Greedy Algorithms for the Multi-capacitated Metric Scheduling Problem

Cesta, Amedeo; Oddi, Angelo; Smith, Stephen F.

doi:10.1007/10720246_17

Cited by 17 publications

(21 citation statements)

References 4 publications

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“…6) used in [Cesta et al, 2000] is inspired by prior work on the Earliest Start Time Algorithm (ESTA) from [Cesta et al, 1998]. The algorithm is a variant of a class of PCP scheduling procedures that utilize a twophase solution generation process.…”

Section: Pcp Search (Pcps)mentioning

confidence: 99%

Iterative flattening search for resource constrained scheduling

et al. 2008

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Iterative Flattening Search (IFS) is a meta-heuristic strategy for solving multi-capacity scheduling problems. Given an initial solution, IFS iteratively applies: (1) a relaxationstep, in which a subset of scheduling decisions are randomly retracted from the current solution; and (2) a flattening-step, in which a new solution is incrementally recomputed from this partial schedule. Whenever a better solution is found, it is retained, and, upon termination, the best solution found during the search is returned. Prior research has shown IFS to be an effective and scalable heuristic procedure for minimizing schedule makespan in multi-capacity resource settings.In this paper we experimentally investigate the impact on IFS performance of algorithmic variants of the flattening step. The variants considered are distinguished by different computational requirements and correspondingly vary in the type and depth of search performed. The analysis is centered around the idea that given a time bound to the overall optimization procedure, the IFS optimization process is driven by two different and contrasting mechanisms: the random sampling performed by iteratively applying the "relaxation/flattening" cycle and the search conducted within the Stephen F. Smith The Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA E-mail: sfs@cs.cmu.edu constituent flattening procedure. On one hand, one might expect that efficiency of the flattening process is key: the faster the flattening procedure, the greater the number of iterations (and number of sampled solutions) for a given time bound; and hence the greater the probability of finding better quality solutions. On the other hand, the use of more accurate (and more costly) flattening procedures can increase the probability of obtaining better quality solutions even if their greater computational cost reduces the number of IFS iterations. Comparative results on well-studied benchmark problems are presented that clarify this tradeoff with respect to previously proposed flattening strategies, identify qualitative guidelines for the design of effective IFS procedures, and suggest some new directions for future work in this area.

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Section: Pcp Search (Pcps)mentioning

confidence: 99%

Iterative flattening search for resource constrained scheduling

et al. 2008

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“…In the Meta-CSP layer, resource constraints are super-imposed, giving rise to a set of resource conflicts that are solved with a number of sequencing decisions [9,10].…”

Section: Ipss-smentioning

confidence: 99%

“…Then, for each set we have increased the number of workers (2,3,4,5,6,7,8,9,10,20,30,40,50). The total time given to solve each problem (time bound) was 120 seconds.…”

Section: Telephone Installation (Ti) Domainmentioning

confidence: 99%

Integrating planning and scheduling in workflow domains

R-Moreno

Borrajo

Cesta

et al. 2007

Expert Systems with Applications

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One of the main obstacles in applying AI planning techniques to real problems is the difficulty to model the domains. Usually, this requires that people that have developed the planning system carry out the modeling phase since the representation depends very much on a deep knowledge of the internal working of the planning tools. On some domains such as Business Process Reengineering (BPR), there has already been work on the definition of languages that allow non-experts entering knowledge on processes into the tools. We propose here the use of one of such BPR languages to enter knowledge on the organisation processes to be used by planning tools. Then, planning tools can be used to semi-automatically generate business process models.As instances of this domain, we will use the workflow modeling tool SHAMASH, where we have exploded its object oriented structure to introduce the knowledge through its user-friendly interface and, using a translator transform it into predicate logic terms. After this conversion, real models can be automatically generated using a planner that integrates Planning and Scheduling, IPSS. We present results in a real workflow domain, the TELEPHONE INSTALLATION (TI) domain.

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“…To date, comparative studies show no clear advantage to either approach [2]. Both heuristics were originally applied to the static job-shop scheduling problem (JSP), and have subsequently been applied to extensions of JSP such as multiple capacitated resource scheduling [5] and the handling of alternative process plans [1]. We directly apply both the B-Slack [19] and Sum-Height [2] heuristics to deciding SSP feasibility.…”

Section: Scheduling Satellite Access Requestsmentioning

confidence: 99%

Exploiting Problem Structure in Scheduling

Howe¹,

Whitley²

2000

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Public reporting burden for this collection of information is estimated to average 1 hour per response, includi gathering and maintaining the data needed, and completing and reviewing the collection of information. ABSTRACT (Maximum 200 words)Many scheduling algorithms have been developed, but surprisingly there is little guidance as to which method to use for a given application. This project addressed this gap be-tween the science and practice of scheduling by developing a methodology for assessing performance and applying it to two problems: scheduling a manufacturing flow shop and scheduling access requests for the Air Force satellite control network. We found flaws in the conventional practice of evaluating scheduling algorithms. In particular, performance on popular benchmark problems does not generalize to problems with realistic problem struc-ture. Additionally, we found that the performance of algorithms previously deemed to be state-of-the-art may be dominated by much simpler and computationally faster algorithms. We were able to explain the differences in performance by characteristic patterns in the search spaces of structured problems. SUBJECT TERMS AbstractMany scheduling algorithms have been developed, but surprisingly there is little guidance as to which method to use for a given application. This project addressed this gap between the science and practice of scheduling by developing a methodology for assessing performance and applying it to two problems: scheduling a manufacturing flow shop and scheduling access requests for the Air Force satellite control network. We found flaws in the conventional practice of evaluating scheduling algorithms. In particular, performance on popular benchmark problems does not generalize to problems with realistic problem structure. Additionally, we found that the performance of algorithms previously deemed to be state-of-the-art may be dominated by much simpler and computationally faster algorithms. We were able to explain the differences in performance by characteristic patterns in the search spaces of structured problems.

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Greedy Algorithms for the Multi-capacitated Metric Scheduling Problem

Cited by 17 publications

References 4 publications

Iterative flattening search for resource constrained scheduling

Iterative flattening search for resource constrained scheduling

Integrating planning and scheduling in workflow domains

Exploiting Problem Structure in Scheduling

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