Constraint Programming

In constraint-based program testing, symbolic execution is a technique which allows to generate test data exercising a given execution path, selected within the program to be tested. Applied to a set of paths covering a sufficient part of the code under test, this technique permits to generate automatically adequate test sets for units of code. As databases are ubiquitous in software, generalizing such a technique for efficient testing of programs manipulating databases is an interesting approach to enhance the reliability of software. In this work, we propose a relational symbolic execution algorithm to be used for testing of simple Java methods, reading and writing with transactional SQL in a relational database, subject to integrity constraints. This algorithm considers the Java method under test as a sequence of operations over a set of constrained relational variables, modeling both the database tables and the method variables. By integrating this relational model of the method and database with the classical symbolic execution process, the algorithm can generate a set of Alloy constraints for any finite path to test in the control-flow graph of the method. Solutions of these constraints are data which constitute a test case, including valid content for the database, which exercises the selected path in the method. A tool implementing the proposed algorithm is demonstrated over a number of examples.

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 outsourcing 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 implemented 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.

The short-term scheduling of multiproduct multistage batch plants is tackled in this paper by means of a constraint programming (CP) methodology. This approach, consisting of both a model and a search strategy, easily handles different features found in industrial environments: finite unit ready times, dissimilar parallel equipment at each stage, sequence-dependent changeovers, topology constraints, forbidden job-equipment assignments, order release times, as well as renewable resources limitations. It can also address various interstage storage and operational policies: UIS, NIS/ZW, NIS/UW, and mixed ones. Besides, it introduces two simple and efficient search methodologies based on domain knowledge, whose great impact on the computational performance is shown. The approach was extensively tested by means of several examples having various difficulty degrees. It rendered good computational results for a variety of interstage storage policies and objective functions. Moreover, this work shows that the default depth-first search strategy does not perform well for scheduling problems.

The aim of Air Traffic Flow Management (ATFM) is to enhance the capacity of the airspace while satisfying Air Traffic Control constraints and airlines requests to optimize their operating costs. This paper presents a design of a new route network that tries to optimize these criteria. The basic idea is to consider direct routes only and to vertically separate intersecting flows of aircrafts by allocating distinct flight levels. This problem is a graph coloring problem that we tackle using Constraint Programming and a greedy algorithm to find cliques of the constraint graph which are used to post global constraints. Through the search for optimal solutions minimizing the number of distinct flight levels allocated, symmetries among equivalent flight levels are dynamically broken, and the variable ordering is guided by the cliques found in the first static step. With an implementation using FaCiLe, our Functional Constraint Library, optimality is achieved for all flow sizes except the smallest one, while the corresponding number of flight levels could fit in the current airspace structure. However, many other constraints should be added to this very simplified model to obtain an operational route network, such that the conclusion is rather the validation of the concept of vertical separation of large flows. This graph coloring technique has also been tested on various benchmarks, featuring good results on real-life instances, which systematically appear to contain large cliques.

This paper presents a logic language for expressing NP search and optimization problems. Specifically, first a language obtained by extending (positive) Datalog with intuitive and efficient constructs (namely, stratified negation, constraints and exclusive disjunction) is introduced. Next, a further restricted language only using a restricted form of disjunction to define (non-deterministically) subsets (or partitions) of relations is investigated. This language,

The aim of this paper is to solve a problem on scheduling with earliness and tardiness costs using constraint programming approach, and to compare the results with the ones from the original timed automata approach. The case study, a production of lacquers, includes some real life features like operating hours or changeover times. Considering the earliness and tardiness costs of the case study, the problem was converted to the problem with objective of minimizing the total waiting time. Then a search procedure was applied.

