Extension of O(n log n) Filtering Algorithms for the Unary Resource Constraint to Optional Activities

Vilím, Petr; Barták, Roman; Čepek, Ondřej

doi:10.1007/s10601-005-2814-0

Cited by 34 publications

(37 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This work extends our single-agent online consistency test [12] to handle multiple agents and shared memory resource constraints. Our online consistency test is a variant of resource edge-finding [17], [33]. The purpose of edge-finding is to determine whether an event must or may execute before or after a set of activities [3].…”

Section: B Multi-agent Task Sequencer Pseudocodementioning

confidence: 99%

Fast Scheduling of Multi-Robot Teams with Temporospatial Constraints

Gombolay¹,

Wilcox²,

Shah³

2013

Robotics: Science and Systems IX

View full text Add to dashboard Cite

Abstract-New uses of robotics in traditionally manual manufacturing processes require the careful choreography of human and robotic agents to support safe and efficient coordinated work. Tasks must be allocated among agents and scheduled to meet temporal deadlines and spatial restrictions on agent proximity. These systems must also be capable of replanning onthe-fly to adapt to disturbances in the schedule and to respond to people working in close physical proximity. In this paper, we present a centralized algorithm, named Tercio, that handles tightly intercoupled temporal and spatial constraints and scales to larger problem sizes than prior art. Our key innovation is a fast, satisficing multi-agent task sequencer that is inspired by real-time processor scheduling techniques but is adapted to leverage hierarchical problem structure. We use this fast task sequencer in conjunction with a MILP solver, and show that we are able to generate near-optimal task assignments and schedules for up to 10 agents and 500 tasks in less than 20 seconds on average. Finally, we demonstrate the algorithm in a multi-robot hardware testbed.

show abstract

Section: B Multi-agent Task Sequencer Pseudocodementioning

confidence: 99%

Fast Scheduling of Multi-Robot Teams with Temporospatial Constraints

Gombolay¹,

Wilcox²,

Shah³

2013

Robotics: Science and Systems IX

View full text Add to dashboard Cite

show abstract

“…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%

“…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. Not-First-Not-Last [5][6][7] checks if a task can be the first or the last among a set of tasks. Its best time complexity is O(N log N ).…”

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

View full text Add to dashboard Cite

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.

show abstract

“…A complementary technique, called "not-first/not-last", deduces that an operation cannot be processed first or last (Torres & Lopez, 2000). (Baptiste & Le Pape, 1996) designed a not-first/not-last algorithm with the time complexity O(n 2 ) and (Vilím, 2004) proposed a filtering algorithm with the time complexity O(n.log n). Some of above mentioned techniques can be extended to cumulative resources, that is, discrete resources with capacity greater than one (more operations can be processed in parallel).…”

Section: Role Of Constraint Satisfaction In Schedulingmentioning

confidence: 99%

New trends in constraint satisfaction, planning, and scheduling: a survey

Barták

Salido

Rossi

2010

The Knowledge Engineering Review

Self Cite

View full text Add to dashboard Cite

During recent years the development of new techniques for constraint satisfaction, planning, and scheduling has received increased attention, and substantial effort has been invested in trying to exploit such techniques to find solutions to real life problems. In this paper, we present a survey on constraint satisfaction, planning and scheduling from the Artificial Intelligence point of view. In particular, we present the main definitions and techniques, and we discuss possible ways of integrating such techniques. We also analyze the role of constraint satisfaction in planning and scheduling, and we hint at some open research issues related to planning, scheduling and constraint satisfaction.

show abstract

Extension of O(n log n) Filtering Algorithms for the Unary Resource Constraint to Optional Activities

Cited by 34 publications

References 8 publications

Fast Scheduling of Multi-Robot Teams with Temporospatial Constraints

Fast Scheduling of Multi-Robot Teams with Temporospatial Constraints

A Position-Based Propagator for the Open-Shop Problem

New trends in constraint satisfaction, planning, and scheduling: a survey

Contact Info

Product

Resources

About