Automated Design of Production Scheduling Heuristics: A Review

Branke, Jürgen; Nguyen, Su; Pickardt, Christoph W.; Zhang, Mengjie

doi:10.1109/tevc.2015.2429314

Cited by 333 publications

(128 citation statements)

References 96 publications

(321 reference statements)

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“…(iii) Advances in methods for both exact optimisation and heuristic methods will allow larger problem instances to be solved more quickly. For example, disruption management may benefit from the use of approaches to automatically design heuristics that have been used successfully in production scheduling applications as presented in Branke et al (2016).…”

Section: Resultsmentioning

confidence: 99%

Disruption management in vehicle routing and scheduling for road freight transport: a review

Eglese

Zambirinis

2018

TOP

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Disruption management is an approach to the rescheduling of operations following an unanticipated event occurring that has been applied in a wide range of applications, including airline scheduling and project management. This review focusses on the use of disruption management in vehicle routing and scheduling applied to road freight distribution. The paper discusses the key features of disruption management and examines the relevant objectives and types of disruption that may occur in this context. Different formulations and solution methods are described. A set of relevant papers are summarised and classified according to the type of disruption addressed, the relevant objectives and the solution approach.

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Section: Resultsmentioning

confidence: 99%

Disruption management in vehicle routing and scheduling for road freight transport: a review

Eglese

Zambirinis

2018

TOP

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“…The attributes used to construct scheduling heuristics can be classified as job attributes, work center attributes, and global or system attributes. A comprehensive list of attributes used in the literature can be found in [20]. The tree-based representation of the traditional GP technique [6,67] and the linear representation in gene expression programming (GEP) [34] are usually applied in the previous studies.…”

Section: Evolving Priority Functionmentioning

confidence: 99%

“…The motivation of this approach is to reduce the time needed to design heuristics from the human experts and to increase the chance to explore a wide range of powerful and undiscovered heuristics. In the last decade, genetic programming [6,67] has been the dominating technique for automated design for production scheduling heuristics [20].…”

Section: Introductionmentioning

confidence: 99%

“…Although there are a number survey papers for production scheduling such as Ouelhadj and Petrovic [109] and Hart et al [43], they just focused on traditional meta-heuristics to find optimal solutions for a set of static scheduling instances. Recently, Branke et al [20] presented the most comprehensive survey of existing studies on hyper-heuristics and production scheduling. In the survey, critical design aspects such as attribute selection, representation, and fitness functions are presented.…”

Section: Introductionmentioning

confidence: 99%

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Genetic programming for production scheduling: a survey with a unified framework

Nguyen

Mei

Zhang

2017

Complex Intell. Syst.

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214

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Genetic programming has been a powerful technique for automated design of production scheduling heuristics. Many studies have shown that heuristics evolved by genetic programming can outperform many existing heuristics manually designed in the literature. The flexibility of genetic programming also allows it to discover very sophisticated heuristics to deal with complex and dynamic production environments. However, as compared to other applications of genetic programming or scheduling applications of other evolutionary computation techniques, the configurations and requirements of genetic programming for production scheduling are more complicated. In this paper, a unified framework for automated design of production scheduling heuristics with genetic programming is developed. The goal of the framework is to provide the researchers with the overall picture of how genetic programming can be applied for this task and the key components. The framework is also used to facilitate our discussions and analyses of existing studies in the field. Finally, this paper shows how knowledge from machine learning and operations research can be employed and how the current challenges can be addressed.

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“…Genetic programming (GP) is a subfield of evolutionary computing [9], that works with variable size LISP-tree representations and thus is able to evolve functions of arbitrary complexity, making it particularly suitable for the design of heuristics. Hyper-heuristics and GP in particular, have been applied in a wide range of contexts, including production scheduling [10], traveling salesman problems [11], bin packing [12], etc.…”

Section: Distinguishes Two Different Categoriesmentioning

confidence: 99%

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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Automated Design of Production Scheduling Heuristics: A Review

Cited by 333 publications

References 96 publications

Disruption management in vehicle routing and scheduling for road freight transport: a review

Disruption management in vehicle routing and scheduling for road freight transport: a review

Genetic programming for production scheduling: a survey with a unified framework

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

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