Project-driven manufacturing, based on the make-to-order or the build-to-order principle and predominant in small and medium-size enterprises (SMEs), calls for an efficient solution of large combinatorial problems, especially in such areas as task scheduling or resource management. This paper addresses the problem of finding a computationally effective approach to scheduling a new project subject to constraints imposed by a multi-project environment. A constraint programming (CP) modeling framework is discussed in the context of an efficient decomposition of the constraint satisfaction problem (CSP) and the evaluation of strategies for pruning the search tree. The proposed approach is illustrated through examples of its application to the evaluation of a new production order.

The hybrid or flexible flowshop problem is a generalization of the flowshop in such a way that every job can be processed by one among several machines on each machine stage. In recent years a number of effective exact methods have been developed. A major reason for this progress is the development of new job and machine based lower bounds as well as the rapidly increasing importance of constraint programming. In this paper we provide the first comprehensive and uniform overview on exact solution methods for flexible flowshops with branching, bounding and propagation of constraints under two different objective functions: minimizing the makespan of a schedule and the mean flow time.

This article concerns the problem of managing the new generation of Agile Earth Observing Satellites (AEOS). This kind of satellites is presently studied by the French Centre National d'Études Spatiales (PLEIADES project). The mission of an Earth Observing Satellite is to acquire images of specified areas on the Earth surface, in response to observation requests from customers. Whereas non-agile satellites such as SPOT5 have only one degree of freedom for acquiring images, the new generation satellites have three, giving opportunities for a more efficient use of the satellite imaging capabilities. Counterwise to this advantage, the selection and scheduling of observations becomes significantly more difficult, due to the larger search space for potential solutions. Hence, selecting and scheduling observations of agile satellites is a highly combinatorial problem. This article sets out the overall problem and analyses its difficulties. Then it presents different methods which have been investigated in order to solve a simplified version of the complete problem: a greedy algorithm, a dynamic programming algorithm, a constraint programming approach and a local search method.

Modeling music theories with computer programs has attracted composers and scholars for a long time. On the one hand, the resulting programs can serve as algorithmic composition tools. On the other hand, such an approach leads to a better understanding of existing as well as newly developed theories, which in turn can lead to a better understanding of music, as well as to better ways to retrieve music from databases. Constraint programming (Apt 2003) has often been used to create computational models of music theories and composition. Constraint-based harmonization systems were surveyed by Pachet and Roy (2001); other examples include purely rhythmic tasks (Sandred 2003), Fuxian counterpoint (Schottstaedt 1989), Ligeti-like textures (Chemillier and Truchet 2001; Laurson and Kuuskankare 2001), and instrument-specific writing (Laurson and Kuuskankare 2001). Many music constraint systems have been proposed in which users implement their own music theory models. Two seminal systems are PWConstraints (Laurson 1996) and Situation (Rueda et al. 1998). Carla (Courtot 1990) is one of the earliest systems. Other examples include the aggregation of the music representation MusES with the constraint system BackTalk (Pachet and Roy 1995), Arno (Anders 2000), OMClouds (Truchet and Codognet 2004), andÖrjan Sandred's PWMC defined on top of PWConstraints (Sandred 2010, in this issue). A survey of music-constraint programming in general and a detailed comparison of existing systems is provided by Anders and Miranda (in press). Each system provides the following components, which are essential for solving musical constraint satisfaction problems (CSP). It defines a music representation (score) where some aspects are

Background and justification Dessert banana and plantain (Musa sp.) are the fourth most important staple food crops in the world after rice, wheat and maize [1]. Dessert banana in particular is a commercially important crop in the global trade, both by volume and value, as a leading fruit [1]. In 2010, world commerce in banana was valued at US 8.05 billion and the total world production of banana is about 106,541,709.00 tons [FAOSTAT, 2012]. For many African, Asian and Latin American countries, banana is as well one of the most important fruit for foreign exchange earnings [FAOSTAT, 2012]. Dessert banana is also the major fruit crop that is most widely grown and consumed in Ethiopia. It is cultivated in several parts where the growing conditions are favorable. Especially in the south and southwestern parts of the country, it is of great socioeconomic importance contributing significantly to the overall wellbeing of the rural communities including food security, income generation and job creation [2]. Currently banana in Ethiopia covers about 59.64% (53,956.13 hectares) of the total fruit area, about 68% (478,251.04 ton) of the total fruit produced, and about 38.3% (2,574.035) of the total fruit producing farmers. On the other hand, about 68.72% (37,076.83 hectares) of land covered by banana, about 77.52% (370,784.17 ton) of the banana production, and 22.38% (1,504,207) of the banana producers in Ethiopia are found in the Southern Nations Nationalities and Peoples Regional State (SNNPRS) [25]. Gamo Gofa, Bench Maji, and Sheka zones are among the major banana producing zones of the SNNPRS [3]. Objectives of the review General objective: • To review the production and marketing of banana in Ethiopia. Specific objectives: • To review the overall status of banana production in the Ethiopia.

