Maintaining Arc-Consistency within Dynamic Backtracking

Jussien, Narendra; Debruyne, Romuald; Boizumault, Patrice

doi:10.1007/3-540-45349-0_19

Cited by 47 publications

(39 citation statements)

References 8 publications

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“…We performed tests using four different ordering cost values a ∈ {40, 80, 160, 320} and two different σ t /d t ∈ {1/3, 1/6}. The planning horizon length takes even values in the range [24,50] when the ordering cost is 40 or 80 and [14,24] when the All experiments were performed on an Intel(R) Centrino(TM) CPU 1.50GHz with 500Mb RAM. The solver used for our test is Choco [15], an open-source solver developed in Java.…”

Section: Resultsmentioning

confidence: 99%

Cost-Based Filtering for Stochastic Inventory Control

Tarim

Hnich

Rossi

et al. 2007

Lecture Notes in Computer Science

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Abstract. An interesting class of production/inventory control problems considers a single product and a single stocking location, given a stochastic demand with a known non-stationary probability distribution. Under a widely-used control policy for this type of inventory system, the objective is to find the optimal number of replenishments, their timings and their respective order-up-to-levels that meet customer demands to a required service level. We extend a known CP approach for this problem using a cost-based filtering method. Our algorithm can solve to optimality instances of realistic size much more efficiently than previous approaches, often with no search effort at all.

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Section: Resultsmentioning

confidence: 99%

Cost-Based Filtering for Stochastic Inventory Control

Tarim

Hnich

Rossi

et al. 2007

Lecture Notes in Computer Science

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“…The calculation of the elimination explanations in (Baker 1994;Ginsberg 1993;Jussien et al 2000) during practical experiments was rather similar. Thus, we assume that inconsistencies are detected rather late, after a lot of superfluous, unsuccessful assignments, i.e.…”

mentioning

confidence: 88%

Intelligent search strategies based on adaptive Constraint Handling Rules

Wolf¹

2005

Theory and Practice of Logic Programming

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The most advanced implementation of adaptive constraint processing with Constraint Handling Rules (CHR) allows the application of intelligent search strategies to solve Constraint Satisfaction Problems (CSP). This presentation compares an improved version of conflict-directed backjumping and two variants of dynamic backtracking with respect to chronological backtracking on some of the AIM instances which are a benchmark set of random 3-SAT problems. A CHR implementation of a Boolean constraint solver combined with these different search strategies in Java is thus being compared with a CHR implementation of the same Boolean constraint solver combined with chronological backtracking in SICStus Prolog. This comparison shows that the addition of "intelligence" to the search process may reduce the number of search steps dramatically. Furthermore, the runtime of their Java implementations is in most cases faster than the implementations of chronological backtracking. More specifically, conflict-directed backjumping is even faster than the SICStus Prolog implementation of chronological backtracking, although our Java implementation of CHR lacks the optimisations made in the SICStus Prolog system.

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“…Then we briefly argue that algorithms based on eliminating explanations [7,9] can be also represented by responsibility sets by showing that an eliminating explanation of a value can be extracted from a responsibility set associated with that value. We start with a brief overview of FC-CBJ.…”

Section: Responsibility Sets As An Alternative Representation Of Confmentioning

confidence: 99%

A CSP Search Algorithm with Responsibility Sets and Kernels

Razgon

Meisels

2007

Constraints

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A CSP search algorithm, like FC or MAC, explores a search tree during its run. Every node of the search tree can be associated with a CSP created by the refined domains of unassigned variables. If the algorithm detects that the CSP associated with a node is insoluble, the node becomes a dead-end. A strategy of pruning "by analogy" states that the current node of the search tree can be discarded if the CSP associated with it is "more constrained" than a CSP associated with some dead-end node. In this paper we present a method of pruning based on the above strategy. The information about the CSPs associated with dead-end nodes is kept in the structures called responsibility sets and kernels. We term the method that uses these structures for pruning RKP, which is abbreviation of Responsibility set, Kernel, Propagation. We combine the pruning method with algorithms FC and MAC. We call the resulting solvers FC-RKP and MAC-RKP, respectively. Experimental evaluation shows that MAC-RKP outperforms MAC-CBJ on random CSPs and on random graph coloring problems. The RKP-method also has theoretical interest. We show that under certain restrictions FC-RKP simulates FC-CBJ. It follows from the fact that intelligent backtracking implicitly uses the strategy of pruning "by analogy."

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Maintaining Arc-Consistency within Dynamic Backtracking

Cited by 47 publications

References 8 publications

Cost-Based Filtering for Stochastic Inventory Control

Cost-Based Filtering for Stochastic Inventory Control

Intelligent search strategies based on adaptive Constraint Handling Rules

A CSP Search Algorithm with Responsibility Sets and Kernels

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