Application Of Constraint Programming Research Articles

Applications of constraint programming in production scheduling problems: A descriptive bibliometric analysis

Production scheduling is a class of widely studied combinatorial optimization problems. Given the complexity of the most addressed production environments several solution procedures have been proposed. In recent years, operational researcher practitioners have been paying attention to solving production scheduling problems using constraint programming, and growing interest in this research domain has been evidenced. The aim of this study is to report a descriptive bibliometric analysis of applications of constraint programming in production scheduling problems. The scope of the study is limited to reviewing 170 scientific papers published between 1992 and 2023 from the Scopus and Web of Science databases. In our proposed research questions, we could address the main topics studied, the most studied performance measures, and the profile of the analyzed documents. Furthermore, we could identify the main gaps and present suggestions for future research.

Transfer-robot task scheduling in job shop

This paper studies a simultaneous scheduling of production and material transfer in a job shop environment. The simultaneous scheduling approach has been recently adopted by warehouse operations, wherein transbots pick up jobs and deliver to pick-machines for processing that requires a simultaneous scheduling of jobs, transbots, and machines. However, both a large proportion of literature and real-world scheduling systems consider only one side of the problem. In our study, machines and transbot are both considered as constraining resources. The contributions of this paper are twofold. First, we propose a novel application of constraint programming for the job shop scheduling problem (JSP) with transbots, significantly outperforming all other benchmark approaches in the literature and proving optimality of the well-known benchmark instances, for the first time. Second, we propose a medium-scale benchmark instance.

Introduction to the special issue on CSP technologies in artificial intelligence

Although constraint programming has evolved as an independent domain of research per se, it remains at the same time one of the most fertile and successful areas of artificial intelligence (A.I.). This special issue is intended to illustrate various current topics and research trends in constraint programming that pertain to A.I. It gathers a series of research contributions that range from the study of tractable classes of non-binary CSPs, to efficient algorithms for the extraction of minimal unsatisfiable subsets in constraint networks, and include the application of constraint programming for deciding the robustness of wireless sensor networks and interval methods for numerical constraint programming. The papers in this issue are all revised and expanded research contributions based on results that were presented at the SAT and CSP technologies track at IEEE ICTAI 2013. They were selected as the papers with the highest review scores amongst the 47 full papers submitted to the track, after all the papers submitted to the track had been thoroughly reviewed by at least three reviewers. The authors of the selected papers in the track were invited to submit substantially expanded and revised contributions for this issue. Each of these expanded contributions underwent two additional reviews. We gratefully acknowledge the reviewers for providing deep insights and useful suggestions that helped improve the papers. Finally, we would like to thank Gilles Pesant and Michela Milano, Editors in chief of Constraints, for inviting us to edit this special issue and for their expert help at the different stages of the process.

ECLiPSe – From LP to CLP

AbstractECLiPSe is a Prolog-based programming system, aimed at the development and deployment of constraint programming applications. It is also used for teaching most aspects of combinatorial problem solving, for example, problem modelling, constraint programming, mathematical programming and search techniques. It uses an extended Prolog as its high-level modelling and control language, complemented by several constraint solver libraries, interfaces to third-party solvers, an integrated development environment and interfaces for embedding into host environments. This paper discusses language extensions, implementation aspects, components, and tools that we consider relevant on the way from Logic Programming to Constraint Logic Programming.

Application of Constraint Programming for Large-scale Planning Systems—Programming, Developments and Challenges—

Application of Constraint Programming for Large-scale Planning Systems—Programming, Developments and Challenges—

Solving production scheduling with earliness/tardiness penalties by constraint programming

This paper deals with an application of constraint programming in production scheduling with earliness and tardiness penalties that reflects the scheduling part of the Just-In-Time inventory strate...

Constraint Programming in Structural Bioinformatics

Bioinformatics aims at applying computer science methods to the wealth of data collected in a variety of experiments in life sciences (e.g. cell and molecular biology, biochemistry, medicine, etc.) in order to help analysing such data and eliciting new knowledge from it. In addition to string processing bioinformatics is often identified with machine learning used for mining the large banks of bio-data available in electronic format, namely in a number of web servers. Nevertheless, there are opportunities of applying other computational techniques in some bioinformatics applications. In this paper, we report the application of constraint programming to address two structural bioinformatics problems, protein structure prediction and protein interaction (docking). The efficient application of constraint programming requires innovative modelling of these problems, as well as the development of advanced propagation techniques (e.g. global reasoning and propagation), which were adopted in Chemera, a system that is currently used to support biochemists in their research.

Three perspectives for solving the job grouping problem

The production efficiency of printed circuit board (PCB) assembly depends strongly on the organization of the component placement jobs. This is characteristic, especially in a high-mix low-volume production environment. The present study discusses the problem of arranging the jobs of one machine into groups in such a way that the job change costs will be minimized when the costs depend on the number of the job groups. This problem is motivated by the practical case where the group utilizes a common machine set-up and the number of set-up occasions is the dominating factor in the production line optimization. The problem is well known and its large instances are hard to solve to optimality. We show how real-life problem instances can be solved by three different methods: efficient heuristics, 0/1-programming, and constraint programming. The first two of these are standard approaches in the field, whereas the application of constraint programming is new for the job grouping problem. The heuristic approach turns out to be efficient: algorithms are fast and produce optimal or nearly optimal groupings. 0/1-programming is capable of finding optimal solutions to small problem instances and it therefore serves as a benchmark to approximative methods. The constraint approach solves moderately large problem instances to optimality and it has the great advantage that changing the problem formulation is relatively easy one can add new constraints or modify the details of the existing ones flexibly.

Constraint programming

No abstract available.

Announcing the Mozart programming system

The Mozart consortium ( German Research Center for Artificial Intelligence (DFKI), Universit~it des Saarlandes (UdS), Swedish Institute of Computer Science (SICS), Universit6 catholique de Louvain (UCL)) is pleased to announce the release of Mozart 1.0, an extensive development platform for open fault-tolerant distributed applications, constraint programming applications, and logic programming applications. The system is available at http://www.mozart-oz.org and runs on many Unix-based platforms and Windows 95/98/NT. The platform implements the Oz language and is the fruit of a decade of research. The platform is released without charge with a very liberal license (XI 1-style) that allows both commercial and non-commercial use. Full source code is available. The platform includes a full-fledged interactive development environment with many libraries and tools. There is extensive documentation including tutorials covering all below mentioned application areas, a complete set of reference manuals, and many demos. The distribution, constraint, and logic programming abilities are the subject of ongoing research. 2 Constraint and logic programming

Practical applications of constraint programming

Constraint programming offers facilities for problem modelling, constraint propagation and search. This paper discusses the resulting benefits for practical applications which exploit these facilities. The modelling facilities are particularly exploited in applications to verification, both of circuits and of real time control systems. The propagation facilities are exploited in applications involving user feedback and graphical interfaces. The search facilities are exploited in applications such as scheduling and resource allocation, which involve combinatorial problems. The paper surveys applications under each of these three headings.