A (μ, λ)-coordination mechanism for agent-based multi-project scheduling

Homberger, Jörg

doi:10.1007/s00291-009-0178-3

Cited by 71 publications

(65 citation statements)

References 51 publications

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“…In the first approach, an agent applies a complete metaheuristic in each negotiation round (Lau et al 2005a,b;Mao et al 2009;Mao 2011). In the second approach, an agent applies only one element from a metaheuristic in each negotiation round (e.g., Homberger 2012;Fink and Homberger 2013). For example, a crossover operator from evolutionary algorithms to generate a new offer from previous ones may be applied, or a probabilistic acceptance rule according to simulated annealing may be utilized to decide about a submitted offer.…”

Section: Negotiation Approachesmentioning

confidence: 99%

“…In the first approach schedule-related information simply consists of starting times of some activities (partial schedules) or of all activities (schedule). In the second approach project ordering lists are used as schedule-related information (Wauters et al 2010(Wauters et al , 2012Homberger 2012). A project ordering list defines the sequence by which the projects have to be scheduled.…”

Section: Negotiation Approachesmentioning

confidence: 99%

“…The approach obtains high quality solutions for instances of the MPSPLib (Homberger et al 2008). Homberger (2012) suggests a meditated negotiation method on the basis of an evolutionary algorithm. The protocol is based on the idea to search for a project ordering list which allows the project agents to generate local schedules with a high solution quality.…”

Section: Negotiation Approachesmentioning

confidence: 99%

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Decentralized Multi-Project Scheduling

Fink

Homberger

2014

Handbook on Project Management and Scheduling Vol. 2

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This chapter is concerned with the decentralized resource-constrained multi-project scheduling problem (DRCMPSP), which is characterized in that individual involved decision makers pursue individual goals, whereas some overall coordination mechanism is needed to resolve conflicts due to the interdependencies between multiple projects. The connection between activities from these projects may result from temporal and resource-orientated constraints. In general, there may be two kinds of autonomous decision makers, on the one hand those that control individual projects, and on the other hand those that control globally available resources. After providing a more detailed description of such kinds of problems and the resulting peculiarities of decentralized decision making, a classification of respective problem types is provided, which leads to related requirements for solution procedures. Overall, there are two basic solution approaches, namely auctions and negotiations. These methods are described in connection with a review of the related literature.

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Section: Negotiation Approachesmentioning

confidence: 99%

Section: Negotiation Approachesmentioning

confidence: 99%

Section: Negotiation Approachesmentioning

confidence: 99%

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Decentralized Multi-Project Scheduling

Fink

Homberger

2014

Handbook on Project Management and Scheduling Vol. 2

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“…A combination of instances from PSPLIB, added with release dates and global resources, were used as instances for the MISTA 2013 challenge (Wauters et al 2016). Also, a combination of RCPSP instances was used to generate multi-project scheduling instances, which are known as MPSPLIB (Homberger 2012).…”

Section: Benchmark Setsmentioning

confidence: 99%

Case mix classification and a benchmark set for surgery scheduling

Leeftink

Hans

2017

J Sched

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Numerous benchmark sets exist for combinatorial optimization problems. However, in healthcare scheduling, only a few benchmark sets are known, mainly focused on nurse rostering. One of the most studied topics in the healthcare scheduling literature is surgery scheduling, for which there is no widely used benchmark set. An effective benchmark set should be diverse, reflect the real world, contain large instances, and be extendable. This paper proposes a benchmark set for surgery scheduling algorithms, which satisfies these four criteria. Surgery scheduling instances are characterized by an underlying case mix, which describes the volume and properties of the surgery types. Given a case mix, unlimited random instances can be generated. A complete surgery scheduling benchmark set should encompass the diversity of prevalent case mixes. We therefore propose a case mix classification scheme, which we use to typify both real-life and theoretical case mixes that span the breadth of possible case mix types. Our full benchmark set contains 20,880 instances, with a small benchmark subset of 146 instances. The instances are generated based on real-life case mixes (11 surgical specialties), as well as theoretical instances. The instances were generated using a novel instance generation procedure, which introduces the concept of "instance proximity" to measure the similarity between two instances, and which uses this concept to generate sets of instances that are as diverse as possible.

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“…A combination of instances from PSPLib, added with release dates and global resources, were used as instances for the MISTA 2013 challenge [318]. Also, a combination of resource constrained project scheduling problem instances was used to generate multi-project scheduling instances, which are known as MPSPLib [141].…”

Section: Benchmark Setsmentioning

confidence: 99%

Organizing integrated processes in cancer care

Leeftink¹

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A (μ, λ)-coordination mechanism for agent-based multi-project scheduling

Cited by 71 publications

References 51 publications

Decentralized Multi-Project Scheduling

Decentralized Multi-Project Scheduling

Case mix classification and a benchmark set for surgery scheduling

Organizing integrated processes in cancer care

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