Constraint Programming Tools Research Articles

Estimation of elevator passenger traffic based on the most likely elevator trip origin-destination matrices

We present a constraint programming formulation for the elevator trip origin-destination matrix estimation problem using a lexicographic bi-criteria optimization method where least squares minimization is applied to the measured counts and the minimum information or the maximum entropy approach to the whole matrix. An elevator trip consists of successive stops in one direction of travel with passengers inside the elevator. It can be defined as a directed network, where the nodes correspond to the stops on the trip and the arcs to the possible origins and destinations of the passengers. The goal is to estimate the most likely counts of passengers for the origin-destination pairs of every elevator trip occurring in a building that are consistent with the measured boarding and alighting counts and any prior information about the trip matrix. These counts are used to make passenger traffic forecasts which, in turn, are used in elevator dispatching to reduce uncertainties related to future passengers and therefore to improve passenger service level. Artificial test data was obtained by simulations of lunch hour traffic in a typical multi-story office building. This resulted in complex problem instances that enable robust performance and quality testing. The results show that the proposed approach outperforms previous alternatives in terms of quality, and can take an advantage of prior information. In addition, the proposed approach satisfies real time elevator group control requirements for estimating elevator trip origin-destination matrices. Practical application: The elevator trip origin-destination matrix estimation problem is important since it makes it possible to obtain complete information and statistics about the elevator passenger traffic. The statistics can be used to model future passengers which, when taken into account in the elevator group control, helps to improve passenger service level. Simulation experiments show that most of the problems occurring in reality can be solved within a reasonable time considering a real application, and the solving algorithms are relatively easy to implement using available constraint programming tools. Hence, this work is undoubtedly of interest to the building and elevator industry.

Tabu search algorithms for minimizing total completion time on a single machine with an actual time-dependent learning effect

In this paper, we investigate a single machine problem with actual time-dependent learning effect considering unequal release times, where the objective is to minimize the total completion time. At first, a mathematical model of the problem was formulated, which was verified to be effective by ILOG CP (a constraint programming tool provided by ILOG). Then a branch-and-bound algorithm incorporating with two dominance properties and two lower bounds was developed to obtain solutions for small size problems. However, since this problem is NP-hard, two tabu search algorithms combined with dominance rules, called TSDR, were proposed for solving problems with large number of jobs. The experimental results demonstrated that the proposed branch-and-bound algorithm had a better performance than CP in small size problems. The TSDR algorithms can also obtain optimal solutions for some situations in small problems. In addition, the proposed TSDR algorithms outperformed the benchmark algorithms in the literature and the advantage became more obvious with the number of jobs increasing.

Heuristic Based Real‐Time Train Rescheduling System

This article deals with the problem of train rescheduling on a railway network. Starting from a defined network topology and initial timetable, the article considers a dynamic train rescheduling in response to disturbances that have occurred. The train rescheduling problem was mapped into a special case of the job shop scheduling problem and solved by applying a constraint programming approach. To improve the time performance of available constraint programming tool and to satisfy a selected objective function, a combination of three classes of heuristics are proposed: bound heuristics, separation heuristics, and search heuristics. Experimental evaluation of the implemented software in Belgrade railway dispatching area indicates that the proposed approach is capable of providing the support to a real‐life operational railway control. In our solution, the dispatcher has the possibility of choosing the most suitable optimization criterion from the set of seven available ones. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 67(1), 32–48 2016

The Complexity of Integer Bound Propagation

Bound propagation is an important Artificial Intelligence technique used in Constraint Programming tools to deal with numerical constraints. It is typically embedded within a search procedure (”branch and prune”) and used at every node of the search tree to narrow down the search space, so it is critical that it be fast. The procedure invokes constraint propagators until a common fixpoint is reached, but the known algorithms for this have a pseudo-polynomial worst-case time complexity: they are fast indeed when the variables have a small numerical range, but they have the well-known problem of being prohibitively slow when these ranges are large. An important question is therefore whether strongly-polynomial algorithms exist that compute the common bound consistent fixpoint of a set of constraints. This paper answers this question. In particular we show that this fixpoint computation is in fact NP-complete, even when restricted to binary linear constraints.

