需求区间型运输问题的求解算法

为了便于建立与需求区间型运输问题有关的决策支持系统，本给出了一个求解需求区间型运输问题的数值算法，证明了算法的理论依据，并举例说明算法的应用，该算法能求得问题的最优解，并具有易于编程实现、收敛性好等优点，大量数值实验表明该算法有较高的计算效率。     

多目标运输问题的Fnzzy线性规划解法

经典运输问题是一类特殊的单目标线性规划问题，可用表上作业法或单纯形法求其最优解［１］．近年来，许多学者研究了多目标运输问题，提出了相应的求解算法［６－９］．本文应用Ｆｕｚｚｙ线性规划的方法，给出了多目标运输问题的又一求解算法．     

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。     

多目标运输问题的Fuzzy线性规划解法

经典运输问题是一类特殊的单目标线性规划问题，可用表上作业法或单纯形法求其最优解。近年来，许多学研究了多目标运输问题，提出了相应的求解算法。本应用Fuzzy线性规划的方法，给出了多目标运输问题的又一求解算法。     

受时间约束的运输问题求解的一种算法

本针对受时间约束的运输问题，提出了一种基于表上作业法的优化求解算法，并用算例说明了该算法的求解过程。     

多重运输调度问题的表搜索算法

本文提出了多重运输调度问题的表搜索算法，实际运算表明，这些算法具有较快的收敛速度，为解决实际问题提供了一条有效的途径。     

流向受限运输问题的解法及其应用

本文提出一种解流向受限运输问题的算法.首先给出数学模型,利用位势法解普通运输问题的原理,建立了一种改进型位势法解流向受限运输问题.算法包含了解普通运输问题.对算法进行了理论证明后,研制了软件.用软件在微机 IBM-PC/XT 上编制一个全国性的物资调运计划仅用时间2分钟.长期实用证明方法是有效的.该软件已提供物资调运部门使用20个月.     

平衡和不平衡运输问题与分配问题的通用迭代算法

将不平衡运输问题转化成网络最短路问题,利用Floyd算法规则,给出了一种既可以解平衡和不平衡运输问题,又可以解平衡和不平衡分配问题的通用迭代算法。与专门用于解运输问题的闭合回路法和专门用于解分配问题的匈牙利法相比,这种算法不但具有通用的优点,而且更便于在计算机上运行。     

军械物资供应系统中的多目标运输问题

建立了军械物资运输问题的模糊多目标线性规划模型，运用一种解模糊函数和一种基于线性隶属函数的模糊规划算法求其调和解。方法简便、有效，可为部队军械物资的运输供应高效化提供科学依据。     

带严格时间限制的大宗物资运输模型及算法

阐述了带严格时间限制的大宗物资运输问题的特性，应用目标规划建立了单个发点多收点问题的数学模型，根据模型约束条件多的特点，给出了求解算法，并编制了相应的计算机程序。     

An algorithm for finding a matroid basis which maximizes the product of the weights of the elements

Consider the problem of finding a spanning tree in an edge-weighted connected graph that maximizes the product of its edge weights, where negative edge weights are allowed. We generalize this problem to matroids and give a polynomial time algorithm for its solution.     

Customer order scheduling to minimize the number of late jobs

In the order scheduling problem, every job (order) consists of several tasks (product items), each of which will be processed on a dedicated machine. The completion time of a job is defined as the time at which all its tasks are finished. Minimizing the number of late jobs was known to be strongly NP-hard. In this note, we show that no FPTAS exists for the two-machine, common due date case, unless P = NP. We design a heuristic algorithm and analyze its performance ratio for the unweighted case. An LP-based approximation algorithm is presented for the general multicover problem. The algorithm can be applied to the weighted version of the order scheduling problem with a common due date.     

A decomposition algorithm for linear relaxation of the weightedr-covering problem

In this paper the linear relaxation of the weightedr-covering problem (r-LCP) is considered. The dual problem (c-LMP) is the linear relaxation of the well-knownc-matching problem and hence can be solved in polynomial time. However, we describe a simple, but nonpolynomial algorithm in which ther-LCP is decomposed into a sequence of 1-LCP’s and its optimal solution is obtained by adding the optimal solutions of these 1-LCP’s. An 1-LCP can be solved in polynomial time by solving its dual as a max-flow problem on a bipartite graph. An accelerated algorithm based on this decomposition scheme to solve ar-LCP is also developed and its average case behaviour is studied.     

