Solving a Dynamic Real-Life Vehicle Routing Problem

Goel, Asvin; Gruhn, Volker

doi:10.1007/3-540-32539-5_58

Cited by 18 publications

(17 citation statements)

References 12 publications

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“…Campbell and Savelsberg (2004) modify an insertion heuristic in such a way that it considers maximum shift times for drivers. Goel and Gruhn (2006) introduce a large neighborhood search algorithm for a VRP which takes into account maximum driving times according to the former Regulation (EEC) No 3820/85. Goel (2009) considers parts of the current Regulation (EC) No 561/2006 in a large neighborhood search algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Programming Heuristic for the Vehicle Routing Problem with Time Windows and European Community Social Legislation

Kok

Meyer

Kopfer

et al. 2010

Transportation Science

115

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In practice, apart from the problem of vehicle routing, schedulers also face the problem of finding feasible driver schedules complying with complex restrictions on drivers' driving and working hours. To address this complex interdependent problem of vehicle routing and break scheduling, we propose a restricted dynamic programming heuristic for the vehicle routing problem with time windows and the full European social legislation on drivers' driving and working hours. The problem we consider includes all rules in this legislation, whereas 1 in the literature only a basic set of rules has been addressed. In addition to this basic set of rules, the legislation contains a set of modifications that allow for more flexibility. To include the legislation in the restricted dynamic programming heuristic, we propose a break scheduling heuristic. Computational results show that our method finds solutions to benchmark instances -which only consider the basic set of rules -with 18% less vehicles and 5% less travel distance than state of the art approaches. Moreover, our results are obtained with significant less computational effort. Furthermore, the results show that including a set of rules on drivers' working hours -which has been generally ignored in the literature -has a significant impact on the resulting vehicle schedules: 3.9% more vehicle routes and 1.0% more travel distance are needed. Finally, using the modified rules of the legislation leads to an additional reduction of 4% in the number of vehicles and of 1.5% regarding the travel distance. Therefore, the modified rules should be exploited in practice.

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Section: Introductionmentioning

confidence: 99%

A Dynamic Programming Heuristic for the Vehicle Routing Problem with Time Windows and European Community Social Legislation

Kok

Meyer

Kopfer

et al. 2010

Transportation Science

115

View full text Add to dashboard Cite

show abstract

“…Orthogonal to previous categories, in the last class, real-world applications, we can find bespoke methods to solve day-to-day problems that real companies have to confront (Savelsbergh and Sol 1998;Caramia et al 2002;Krumke et al 2002;Goel and Gruhn 2006). Generally, most of the methods use sophisticated integer programming algorithms mixed with ad-hoc heuristics to simplify the inherent mathematical model and reduce the execution times.…”

Section: Related Work: Dynamic Vehicle Routing Problemmentioning

confidence: 97%

DVRP: a hard dynamic combinatorial optimisation problem tackled by an evolutionary hyper-heuristic

Garrido

Riff

2010

J Heuristics

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In this paper we propose and evaluate an evolutionary-based hyperheuristic approach, called EH-DVRP, for solving hard instances of the dynamic vehicle routing problem. A hyper-heuristic is a high-level algorithm, which generates or chooses a set of low-level heuristics in a common framework, to solve the problem at hand. In our collaborative framework, we have included three different types of low-level heuristics: constructive, perturbative, and noise heuristics. Basically, the hyper-heuristic manages and evolves a sophisticated sequence of combinations of these low-level heuristics, which are sequentially applied in order to construct and improve partial solutions, i.e., partial routes. In presenting some design considerations, we have taken into account the allowance of a proper cooperation and communication among low-level heuristics, and as a result, find the most promising sequence to tackle partial states of the (dynamic) problem. Our approach has been evaluated using the Kilby's benchmarks, which comprise a large number of instances with different topologies and degrees of dynamism, and we have compared it with some well-known methods proposed in the literature. The experimental results have shown that, due to the dynamic nature of the hyper-heuristic, our proposed approach is able to adapt to dynamic scenarios more naturally than low-level heuristics. Furthermore, the hyper-heuristic can obtain high-quality solutions when compared with other (meta) heuristic-based methods. Therefore, the findings of this contribution justify the employment of hyper-heuristic techniques in such changing environments, and we believe that further contributions could be successfully proposed in related dynamic problems.

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“…The work relies on reoptimization approaches. Goel and Gruhn present a problem in which trucks serve customers and a percentage of customers are unknown. Trucks differ in their capacity, speed, and travel costs.…”

Section: Literature Reviewmentioning

confidence: 99%

Same‐day delivery with heterogeneous fleets of drones and vehicles

2018

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In this paper, we analyze how drones can be combined with regular delivery vehicles to improve same‐day delivery performance. To this end, we present a dynamic vehicle routing problem with heterogeneous fleets. Customers order goods over the course of the day. These goods are delivered either by a drone or by a regular transportation vehicle within a delivery deadline. Drones are faster, but have a limited capacity as well as require charging after use. In the same‐day context, vehicle capacity is not a constraint, but vehicles are slow due to urban traffic. To decide whether an order is delivered by a drone or by a vehicle, we present a policy function approximation based on geographical districting. Our computational study reveals two major implications. First, geographical districting is highly effective increasing the expected number of same‐day deliveries. Second, a combination of drone and vehicle fleets may significantly reduce the required delivery resources.

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Solving a Dynamic Real-Life Vehicle Routing Problem

Cited by 18 publications

References 12 publications

A Dynamic Programming Heuristic for the Vehicle Routing Problem with Time Windows and European Community Social Legislation

A Dynamic Programming Heuristic for the Vehicle Routing Problem with Time Windows and European Community Social Legislation

DVRP: a hard dynamic combinatorial optimisation problem tackled by an evolutionary hyper-heuristic

Same‐day delivery with heterogeneous fleets of drones and vehicles

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