A global constraint for total weighted completion time for unary resources

Kovács, András; Beck, J. Christopher

doi:10.1007/s10601-009-9088-x

Cited by 8 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The notion of regret and its use in the context of costbased filtering was originally introduced for dealing with constraints having a cost variable (Focacci, Lodi, and Milano 1999) and used more extensively later on, e.g. (Focacci, Lodi, and Milano 2002;Sellmann, Gellermann, and Wright 2007;Kovács and Beck 2011). This paper shows how to use this notion of regret for providing a GAC filtering algorithm for a conjunction of a linear inequality and an atleast constraint.…”

Section: Discussionmentioning

confidence: 99%

GAC for a Linear Inequality and an Atleast Constraint with an Application to Learning Simple Polynomials

Razakarison

Beldiceanu

Carlsson³

et al. 2021

SOCS

View full text Add to dashboard Cite

We provide a filtering algorithm achieving GAC for the conjunction of constraints atleast (b, [x(0),x(1),...,x(n-1)], V) and (a(0)*x(0) +...+ a(n-1)*x(n-1)) <= c, where the atleast constraint enforcesb variables out of x(0), x(1), ..., x(n-1) to be assigned to avalue in the set V. This work was motivated by learning simplepolynomials, i.e. finding the coefficients of polynomialsin several variables from example parameter and function values.We additionally require that coefficients be integers, andthat most coefficients be assigned to zero or integers close to0. These problems occur in the context of learning constraintmodels from sample solutions of different sizes. Experimentswith this more global filtering show an improvement by severalorders of magnitude compared to handling the constraintsin isolation or with cost gcc, while also out-performing a0/1 MIP model of the problem.

show abstract

Section: Discussionmentioning

confidence: 99%

GAC for a Linear Inequality and an Atleast Constraint with an Application to Learning Simple Polynomials

Razakarison

Beldiceanu

Carlsson³

et al. 2021

SOCS

View full text Add to dashboard Cite

show abstract

“…These methods have been much more effective for makespan minimization than for summation cost functions, as the total flow time. Nevertheless, in [28] the authors propose a global constraint involving unary resources and weighted completion time that propagates constraints quite well. So, we plan to use this method for reducing the search space and devising better heuristic estimations for guiding the search, as it was done in [33,34] for the job shop scheduling problem with makespan minimization.…”

Section: Future Researchmentioning

confidence: 99%

Solving the job shop scheduling problem with operators by depth-first heuristic search enhanced with global pruning rules

et al. 2015

View full text Add to dashboard Cite

The job shop scheduling problem with an additional resource type has been recently proposed to model the situation where each operation in a job shop has to be assisted by one of a limited set of human operators. We confront this problem with the objective of minimizing the total flow time, which makes the problem more interesting from a practical point of view and harder to solve than the version with makespan minimization. To solve this problem we propose an enhanced dept-first search algorithm. This algorithm exploits a schedule generation schema termed OG&T , two admissible heuristics and some powerful pruning rules. In order to diversify the search, we also consider a variant of this algorithm with restarts. We have conducted an experimental study across several benchmarks. The results of this study show that the global pruning rules are really effective and that the proposed algorithms are quite competent for solving this problem.

show abstract

“…Below we present experiments comparing the performance of FCFS, SP T sum , makespan and completionTime models for minimizing the mean flow time over a long time horizon in our two problem settings. The completion-Time model was implemented via constraint programming in Ilog Scheduler 6.5 and uses the completion global constraint (Kovács and Beck 2011). The remaining methods were implemented using C++.…”

Section: Mean Flow Time In Dynamic Flow Shop Environmentsmentioning

confidence: 99%

Long-Run Stability in Dynamic Scheduling

Terekhov

Tran

Down

et al. 2012

ICAPS

Self Cite

View full text Add to dashboard Cite

Stability analysis consists of identifying conditions under which the number of jobs in a system is guaranteed to remain bounded over time. To date, such long-run performance guarantees have not been available for periodic approaches to dynamic scheduling problems. However, stability has been extensively studied in queueing theory. In this paper, we introduce stability to the dynamic scheduling literature and demonstrate that stability guarantees can be obtained for methods that build the schedule for a dynamic problem by periodically solving static deterministic sub-problems. Specifically, we analyze the stability of two dynamic environments: a two-machine flow shop, which has received significant attention in scheduling research, and a polling system with a flow-shop server, an extension of systems typically considered in queueing. We demonstrate that, among stable policies, methods based on periodic optimization of static schedules may achieve better mean flow times than traditional queueing approaches.

show abstract

A global constraint for total weighted completion time for unary resources

Cited by 8 publications

References 31 publications

GAC for a Linear Inequality and an Atleast Constraint with an Application to Learning Simple Polynomials

GAC for a Linear Inequality and an Atleast Constraint with an Application to Learning Simple Polynomials

Solving the job shop scheduling problem with operators by depth-first heuristic search enhanced with global pruning rules

Long-Run Stability in Dynamic Scheduling

Contact Info

Product

Resources

About