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。     

C运输问题

在传统的运输问题中 ,总假设所有产地 (发点 )的产量之和或所有销地 (收点 )的销量之和就是货物的总运输量 .但在实践中 ,特别是在一些与环境有关的资源、稀有资源或不可再生资源的开发利用过程中 ,由于受环境保护或政策限制 ,常常对这些资源的开采和运输有一定的数量限制 .这一类对总运输量有数量限制的运输问题不同于 A运输问题和 B运输问题 ,我们把它称为 C运输问题 .事实上 ,C运输问题是 A运输问题和 B运输问题的推广 .将给出 C运输问题的数学模型和求解方法 .     

多约束水平运输问题的时间费用均衡

基于为顾客提供最佳服务的现代营销理念,剖析了从一定产地到一定销地所发生的运输问题,构建了时间用均衡的目标模式及多目标约束水平MC^2运输问题的数学模型,本文在建立一系列定义,定理的基础上,提出了认明时间费用均衡解空间的优化决策方法,并结合示例展现了这一方法在解决客观实际问题中的有效性。     

关于运输问题最优解的进一步讨论

首先给出了运输问题最优解的相关概念,将最优解扩展到广义范畴,提出狭义多重最优解和广义多重最优解的概念及其区别.然后给出了惟一最优解、多重最优解、广义有限多重最优解、广义无限多重最优解的判定定理及其证明过程.最后推导出了狭义有限多重最优解个数下限和广义有限多重最优解个数上限的计算公式,并举例验证了结论的正确性.     

有整数限制的运输问题

经典的运输问题是一个线性规划模型。本文讨论了把产地运输到销地的物资数量限制为非负整数时的运输问题，从理论上证明了这种有整数限制的运输问题模型可以转化为相应的线性规划模型来求解，有效地降低了计算难度。     

R~n 空间一类广义 MAXMIN 问题的最优性条件

本文拟应用凸锥分离定理给出 R~n 空间一类广义maxmin问题的最优性条件.第2节首先给出了有关的预备性定义及引理.第3节研究了3种GMM(D,f)模型,给出了相应的最优性条件.第4节讨论了 GMM(D,f)最优解与 R~(?) 空间广义向量极值问题GVP(D,f)(见定义2.1)的弱有效解的一个关系.     

初值带δ函数的运输方程的黎曼问题（英文）

研究了具有δ初值的运输方程的黎曼问题,并且构造了在广义Rankine-Hugoniot条件和δ熵条件下的全局解.更进一步,通过对初值的扰动得到了广义解的稳定性.     

具有学习效应的三层供应链排序问题

研究了具有学习效应的三层供应链排序问题. 多个客户分布在不同位置,每个客户都有订 单需要制造商进行生产. 制造商需要针对每一个不同订单的客户从不同的地方进购对应的原材料进行生产,生产完工后需要利用有限的车辆将工件运输到相应客户处. 要求每辆运输车装载尽可 能多的货物才开始运输. 利用动态规划算法研究了最大流程时间、总流程时间以及最大延迟三个目标函数.     

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.     

一类半线性方程的Dirichlet问题

本文利用概率方法证明了如下的 Dizihclet 问题的解的存在性:其中 D 是 F 中的一个有界规则区域,μ和 v 是属于广义 Kato 类的符号测度,f 是 B~1上的连续可微函数连g 是 D 上的一个连续函数.     

A heuristic approach to the multi-period multi-commodity transportation problem

This paper describes an approach to solving a real-world problem which involves the transportation of multiple types of commodities from a number of sources to a number of destinations in discrete time periods, using a capacitated heterogeneous fleet of vehicles. The preliminary objective is to minimize the total number of discrete periods needed to complete the entire operation. The problem is first formulated as a mixed integer programme and its tractability is then greatly improved by reformulating it through backward decomposition into two separate models and solved iteratively. A heuristic approach harnessing specific features of the second approach is developed for solving large size problems to obtain near-optimal solutions within reasonable time. The design of the heuristic also takes into consideration the secondary objectives of minimizing the total vehicle capacity used and minimizing the total capacity of sources needed to satisfy the demands at the destinations. Computational results are provided for a variety of randomly generated problems as well as problems from the literature. The approach described here may be applied to the multi-period transportation of personnel and goods from multiple starting points to multiple destinations in both military and civilian applications.     

The freight allocation problem with lane cost balancing constraint

We consider a problem faced by a buying office for one of the largest retail distributors in the world. The buying office plans the distribution of goods from Asia to various destinations across Europe. The goods are transported along shipping lanes by shipping companies, many of which have collaborated to form strategic alliances; each lane must be serviced by a minimum number of companies belonging to a minimum number of alliances. The task involves purchasing freight capacity from shipping companies for each lane based on projected demand, and subject to minimum quantity requirements for each selected shipping company, such that the total transportation cost is minimized. In addition, the allocation must not assign an overly high proportion of freight to the more expensive shipping companies servicing any particular lane, which we call the lane cost balancing constraint.This study is the first to consider the lane cost balancing constraint in the context of freight allocation. We formulate the freight allocation problem with this lane cost balancing constraint as a mixed integer programming model, and show that even finding a feasible solution to this problem is computationally intractable. Hence, in order to produce high-quality solutions in practice, we devised a meta-heuristic approach based on tabu search. Experiments show that our approach significantly outperforms the branch-and-cut approach of CPLEX 11.0 when the problem increases to practical size and the lane cost balancing constraint is tight. Our approach was developed into an application that is currently employed by decision-makers at the buying office in question.     

