A Bionomic Approach to the Capacitated p-Median Problem

This paper advocates the use of the bionomic algorithm, a recently proposed metaheuristic technique, as an effective method to solve capacitated p-median problems (CPMP). Bionomic algorithms already proved to be an effective framework for finding good solutions to combinatorial optimization problems, when good local optimization algorithms are available. The paper also presents an effective local search technique for the CPMP. Computational results show the effectiveness of the proposed approach, when compared to the best performing heuristics so far presented in the literature.     

An Efficient Genetic Algorithm for the p-Median Problem

We propose a new genetic algorithm for a well-known facility location problem. The algorithm is relatively simple and it generates good solutions quickly. Evolution is facilitated by a greedy heuristic. Computational tests with a total of 80 problems from four different sources with 100 to 1,000 nodes indicate that the best solution generated by the algorithm is within 0.1% of the optimum for 85% of the problems. The coding effort and the computational effort required are minimal, making the algorithm a good choice for practical applications requiring quick solutions, or for upper-bound generation to speed up optimal algorithms.     

Density Based Problem Space Search for the Capacitated Clustering p-Median Problem

In the Capacitated Clustering Problem (CCP), a given set of n weighted points is to be partitioned into p clusters such that, the total weight of the points in each cluster does not exceed a given cluster capacity. The objective is to find a set of p centers that minimises total scatter of points allocated to them. In this paper a new constructive method, a general framework to improve the performance of greedy constructive heuristics, and a problem space search procedure for the CCP are proposed. The constructive heuristic finds patterns of natural subgrouping in the input data using concept of density of points. Elements of adaptive computation and periodic construction–deconstruction concepts are implemented within the constructive heuristic to develop a general framework for building efficient heuristics. The problem-space search procedure is based on perturbations of input data for which a controlled perturbation strategy, intensification and diversification strategies are developed. The implemented algorithms are compared with existing methods on a standard set of bench-marks and on new sets of large-sized instances. The results illustrate the strengths of our algorithms in terms of solution quality and computational efficiency.     

Solving the p-Median Problem with a Semi-Lagrangian Relaxation

Lagrangian relaxation is commonly used in combinatorial optimization to generate lower bounds for a minimization problem. We study a modified Lagrangian relaxation which generates an optimal integer solution. We call it semi-Lagrangian relaxation and illustrate its practical value by solving large-scale instances of the p-median problem. This work was partially supported by the Fonds National Suisse de la Recherche Scientifique, grant 12-57093.99 and the Spanish government, MCYT subsidy dpi2002-03330.     

遗传算法求解带容量限制的最小费用流问题

研究了带容量限制的带固定费用和可变费用的最小费用流问题,发现该问题是混合0-1整数规划问题,不存在多项式算法.在研究了最优解的结构后,结合最优解的结构特点为之设计了遗传算法,然后构造了一个100个节点的特殊网络,用计算机做了100例计算,验证了该算法具有很好的近似比和很快的收敛速度.     

A Hybrid Heuristic for the p-Median Problem

Given n customers and a set F of m potential facilities, the p-median problem consists in finding a subset of F with p facilities such that the cost of serving all customers is minimized. This is a well-known NP-complete problem with important applications in location science and classification (clustering). We present a multistart hybrid heuristic that combines elements of several traditional metaheuristics to find near-optimal solutions to this problem. Empirical results on instances from the literature attest the robustness of the algorithm, which performs at least as well as other methods, and often better in terms of both running time and solution quality. In all cases the solutions obtained by our method were within 0.1% of the best known upper bounds.     

求解旅行商问题的搜寻者遗传算法

针对简单遗传算法易陷入局部最优及收敛速度慢的不足,提出一种改进遗传算法-基于启发式策略的搜寻者遗传算法.首先将搜寻者优化算法中的模糊思想和近邻策略相结合改进变异算子,增强种群多样性,避免陷入局部最优;然后针对路径优化问题基于启发式策略设计反转算子,使得路径中不存在交叉边,加快收敛速度;最后将改进遗传算法用于求解旅行商问题.结果表明,改进遗传算法的求解精度和求解效率明显优于基本遗传算法.     

