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Author: Norman Sadeh Publisher: ISBN: Category : Combinatorial analysis Languages : en Pages : 0
Book Description
Abstract: "Practical Constraint Satisfaction Problems (CSPs) such as design of integrated circuits or scheduling generally entail large search spaces with hundreds or even thousands of variables, each with hundreds or thousands of possible values. Often, only a very tiny fraction of all these possible assignments participates in a satisfactory solution. This article discusses techniques that aim at reducing the effective size of the search space to be explored in order to find a satisfactory solution by judiciously selecting the order in which variables are instantiated and the sequence in which possible values are tried for each variable. In the CSP literature, these techniques are commonly referred to as variable and value ordering heuristics. Our investigation is conducted in the job shop scheduling domain. We show that, in contrast with problems studied earlier in the CSP literature, generic variable and value heuristics do not perform well in this domain. This is attributed to the difficulty of these heuristics to properly account for the tightness of constraints and/or the connectivity of the constraint graphs induced by job shop scheduling CSPs. A new probabilistic framework is introduced that better captures these key aspects of the job shop scheduling search space. Empirical results show that variable and value ordering heuristics derived within this probabilistic framework often yield significant improvements in search efficiency and significant reductions in the search time required to obtain a satisfactory solution. The research reported in this article was the first one, along with the work of Keng and Yun [Keng 89], to use CSP problem solving paradigm to solve job shop scheduling problems. The suite of benchmark problems it introduced has been used since then by a number of other researchers to evaluate alternative techniques for the job shop scheduling CSP. The article briefly reviews some of these more recent efforts and shows that our variable and value ordering heuristics remain quite competitive."
Author: Norman Sadeh Publisher: ISBN: Category : Combinatorial analysis Languages : en Pages : 0
Book Description
Abstract: "Practical Constraint Satisfaction Problems (CSPs) such as design of integrated circuits or scheduling generally entail large search spaces with hundreds or even thousands of variables, each with hundreds or thousands of possible values. Often, only a very tiny fraction of all these possible assignments participates in a satisfactory solution. This article discusses techniques that aim at reducing the effective size of the search space to be explored in order to find a satisfactory solution by judiciously selecting the order in which variables are instantiated and the sequence in which possible values are tried for each variable. In the CSP literature, these techniques are commonly referred to as variable and value ordering heuristics. Our investigation is conducted in the job shop scheduling domain. We show that, in contrast with problems studied earlier in the CSP literature, generic variable and value heuristics do not perform well in this domain. This is attributed to the difficulty of these heuristics to properly account for the tightness of constraints and/or the connectivity of the constraint graphs induced by job shop scheduling CSPs. A new probabilistic framework is introduced that better captures these key aspects of the job shop scheduling search space. Empirical results show that variable and value ordering heuristics derived within this probabilistic framework often yield significant improvements in search efficiency and significant reductions in the search time required to obtain a satisfactory solution. The research reported in this article was the first one, along with the work of Keng and Yun [Keng 89], to use CSP problem solving paradigm to solve job shop scheduling problems. The suite of benchmark problems it introduced has been used since then by a number of other researchers to evaluate alternative techniques for the job shop scheduling CSP. The article briefly reviews some of these more recent efforts and shows that our variable and value ordering heuristics remain quite competitive."
Author: Norman Sadeh Publisher: ISBN: Category : Combinatorial analysis Languages : en Pages : 51
Book Description
Abstract: "Practical Constraint Satisfaction Problems (CSPs) such as design of integrated circuits or scheduling generally entail large search spaces with hundreds or even thousands of variables, each with hundreds or thousands of possible values. Often, only a very tiny fraction of all these possible assignments participates in a satisfactory solution. This article discusses techniques that aim at reducing the effective size of the search space to be explored in order to find a satisfactory solution by judiciously selecting the order in which variables are instantiated and the sequence in which possible values are tried for each variable. In the CSP literature, these techniques are commonly referred to as variable and value ordering heuristics. Our investigation is conducted in the job shop scheduling domain. We show that, in contrast with problems studied earlier in the CSP literature, generic variable and value heuristics do not perform well in this domain. This is attributed to the difficulty of these heuristics to properly account for the tightness of constraints and/or the connectivity of the constraint graphs induced by job shop scheduling CSPs. A new probabilistic framework is introduced that better captures these key aspects of the job shop scheduling search space. Empirical results show that variable and value ordering heuristics derived within this probabilistic framework often yield significant improvements in search efficiency and significant reductions in the search time required to obtain a satisfactory solution. The research reported in this article was the first one, along with the work of Keng and Yun [Keng 89], to use CSP problem solving paradigm to solve job shop scheduling problems. The suite of benchmark problems it introduced has been used since then by a number of other researchers to evaluate alternative techniques for the job shop scheduling CSP. The article briefly reviews some of these more recent efforts and shows that our variable and value ordering heuristics remain quite competitive."
