Dispatching of an Electric Monorail System: Applying Metaheuristics to an Online Pickup and Delivery Problem

Gutenschwager, Kai; Niklaus, Christian; Voβ, Stefan

doi:10.1287/trsc.1030.0066

Cited by 28 publications

(17 citation statements)

References 37 publications

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“…Existing static solutions can be applied to form new schedules from scratch as new information arrives. Alternatively, heuristics (Popken 2006;Rubinstein, Smith, and Barbulescu 2012) or metaheuristics such as Tabu search and simulated annealing (Gutenschwager, Niklaus, and Voß 2004) can be applied to extend and adjust schedules to incorporate new tasks. Some work has been done on responding to dynamic events such as cancellations, traffic delays, and accidents (Haghani and Jung 2005;Xiang, Chu, and Chen 2008).…”

Section: Related Workmentioning

confidence: 99%

Online pickup and delivery planning with transfers for mobile robots

Coltin

Veloso

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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We have deployed a fleet of robots that pickup and deliver items requested by users in an office building. Users specify time windows in which the items should be picked up and delivered, and send in requests online. Our goal is to form a schedule which picks up and delivers the items as quickly as possible at the lowest cost. We introduce an auction-based scheduling algorithm which plans to transfer items between robots to make deliveries more efficiently. The algorithm can obey either hard or soft time constraints. We discuss how to replan in response to newly requested items, cancelled requests, delayed robots, and robot failures. We demonstrate the effectiveness of our approach through execution on robots, and examine the effect of transfers on large simulated problems.

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Section: Related Workmentioning

confidence: 99%

Online pickup and delivery planning with transfers for mobile robots

Coltin

Veloso

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…Applications include full truckload transportation problems (Tjokroamidjojo et al 2006), the dispatching of automated guided vehicles (Mes et al 2007) and dispatching of passenger transportation with vehicles that can only carry a single person (Gutenschwager et al 2004).…”

Section: Literature Overviewmentioning

confidence: 99%

Distribution of waiting time for dynamic pickup and delivery problems

Vonolfen

Affenzeller

2014

Ann Oper Res

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Pickup and delivery problems have numerous applications in practice such as parcel delivery and passenger transportation. In the dynamic variant of the problem, not all information is available in advance but is revealed during the planning process. Thus, it is crucial to anticipate future events in order to generate high-quality solutions. Previous work has shown that the use of waiting strategies has the potential to save costs and maximize service quality. We adapt various waiting heuristics to the pickup and delivery problem with time windows. Previous research has shown, that specialized waiting heuristics utilizing anticipatory knowledge potentially outperform general heuristics. Direct policy search based on evolutionary computation and a simulation model is proposed as a methodology to automatically specialize waiting strategies to different problem characteristics. Based on the strengths of the previously introduced waiting strategies, we propose a novel waiting heuristic that can utilize historical request information based on an intensity measure which does not require an additional data preprocessing step. The performance of the waiting heuristics is evaluated on a single set of benchmark instances containing various instance classes that differ in terms of spatial and temporal properties. The diverse set of benchmark instances is used to analyze the influence of spatial and temporal instance properties as well as the degree of dynamism to the potential savings that can be achieved by anticipatory waiting and the incorporation of knowledge about future requests.

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“…The dynamic (real-time) vehicle routing problem with time windows (DVRPTW) in particular is still even harder to solve than the static problem since customer requests are generated or do occur dynamically, mainly during the execution of the problem-solving procedure. Work and surveys on DVRPTW may be found in [7][8][9][10][11][12][13][14][15][16][17][18][19]. From the various heuristic methods reported for the DVRPTW, tabu search and genetic algorithms techniques have demonstrated the best performance [11,13,19].…”

Section: Introductionmentioning

confidence: 99%