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Models and algorithms for workforce scheduling and routing problems in emergency response services

Models and algorithms for workforce scheduling and routing problems in emergency response services
Models and algorithms for workforce scheduling and routing problems in emergency response services
Emergency response services play a key role in protecting public safety and health, and therefore developing effective and efficient response systems is of critical importance. In this thesis, we focus on the workforce scheduling and routing problems (WSRPs) that are commonly faced by emergency response organisations.

We first present a simulation model for real-time emergency vehicle dispatching and routing, developed based on a case study of a British company providing emergency road services. The developed model is used to evaluate system performance, test scenarios and compare the effectiveness of different dispatching policies.

The results of simulation study motivate us to design more advanced heuristic algorithms for the static WSRP. To this purpose, we develop a simple and fast algorithm based on the iterated local search (ILS) framework. The performance of the proposed algorithm is evaluated on benchmark instances against an off-the-shelf optimizer and an existing adaptive large neighbourhood search algorithm. The proposed ILS algorithm is also applied to solve the skill vehicle routing problem, which can be viewed as a special case of the WSRP.

To further improve the decision making, we exploit the stochastic information about future requests and integrate this part of information into the solution method for the dynamic WSRP. A stochastic set-partitioning model is described and integrated with a sampling-based approach. The proposed model uses a two-stage framework, where the first-stage is concerned with finding a set of feasible routes covering known requests, while the second-stage estimates the effect of the same routes with respect to future requests. The performance of the proposed model is evaluated against a deterministic model and a naive greedy heuristic within a simulation framework.
University of Southampton
Xie, Fulin
8ab88939-a499-4a79-b7fb-f533033212f5
Xie, Fulin
8ab88939-a499-4a79-b7fb-f533033212f5
Potts, Christopher
58c36fe5-3bcb-4320-a018-509844d4ccff
Bektas, Tolga
0db10084-e51c-41e5-a3c6-417e0d08dac9

Xie, Fulin (2018) Models and algorithms for workforce scheduling and routing problems in emergency response services. University of Southampton, Doctoral Thesis, 179pp.

Record type: Thesis (Doctoral)

Abstract

Emergency response services play a key role in protecting public safety and health, and therefore developing effective and efficient response systems is of critical importance. In this thesis, we focus on the workforce scheduling and routing problems (WSRPs) that are commonly faced by emergency response organisations.

We first present a simulation model for real-time emergency vehicle dispatching and routing, developed based on a case study of a British company providing emergency road services. The developed model is used to evaluate system performance, test scenarios and compare the effectiveness of different dispatching policies.

The results of simulation study motivate us to design more advanced heuristic algorithms for the static WSRP. To this purpose, we develop a simple and fast algorithm based on the iterated local search (ILS) framework. The performance of the proposed algorithm is evaluated on benchmark instances against an off-the-shelf optimizer and an existing adaptive large neighbourhood search algorithm. The proposed ILS algorithm is also applied to solve the skill vehicle routing problem, which can be viewed as a special case of the WSRP.

To further improve the decision making, we exploit the stochastic information about future requests and integrate this part of information into the solution method for the dynamic WSRP. A stochastic set-partitioning model is described and integrated with a sampling-based approach. The proposed model uses a two-stage framework, where the first-stage is concerned with finding a set of feasible routes covering known requests, while the second-stage estimates the effect of the same routes with respect to future requests. The performance of the proposed model is evaluated against a deterministic model and a naive greedy heuristic within a simulation framework.

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Models and Algorithms for Workforce Scheduling and Routing Problems in Emergency Response Services - Version of Record
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Published date: May 2018

Identifiers

Local EPrints ID: 422176
URI: https://eprints.soton.ac.uk/id/eprint/422176
PURE UUID: 054d0acc-52b1-4bb5-b2c2-9d16733ea7de
ORCID for Tolga Bektas: ORCID iD orcid.org/0000-0003-0634-144X

Catalogue record

Date deposited: 18 Jul 2018 16:30
Last modified: 14 Mar 2019 01:39

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