Objectives: The study was conducted to determine the effects of MCIT on the affected upper extremity among Mild Moderate Spastic Hemiplegic children. Study Design: Randomized Control Trial (RCT) Study Settings & Participants: An experimental study was conducted on 30 participants at a renowned CP Centre in Karachi. Procedure: The participants were randomly divided into Group A that received MCITwhereas group Bwas given conventional therapy.Group Aparticipants restraint non-affected arm and involve in different activities by using the affected arm while the other group performed different selected activities using both the arms.Data was recorded in a treatment sheet where PAFTwas used as a measurement tool.Data was analyzedusing SPSS version 20 Results: Results revealed significant improvements on affected hand in both the groups but MCIT isfound to be more effective than conventional therapy. Results were independent of children age and genderbut were dependent on degree of spastici...

The scheduling needs of umpires and referees differ from the needs of sports teams. In some sports leagues, such as Major League Baseball in the United States, umpires travel throughout the league's territory; they do not have a "home base." For such leagues, balancing the need to minimize umpire travel and the objective that an umpire should not handle the games of a particular team too frequently is important. We have used our approach, which is based on network optimization and simulated annealing, to successfully schedule Major League Baseball umpires. To develop this approach, we created the traveling umpire problem, which includes the major umpire scheduling issues and also provides a test bed for alternative techniques.

This paper presents two heuristics algorithms to solve Japanese puzzles, both black and white puzzles and color puzzles. First, we present ad-hoc heuristics which use the information in rows, columns, and puzzle's constraints to obtain the solution of the puzzle. The best heuristic developed for black and white puzzles is then extended to solving color Japanese puzzles. We show the performance of the proposed heuristics in several examples from a well known web page devoted to this kind of puzzles. Comparison with an existing solver based on constraint programming and with a genetic algorithm is carried out.

Sebuah fungsi yang didefinisikan oleh pembuat program selain dapat dipanggil oleh fungsi main() juga dapat dipanggil oleh fungsi lain. Seperti kita ketahui bahwa bentuk umum sebuah fungsi adalah sebagai berikut:
Tipe_nilai_balik nama_fungsi(daftar_argumen) { instruksi; }
Daftar argumen berisikan parameter yang dituliskan pada saat pendefinisian fungsi disebut parameter formal, sedangkan daftar argumen berisikan parameter yang dituliskan pada saat fungsi dipanggil oleh fungsi lain disebut parameter aktual.

