Constraint Satisfaction Optimization Problem Research Articles

CSOP+RP: a novel constraints satisfaction model for requirements prioritization in large-scale software systems

One of the main factors in the failure of software projects is the lack of attention to their requirements prioritization. In this paper, we propose a decision-oriented methodology with a novel model for requirements prioritization (RP) in large-scale software systems. The model is formulated based on the constraint satisfaction optimization problems (CSOP) approach, which we call CSOP+RP. The main objective of the model is to maximize the quality of the software in total, subject to the constraints on the budgets and importance level that pre-determined by the administrator. To evaluate CSOP+RP, we applied it to the police command-and-control system (PCCS), which is extensively used during the outbreak of the Coronavirus disease as an incident in terms of quality and speed of service. The results of various experiments show that the proposed model with its specific capabilities can find reasonable and a solution near optimal. Moreover, the sensitivity analysis indicates that the model is very sensitive to its parameters. Although, we applied the model to CCPS, the CSOP+RP is very general so that it can be applied to different types of software projects. Additionally, the model could be extended to other aspects and criteria of the RP problem and could play definite roles for optimal management of system resources.

Application of an improved genetic algorithm to Hamiltonian circuit problem

In the last few years, there has been an increasing interest in Random Constraint Satisfaction Problems (CSP) from both experimental and theoretical points of view. To consider a variant instance of the problems, we used a random benchmark. In the present paper, some work has been done to find the shortest Hamiltonian circuit among specified nodes in each superimposed graph (SGs). The Hamiltonian circuit is a circuit that visits each node in the graph exactly once. The Hamiltonian path may be constructed and adjusted according to specific constraints such as time limits. A new constraint satisfaction optimization problem model for the circuit Hamiltonian circuit problem in a superimposed graph has been presented. To solve this issue, we propose amelioration for the genetic algorithm using Dijkstra’s algorithm, where we create the improved genetic algorithm (IGA). To evaluate this approach, we compare the CPU and fitness values of the IGA to the results provided by an adapted genetic algorithm to find the shortest Hamiltonian circuit in a superimposed graph.

Local-Consistency Web Services Composition Approach Based On Harmony Search

Designing of the composite services raises many challenges, as the huge number of equivalent ones and the complexity of user’s requirements. Thus, in order to achieve user satisfaction and problem resolution, we have to choose among a set of functionally equivalent services. So, the composition can be regarded as a valued constraint satisfaction optimization problem, which involved a valuation of requirements attributes. Next, the skyline operator was applied to our search space. Skyline is an operator based on Pareto dominance, used to help in reducing the size of a search space for many applications such as web service composition. However, this technique has some drawbacks such as the important number of solutions which it couldn’t, usually, consider all users’ preferences or context. In this paper, we propose a new approach based on local consistency reinforcement methods (node and arc-consistency), to enhance the skyline approach and to reach the user needs. The idea is to keep only the highest service quality in the skyline set. Then, the Harmony Search algorithm adopted to catch an optimal or near-optimal composition. The efficiency of these algorithms is empirically studied, showing the capacity of our approach in reducing skyline size and keeping only the personalized services.

Optimizing satisfaction in a multi-courses allocation problem combined with a timetabling problem

The resource allocation problem and the timetabling problem are traditional kinds of NP-hard problems. Both problems can be found in universities where students can select courses they would like to attend before or after the timetabling is done. When demand exceeds capacity, the universities may allocate the available seats independently from the timetabling, but students may have then to decide which courses they are going to attend because of clashes in their timetable. To avoid this situation, some universities prepare their timetable considering students selection. In addition to that, students may submit preferences over courses, and the school administration has to assign seats and do the timetable considering both preferences and clashes. In this paper, both problems, seats allocation and timetabling, have been modeled separately and combined as constraint satisfaction optimization problems (CSOP). Two algorithms have been designed and implemented to find a solution to both problems simultaneously maximizing the satisfaction of students using a CSOP solver and an Ant colony optimization algorithm for the timetabling problem. The results of both algorithms are then compared. The allocation and timetabling procedures are based on preferences for courses defined by students, and on the administration’s constraints at Ecole Hoteliere de Lausanne. Three real data sets have been used to carry out a complete experimental analysis. High-quality solutions are obtained in a few minutes with both approaches; those solutions are currently used at Ecole Hoteliere de Lausanne.

