Helmut Simonis

Solving large combinatorial problems in logic programming

Abstract Many problems in operations research and hardware design are combinatorial problems which can be seen as search problems with constraints. We present an application of CHIP ( C onstraint H andling I n P rolog) to large problems in disjunctive scheduling, graph coloring, and firmware design. chip is a constraint logic-programming language combining the declarative aspects of PROLOG with the efficiency of constraint-solving techniques. It is shown that it allows a natural expression of problems to be executed as efficiently as special-purpose programs written in procedural languages.

Constraint satisfaction using constraint logic programming

Cosytec, Parc Club Orsay-University, 4, rue Jean-Rostand, 91893 Orsay Cedex, France Abstract Van Hentenryck, P., H. Simonis and M. Dincbas, Constraint satisfaction using constraint logic programming, Artificial Intelligence 58 (1992) 113-159. Constraint logic programming (CLP) is a new class of declarative programming lan- guages whose primitive operations are based on constraints (e.g. constraint solving and constraint entailment). CLP languages naturally combine constraint propagation with nondeterministic choices. As a consequence, they are particularly appropriate for solv- ing a variety of combinatorial search problems, using the global search paradigm, with short development time and efficiency comparable to procedural tools based on the same approach. In this paper, we describe how the CLP language cc(FD), a successor of CHIP using consistency techniques over finite domains, can be used to solve two practical applications: test-pattern generation and car sequencing. For both applications, we present the cc(FD) program, describe how constraint solving is performed, report experimental results, and compare the approach with existing tools. 1.

Modelling Producer/Consumer Constraints

In this paper we describe the modelling of producer/consumer constraints with the CHIP system. Producer/consumer constraints arise in scheduling problems with consumable resources like raw materials or money, in particular for batch based processing. The constraint assures that at each time point enough consumable resources are available. The modelling with CHIP uses the cumulative constraint to express conditions in a very declarative way, yet obtains very good propagation due to the reasoning power build into the cumulative constraint. We show that with producer/consumer constraints many resource scheduling problems can be easily expressed and give examples of its industrial use.

Extending Equation Solving and Constraint Handling in Logic Programming

Publisher Summary By virtue of its relational form, nondeterministic computation and unification mechanism, Prolog can be seen as a good candidate for a powerful constraint programming language. Unification is used to solve equality constraints among terms of this universe. This chapter explains how Prolog is adequate for solving problems stated by the means of constraints, and the necessity to extend the computation domains and the related constraint solving techniques. A unification algorithm is presented for boolean terms that computes the unique most general unifier, if it exists. Two applications of boolean unification is presented in the digital circuit design—proof of correctness and specialization. Finite domains are introduced in Prolog and the associated constraint handling techniques to solve constraint satisfaction problems. It discusses the use of these techniques on two examples—a cryptarithmetic puzzle and a warehouse location problem from Operations Research.

Search-Tree Visualisation

This chapter describes a visual tool for debugging and analysis of the search-trees generated by finite domain constraint programs. The tool allows to navigate in the search-tree in a flexible way and gives, for any node of the search-tree, a clear view of the current state of the program execution. The tool provides graphical representations of the form of the search-tree, of constraints and variables of the program and of the propagation steps performed after each decision in the tree. The debugger is used via a set of meta-predicates which annotate the search routine given by the user, which allows great flexibility in adapting the program to the needs of different users. The tool is now part of the CHIP constraint programming environment and covers important aspects both of correctness and performance debugging.

The CHIP System and Its Applications

This tutorial presents the CHIP constraint logic programming system and some of its applications. We present the different constraint solving modules in the CHIP environment with special emphasis on the high-level global constraints in the finite domain solver.

Tuning Parameters of Large Neighborhood Search for the Machine Reassignment Problem

Data centers are a critical and ubiquitous resource for providing infrastructure for banking, Internet and electronic commerce. One way of managing data centers efficiently is to minimize a cost function that takes into account the load of the machines, the balance among a set of available resources of the machines, and the costs of moving processes while respecting a set of constraints. This problem is called the machine reassignment problem. An instance of this online problem can have several tens of thousands of processes. Therefore, the challenge is to solve a very large sized instance in a very limited time. In this paper, we describe a constraint programming-based Large Neighborhood Search (LNS) approach for solving this problem. The values of the parameters of the LNS can have a significant impact on the performance of LNS when solving an instance. We, therefore, employ the Instance Specific Algorithm Configuration (ISAC) methodology, where a clustering of the instances is maintained in an offline phase and the parameters of the LNS are automatically tuned for each cluster. When a new instance arrives, the values of the parameters of the closest cluster are used for solving the instance in the online phase. Results confirm that our CP-based LNS approach, with high quality parameter settings, finds good quality solutions for very large sized instances in very limited time. Our results also significantly outperform the hand-tuned settings of the parameters selected by a human expert which were used in the runner-up entry in the 2012 EURO/ROADEF Challenge.

A constraint seeker: finding and ranking global constraints from examples

In this paper we describe a Constraint Seeker application which provides a web interface to search for global constraints in the global constraint catalog, given positive and negative, fully instantiated (ground) examples. Based on the given instances the tool returns a ranked list of matching constraints, the rank indicating whether the constraint is likely to be the intended constraint of the user. We give some examples of use cases and generated output, describe the different elements of the search and ranking process, discuss the role of constraint programming in the different tools used, and provide evaluation results over the complete global constraint catalog. The Constraint Seeker is an example for the use of generic meta-data provided in the catalog to solve a specific problem.

Constraint Applications in Networks

Publisher Summary This chapter discusses the use of constraint programming (CP) for network applications. Network problems that arise in different domains are discussed. The chapter focuses on three areas: (1) electrical networks, (2) water (oil) networks, and (3) data networks. The applications for data networks cover a wide range of problems, from design, to risk analysis and operational control. Classical finite domain constraint programming currently seems to be rather limited for these problems; this clearly is a field where hybrid systems are achieving much better results. It discusses two main contributing factors: one is the important role of cost optimization, the other the large scale of the problems together with the fine granularity of the decisions. The chapter also reviews global constraints for graph based problems that are very useful for rapid application development in other domains. It concludes that there are a number of proposals for new global constraints, which may help to solve some of these problems in a more declarative way.

Propositional Calculus Problems in CHIP

This paper describes how to model and solve boolean satisfiability problems with the constraint logic programming language CHIP. Although CHIP has not been developed as a specialised propositional calculus prover, it can solve these problems quite efficiently. Several different methods of describing satisfiability problems in CHIP are presented and compared. This flexibility of modelling is a major advantage of CHIP over closed problem solvers. We have evaluated various sets of benchmarks taken from [31] [16] [21]. With one exception, CHIP performs as well or better as specialised programs on these examples. We also shortly discuss an alternative modeling technique using finite domain variables not restricted to 0/1 values.