Metaheuristics for the dynamic stochastic dial-a-ride problem with expected return transports

Abstract

The problem of transporting patients or elderly people has been widely studied in literature and is usually modeled as a dial-a-ride problem (DARP). In this paper we analyze the corresponding problem arising in the daily operation of the Austrian Red Cross. This nongovernmental organization is the largest organization performing patient transportation in Austria. The aim is to design vehicle routes to serve partially dynamic transportation requests using a fixed vehicle fleet. Each request requires transportation from a patient's home location to a hospital (outbound request) or back home from the hospital (inbound request). Some of these requests are known in advance. Some requests are dynamic in the sense that they appear during the day without any prior information. Finally, some inbound requests are stochastic. More precisely, with a certain probability each outbound request causes a corresponding inbound request on the same day. Some stochastic information about these return transports is available from historical data. The purpose of this study is to investigate, whether using this information in designing the routes has a significant positive effect on the solution quality. The problem is modeled as a dynamic stochastic dial-a-ride problem with expected return transports. We propose four different modifications of metaheuristic solution approaches for this problem. In detail, we test dynamic versions of variable neighborhood search (VNS) and stochastic VNS (S-VNS) as well as modified versions of the multiple plan approach (MPA) and the multiple scenario approach (MSA). Tests are performed using 12 sets of test instances based on a real road network. Various demand scenarios are generated based on the available real data. Results show that using the stochastic information on return transports leads to average improvements of around 15%. Moreover, improvements of up to 41% can be achieved for some test instances.

A look-ahead partial routing framework for the stochastic and dynamic vehicle routing problem

“…For example, the set of customers that needs to be serviced may not be known in advance. Instead, new customers may arrive in real time throughout the planning horizon [10,15,27]. In some cases, the demand of a customer may not be known when the service request is made and when routing decisions have to be made.…”

“…The problem arises naturally from a broad spectrum of real-world applications, including courier routing [9,15], service scheduling [3,4], Dial-a-Ride systems [2,8,26,27], etc. Depending on which element or elements of the problem become dynamic, numerous variations of the DVRP exist.…”

Towards Designing an Assistant for Semi-Automatic EMS Dispatching

“…For the online case, but when rescheduling is possible, Kergosien et al [26] propose a tabu search heuristic, which is called every time a new transport emerges. Schilde et al [38] study the problem of incorporating unknown but expected return transports for the patients. They model it as a dynamic stochastic problem and propose four advanced variants of metaheuristics.…”

“…Operations Research (OR) can not only help finding optimal locations for the ambulances (and relocate them over the day if applicable) [8], but also dispatch ambulances to emergencies [7], or schedule patient transports in order to minimize the tardiness of the transports [38].…”

Heuristics for dynamic and stochastic routing in industrial shipping

“…Hvattum et al (2007) extended the problem by allowing the demand to be unknown for customers that had placed an order in advance. Schilde et al (2011) considered variants of the methods of Bent and Van Hentenryck, as well as two variants of variable neighborhood search, for a dynamic stochastic dial-a-ride problem. Flatberg et al (2007) considered issues arising when trying to apply heuristics for stochastic and dynamic vehicle routing problems in practice, including how to model probabilities for future events based on historical data.…”