Dual-ascent methods for large-scale multicommodity flow problems

The capacitated multicommodity network flow problem presents itself in a number of problem contexts including transportation, communication, and production. To solve the large-scale multicommodity flow problems encountered in these fields, we develop dual-ascent heuristics and a primal solution generator. The dual-ascent solutions, in addition to determining lower bounds on the optimal objective function value, provide advanced starting solutions for use with primal-based solution techniques. The primal solution generator uses the dual-ascent solution to obtain heuristically primal solutions to the multicommodity flow problems. Computational experiments performed on three test problem sets show that the dual-ascent and primal heuristic procedures typically determine nearoptimal solutions quickly. In addition, by using the dual-ascent procedure to obtain advanced starting solutions, run times for optimal multicommodity flow procedures are reduced significantly and greatly improved solutions are obtained by the new primal solution generator. © 1993 John Wiley & Sons, Inc.     

Optimal multicommodity network flows with resource allocation

The problem of determining multicommodity flows over a capacitated network subject to resource constraints may be solved by linear programming; however, the number of potential vectors in most applications is such that the standard arc-chain formulation becomes impractical. This paper describes an approach—an extension of the column generation technique used in the multicommodity network flow problem—that simultaneously considers network chain selection and resource allocation, thus making the problem both manageable and optimal. The flow attained is constrained by resource availability and network capacity. A minimum-cost formulation is described and an extension to permit the substitution of resources is developed. Computational experience with the model is discussed.     

Computational study of the multiechelon production planning problem

In this article we try to identify appropriate solution procedures for different types of multiechelon production planning problems. We conduct an extensive computational study on uncapacitated multiechelon production planning problems with serial and assembly types of bill-of-material structures. Problems are formulated as both single-source fixed charge network problems and as multicommodity flow problems with fixed charges. Solution procedures considered are branch and cut, Lagrangean relaxation (for the network formulation), and branch and bound (for the multicommodity formulation). Three hundred problems with various problem structures are tested. Our conclusions suggest the best approach for each type of problem structure. © 1997 John Wiley & Sons, Inc.     

The multicommodity network flow model revised to include vehicle per time period and node constraints

The minimum-cost formulation of the problem of determining multicommodity flows over a capacitated network subject to resource constraints has been treated in previobs papers. In those treatments only capacitated arcs were assumed and a uniform unit of measure like short tons was used for all commodities. This paper treats the effect of constraints on the nodes of the network, allows the commodities to be measured in their “natural” units and allows the network capacities to be expressed in vehicles per time period-in some cases giving a more accurate representation of the capacities of the network. This paper describes the solution procedure which uses the column generation technique; it also discusses computational experience.     

变量有界线性目标规划的对偶算法

本文讨论了变量有界的线性目标规划问题,给出了求解这类问题的一个对偶算法,此方法与变量有界线性规划问题的对偶算法相类似。文中证明了算法的有效性,并举例说明了计算过程。     

Adjacent vertices on transportation polytopes

To solve linear fixed charge problems with Murty's vertex ranking algorithm, one uses a simplex algorithm and a procedure to determine the vertices adjacent to a given vertex. In solving fixed charge transportation problems, the simplex algorithm simplifies to the stepping-stone algorithm. To find adjacent vertices on transportation polytopes, we present a procedure which is a simplification of a more general procedure for arbitrary polytopes.     

用Excel演示大M单纯形法

大M单纯形法简称大M法。在大M法中，要求M足够大，通常，M作为符号参与运算。大M法单纯形中的数据均可表为aM＋b的形式，如果用有序数对〈a，b〉等价表示aM＋b，则大M法单纯形中的M被形式上消去，使得，大M法可用Excel演示。     

Computing bounds for the optimal value in linear programming

Consider a standard linear programming problem and suppose that there are bounds available for the decision variables such that those bounds are not violated at an optimal solution of the problem (but they may be violated at some other feasible solutions of the problem). Thus, these bounds may not appear explicitly in the problem, but rather they may have been derived from some prior knowledge about an optimal solution or from the explicit constraints of the problem. In this paper, the bounds on variables are used to compute bounds on the optimal value when the problem is being solved by the simplex method. The latter bounds may then be used as a termination criteria for the simples iterations for the purpose of finding a “sufficiently good” near optimal solution. The bounds proposed are such that the computational effort in evaluating them is insignificant compared to that involved in the simplex iterations. A numerical example is given to demonstrate their performance.     

An efficient algorithm for the transshipment along a single road problem

This paper presents a specialized algorithm for the transshipment along a single road problem. The problem is a specially structured network flow problem. For larger problems, the specialized algorithm is in excess of a hundred times faster than the primal simplex method on a graph.     