Forward Constraint-Based Algorithms for Anytime Planning

This paper presents a generic anytime forward-search constraint-based algorithm for solving planning problems expressed in the CNT framework (Constraint Network on Timelines). It is generic because it allows many kinds of search to be covered, from complete tree search to greedy search. It is anytime because some parameter settings, together with domain-specific knowledge, allow high quality plans to be produced very quickly and to be further improved. It is forward because it systematically considers the decisions to be made in a chronological order. It is finally constraint-based because it is built on top of the CNT framework which is an extension of the CSP framework able to model discrete event dynamic systems and because it is implemented on top of the Choco constraint programming tool from which it inherits all the constraint handling machinery. Experimental comparisons are made in terms of quality profile with other domain-dependent and domain-independent planners.

Using OBDDs to handle dynamic constraints

For many real-life problems naturally modeled as constraints systems, we have to manage dynamically systems of constraints. So, a model based on the formalism of finite Constraint Satisfaction Problems (CSPs) (Montanari, 1974) has been proposed with Dynamic CSPs (DCSPs) to handle this kind of problem (Dechter and Dechter, 1988; Janssen et al., 1990). Some classical techniques defined in the field of CSPs are usable in DCSPs, but the management of dynamicity induces new problems such as management of over-constrained systems and consistency maintenance. At present, Constraint Programming tools generally do not offer a framework for integrating dynamic constraints. The purpose of this paper is to introduce an efficient way to solve DCSPs based on a logical approach. We use and extend Ordered Binary Decision Diagrams (OBDDs) (Bryant, 1986) and propose a particular coding for dynamicity. We show that our approach allows to solve some major questions in the field of DCSPs.

Constraint Databases: Promising Technology or Just IntellectualExercise?

This paper describes the state of Constraint Databases (CDBs), a young discipline at the intersection of Database Management, Constraint Programming, Computational Geometry and Operations Research. As in Constraint Logic Programming, constraints in CDBs are a first class data type, and can play many modeling roles including spatial and temporal behavior, complex design requirements, and partial and incomplete information, for which existing databases have proven inadequate. We motivate the importance of CDBs, outline the work in the area that has been done, the current trends, and future directions and challenges. We briefly discuss (1) constraint modeling, canonical forms and algebras, (2) data models and query languages, (3) indexing and approximation-based filtering, (4) constraint algebra algorithms and global optimization, and (5) systems and case studies. We argue that CDBs are a promising technology that will impact many important application realms, and furthermore have the potential to be integrated into future database systems, and operations research and constraint programming tools.

Constraint databases

This paper describes the state of Constraint Databases (CDBs), a young discipline at the intersection of Database Management, Constraint Programming, Computational Geometry and Operations Research. As in Constraint Logic Programming, constraints in CDBs are a first class data type, and can play many modeling roles including spatial and temporal behavior, complex design requirements, and partial and incomplete information, for which existing databases have proven inadequate. We motivate the importance of CDBs, outline the work in the area that has been done, the current trends, and future directions and challenges. We briefly discuss (1) constraint modeling, canonical forms and algebras, (2) data models and query languages, (3) indexing and approximation-based filtering, (4) constraint algebra algorithms and global optimization, and (5) systems and case studies. We argue that CDBs are a promising technology that will impact many important application realms, and furthermore have the potential to be integrated into future database systems, and operations research and constraint programming tools.

Implementation of resource constraints in ILOG SCHEDULE: a library for the development of constraint-based scheduling systems

It has been argued that the use of constraint-based techniques and tools enables the implementation of precise, flexible, efficient and extensible scheduling systems; precise and flexible as the system can take into account any constraint expressible in the constraint language; efficient in as much as highly optimised constraint propagation procedures are now available; extensible as the consideration of a new type of constraint may require (especially in an object-oriented framework) only an extension to the constraint system or, in the worst case, the implementation of additional decision-making modules (without needs for modification of the existing code). The paper presents ILOG SCHEDULE, a C++ library enabling the representation of a wide collection of scheduling constraints in terms of ‘resources’ and ‘activities’. ILOG SCHEDULE is based on SOLVER, the generic software tool for object-oriented constraint programming from ILOG. SOLVER variables and constraints can be accessed from SCHEDULE activities and resources. As a result, SCHEDULE users can make use of SOLVER to represent specific constraints, and implement and combine the specific problem-solving strategies that are the most appropriate for the scheduling application under consideration. It is hoped—and expected—that object-oriented constraint programming tools like SCHEDULE will enable the industry to make decisive stepstoward the implementation of ‘state-of-the-art’ highly flexible, constraint-based scheduling applications.