Decomposing combinatorial auctions and set packing problems

Combinatorial auctions allow bidders to bid on bundles of items rather than just on single items. The winner determination problem in combinatorial auctions is the problem of determining the allocation of items to bidders such that the sum of the accepted bid prices is maximized. This problem is equivalent to the well-known maximum-weight set packing problem. Even though these problems are NP-hard in general, they can be solved in polynomial time on instances whose associated item graphs have bounded treewidth (called structured item graphs). However, the tractability of determining whether for a given problem instance a structured item graph of fixed treewidth exists (and if so, computing one efficiently) was an open problem. In this article, we solve this problem by proving that deciding the existence of structured item graphs is computationally intractable, even for treewidth 3. Motivated by this unfavorable complexity result, we investigate other structural restrictions, and we show that the notion of hypertree decomposition, a well-studied measure of hypergraph cyclicity, turns out to be most useful here. Indeed, we show that the winner determination problem is solvable in polynomial time on instances whose dual auction hypergraphs have bounded hypertree width. Our solution method is based on encoding winner determination via a constraint satisfaction optimization problem and on exhibiting an algorithm to solve this latter problem efficiently for such structurally restricted instances. The class of tractable instances identified by our approach, while being efficiently recognizable, properly contains the class of instances having a structured item graph. Moreover, on the larger class, our method solves winner determination with the same asymptotic complexity as the best algorithm proposed in the literature for the subclass of structured item graphs. Hypertree decompositions can equally profitably be applied to the maximum-weight independent set problem, which is the dual problem of maximum-weight set packing.

제약만족 최적화 문제를 위한 백트래킹 탐색의 구조화

It is very hard to obtain a general algorithm for solution of all the constraint satisfaction optimization problems. However, if the whole problem is separated into subproblems by characteristics of decision variables, we can assume that an algorithm to obtain solutions of these subproblems is easier. Under the assumption, we propose a problem classifying rule which subdivide the whole problem, and develop backtracking algorithms fit for these subproblems. One of the methods of finding a quick solution is efficiently arrange for any order of the search tree nodes. We choose the cluster head positioning problem in wireless sensor networks in which static characteristics is dominant and interference minimization problem of RFID readers that has hybrid mixture of static and dynamic characteristics. For these problems, we develop optimal variable ordering algorithms, and compare with the conventional methods. As a result of classifying the problem into subproblems, we can realize a backtracking framework for systematic search. We also have shown that developed backtracking algorithms have good performance in their quality.

선형 제약 만족 최적화 문제를 위한 정수계획법 기반 지역 탐색 기법

Linear constraint satisfaction optimization problem is a kind of combinatorial optimization problem involving linearly expressed objective function and complex constraints. Integer programming is known as a very effective technique for such problem but require very much time and memory until finding a suboptimal solution. In this paper, we propose a method to improve the search performance by integrating local search and integer programming. Basically, simple hill-climbing search, which is the simplest form of local search, is used to solve the given problem and integer programming is applied to generate a neighbor solution. In addition, constraint programming is used to generate an initial solution. Through the experimental results using N-Queens maximization problems, we confirmed that the proposed method can produce far better solutions than any other search methods.

제약 만족 최적화 문제의 해결을 위한 지역 탐색과 제약 프로그래밍의 결합

제약 만족 최적화 문제는 복잡한 제약 조건을 포함하는 동시에 비용을 최소화하는 최적화 문제로 정의된다. 지역 탐색과 제약 프로그래밍은 각각 이와 같은 문제의 해결을 위한 도구로서 활용되어 왔다. 본 논문에서는 탐색 성능 향상을 위해 지역 탐색과 제약 프로그래밍을 결합하는 방안을 제시하고 있다. 기본적으로 대상 문제의 해결을 위해 지역 탐색을 사용한다. 그러나 지역 탐색만을 사용할 경우 제약 조건을 모두 만족하는 실행 가능한 이웃해를 생성하는 것이 매우 힘들어진다. 따라서 본 논문에서는 이웃해 생성을 위한 도구로 제약 프로그래밍을 도입하였다. 가중치가 부여된 N-Queens 문제를 대상으로 한 실험 결과, 본 논문에서 제시한 방법을 통해 탐색 성능을 획기적으로 향상시킬 수 있음을 확인할 수 있었다. Constraint satisfaction optimization problem is a kind of optimization problem involving cost minimization as well as complex constraints. Local search and constraint programming respectively have been used for solving such problems. In this paper, I propose a method to integrate local search and constraint programming to improve search performance. Basically, local search is used to solve the given problem. However, it is very difficult to find a feasible neighbor satisfying all the constraints when we use only local search. Therefore, I introduced constraint programming as a tool for neighbor generation. Through the experimental results using weighted N-Queens problems, I confirmed that the proposed method can significantly improve search performance.