二层线性规划的自适应遗传算法

提出了一种自适应遗传算法来求解二层线性规划问题.该方法克服了难以确定合适的交叉概率和变异概率的困难.另外,在该方法中还采用了其它一些技巧不仅解决了在采用遗传算法经常出现的有些个体不可行的问题,而且还改进了算法的效率.     

A Fast Algorithm for Solving a Simple Search Problem

Computational Mathematics and Mathematical Physics - A fast algorithm for solving the Danskin problem is proposed. The dependence of its solution on parameters is analyzed.     

A Memory Adaptive Reasoning Technique for Solving the Capacitated Minimum Spanning Tree Problem

In this paper we propose a hybrid memory adaptive heuristic for solving the Capacitated Minimum Spanning Tree (CMST) problem. We augment the problem formulation with additional non-redundant constraints via use of adaptive memory, to improve upon the performance of an elementary heuristic (the Esau-Williams heuristic). Our methodology is tested against many of the previously reported heuristics for the CMST. We conclude that our generalized procedure performs on par with the best of these approaches in terms of solution quality, while expending a very modest amount of computational effort.     

An Improved Algorithm for the Capacitated Facility Location Problem

In this paper we consider the classical capacitated facility location problem. A branch and bound algorithm is presented which measurably improves upon the recent results of Akinc and Khumawala. The use of a specialized Lagrangean relaxation results in significantly tighter bounds than those for the traditional continuous relaxation. These bounds, when combined with penalties derived from the Lagrangean relaxation, enable many integer variables to be fixed at specific values. This results in fewer branches, and indeed for certain test problems taken from the literature, branching is not required. Average computation time for a battery of test problems from the literature has been reduced (conservatively) by a factor of 3.     

A Pseudo-Polynomial Primal-Dual Algorithm for Globally Solving a Production-Transportation Problem

In this paper, we propose a primal-dual algorithm for solving a class ofproduction-transportation problems. Among m( 2) sources two factoriesexist, which produce given goods at some concave cost and supply them to nterminals. We show that one can globally minimize the total cost ofproduction and transportation by solving a Hitchcock transportation problemwith m sources and n terminals and a minimum linear-cost flow problem withm+n nodes. The number of arithmetic operations required by the algorithm ispseudo-polynomial in the problem input length.     

A Branch-and-Price Algorithm for the Capacitated Arc Routing Problem with Stochastic Demands

We address the Capacitated Arc Routing Problem with Stochastic Demands (CARPSD), which we formulate as a Set Partitioning Problem. The CARPSD is solved by a Branch-and-Price algorithm, which we apply without graph transformation. The demand’s stochastic nature is incorporated into the pricing problem. Computational results are reported.     

A Lagrangean Heuristic for a Modular Capacitated Location Problem

This paper considers the Modular Capacitated Location Problem (MCLP) which consists of finding the location and capacity of the facilities, to serve a set of customers at a minimum total cost. Each customer has an associated demand and the capacity of each potential location must be chosen from a finite and discrete set of available capacities. Practical applications of this problem can be found in the location of warehouses, schools, health care services or other types of public services. For the MCLP different mixed integer linear programming models are proposed. The authors develop upper and lower bounds on the problem's optimal value and present computational results with randomly generated tests problems.     

运输问题求解的一种网络算法

本着重探讨了在网络图上求运输问题的初始解的方法，并指出在求解受时间约束的运输问题时得到的初始解，在很大程度就是该问题的最优解，通过实例说明了该算法。     

求解非线性互补问题的一个下降算法

在[1]中,Soldov将非线性互补问题等价地转化成一个带非负约束的优化问题,基于这种转化形式,我们给出了一种求解非线性互补问题的下降算法,在映射为强单调时,证明了算法的全局收敛性。     

求解非线性互补问题的一个下降算法(英文)

在[1]中,Solodov将非线性互补问题等价地转化成一个带非负约束的优化问题.基于这种转化形式,我们给出了一种求解非线性互补问题的下降算法.在映射为强单调时,证明了算法的全局收敛性.     

An Algorithm for Solving a Resource Allocation Problem

The paper discusses the solution of a resource allocation problem and a new method for solving a special case of the problem. An algorithm for solving the general problem is presented, and computational experience comparing it with existing methods is given.