基于约束放松的电子商务协同谈判模型

在众多的自动谈判模式中，协同谈判是一种新型的多线程谈判形式。文章分析了面向电子商务的协同谈判中的冲突问题，提出了解决谈判冲突的方法——满意度函数法。该方法在谈判模型中用约束网来表示谈判中的变量和约束关系，通过约束放松来解决谈判中的冲突。作者在对约束及约束网进行详细分析的基础上设计了约束传播算法，用它来求解约束网问题。文章通过实例验证了提出的论点和方法的正确性。采用这种基于约束放松的协同谈判模型可以有效地解决谈判中的冲突，提高谈判的效率。该方法的应用可为解决多个谈判的组合问题提供一种新的思路。     

不可微规划的邻近控制簇算法

本文提出了一个带非线性约束的凸不可微规划的邻近控制簇算法，并给出了一种加权技术．在Ｓｌａｔｅｒ约束规格满足的条件下，证明了算法的整体收敛性．数字例子表明，该算法是处理该类问题的一种有效方法．     

一种部分约束满足车辆路线问题及其求解算法

描述了一类过度约束车辆路线问题，其中可用车辆数较少而时间窗口等其它约束又不允许放松，因而导致不存在满足所有约束的可行解。此时问题求解可以转化为一类部分约束满足问题来处理，相应的优化目标是最小化未访问顾客的损失和。本给出了求解这类特殊问题的一种禁忌搜索算法设计，并通过规模不同的几个算例与其它常用方法进行了比较。最后分析了模型和算法的实用意义。     

自适应蚁群算法及其在多约束多目标柔性Job-Shop调度中的应用

实际生产系统的车间作业调度一般是多约束多目标柔性Job-Shop调度,比经典的Job-Shop调度更复杂,存在多约束、多目标、动态柔性、建模复杂等特性.建立了多约束多目标柔性Job-Shop调度模型,提出了一种自适应蚁群算法,采用自适应机制和遗传原理防止算法过早停滞和加快收敛速度.西安航空发动机(集团)有限公司制造单元调度实例表明,提出的自适应蚁群算法是求解多约束多目标柔性Job-Shop调度的有效方法.     

线性约束优化的信赖域仿射尺度算法

对线性约束优化问题提出一种信赖域仿射尺度算法，在没有非退化假设的条件下，证明了该算法产生的无限序列{x-k}的任一极限点都满足一阶必要条件，且至少存在一个极限点满足二阶必要条件.     

线性互补约束规划问题的一种Filter算法

本文研究了求解线性互补约束规划问题的算法问题.首先基于广义互补函数和摄动技术将问题转化为带参数的非线性优化问题,利用SlQP-Filter算法方法,求解线性互补约束规划问题的一种Filter算法.在适当条件下,证明了该算法的全局收敛性.     

模糊推理三Ⅰ约束算法的一般表示

讨论模糊推理三Ⅰ约束算法的表示问题。首先，改进FMP及FMT问题的三Ⅰ约束原则,进而，为使更多的蕴涵算子纳入统一的算法表示之下。基于较弱的条件建立三Ⅰ约束算法的一般表示。如此，现有的三Ⅰ约束算法的统一表示被推广到一种新的形式。     

一类带单源约束的选址运输问题算法研究

带单源约束的选址运输问题是在经典的选址运输问题基础上考虑每个顾客需求的产品仅由一家工厂供应的情况。所建立的模型是整数规划，是NP难的。本文先考虑了开办费用为零的带单源约束的选址运输问题，即带单源约束的运输问题。松弛其中一种变量约束，借鉴求解运输问题的表上作业法，给出了一种修正的表上作业法，然后将算法推广。最后给出了将算法应用在Excel随机生成的测试问题上所得到的结果，与LINDO求得的最优解相比，差距很小。由此得出结论：对规模较小的带单源约束的选址运输问题，本文提出的算法是简便且行之有效的。     

退化约束条件下的依赖域算法及其收敛性

本文建立带退化线性等式与不等式约束最优化问题的一种依赖域算法，方法用一系列以原点为内点的一般紧集为依赖域。讨论了方法的收敛性，证明了迭代点列必有一聚点为原问题的Ｋｕｈｎ－Ｔｕｃｋｅｒ点，最后，在一定假设下，讨论了算法的超线性收敛性。     

混合约束下广义几何规划的一种SQP算法

针对带等式和不等式约束的广义几何规划问题，构造了一种ＳＱＰ算法并证明了该算法的全局收敛性和局部二阶收敛性。     

Constraint Propagation in Graph Coloring

In this paper we propose a method for integrating constraint propagation algorithms into an optimization procedure for vertex coloring with the goal of finding improved lower bounds. The key point we address is how to get instances of Constraint Satisfaction Problems (CSPs) from a graph coloring problem in order to give rise to new lower bounds outperforming the maximum clique bound. More precisely, the algorithms presented have the common goal of finding CSPs in the graph for which infeasibility can be proven. This is achieved by means of constraint propagation techniques which allow the algorithms to eliminate inconsistencies in the CSPs by updating domains dynamically and rendering such infeasibilities explicit. At the end of this process we use the largest CSP for which it has not been possible to prove infeasibility as an input for an algorithm which enlarges such CSP to get a feasible coloring. We experimented with a set of middle-high density graphs with quite a large difference between the maximum clique and the chromatic number.     

