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.
Highlights
Roadside assistance is a service that helps the drivers of cars, motorcycles, and bicycles whose vehicles have suffered a mechanical failure that has left them stranded.Resolving one of these incidents may involve starting a car, diagnosing and repairing the problem, towing a vehicle, changing a flat tire, freeing a vehicle that is stuck in the snow, or helping people who have been trapped
All the results presented in this article have been implemented and are currently part of the Real Automòbil Club de Catalunya (RACC) resource allocation software
The aim of this section is to present an innovative method of resource scheduling and allocation of roadside services in real-time while minimizing the operational costs, minimizing the arrival times, and satisfying all the requirements and business constraints
Summary
Roadside assistance is a service that helps the drivers of cars, motorcycles, and bicycles whose vehicles have suffered a mechanical failure (or accident) that has left them stranded. Operational and tactical decisions often require smaller granularity: having a good approximation of the number of incidents of a certain type at a specific time of day would allow them to improve the organization of work shifts and to locate a resource with a skill set closer to where an assignable incident is most likely to occur. The main objective of this work is to propose an approach that automatically assigns 100% of incoming incidents, minimizing operating costs while respecting all business requirements. As said above, this is not a theoretical problem but a real customer problem, with incidents that arise in real-time and must be assigned in a matter of seconds.
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