Constraint satisfaction techniques in planning and scheduling

Barták, Roman; Salido, Miguel Á.; Rossi, Francesca

doi:10.1007/s10845-008-0203-4

Cited by 77 publications

(50 citation statements)

References 21 publications

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“…Incomplete search methods search the space either non-systematically or in a systematical manner (Bartak et al 2008). Incomplete search methods dealing with the constraints were grouped into four categories by Koziel and Michalewicz (1999):…”

Section: Constraint Handling Methodsmentioning

confidence: 99%

Artificial bee colony algorithm for large-scale problems and engineering design optimization

2010

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Engineering design problems are generally large scale or nonlinear or constrained optimization problems. The Artificial Bee Colony (ABC) algorithm is a successful tool for optimizing unconstrained problems. In this work, the ABC algorithm is used to solve large scale optimization problems, and it is applied to engineering design problems by extending the basic ABC algorithm simply by adding a constraint handling technique into the selection step of the ABC algorithm in order to prefer the feasible regions of entire search space. Nine well-known large scale unconstrained test problems and five well-known constrained engineering problems are solved by using the ABC algorithm and the performance of ABC algorithm is compared against those of state-of-the-art algorithms.

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Section: Constraint Handling Methodsmentioning

confidence: 99%

Artificial bee colony algorithm for large-scale problems and engineering design optimization

2010

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“…Felfernig et al (2003) and Romero Bejarano et al (2014) proposed a methodology based on ontology and associated inference techniques for the configuration problem. Mida and Vernadat (2009) and Barták, Salido, and Rossi (2010) presented a constraint-satisfaction problem or CSP approach for process planning and scheduling. Aldanondo and Vareilles (2008) and Zhang, Vareilles, and Aldanondo (2013) explained how the concurrent configuration of a product and a process can be considered as a single constraint-satisfaction problem.…”

Section: Models and Tools For Aiding System And Process Design At Thementioning

confidence: 99%

Readiness, feasibility and confidence: how to help bidders to better develop and assess their offers

Sylla

Vareilles

Coudert

et al. 2017

International Journal of Production Research

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao. In a bidding process, the bidder must define and evaluate potential offers in order to propose the most suitable one to the potential customer. Proposing attractive but also realistic offers to various potential customers is a key factor for the bidder to stay competitive. In order to achieve this, the bidder needs to be very sure about the technical specifications and the constructability of the proposal. However, performing a detailed design is resource and time-consuming. This article proposes the foundation of a new framework which can help bidders to define the right offer: (i) in the context of a non-routine design process, while avoiding a detailed design and (ii) taking into account two new indicators that reflect the bidder's confidence that they can meet the commitments once the offer is accepted. The first indicator (OCS) characterises the Overall Confidence in the technical System, while the second one (OCP) gives the Overall Confidence in the delivery Process. Both OCS and OCP are based firstly on two factual objective indicators, Technology Readiness Level (TRL) for OCS and Activity Feasibility Level (AFL) for OCP, and secondly on two human-based subjective indicators, Confidence In System (CIS) for the OCS and Confidence In Process for the OCP. An illustrative application shows how this framework can really help bidders define an offer, while avoiding detailed design and enable them to evaluate the confidence level in each potential offer.

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“…Some planners use the instantiation in order to encode planning problems into different formalisms such as Sat (Kautz & Selman, 1992, 1999Rintanen, 2012) or Csp (Barták, Salido, & Rossi, 2010;Kambhampati, 2000;Lopez & Bacchus, 2003) in order to benefit from the recent performance improvements of Sat and Csp solvers. Other planners use the instantiation to efficiently compute heuristics (Hoffmann & Nebel, 2001) or speedup the search algorithm by using a compact encoding (Helmert, 2009).…”

Section: Instantiation Modulementioning

confidence: 99%

PDDL4J: a planning domain description library for java

Pellier

Fiorino

2017

Journal of Experimental & Theoretical Artificial Intelligen

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Pddl4j (Planning Domain Description Library for Java) is an open source toolkit for Java cross-platform developers meant (1) to provide state-of-the-art planners based on the Pddl language, and (2) to facilitate research works on new planners. In this article, we present an overview of the Automated Planning concepts and languages. We present some planning systems and their most significant applications. Then, we detail the Pddl4j toolkit with an emphasis on the available informative structures, heuristics and search algorithms.

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Constraint satisfaction techniques in planning and scheduling

Cited by 77 publications

References 21 publications

Artificial bee colony algorithm for large-scale problems and engineering design optimization

Artificial bee colony algorithm for large-scale problems and engineering design optimization

Readiness, feasibility and confidence: how to help bidders to better develop and assess their offers

PDDL4J: a planning domain description library for java

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