Approximations and Randomization to Boost CSP Techniques

In recent years we have seen an increasing interest in combining constraint satisfaction problem (CSP) formulations and linear programming (LP) based techniques for solving hard computational problems. While considerable progress has been made in the integration of these techniques for solving problems that exhibit a mixture of linear and combinatorial constraints, it has been surprisingly difficult to successfully integrate LP-based and CSP-based methods in a purely combinatorial setting. Our approach draws on recent results on approximation algorithms based on LP relaxations and randomized rounding techniques, with theoretical guarantees, as well on results that provide evidence that the runtime distributions of combinatorial search methods are often heavy-tailed. We propose a complete randomized backtrack search method for combinatorial problems that tightly couples CSP propagation techniques with randomized LP-based approximations. We present experimental results that show that our hybrid CSP/LP backtrack search method outperforms the pure CSP and pure LP strategies on instances of a hard combinatorial problem.     

Reliable amplitude and frequency estimation for biased and noisy signals

Robust estimation of the amplitude, frequency and bias of unknown noisy sinusoidal signals is considered in this paper. It is only assumed that the measurements noise is bounded without any additional information such as stationarity, uncorrelation or type of distribution. In this context, the aim is to compute the set of all admissible values that are consistent with the measurements and with the error bound. The estimation problem is formulated as a Constraint Satisfaction Problem (CSP) where the amplitude, frequency and bias constitute the variables and a function relating them to the output is the constraint. Interval constraint propagation techniques are used to solve, in a guaranteed way, this problem. In order to illustrate the principle and the efficiency of the approach, numerical simulations are provided.     

A Constraint-Based Method for Project Scheduling with Time Windows

This paper presents a heuristic algorithm for solving RCPSP/max, the resource constrained project scheduling problem with generalized precedence relations. The algorithm relies, at its core, on a constraint satisfaction problem solving (CSP) search procedure, which generates a consistent set of activity start times by incrementally removing resource conflicts from an otherwise temporally feasible solution. Key to the effectiveness of the CSP search procedure is its heuristic strategy for conflict selection. A conflict sampling method biased toward selection of minimal conflict sets that involve activities with higher-capacity requests is introduced, and coupled with a non-deterministic choice heuristic to guide the base conflict resolution process. This CSP search is then embedded within a larger iterative-sampling search framework to broaden search space coverage and promote solution optimization. The efficacy of the overall heuristic algorithm is demonstrated empirically on a large set of previously studied RCPSP/max benchmark problems.     

On global warming: Flow-based soft global constraints

In case a CSP is over-constrained, it is natural to allow some constraints, called soft constraints, to be violated. We propose a generic method to soften global constraints that can be represented by a flow in a graph. Such constraints are softened by adding violation arcs to the graph and then computing a minimum-weight flow in the extended graph to measure the violation. We present efficient propagation algorithms, based on different violation measures, achieving domain consistency for the alldifferent constraint, the global cardinality constraint, the regular constraint and the same constraint. This work was for a large part carried out while the author was at the Centrum voor Wiskunde en Informatica, Amsterdam, The Netherlands     

Constraint satisfaction problems: Algorithms and applications

A constraint satisfaction problem (CSP) requires a value, selected from a given finite domain, to be assigned to each variable in the problem, so that all constraints relating the variables are satisfied. Many combinatorial problems in operational research, such as scheduling and timetabling, can be formulated as CSPs. Researchers in artificial intelligence (AI) usually adopt a constraint satisfaction approach as their preferred method when tackling such problems. However, constraint satisfaction approaches are not widely known amongst operational researchers. The aim of this paper is to introduce constraint satisfaction to the operational researcher. We start by defining CSPs, and describing the basic techniques for solving them. We then show how various combinatorial optimization problems are solved using a constraint satisfaction approach. Based on computational experience in the literature, constraint satisfaction approaches are compared with well-known operational research (OR) techniques such as integer programming, branch and bound, and simulated annealing.     

Ship maintenance scheduling by genetic algorithm and constraint-based reasoning

Ship maintenance scheduling is a process to decide start times of maintenance activities that satisfy all precedence and resource constraints and optimize the ship availability. In this paper, ship maintenance scheduling is modelled as a constraint satisfaction problem (CSP). The variables of CSP are the start times and its domain values are the start and horizon of the schedule. To solve the ship maintenance scheduling problem in the Royal Malaysian Navy, we have adopted a constraint-based reasoning (CBR) which requires start times of the first activities of maintenance cycles to solve the problem by the CBR. Thus, we adopt a genetic algorithm (GA) to find the start times of the first activities. The simulation results showed the effectiveness of the present hybrid algorithm.     

Regular-SAT: A many-valued approach to solving combinatorial problems

Regular-SAT is a constraint programming language between CSP and SAT that—by combining many of the good properties of each paradigm—offers a good compromise between performance and expressive power. Its similarity to SAT allows us to define a uniform encoding formalism, to extend existing SAT algorithms to Regular-SAT without incurring excessive overhead in terms of computational cost, and to identify phase transition phenomena in randomly generated instances. On the other hand, Regular-SAT inherits from CSP more compact and natural encodings that maintain more the structure of the original problem. Our experimental results—using a range of benchmark problems—provide evidence that Regular-SAT offers practical computational advantages for solving combinatorial problems.     

Solving Fixed-Charge Network Flow Problems with a Hybrid Optimization and Constraint Programming Approach

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.     

A Hybrid Approach to Scheduling with Earliness and Tardiness Costs

A hybrid technique using constraint programming and linear programming is applied to the problem of scheduling with earliness and tardiness costs. The linear model maintains a set of relaxed optimal start times which are used to guide the constraint programming search heuristic. In addition, the constraint programming problem model employs the strong constraint propagation techniques responsible for many of the advances in constraint programming for scheduling in the past few years. Empirical results validate our approach and show, in particular, that creating and solving a subproblem containing only the activities with direct impact on the cost function and then using this solution in the main search, significantly increases the number of problems that can be solved to optimality while significantly decreasing the search time.