Conference Paper

# Making AC-3 an Optimal Algorithm.

Conference: Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI 2001, Seattle, Washington, USA, August 4-10, 2001

Source: DBLP

- [Show abstract] [Hide abstract]

**ABSTRACT:**For the enterprises organised in several distributed production sites, usually, production scheduling models presume either an instantaneous delivery of products or an unlimited number of available vehicles for transporting products. However, the transportation of the intermediate products to the sites is an important activity within the whole process of manufacturing, and the efficient coordination of production and transportation becomes a challenging problem in the actual higher collaborative and competitive environments. This work focuses on the integrated production and transportation scheduling properly managing the resources capacity, material flows and temporal interdependencies between sites. A case-study is reported and the industrial problem under consideration is modelled as a constraint satisfaction problem (CSP). Besides scheduling under resource constraints, the model presented in this paper expands the packing problem to the area of transportation operations scheduling. It is implemented under the constraint programming language CHIP V5. The provided solutions determine values for the various variables associated to the production and transportation operations realised on the whole multi-site, as well as the curves with the profile of the total consumption of resources in time.International Journal of Computer Integrated Manufacturing 01/2012; · 0.94 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**a b s t r a c t This paper concerns project scheduling under resource constraints. Traditionally, the objective is to find a unique solution that minimizes the project makespan, while respecting the precedence constraints and the resource constraints. This work focuses on developing a model and a decision support framework for industrial application of the cumulative global constraint. For a given project scheduling, the proposed approach allows the generation of different optimal solutions relative to the alternate availability of out-sourcing and resources. The objective is to provide a decision-maker an assistance to construct, choose, and define the appropriate scheduling program taking into account the possible capacity resources. The industrial problem under consideration is modeled as a constraint satisfaction problem (CSP). It is imple-mented under the constraint programming language CHIP V5. The provided solutions determine values for the various variables associated to the tasks realized on each resource, as well as the curves with the profile of the total consumption of resources on time.Computers & Industrial Engineering 09/2010; 61:357-363. · 1.69 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**Arc-consistency algorithms are the workhorse of backtrackers that maintain arc-consistency (MAC). This paper will provide experimental evidence that, despite common belief to the contrary, it is not always necessary for a good arc-consistency algorithm to have an optimal worst-case time-complexity. Sacrificing this optimality allows MAC solvers that (1) do not need additional data structures during search, (2) have an excellent average time-complexity, and (3) have a space-complexity that improves significantly on that of MAC solvers that have optimal arc-consistency components. Results will be presented from an experimental comparison between MAC-2001, MAC-3d and related algorithms. MAC-2001 has an arc-consistency component with an optimal worst-case time-complexity, whereas MAC-3d does not. MAC-2001 requires additional data structures during search, whereas MAC-3d does not. MAC-3d has a O(e+nd) of space-complexity, where n is the number of variables, d the maximum domain size, and e the number of constraints. We shall demonstrate that MAC-2001's space-complexity is O(edmin(n,d)). Our experimental results indicate that MAC-2001 was slower than MAC-3d for easy and hard random problems. For real-world problems things were not as clear.Artificial Intelligence Review 06/2004; 21(3-4). · 1.57 Impact Factor

Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.