Optimization of the Real-Time Response to Roadside Incidents through Heuristic and Linear Programming

Roman Buil, Jésica de Armas, Daniel Riera and Sandra Orozco
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Roman Buil: Studies of Computer Science, Multimedia and Telecommunications, Universitat Oberta de Catalunya, Rambla Poble Nou, 18, 08018 Barcelona, Spain
Daniel Riera: Studies of Computer Science, Multimedia and Telecommunications, Universitat Oberta de Catalunya, Rambla Poble Nou, 18, 08018 Barcelona, Spain
Sandra Orozco: Accenture S.L., Passeig de Sant Gervasi, 51, 08022 Barcelona, Spain

Mathematics, 2021, vol. 9, issue 16, 1-20

Abstract: This paper presents a solution for a real-world roadside assistance problem. Roadside incidents can happen at any time. Depending on the type of incident, a specific resource from the roadside assistance company can be sent on site. The problem of allocating resources to these road-side incidents can be stated as a multi-objective function and a large set of constraints, including priorities and preferences, resource capacities and skills, calendars, and extra hours. The request from the client is to a have real-time response and to attempt to use only open source tools. The optimization objectives to consider are the minimization of the operational costs and the minimization of the time to arrive to each incident. In this work, an innovative approach to near-optimally solving this problem in real-time is proposed, combining a heuristic approach and linear programming. The results show the great potential of this approach: operational costs were reduced by 19%, the use of external providers was reduced to half, and the productivity of the resources owned by the client was significantly increased.

Keywords: roadside assistance; resources scheduling optimization; real-time allocation; multi-objective function (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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