Adjustment of heads and tails for the job-shop problem

Carlier, Jacques; Pinson, Éric

doi:10.1016/0377-2217(94)90379-4

Cited by 190 publications

(81 citation statements)

References 10 publications

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“…The Shaving filtering technique (Carlier and Pinson (1994), Martin and Shmoys (1996)) updates the task time windows by assessing the earliest and latest start times. For each unassigned task t i , at each node, it temporarily assigns t i a starting time (either est i or lst i ) and propagates the assignment using the filtering algorithms (Edge-Finding, etc.).…”

Section: Traditional Constraint Programming Approach ("Heavy Model")mentioning

confidence: 99%

Solving Variants of the Job Shop Scheduling Problem Through Conflict-Directed Search

Grimes

Hébrard

2015

INFORMS Journal on Computing

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We introduce a simple technique for disjunctive machine scheduling problems and show that this method can match or even outperform state of the art algorithms on a number of problem types. Our approach combines a number of generic search techniques such as restarts, adaptive heuristics and solution guided branching on a simple model based on a decomposition of disjunctive constraints and on the reification of these disjuncts. This paper describes the method and its application to variants of the job shop scheduling problem (JSP ).We show that our method can easily be adapted to handle additional side constraints and different objective functions, often outperforming the state of the art and closing a number of open problems.* Moreover, we perform in-depth analysis of the various factors that makes this approach efficient. We show that, while most of the factors give moderate benefits, the variable and value ordering components are key.

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Section: Traditional Constraint Programming Approach ("Heavy Model")mentioning

confidence: 99%

Solving Variants of the Job Shop Scheduling Problem Through Conflict-Directed Search

Grimes

Hébrard

2015

INFORMS Journal on Computing

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“…We implemented also the NFNL and EF techniques following the algorithms described in [15]. Note that the implementations of EF and NFNL described in that book run in O(N 2 ) but they use much simpler data structures than the theoretically most efficient algorithm described respectively in [3] and [7]. Finally, we modeled the OpenShop Problem as described in the first section with the NFNL, EF and PS or PSB propagators and the AllDifferent constraint.…”

Section: Methodsmentioning

confidence: 99%

“…The more prominent techniques are Edge-Finding (EF) and Not-First-Not-Last (NFNL). Edge-Finding [1][2][3][4] consists in testing whether a particular task must start before or after a set of tasks. It can be implemented with a time complexity of O(N log N ) where N is the number of tasks on one machine or one job.…”

Section: Introductionmentioning

confidence: 99%

A Position-Based Propagator for the Open-Shop Problem

Monette

Deville

Dupont

2007

Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems

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Abstract. The Open-Shop Problem is a hard problem that can be solved using Constraint Programming or Operation Research methods. Existing techniques are efficient at reducing the search tree but they usually do not consider the absolute ordering of the tasks. In this work, we develop a new propagator for the One-Machine Non-Preemptive Problem, the basic constraint for the Open-Shop Problem. This propagator takes this additional information into account allowing, in most cases, a reduction of the search tree. The underlying principle is to use shaving on the positions. Our propagator applies on one machine or one job and its time complexity is in O(N 2 log N ), where N is either the number of jobs or machines. Experiments on the Open-Shop Problem show that the propagator adds pruning to state-of-the-art constraint satisfaction techniques to solve this problem.

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“…In this case, each resource can execute at most one activity at a time. For instance, the reader can consult (Baptiste et al, 1998),or (Carlier and Pinson, 1994) for a presentation of the edge-finder algorithm with the lowest worst case complexity so far.…”

Section: Examplesmentioning

confidence: 99%

Global Constraints and Filtering Algorithms

Régin¹

2004

Constraint and Integer Programming

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Constraint programming (CP) is mainly based on filtering algorithms; their association with global constraints is one of the main strengths of CP. This chapter is an overview of these two techniques. Some of the most frequently used global constraints are presented. In addition, the filtering algorithms establishing arc consistency for two useful constraints, the alldiff and the global cardinality constraints, are fully detailed. Filtering algorithms are also considered from a theoretical point of view: three different ways to design filtering algorithms are described and the quality of the filtering algorithms studied so far is discussed. A categorization is then proposed. Over-constrained problems are also mentioned and global soft constraints are introduced.

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Adjustment of heads and tails for the job-shop problem

Cited by 190 publications

References 10 publications

Solving Variants of the Job Shop Scheduling Problem Through Conflict-Directed Search

Solving Variants of the Job Shop Scheduling Problem Through Conflict-Directed Search

A Position-Based Propagator for the Open-Shop Problem

Global Constraints and Filtering Algorithms

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