Asymptotic Properties of Random Multidimensional Assignment Problems

The multidimensional assignment problem (MAP) is a NP-hard combinatorial optimization problem, occurring in many applications, such as data association. In this paper, we prove two conjectures made in Ref. 1 and based on data from computational experiments on MAPs. We show that the mean optimal objective function cost of random instances of the MAP goes to zero as the problem size increases, when assignment costs are independent exponentially or uniformly distributed random variables. We prove also that the mean optimal solution goes to negative infinity when assignment costs are independent normally distributed random variables.     

Heuristic Solution Methods for the Multilevel Generalized Assignment Problem

The multilevel generalized assignment problem is a problem of assigning agents to tasks where the agents can perform tasks at more than one efficiency level. A profit is associated with each assignment and the objective of the problem is profit maximization. Two heuristic solution methods are presented for the problem. The heuristics are developed from solution methods for the generalized assignment problem. One method uses a regret minimization approach whilst the other method uses a repair approach on a relaxation of the problem. The heuristics are able to solve moderately large instances of the problem rapidly and effectively. Procedures for deriving an upper bound on the solution of the problem are also described. On larger and harder instances of the problem one heuristic is particularly effective.     

有资格限制的指派问题的求解方法

在实际的指派工作中，常会遇到某个人有没有资格去承担某项工作的问题，因此，本建立了有资格限制的指派问题的数学模型。在此数学模型中，将效益矩阵转化为判定矩阵，由此给出了判定此种指派问题是否有解的方法；在有解的情况下，进一步将效益矩阵转化为求解矩阵，从而将有资格限制的指派问题化为传统的指派问题来求解。最后给出了一个数值例子来说明这样的处理方法是有效的。     

双目标瓶颈指派问题的遗传算法

给出一种双目标瓶颈指派问题的新模型,本模型结合了决策者和工人两方面的因素,特别之处在于考虑到了工人对工作的排名偏好.进而,将双目标瓶颈指派问题转化为单目标规划,并设计了解此问题的遗传算法,算法的解均为双目标瓶颈指派问题的Pareto最优解.     

The Snow Disposal Assignment Problem

Snow removal and disposal are expensive winter activities that affect the quality of life and the environment in cities throughout the world. To facilitate traffic flow in urban regions that receive heavy snowfall, snow is first ploughed from streets and sidewalks and then hauled to disposal sites. A city is typically divided into many sectors that are cleared of snow concurrently. This paper briefly describes urban snow removal and disposal operations and analyses the snow disposal assignment problem for assigning snow removal sectors to snow disposal sites. The snow disposal assignment problem is formulated as a multi-resource generalized assignment problem. A two-phase heuristic solution procedure is developed, and solutions and sensitivity analyses are presented using data for the City of Montreal.     

Some Fixed-Point Results for the Dynamic Assignment Problem

In previous work the authors consider the dynamic assignment problem, which involves solving sequences of assignment problems over time in the presence of uncertain information about the future. The algorithm proposed by the authors provides generally high-quality but non-optimal solutions. In this work, though, the authors prove that if the optimal solution to a dynamic assignment problem in one of two problem classes is unique, then the optimal solution is a fixed point under the algorithm.     

Repulsive Assignment Problem

Standard assignment is the problem of obtaining a matching between two sets of respectively persons and positions so that each person is assigned exactly one position and each position receives exactly one person, while a linear decision maker utility function is maximized. We introduce a variant of the problem where the persons individual utilities are taken into account in a way that a feasible solution must satisfy not only the standard assignment constraints, but also an equilibrium constraint of the complementarity type, which we call repulsive. The equilibrium constraint can be, in turn, transformed into a typically large set of linear constraints. Our problem is NP-hard and it is a special case of the assignment problem with side constraints. We study an exact penalty function approach which motivates a heuristic algorithm. We provide computational experiments that show the usefulness of a heuristic mechanism inspired by the exact approach. The heuristics outperforms a state-of-the-art integer linear programming solver.     

人力资源多维分配问题的混合算法

针对已有多维分配问题求解算法复杂、耗时长及精度低等问题,本文将二部图中寻求最优匹配的方法进行推广,运用试分配、饱和路调整和增广路调整对多维分配问题的最优解进行搜索,提出了求解人力资源多维分配问题的最小零面优先分配混合算法和随机试分配混合算法,对算法的有效性进行了理论证明,并分析了算法的时间和空间复杂度;同时通过这两种混合算法对初始零元素数不同的代价矩阵求解时间的计算,以及与Lagrangian松弛算法和剪枝法的耗时、精度的对比,分别得到了两种混合算法的适用性和高效性,最后通过算例验证了算法的有效性。     

The Stochastic Location-Assignment Problem on a Tree

In the assignment problem units of supply are assigned on a one-to-one basis to units of demand so as to minimize the sum of the cost associated with each supply-to-demand matched pair. Defined on a network, the supplies and demands are located at vertices and the cost of a supply-to-demand matched pair is the distance between them. This paper considers a two-stage stochastic program for locating the units of supply based upon only a probabilistic characterization of demand. The objective of the first-stage location problem is to minimize the expected cost of the second-stage assignment problem. Principal results include showing that the problem is NP-hard on a general network, has a simple solution procedure on a line network, and is solvable by a low order polynomial greedy procedure on a tree network. Potential applications are discussed.     

