simulation
KW - Two-stage Stochastic
KW - Uncertainties A Priori Optimisation
UR - http://www.scopus.com/inward/record.url?scp=85118187681&partnerID=8YFLogxK
U2 - 10.4230/OASIcs.ATMOS.2021.4
DO - 10.4230/OASIcs.ATMOS.2021.4
M3 - Conference contribution
AN - SCOPUS:85118187681
T3 - OpenAccess Series in Informatics
BT - 21st Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2021)
A2 - Muller-Hannemann, Matthias
A2 - Perea, Federico
PB - Schloss Dagstuhl - Leibniz-Zentrum für Informatik
T2 - 21st Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems 2021
Y2 - 9 September 2021 through 10 September 2021
ER -