有容量限制的运输问题

具有容量限制的运输问题可以用有界变量的线性规划问题求解,但是问题的规模往往变得很大,给求解带来不便。本文给出求解这一问题的表上作业法。     

目标规划的求解

目标规划的求解可采用图示法、单纯形法、分级法等,文中用Excel演示单纯形法和分级法。     

A forward network simplex algorithm for solving multiperiod network flow problems

An optimization model which is frequently used to assist decision makers in the areas of resource scheduling, planning, and distribution is the minimum cost multiperiod network flow problem. This model describes network structure decision-making problems over time. Such problems arise in the areas of production/distribution systems, economic planning, communication systems, material handling systems, traffic systems, railway systems, building evacuation systems, energy systems, as well as in many others. Although existing network solution techniques are efficient, there are still limitations to the size of problems that can be solved. To date, only a few researchers have taken the multiperiod structure into consideration in devising efficient solution methods. Standard network codes are usually used because of their availability and perceived efficiency. In this paper we discuss the development, implementation, and computational testing of a new technique, the forward network simplex method, for solving linear, minimum cost, multiperiod network flow problems. The forward network simplex method is a forward algorithm which exploits the natural decomposition of multiperiod network problems by limiting its pivoting activity. A forward algorithm is an approach to solving dynamic problems by solving successively longer finite subproblems, terminating when a stopping rule can be invoked or a decision horizon found. Such procedures are available for a large number of special structure models. Here we describe the specialization of the forward simplex method of Aronson, Morton, and Thompson to solving multiperiod network network flow problems. Computational results indicate that both the solution time and pivot count are linear in the number of periods. For standard network optimization codes, which do not exploit the multiperiod structure, the pivot count is linear in the number of periods; however, the solution time is quadratic.     

球约束凸二次规划的一个算法

针对球约束凸二次规划问题,利用Lagrange对偶将其转化为无约束优化问题,然后运用单纯形法对其求解,获得原问题的最优解。最后,对文中给出的算法给出了论证。     

整数线性规划问题的一个新算法

本文给出求解整数线性规划问题的一个算法。基本思想是通过求出其伴随线性规划问题的最优单纯形表,把整数线性规划化成正整数系数的不定方程,然后从不定方程的非负整数解集中选取一组满足整数线性规划的约束条件的解,作为整数线性规划的最优解。     

Subspace dynamic‐simplex linear interpolation search for mixed‐integer black‐box optimization problems

Design and management of complex systems with both integer and continuous decision variables can be guided using mixed‐integer optimization models and analysis. We propose a new mixed‐integer black‐box optimization (MIBO) method, subspace dynamic‐simplex linear interpolation search (SD‐SLIS), for decision making problems in which system performance can only be evaluated with a computer black‐box model. Through a sequence of gradient‐type local searches in subspaces of solution space, SD‐SLIS is particularly efficient for such MIBO problems with scaling issues. We discuss the convergence conditions and properties of SD‐SLIS algorithms for a class of MIBO problems. Under mild conditions, SD‐SLIS is proved to converge to a stationary solution asymptotically. We apply SD‐SLIS to six example problems including two MIBO problems associated with petroleum field development projects. The algorithm performance of SD‐SLIS is compared with that of a state‐of‐the‐art direct‐search method, NOMAD, and that of a full space simplex interpolation search, Full‐SLIS. The numerical results suggest that SD‐SLIS solves the example problems efficiently and outperforms the compared methods for most of the example cases. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 305–322, 2017     

An approximative algorithm for the fixed charge problem

This paper describes an approximate solution method for solving the fixed charge problem. This heuristic approach is applied to a set of test problems to explore the margin of error. The results indicate that the proposed fixed charge simplex algorithm is capable of finding optimal or near optimal solutions to moderate sized fixed charge problems. In the absence of an exact method, this heuristic should prove useful in solving this fundamental nonlinear programming problem.     

基于静止标量磁强计的运动舰船定位问题的研究

建立了静止标量磁强计对运动舰船定位的模型,并给出了用遗传算法求全局较优解、然后用单纯形法进行精确局部搜索的求解参数的方法.仿真实验表明这种方法有效、可行.     

A comparison of the primal-simplex and complementary pivot methods for linear programming

A comparison of the complementary pivot method of Lemke-Howson and the more commonly used primal-simplex method for solving linear programming problems in symmetric dual form has been made. In our tests the complementary pivot method shows a definite superiority over the simplex method both with regard to the number of iterations and computation time.     

延迟时间模型参数的极大似然估计

介绍了基于客观数据的延迟时间模型参数极大似然估计方法,提出了应用优化理论中的单纯形法求解似然函数的算法,为延迟时间模型的参数估计问题提供了可行的解决方法。通过计算机模拟数据的验证,此方法切实可行,结果可以满足需要。     

线性规划问题新解——改进大M法

借鉴两阶段法的求解思路,在用单纯形法求解线性规划问题时,对大M法进行改进,提出一种新的算法.这种改进后的算法可以有效克服原来两种算法的不足,既能降低理解难度,又能提高算法的效率,保证算法的全局收敛性.