We combine mixed integer linear programming (MILP) and constraint programming (CP) to solve an important class of planning and scheduling problems. Tasks are allocated to facilities using MILP and scheduled using CP, and the two are linked via logic-based Benders decomposition. Tasks assigned to a facility may run in parallel subject to resource constraints (cumulative scheduling). We solve problems in which the objective is to minimize cost, makespan, or total tardiness. We obtain significant computational speedups, of several orders of magnitude for the first two objectives, relative to the state of the art in both MILP and CP. We also obtain better solutions and bounds for problems than cannot be solved to optimality. We address a fundamental planning and scheduling problem that occurs frequently in manufacturing and supply chain contexts. Tasks must be assigned to facilities and scheduled on each facility subject to release dates and deadlines. Some of the tasks assigned to a given facility may run in parallel if desired, provided the total resource consumption at any time remains within limits ("cumulative scheduling"). Precedence and other side constraints may be imposed. The objective is to minimize cost, makespan, or total tardiness. Although problems of this sort arise often, they have proved to be quite difficult to solve. In the absence of an effective formal algorithm, a common-sense approach is often used in practice. The problem is decomposed into an assignment portion and a scheduling portion. Central managers assign tasks to facilities, whereupon operations managers develop detailed schedules for the tasks assigned to each facility. Since the schedule for one or more facilities may prove infeasible or otherwise unsuitable, the schedulers may telephone headquarters and ask for a different allocation of tasks. The give-and-take continues until a mutually satisfactory solution is reached. However, an informal process of this sort is time consuming, and even if carried to completion it offers no guarantee of obtaining a good solution. One possibility that suggests itself is to apply Benders decomposition, which mimics the informal feedback loop in a rigorous fashion. The Benders master problem allocates tasks

Matching highly skilled people to available positions is a high-stakes task that requires careful consideration by experienced resource managers. A wrong decision may result in significant loss of value due to understaffing, underqualification or overqualification of assigned personnel, and high turnover of poorly matched workers. While the importance of quality matching is clear, dealing with pools of hundreds of jobs and resources in a dynamic market generates a significant amount of pressure to make decisions rapidly. We present a novel solution designed to bridge the gap between the need for high-quality matches and the need for timeliness. By applying constraint programming, a subfield of artificial intelligence, we are able to deal successfully with the complex constraints encountered in the field and reach near-optimal assignments that take into account all resources and positions in the pool. The considerations include constraints on job role, skill level, geographical location, language, potential retraining, and many more. Constraints are applied at both the individual and team levels. This paper introduces the technology and then describes its use by IBM Global Services, where large numbers of service and consulting employees are considered when forming teams assigned to customer projects.

We apply to fixed charge network flow (FCNF) problems a general hybrid solution method that combines constraint programming and linear programming. FCNF problems test the hybrid approach on problems that are already rather well suited for a classical 0-1 model. They are solved by means of a global constraint that generates specialized constraint propagation algorithms and a projected relaxation that can be rapidly solved as a minimum cost network flow problem. The hybrid approach ran about twice as fast as a commercial mixed integer programming code on fixed charge transportation problems with its advantage increasing with problem size. For general fixed charge transshipment problems, however, it has no effect because the implemented propagation methods are weak.

This paper discusses an in-depth analysis and a solution guide for the OPCDE Challenge. The crackme is rather straightforward and simple for the experienced reverser. However, for a beginner it could be problematic due to the nature of the protection and calculation method. I am going to show a couple of ways to explore how to attack it. Ranging from a pure abstract static analysis, dynamic analysis using a debugger, Concolic and Symbolic execution to Constraint solvers.

This research presents the design, usage, and evaluation of a highly generic music con- straint system called Strasheela. Strasheela simplifies the definition of musical constraint satisfaction problems (CSP) by predefining building blocks required for such problems. At the same time, Strasheela preserves a high degree of generality and is reasonably efficient.

Strasheela is highly generic, because it is highly programmable. In particular, three fundamental components are more programmable in Strasheela compared with existing music constraint systems: the music representation, the rule application mechanism, and the search process.

Strasheela features an expressive symbolic music representation. This representation sup- ports explicitly storing score information by sets of score objects, their attributes, and their hierarchic nesting. Any information available in the score is accessible from any object in the score and can be used to obtain derived information. The representation is complemented by the notion of variables: score information can be unknown and such information can be constrained.

This research proposes a rule formalism which combines convenience and full user con- trol to express which score variable sets are constrained by a given rule. A rule is a first-class function. A rule application mechanism is a higher-order function. A rule ap- plication mechanism traverses the score in order to apply a given rule to variable sets. This text presents rule application mechanisms suitable for a large set of musical CSPs and reproduces important mechanisms of existing systems.

