Measures of dynamism and urgency in logistics

Lon, Rinde R.S. van; Ferrante, Eliseo; Turgut, Ali Emre; Wenseleers, Tom; Berghe, Greet Vanden; Holvoet, Tom

doi:10.1016/j.ejor.2016.03.021

Cited by 20 publications

(26 citation statements)

References 35 publications

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“…For this, a series of communication, coordination and negotiation protocols is established among the agents. In this case, collaboration between the system components facilitates problems solving and reaching out the defined objectives [11][12][13][14][15].…”

Section: Multi-agent Systems In Goods Distributionmentioning

confidence: 99%

Multi-agent Model for Urban Goods Distribution

Serna¹,

Marín²,

Urán³

et al. 2018

RCS

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This paper proposes a multi-agent model to evaluate the last mile distribution process in urban areas, in a network that includes a single depot, multiple customers and multiple homogeneous and capacitated vehicles. The model is based on the behaviors of the different stakeholders and their interactions, including coordination and collaboration of agents in the allocations and routing processes. The performance of the Solomon Algorithm is the starting point of the proposed model and the comparison of both results shows the advantage of using multi-agent modelling to reduce distances and number of vehicles needed for urban freight transportation.

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Section: Multi-agent Systems In Goods Distributionmentioning

confidence: 99%

Multi-agent Model for Urban Goods Distribution

Serna¹,

Marín²,

Urán³

et al. 2018

RCS

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“…As a result, there is an urgent need for efficient and effective dispatching technology to support meal delivery operations. However, meal delivery routing problems are quite challenging, not in the least because of the dynamism and urgency of arriving orders (van Lon et al, 2016). Meal delivery is arguably the ultimate challenge in last mile logistics: a typical order is expected to be delivered within an hour (much less if possible), and within minutes of the food becoming ready (Reyes et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Provably High-Quality Solutions for the Meal Delivery Routing Problem

Yıldız

Savelsbergh

2019

Transportation Science

139

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Online restaurant aggregators with integrated meal delivery networks have become more common and more popular in the past few years. Meal delivery is arguably the ultimate challenge in last mile logistics: a typical order is expected to be delivered within an hour (much less if possible), and within minutes of the food becoming ready. We introduce a novel formulation for a meal delivery routing problem (in which we assume perfect information about order arrivals), and develop a simultaneous column and row generation method for its solution. The analysis of the results of an extensive computational study, using instances derived from real-life data, demonstrates the efficacy of the solution approach, and provides valuable insights into, among others, the (potential) benefits of order bundling, courier shift scheduling, and demand management.

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“…Dynamism is defined in van Lon et al [3]. Informally, a scenario that changes continuously is said to be dynamic while a scenario that changes occasionally is said to be less dynamic.…”

Section: Dynamismmentioning

confidence: 99%

“…In [3] urgency is defined as the maximum reaction time available to the fleet of vehicles in order to respond to an incoming order. Or more formally:…”

Section: Urgencymentioning

confidence: 99%

“…In a purely dynamic PDPTW, no order is known before the operating hours. When a new order is announced, the available computation time for an algorithm is limited by the order's urgency, the amount of available time until the order needs to be serviced [3]. Together, the dynamism, urgency, and scale of a problem, directly affect the amount of computations that need to be done as well as how much time is available for performing them [4].…”

Section: Introductionmentioning

confidence: 99%

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Optimizing agents with genetic programming: an evaluation of hyper-heuristics in dynamic real-time logistics

Lon

Branke

Holvoet

2017

Genet Program Evolvable Mach

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warwick.ac.uk/lib-publicationsOriginal citation: van Lon, Rinde R. S., Branke, Jürgen and Holvoet, Tom. (2017) Optimizing agents with genetic programming : an evaluation of hyper-heuristics in dynamic real-time logistics. Genetic Programming and Evolvable Machines. Permanent WRAP URL:http://wrap.warwick.ac.uk/86320 Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Publisher's statement:The final publication is available at Springer via https://doi.org/10.1007/s10710-017-9300-5 A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. Abstract Dynamic pickup and delivery problems (PDPs) require online algorithms for managing a fleet of vehicles. Generally, vehicles can be managed either centrally or decentrally. A common way to coordinate agents decentrally is to use the contract-net protocol (CNET) that uses auctions to allocate tasks among agents. To participate in an auction, agents require a method that estimates the value of a task. Typically this method is an optimization algorithm. Recently, hyper-heuristics has been proposed for automated design of heuristics. Two properties of automatically designed heuristics are particularly promising: 1) a generated heuristic computes quickly, it is expected therefore that hyper-heuristics heuristics perform especially well for urgent problems, and 2) by using simulationbased evaluation, hyper-heuristics can learn from the past and can therefore create a 'rule of thumb' that anticipates situations in the future. In the present paper we empirically evaluate whether hyper-heuristics, more specifically genetic programming (GP), can be used to improve agents decentrally coordinated via CNET. We compare several GP settings and compare the resulting heuristic with existing centralized and decentralized algorithms on a dynamic PDP dataset with varying levels of dynamism, urgency, and scale. The results indicate that the evolved heuristic always outperforms the optimization algorithm in the decentralized MAS and often outperforms the centralized optimization algorithm. Our paper shows that des...

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Measures of dynamism and urgency in logistics

Cited by 20 publications

References 35 publications

Multi-agent Model for Urban Goods Distribution

Multi-agent Model for Urban Goods Distribution

Provably High-Quality Solutions for the Meal Delivery Routing Problem

Optimizing agents with genetic programming: an evaluation of hyper-heuristics in dynamic real-time logistics

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