An Algorithm for Solving the Shortest Path Improvement Problem on Rooted Trees Under Unit Hamming Distance

Shortest path problems play important roles in computer science, communication networks, and transportation networks. In a shortest path improvement problem under unit Hamming distance, an edge-weighted graph with a set of source–terminal pairs is given. The objective is to modify the weights of the edges at a minimum cost under unit Hamming distance such that the modified distances of the shortest paths between some given sources and terminals are upper bounded by the given values. As the shortest path improvement problem is NP-hard, it is meaningful to analyze the complexity of the shortest path improvement problem defined on rooted trees with one common source. We first present a preprocessing algorithm to normalize the problem. We then present the proofs of some properties of the optimal solutions to the problem. A dynamic programming algorithm is proposed for the problem, and its time complexity is analyzed. A comparison of the computational experiments of the dynamic programming algorithm and MATLAB functions shows that the algorithm is efficient although its worst-case complexity is exponential time.     

A discrete artificial bee colony algorithm for the no-idle permutation flowshop scheduling problem with the total tardiness criterion

In this paper, we present a discrete artificial bee colony algorithm to solve the no-idle permutation flowshop scheduling problem with the total tardiness criterion. The no-idle permutation flowshop problem is a variant of the well-known permutation flowshop scheduling problem where idle time is not allowed on machines. In other words, the start time of processing the first job on a given machine must be delayed in order to satisfy the no-idle constraint. The paper presents the following contributions: First of all, a discrete artificial bee colony algorithm is presented to solve the problem on hand first time in the literature. Secondly, some novel methods of calculating the total tardiness from makespan are introduced for the no-idle permutation flowshop scheduling problem. Finally, the main contribution of the paper is due to the fact that a novel speed-up method for the insertion neighborhood is developed for the total tardiness criterion. The performance of the discrete artificial bee colony algorithm is evaluated against a traditional genetic algorithm. The computational results show its highly competitive performance when compared to the genetic algorithm. Ultimately, we provide the best known solutions for the total tardiness criterion with different due date tightness levels for the first time in the literature for the Taillard’s benchmark suit.     

Generating Functions for Computing the Myerson Value

The complexity of a computational problem is the order of computational resources which are necessary and sufficient to solve the problem. The algorithm complexity is the cost of a particular algorithm. We say that a problem has polynomial complexity if its computational complexity is a polynomial in the measure of input size. We introduce polynomial time algorithms based in generating functions for computing the Myerson value in weighted voting games restricted by a tree. Moreover, we apply the new generating algorithm for computing the Myerson value in the Council of Ministers of the European Union restricted by a communication structure.     

Parameter estimation of partially observed continuous time stochastic processes via the EM algorithm

An algorithm is presented for the problem of maximum likelihood (ML) estimation of parameters of partially observed continuous time random processes. This algorithm is an extension of the EM algorithm [3] used in the time series literature, and preserves its main features. It is then applied to the problem of parameter estimation of continuous time, finite state or infinite state (diffusions) Markov processes observed via a noisy sensor. The algorithm in general involves iterations of non-linear smoothing with known parameters and then a non-stochastic maximization. For special cases, including linear models and AR/ARMA processes observed in white noise, each iteration is easily performed with finite dimensional filters. Finally, the algorithm is applied to parameter estimation of “randomly slowly varying” linear systems observed in white noise, and explicit results are derived.     

On maximizing the profit of a satellite launcher: Selecting and scheduling tasks with time windows and setups

At regular times, a satellite launcher company has to plan the use of its launcher to get the maximum profit. In a more formal way, the problem consists of selecting and scheduling a subset of unit-length jobs constrained by capacitated time slots so that the overall cost is a minimum. The data associated with each job are its weight, its time-window and its expected gain when it is performed. With each time slot are associated a setup cost and a capacity. The setup cost of a time slot is due when this time-slot is used to perform at least one job. Moreover the total weight of all jobs scheduled within a time slot is at most the time slot capacity. We first show that the general problem is hard and provide some easy special cases. We then propose a first dynamic-programming polynomial-time algorithm for the special case with unit weights. A second and more efficient dynamic programming algorithm is also provided for the special case of unit weights and agreeable time windows. This last algorithm is finally improved for the special case of equal gains.     

最短时限缺省指派问题的一种解法

将周良泽在 1998年提出的最短时限缺省指派问题转化成赋权二分图的最小权 K-匹配问题。研究了其解的最优性充分及必要条件 ,并给出了适合在图上求解的生长树法及适合在表上直接求解的标号法 ,最后给出一个实例。该解法是一种较简便的算法。     

A tabu search algorithm for the integrated scheduling problem of container handling systems in a maritime terminal

The scheduling problem in a container terminal is characterized by the coordination of different types of equipment. In this paper, we present an integrated model to schedule the equipment. The objective is to minimize the makespan, or the time it takes to serve a given set of ships. The problem is formulated as a Hybrid Flow Shop Scheduling problem with precedence and Blocking constraints (HFSS-B). A tabu search algorithm is proposed to solve this problem. Certain mechanisms are developed and introduced into the algorithm to assure its quality and efficiency. The performance of the tabu search algorithm is analyzed from the computational point of view.