Strasheela is founded on a constraint programming model which makes the search process programmable at a high-level. The Strasheela user can optimise the search for a partic- ular constraint satisfaction problem by programming a distribution strategy (a dynamic variable and value ordering) independent of the problem definition. Special distribution strategies for efficiently solving various musical CSPs – including complex polyphonic problems – are presented.

A Constraint Satisfaction Problem (CSP) is a powerful framework for representing and solving constraint problems. When solving a CSP using a backtrack search method, one important factor that reduces the size of the search space drastically is the order in which variables and values are examined. Many heuristics for static and dynamic variable ordering have been proposed and the most popular and powerful are those that gather information about the failures during the constraint propagation phase, in the form of constraint weights. These later heuristics are called conflict driven heuristics. In this paper, we propose two of these heuristics respectively based on Hill Climbing (HC) and Ant Colony Optimization (ACO) for weighing constraints. In addition, we propose two new value ordering techniques, respectively based on HC and ACO, that rank the values based on their ability to satisfy the constraints attached to their corresponding variables. Several experiments were conducted on various types of problems including random, quasi random and patterned problems. The results show that the proposed variable ordering heuristics, are successful especially in the case of hard random problems. Also, when using the proposed value and variable ordering together, we can improve the performance particularly in the case of random problems.

Book Review T exts in constraint programming are a little like buses. You wait forever for one to come along, and then two come along at once. In this case, there has been a large gap in the market for a theoretical introduction to constraint programming ever since Edward Tsang's Foundations of Constraint Satisfaction (1993) went out of print. 1 Therefore, we are very pleased to see two books written by two of the leading researchers in this field come along to fill the gap. Constraint programming is a very active research area within AI. It is a highly successful technology for solving a wide range of combinatorial problems, including scheduling, rostering, assignment, routing, and design. A number of companies, like ILOG, Dash Optimization, and Parc Technologies, market model building and constraint programming toolkits, which are used by companies as diverse as Amazon.com, British Airways,

This paper presents a logical abstraction of the reachability graph of a timed Petri net using constraint programming. We apply it to the scheduling of transient inter-production states for cyclic productions in Flexible Manufacturing System. So, we propose to adapt the approach of based on the search of the accessibility by means of constraints using concepts of partial marking and partial step which allow a logical abstraction of the reachability graph of a Petri net. Having the timed Petri net (where a duration is associated to each transition), we propagate time to the obtained steps. In fact, we associate, to each marking extracted from a step, a timestamp vector: each timestamp corresponds to the date of the last token produced in a place at a step. Then, under temporal constraints, we solve scheduling problems, using constraint programming.

CREAM (“Constraints with Rules to EAse Modelling”) is a general rule-based modelling language designed to make easy to use for engineers the formulation of combinatorial problems and the integration of domain-specific knowledge in libraries.
This work had a practical application within the European STReP Net-WMS, which aimed at tackling complex real-world 3D packing problems (with rotations) subject to weight and stability constraints from the automotive industry (PSA, FIAT).

This paper describes the Rostering Engine (RE) that we have developed for the Hospital Authority (HA), Hong Kong as part of their Staff Rostering System (SRS) using AI constraint-programming techniques. The Hospital Authority manages over 40 public hospitals in Hong Kong. With close to 1500 wards total, the amount of resources needed to produce weekly staff rosters for each ward

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

Timetabling problems have been frequently studied due to their wide range of applications. However, they are often solved manually because of the lack of appropriate computer tools. Although many approaches mainly based on local search or constraint programming seem to have been quite successful in recent years, they are often highly dedicated to specific problems and encounter difficulties to take the dynamic and over-constrained nature of such problems. We were confronted with such an over-constrained and dynamic problem in our institution. This paper deals with a timetabling system based on constraint programming with the use of explanations to offer a dynamic behaviour and to allow automatic relaxations of constraints. Our tool has successfully answered the needs of the current planner by providing solutions in a few minutes instead of a week of manual design. We present in this paper the techniques used, the results obtained and a discussion on the effects of the automation of the timetabling process.