Solving the short-term electrical generation scheduling problem by an adaptive evolutionary approach

In this paper, we introduce an adaptive evolutionary approach to solve the short-term electrical generation scheduling problem (STEGS). The STEGS is a hard constraint satisfaction optimization problem. The algorithm includes various strategies proposed in the literature to tackle hard problems with constraints such as: the representation used a non-binary coding scheme that drastically reduces the search space compared with the traditional evolutionary approaches. Specialized operators are especially designed for this problem and for this kind of representation, which also includes a local search procedure. Furthermore, the algorithm is guided by an adaptive parameter control strategy. We used some very well known benchmarks for STEGS to evaluate our approach. The results are very encouraging and we have obtained new better values for all the systems tested. Our aim here is to show that evolutionary approaches can be considered as good techniques to be used to solve real-world highly constrained problems.

A coordination framework for supply chain inventory alignment

PurposeA traditional supply chain, all too often, is a sequence of weakly connected activities both within and outside the organization and leads to many misalignments. Tackling this inherent lack of coordination is a major value creation opportunity for supply chain managers. This paper aims to present a coordination framework, called ASCEND, to align the inventory decisions in decentralized supply chains.Design/methodology/approachThe framework rests on three pillars – multi‐agent technology, coordination theory, and optimization technology. Inventory alignment is transformed into a decentralized constraint satisfaction optimization problem, which is then solved using a genetic algorithm‐based coordination process. The guiding principle of this framework is the establishment of a proper incentive alignment mechanism through cost‐sharing and service level contracts, taking into account the associated dependencies.FindingsThe results of the pilot study indicate that the coordination framework reduces the supply chain inventory‐holding cost, compared with the “as‐is” scenario. Interestingly, the results also indicate that not all the supply chain partners will experience lower inventory costs in the absence of a proper incentive alignment mechanism. Only through cost sharing and service level contracts can the value of coordination be realized by each partner in a supply chain network!Practical implicationsThe coordination approach is applied to a pilot supply chain. The results of the case study lead to 10.4 percent (average) reduction in supply chain inventory‐holding costs, validating the efficacy of the coordination framework.Originality/valueThis research has resulted in the development of a supply chain coordination framework, which requires the establishment of a proper incentive alignment mechanism, based on cost‐sharing and service level contracts, and taking into account the associated dependencies.

A data mining-constraint satisfaction optimization problem for cost effective classification

We propose a data mining-constraint satisfaction optimization problem (DM–CSOP) where it is desired to maximize the number of correct classifications at a lowest possible information acquisition cost. We show that the problem can be formulated as a set of several binary variable knapsack optimization problems, which are solved sequentially. We propose a heuristic hybrid simulated annealing and gradient-descent artificial neural network (ANN) procedure to solve the DM-CSOP. Using a real-world heart disease data set, we show that the proposed hybrid procedure provides a low-cost and high-quality solution when compared to a traditional ANN classification approach. The massive proliferation of very large databases in organizations makes it necessary to design cost effective and efficient data mining systems. This paper proposes a data mining constraint satisfaction optimization problem, which provides a high quality cost effective solution for a binary classification problem.

On the Hamming distance of constraint satisfaction problems

In this paper we consider a new optimization problem, called M AX H AMMINGD ISTANCE ( F) where F is a family of Boolean constraints. This problem consists in finding two satisfying assignments that differ in the maximum number of variable values: in other words, the problem looks for the maximum difference between two models of the constraints given in input. We give a complete classification of the approximability properties of M AX H AMMINGD ISTANCE ( F) by using a specialization of the criteria introduced by Schaefer in order to classify constraint satisfaction problems and subsequently used by Khanna, Sudan, Trevisan, and Williamson to classify constraint satisfaction optimization problems.

An artificial neural network satisfiability tester

An artificial neural network tester for the satisfiability problem of propositional calculus is presented. Satisfiability is treated as a constraint satisfaction optimization problem and, contrary to most of the existing satisfiability testers, the expressions are converted into disjunctive normal form before testing. The artificial neural network is based on the principles of harmony theory. Its basic characteristics are the simulated annealing procedure and the harmony function; the latter constitutes a measure of the satisfiability of the expression under the current truth assignment to its variables. The tester is such that: (a) the satisfiability of any expression is determined; (b) a truth assignment to the variables of the expression is output which renders true the greatest possible number of clauses; (c) all the truth assignments which render true the maximum number of clauses can be produced. © 2001 John Wiley & Sons, Inc.