Extending chip in order to solve complex scheduling and placement problems

Aggoun, Abderrahmane; Beldiceanu, Nicolas

doi:10.1016/0895-7177(93)90068-a

Cited by 248 publications

(189 citation statements)

References 11 publications

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“…Random instances have either 10 or 20 activities. Their processing times were chosen within [1,10], their heights within [1,5]. We used the first fail [6] search strategy (the current default strategy of Choco) and compared our algorithms with the corresponding state of the art algorithms [2], both combined with the Time- Table (TT) filtering algorithm of Letort et al [8].…”

Section: Methodsmentioning

confidence: 99%

“…We examine if scheduling an activity a at its minimum schedule does not lead to a failure of condition (1). We first define the available energy for a over interval [t 1 , t 2 [ as the capacity of the interval minus the minimum intersection of all other activities: The characterization of Proposition 2 is proved to be sufficient in [2] (Proposition 19) for the ER checker.…”

Section: Introductionmentioning

confidence: 99%

“…Usually, variables p a and h a are fixed integers, as well as C. In this paper, we make such assumptions. A solution to a CuSP is a schedule that satisfies the following constraints: ∀a ∈ A : s a + p a = e a ∧ ∀t ∈ N : t∈[sa,ea[,a∈A h a ≤ C In CP, this problem is generally represented by the global constraint Cumulative [1]. The Energetic Reasoning of Baptiste et al (ER) is one of the most powerful filtering algorithms for Cumulative [2].…”

Section: Introductionmentioning

confidence: 99%

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A New Characterization of Relevant Intervals for Energetic Reasoning

Derrien

Petit

2014

Lecture Notes in Computer Science

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Abstract. Energetic Reasoning (ER) is a powerful filtering algorithm for the Cumulative constraint. Unfortunately, ER is generally too costly to be used in practice. One reason of its bad behavior is that many intervals are considered as relevant, although most of them should be ignored. In the literature, heuristic approaches have been developed in order to reduce the number of intervals to consider, leading to a loss of filtering. In this paper, we provide a sharp characterization that allows to reduce the number of intervals by a factor seven without loss of filtering.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Characterization of Relevant Intervals for Energetic Reasoning

Derrien

Petit

2014

Lecture Notes in Computer Science

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“…Each activity is therefore associated to a unique auxiliary power line and to a unique converter. For each auxiliary power line, all the activities supplied by this line are constrained by CUMULATIVE constraint [1] with capacity equal to this threshold. Similarly another a CUMULATIVE constraint is associated to each converter, and a last one is associated to the main power line, involving all activities of the problem.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…The international Rosetta/Philae project is an European consortium mission approved in 1993, and is under the leadership of the German Aerospace Research Institute (DLR) and ESA 1 . The spacecraft was launched in 2004 by Ariane 5, and is set to travel more than six billion kilometers to finally reach and land on the comet 67P /ChuryumovGerasimenko in 2014 in order to analyze the comet structure.…”

Section: Introductionmentioning

confidence: 99%

Scheduling Scientific Experiments on the Rosetta/Philae Mission

Simonin

Artigues

Hébrard

et al. 2012

Lecture Notes in Computer Science

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Abstract. The Rosetta/Philae mission was launched in 2004 by the European Space Agency (ESA). It is scheduled to reach the comet 67P /ChuryumovGerasimenko in 2014 after traveling more than six billion kilometers. The Philae module will then be separated from the orbiter (Rosetta) to attempt the first ever landing on the surface of a comet. If it succeeds, it will engage a sequence of scientific exploratory experiments on the comet. In this paper we describe a constraint programming model for scheduling the different experiments of the mission. A feasible plan must satisfy a number of constraints induced by energetic resources, precedence relations on activities, or incompatibility between instruments. Moreover, a very important aspect is related to the transfer (to the orbiter then to Earth) of all the data produced by the instruments. The capacity of inboard memories and the limitation of transfers within visibility windows between lander and orbiter, make the transfer policy implemented on the lander's CPU prone to data loss. We introduce a global constraint to handle data transfers. The goal of this constraint is to ensure that data-producing activities are scheduled in such a way that no data is lost. Thanks to this constraint and to the filtering rules we propose, mission control engineers are now able to compute feasible plans in a few seconds for scenarios where minutes or even hours were previously often required. Moreover, in many cases, data transfers are now much more accurately simulated, thus increasing the reliability of the plans.

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Constraint Programming

Mathieu¹,

Keisu²

2000

Logic‐Based Methods for Optimization

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Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, operations research, algorithms, graph theory and elsewhere. The basic idea in constraint programming is that the user states the constraints and a general purpose constraint solver is used to solve them. Constraints are just relations, and a constraint satisfaction problem (CSP) states which relations should hold among the given decision variables. More formally, a constraint satisfaction problem consists of a set of variables, each with some domain of values, and a set of relations on subsets of these variables. For example, in scheduling exams at an university, the decision variables might be the times and locations of the different exams, and the constraints might be on the capacity of each examination room (e.g., we cannot schedule more students to sit exams in a given room at any one time than the room's capacity) and on the exams scheduled at the same time (e.g., we cannot schedule two exams at the same time if they share students in common). Constraint solvers take a real-world problem like this represented in terms of decision variables and constraints, and find an assignment to all the variables that satisfies the constraints. Extensions of this framework may involve, for example, finding optimal solutions according to one or more optimization criterion (e.g., minimizing the number of days over which exams need to be scheduled), finding all solutions, replacing (some or all) constraints with preferences, and considering a distributed setting where constraints are distributed among several agents.Constraint solvers search the solution space systematically, as with backtracking or branch and bound algorithms, or use forms of local search which may be incomplete. Systematic method often interleave search (see Section 4.3) and inference, where inference consists of propagating the information contained in one constraint to the neighboring constraints (see Section 4.2). Such inference reduces the parts of the search space that need to be visited. Special propagation procedures can be devised to suit specific constraints (called global constraints), which occur often in real life. Such global constraints are an important component in the success of constraint pro- Backtracking SearchA backtracking search for a solution to a CSP can be seen as performing a depth-first traversal of a search tree. This search tree is generated as the search progresses. At a

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Extending chip in order to solve complex scheduling and placement problems

Cited by 248 publications

References 11 publications

A New Characterization of Relevant Intervals for Energetic Reasoning

A New Characterization of Relevant Intervals for Energetic Reasoning

Scheduling Scientific Experiments on the Rosetta/Philae Mission

Constraint Programming

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