Multi-agent systems are communities of problem-solving entities that can perceive and act upon their environments to achieve their individual goals as well as joint goals. The work on such systems integrates many technologies and concepts in artificial intelligence and other areas of computing. For this reason, over recent years, the agent paradigm gained popularity in many subfields of computer science. A full spectrum of multi-agent systems applications have been and are being developed; from search engines to educational aids to electronic commerce and trade, e-procurement, recommendation systems, simulation and routing, to cite only some.

This paper explains why learning to solve constraint problems is so difficult, and describes a set of methods that has been effective on a broad variety of problem classes. The primary focus is on learning an effective search algorithm as a weighted subset of ordering heuristics. Experiments show the impact of several novel techniques on a variety of problems. When a planning problem is cast as a constraint satisfaction problem (CSP), it can use the representational expressiveness and inference power inherent in constraint programming (Nareyek et al. 2005). If such an encoding lacks the necessary planning knowledge, a program might learn effective solution methods. Our thesis is that it is possible to learn to solve constraint satisfaction problems from experience. In this scenario, a program is given a set of CSPs and a set of search heuristics. It is then expected to learn an effective search algorithm, represented as a weighted combination of some subset of those heuristics. From...

In current model-driven development approaches, software models are the primary artifacts of the development process. Therefore, assessment of their correctness is a key issue to ensure the quality of the final application. Research on model consistency has focused mostly on the models' static aspects. Instead, this paper addresses the verification of their dynamic aspects, expressed as a set of operations defined by means of pre/postcondition contracts. This paper presents an automatic method based on Constraint Programming to verify UML models extended with OCL constraints and operation contracts. In our approach, both static and dynamic aspects are translated into a Constraint Satisfaction Problem. Then, compliance of the operations with respect to several correctness properties such as operation executability or determinism are formally verified.

Searching objects within a catalog is a problem of increasing importance, as the general public has access to increasing volumes of data. Constraint programming has addressed the case of searching databases of complex products that can be customized and build from components, through constraint-based configurators. We address the issue of searching objects within catalogs of simpler items for which no logic description is available. We propose to embed constraint technology in a search assistant supporting dynamic and concise dialogs based on the exchange of constraint formulations between the client and the server. At each iteration, the server analyses the data available in the catalog, computes abstractions, cluster decompositions and relaxations in order to provide the user with alternative for either explicitly focusing the search (adding constraints to the user's wish) or enlarging it (relaxing, to some extent, a subset of the user's constraints). This cooperative system is currently used for Intranets, customer relationship management systems and e-commerce websites.

From an operational point of view, Ready-Mixed Concrete Suppliers are faced with challenging operational problems such as the acquisition of raw materials, scheduling of production facilities, and the transportation of concrete. This paper is centered around the logistical and distributional part of the operation: the scheduling and routing of concrete, commonly known as the Concrete Delivery Problem (CDP). The problem aims at finding efficient routes for a fleet of (heterogeneous) vehicles, alternating between concrete production centers and construction sites, and adhering to strict scheduling and routing constraints. Thus far, a variety of CDPs and solution approaches have appeared in academic research. However, variations in problem definitions and the lack of publicly available benchmark data inhibit a mutual comparison of these approaches. Therefore, this work presents a more fundamental version of CDP, while preserving the main characteristics of the existing problem variations. Both exact and heuristic algorithms for CDP are proposed. The exact solution approaches include a Mixed Integer Programming (MIP) model and a Constraint Programming model. Similarly, two heuristics are studied: the first heuristic relies on an efficient best-fit scheduling procedure, whereas the second heuristic utilizes the MIP model to improve delivery schedules locally. Computational experiments are conducted on new, publicly accessible, data sets; results are compared against lower bounds on the optimal solutions.