A Problem of Assignment with Inter-Related Costs

There is a class of assignment problems where the cost function depends on the assignment of pairs of variables. A method of finding the optimum solution by a systematic exploration of a limited part of the solution space is presented by means of a numerical example. It is not possible to give a formula for the length of the computation since this will depend on the actual costs of the particular problem being solved.     

On A Special Case of the Quadratic Assignment Problem with an Application to Storage-and-Retrieval Devices

In a storage-and-retrieval device, items are retrieved on demand from a storage bank by a picking mechanism. Many varieties of these robotic devices are in use in manufacturing, logistics and computer peripherals. In printed circuit board manufacturing, storage-and-retrieval is intertwined with component placement and product clustering. Under certain circumstances, the problem of assigning items by type to storage slots to minimize the expected retrieval time is a quadratic assignment problem. Although such models are very difficult to solve to optimality, an important special case considered here admits an easy solution, namely, the well known “organ pipe” arrangement of items.     

分派问题的灵敏度分析及其应用

讨论分派问题的效率矩阵的元素发生变化时,对最优解的影响;在保持分派问题最优解不变的情况下,效率矩阵的元素的变化范围;及当分派问题的最优解发生变化后,如何用简单的方法求得新的最优解等.     

运输问题的合作对策求解方法

产地间或销地间往往存在竞争,在这种情况下,使用运输问题最优化方法是不合理的。因此,从个体理性的视角提出运输问题的合作对策求解方法,方法将运输问题看作是一个博弈问题,各个产地或销地是博弈的局中人,求解其纳什均衡与纳什讨价还价解。在此基础上,说明了运输问题的非合作形式是一个指派问题,并证明指派问题的最优解是一个纳什均衡点。接着,通过实验验证运输问题的最优解是一个纳什讨价还价解,满足产地或销地的自身利益。在此基础上,针对纳什讨价还价解不唯一的问题,从决策者的视角给出最大可能激励成本的计算方法。最后,为弥补纳什讨价还价解不唯一及纳什讨价还价解不允许出现子联盟的缺陷,给出运输收益分配或成本分摊的Shapely值计算方法。     

Asymptotic Results for Random Multidimensional Assignment Problems

The multidimensional assignment problem (MAP) is an NP-hard combinatorial optimization problem occurring in applications such as data association and target tracking. In this paper, we investigate characteristics of the mean optimal solution values for random MAPs with axial constraints. Throughout the study, we consider cost coefficients taken from three different random distributions: uniform, exponential and standard normal. In the cases of uniform and exponential costs, experimental data indicates that the mean optimal value converges to zero when the problem size increases. We give a short proof of this result for the case of exponentially distributed costs when the number of elements in each dimension is restricted to two. In the case of standard normal costs, experimental data indicates the mean optimal value goes to negative infinity with increasing problem size. Using curve fitting techniques, we develop numerical estimates of the mean optimal value for various sized problems. The experiments indicate that numerical estimates are quite accurate in predicting the optimal solution value of a random instance of the MAP.     

一类特殊二维0-1规划的广义指派模型求解

二维0-1整数规划模型应用广泛,对广义指派问题的研究,解决了一些二维0-1整数规划问题.但有些实际问题具有特殊上限约束,目前还没有对应的方法.针对该实际情形,本文建立了相应的数学模型,利用对指派模型的推广,求得问题最优解,从理论上解决了这一类特殊约束二维0-1整数规划的最优解求取问题.并通过算例说明了方法的使用.     

一类二阶段指派问题

经典的指派问题是研究资源与任务的二元匹配。作为推广,三维指派问题是研究资源、任务与作业的三元匹配。前者已有成熟的有效算法,后者是著名的NP困难问题。本文讨论介于二者之间的一类二阶段指派问题,即可分解为二阶段决策的特殊三维匹配问题,给出多项式时间算法。     

On the Use of Exact and Heuristic Cutting Plane Methods for the Quadratic Assignment Problem

This paper uses the formulation of the quadratic assignment problem as that of minimizing a concave quadratic function over the assignment polytope. Cutting plane procedures are investigated for solving this problem. A lower bound derived on the number of cuts needed for termination indicates that conventional cutting plane procedures would require a huge computational effort for the exact solution of the quadratic assignment problems. However, several heuristics which are derived from the cutting planes produce optimal or good quality solutions early on in the search process. An illustrative example and computational results are presented.     

指派问题的纳什均衡解

为弥补传统指派问题解不符合个体理性的不足,提出指派问题的纳什均衡解,并证明有限指派问题有且仅有纯纳什均衡解。相比传统的指派问题解,纯纳什均衡符合Pareto最优,是个体理性视角下的最优解。在此基础上,给出一个综合考虑个体理性与集体理性的求解方法。     

A Special Multi-Objective Assignment Problem

The following assignment problem is considered. There are n activities to be assigned to n personnel. The cost of assigning activity i to person j is c _ij . It is required to find all the efficient assignments, i.e. those for which there exists no other assignment which has at least as small costs for each person and strictly smaller costs for at least one person. The main results are as follows. In Theorem 1 it is shown that whereas, for many integer problems, the standard scalar weighting factor approach will not produce all the efficient solutions, in this case it will. In Theorem 2 it is shown that when each efficient vector is determined by a single assignment solution, the efficient set is identical to the set of efficient vertices of the convex hull of the assignment solution set.     

A New Heuristic for the Quadratic Assignment Problem

We propose a new heuristic for the solution of the quadratic assignment problem. The heuristic combines ideas from tabu search and genetic algorithms. Run times are very short compared with other heuristic procedures. The heuristic performed very well on a set of test problems.