Author: Norman Sadeh Publisher: ISBN: Category : Mathematical optimization Languages : en Pages : 51
Book Description
Our investigation is conducted in the domain of job shop scheduling. It is shown that, in this domain, generic CSP heuristics are usually not sufficient to guide the search for a feasible solution. This is because these heuristics fail to properly account for the tightness of constraints and/or the connectivity of the constraint graph. Instead, a probabilistic model of the search space is used to define new heuristics, which better account for these problem characteristics. Experimental results indicate that these new heuristics yield important improvements in both search efficiency and search time."
Author: Norman Sadeh Publisher: ISBN: Category : Mathematical optimization Languages : en Pages : 0
Book Description
Our investigation is conducted in the domain of job shop scheduling. It is shown that, in this domain, generic CSP heuristics are usually not sufficient to guide the search for a feasible solution. This is because these heuristics fail to properly account for the tightness of constraints and/or the connectivity of the constraint graph. Instead, a probabilistic model of the search space is used to define new heuristics, which better account for these problem characteristics. Experimental results indicate that these new heuristics yield important improvements in both search efficiency and search time."
Author: Carnegie-Mellon University. Robotics Institute Publisher: ISBN: Category : Operations research Languages : en Pages : 26
Book Description
Abstract: "This paper studies a version of the job shop scheduling problem in which some operations have to be scheduled within non-relaxable time windows (i.e. earliest/latest possible start time windows). This problem is a well-known NP-complete Constraint Satisfaction Problem (CSP). A popular method for solving this type of problems [sic] involves using depth-first backtrack search. In our earlier work, we focused on the development of consistency enforcing techniques and variable/value ordering heuristics that improve the efficiency of this search procedure. In this paper, we combine these techniques with new look-back schemes that help the search procedure recover from so-called deadend search states (i.e. partial solutions that cannot be completed without violating some constraints). More specifically, we successively describe three 'intelligent' backtracking schemes: (1) Dynamic Consistency Enforcement dynamically identifies critical subproblems and determines how far to backtrack by selectively enforcing higher levels of consistency among variables participating in these critical subproblems, (2) Learning Ordering From Failure dynamically modifies the order in which variables are instantiated based on earlier conflicts, and (3) Incomplete Backjumping Heuristic abandons areas of the search space that appear to require excessive computational efforts. These schemes are shown to (1) further reduce the average complexity of the backtrack search procedure, (2) enable our system to efficiently solve problems that could not be solved otherwise due to excessive computation cost, and (3) be more effective at solving job shop scheduling problems than other look-back schemes advocated in the literature."
Author: Carnegie Mellon University. Robotics Institute Publisher: ISBN: Category : Operations research Languages : en Pages : 0
Book Description
Abstract: "This paper studies a version of the job shop scheduling problem in which some operations have to be scheduled within non-relaxable time windows (i.e. earliest/latest possible start time windows). This problem is a well-known NP-complete Constraint Satisfaction Problem (CSP). A popular method for solving this type of problems [sic] involves using depth-first backtrack search. In our earlier work, we focused on the development of consistency enforcing techniques and variable/value ordering heuristics that improve the efficiency of this search procedure. In this paper, we combine these techniques with new look-back schemes that help the search procedure recover from so-called deadend search states (i.e. partial solutions that cannot be completed without violating some constraints). More specifically, we successively describe three 'intelligent' backtracking schemes: (1) Dynamic Consistency Enforcement dynamically identifies critical subproblems and determines how far to backtrack by selectively enforcing higher levels of consistency among variables participating in these critical subproblems, (2) Learning Ordering From Failure dynamically modifies the order in which variables are instantiated based on earlier conflicts, and (3) Incomplete Backjumping Heuristic abandons areas of the search space that appear to require excessive computational efforts. These schemes are shown to (1) further reduce the average complexity of the backtrack search procedure, (2) enable our system to efficiently solve problems that could not be solved otherwise due to excessive computation cost, and (3) be more effective at solving job shop scheduling problems than other look-back schemes advocated in the literature."
Author: Thomas E. Morton Publisher: John Wiley & Sons ISBN: 9780471578192 Category : Business & Economics Languages : en Pages : 718
Book Description
Reflects exact and heuristic methods of scheduling techniques suitable for creating customized sequencing and scheduling systems for flexible manufacturing, project management, group and cellular manufacturing operations. Summarizes complex computational studies demonstrating how they work in practice. Contains new theories and techniques developed by the author. Includes a software disk to reinforce and practice the methods described.
Author: Thomas Schiex Publisher: Springer Nature ISBN: 303030048X Category : Mathematics Languages : en Pages : 788
Book Description
This book constitutes the proceedings of the 25th International Conference on Principles and Practice of Constraint Programming, CP 2019, held in Stamford, CT, USA, France, in September/October 2019. The 44 full papers presented in this volume were carefully reviewed and selected from 118 submissions. They deal with all aspects of computing with constraints including theory, algorithms, environments, languages, models, systems, and applications such as decision making, resource allocation, scheduling, configuration, and planning. The papers were organized according to the following topics/tracks: technical track; application track; multi-agent and parallel CP track; testing and verification track; CP and data science track; computational sustainability; and CP and life sciences track.
Author: Toby Walsh Publisher: Springer ISBN: 3540455787 Category : Computers Languages : en Pages : 794
Book Description
This book constitutes the refereed proceedings of the 7th International Conference on Principles and Practice of Constraint Programming, CP 2001, held in Paphos, Cyprus, in November/December 2001. The 37 revised full papers, 9 innovative applications presentations, and 14 short papers presented were carefully reviewed and selected from a total of 135 submissions. All current issues in constraint processing are addressed, ranging from theoretical and foundational issues to advanced and innovative applications in a variety of fields.
Author: Peter van Beek Publisher: Springer ISBN: 3540320504 Category : Computers Languages : en Pages : 906
Book Description
The 11th International Conference on the Principles and Practice of Constraint Programming (CP 2005) was held in Sitges (Barcelona), Spain, October 1-5, 2005. Information about the conference can be found on the web at http://www.iiia.csic.es/cp2005/.Informationaboutpastconferencesinthe series can be found athttp://www.cs.ualberta.ca/~ai/cp/. The CP conference series is the premier international conference on c- straint programming and is held annually. The conference is concerned with all aspects of computing with constraints, including: algorithms, applications, environments, languages, models and systems. This year, we received 164 submissions. All of the submitted papers received atleastthreereviews, andthepapersandtheirreviewswerethenextensivelyd- cussed during an online Program Committee meeting. As a result, the Program Committee chose 48 (29.3%) papers to be published in full in the proceedings and a further 22 (13.4%)papers to be published as short papers.The full papers werepresentedattheconferencein twoparalleltracksandtheshortpaperswere presented as posters during a lively evening session. Two papers were selected by a subcommittee of the ProgramCommittee--consisting of Chris Beck, Gilles Pesant, and myself--to receive best paper awards. The conference program also includedexcellentinvitedtalksbyHþ ectorGe?ner, IanHorrocks, FrancescaRossi, and Peter J. Stuckey. As a permanent record, the proceedings contain four-page extended abstracts of the invited talks.
Author: Norman Sadeh
Author: Norman Sadeh
Author: Norman Sadeh
Author: Norman Sadeh
Author: Norman Sadeh
Author: Carnegie-Mellon University. Robotics Institute
Author: Carnegie Mellon University. Robotics Institute
Author: Thomas E. Morton
Author: Thomas Schiex
Author: Toby Walsh
Author: Peter van Beek