An exact algorithm for a vehicle routing problem with time windows and multiple use of vehicles

The vehicle routing problem with multiple use of vehicles is a variant of the classical vehicle routing problem. It arises when each vehicle performs several routes during the workday due to strict time limits on route duration (e.g., when perishable goods are transported). The routes are defined over customers with a revenue, a demand and a time window. Given a fixed-size fleet of vehicles, it might not be possible to serve all customers. Thus, the customers must be chosen based on their associated revenue minus the traveling cost to reach them. We introduce a branch-and-price approach to address this problem where lower bounds are computed by solving the linear programming relaxation of a set packing formulation, using column generation. The pricing subproblems are elementary shortest path problems with resource constraints. Computational results are reported on euclidean problems derived from well-known benchmark instances for the vehicle routing problem with time windows.     

Time variant multi-objective linear fractional interval-valued transportation problem

This paper studies a time-variant multi-objective linear fractional transportation problem. In reality, transported goods should reach in destinations within a specific time. Considering the importance of time, a time-variant multi-objective linear fractional transportation problem is formulated here. We take into account the parameters as cost, supply and demand are interval valued that involved in the proposed model, so we treat the model as a multi-objective linear fractional interval transportation problem. To solve the formulated model, we first convert it into a deterministic form using a new transformation technique and then apply fuzzy programming to solve it. The applicability of our proposed method is shown by considering two numerical examples. At last, conclusions and future research directions regarding our study is included.     

Synchronizing production and air transportation scheduling using mathematical programming models

Traditional scheduling problems assume that there are always infinitely many resources for delivering finished jobs to their destinations, and no time is needed for their transportation, so that finished products can be transported to customers without delay. So, for coordination of these two different activities in the implementation of a supply chain solution, we studied the problem of synchronizing production and air transportation scheduling using mathematical programming models. The overall problem is decomposed into two sub-problems, which consists of air transportation allocation problem and a single machine scheduling problem which they are considered together. We have taken into consideration different constraints and assumptions in our modeling such as special flights, delivery tardiness and no delivery tardiness. For these purposes, a variety of models have been proposed to minimize supply chain total cost which encompass transportation, makespan, delivery earliness tardiness and departure time earliness tardiness costs.     

Polyhedral study of the capacitated vehicle routing problem

The capacitated vehicle routing problem (CVRP) considered in this paper occurs when goods must be delivered from a central depot to clients with known demands, usingk vehicles of fixed capacity. Each client must be assigned to exactly one of the vehicles. The set of clients assigned to each vehicle must satisfy the capacity constraint. The goal is to minimize the total distance traveled. When the capacity of the vehicles is large enough, this problem reduces to the famous traveling salesman problem (TSP). A variant of the problem in which each client is visited by at least one vehicle, called the graphical vehicle routing problem (GVRP), is also considered in this paper and used as a relaxation of CVRP. Our approach for CVRP and GVRP is to extend the polyhedral results known for TSP. For example, the subtour elimination constraints can be generalized to facets of both CVRP and GVRP. Interesting classes of facets arise as a generalization of the comb inequalities, depending on whether the depot is in a handle, a tooth, both or neither. We report on the optimal solution of two problem instances by a cutting plane algorithm that only uses inequalities from the above classes.This work was supported in part by NSF grant DDM-8901495.     

A priori policy evaluation and cyclic-order-based simulated annealing for the multi-compartment vehicle routing problem with stochastic demands

We develop methods to estimate and exactly calculate the expected cost of a priori policies for the multi-compartment vehicle routing problem with stochastic demands, an extension of the classical vehicle routing problem where customer demands are uncertain and products must be transported in separate partitions. We incorporate our estimation procedure into a cyclic-order-based simulated annealing algorithm, significantly improving the best-known solution values for a set of benchmark problems. We also extend the updating procedure for a cyclic order’s candidate route set to duration-constrained a priori policies.     

Optimal Solution of a Vehicle-routeing Problem: Transporting Mentally Handicapped Adults to an Adult Training Centre

Many organizations must devise tours for vehicles to collect from or deliver to a given set of destinations—the vehicle-routeing problem. This paper presents the optimal solution to a real-world problem with 38 destinations and four vehicles, and is thought to be the largest such problem that has been solved optimally. The organization concerned is a local authority (Berkshire County Council) which has aims other than profit maximization. Therefore explicit consideration is given to the multi-objective nature of the problem. Attempts to solve the problem using a commercial mathematical-programming package failed, but a specially written computer program was run on a mini-computer. In the optimal solution, total travel time is reduced by 15.7% and total distance by 11.5%, while the number of empty seats in each vehicle is more equally distributed.     

基于LINGO的物资运输最短时间计算

根据应急物资运输问题时效性强的要求,建立了完成物资运输任务最短时间问题的数学模型,给出了利用二分法进行搜索的LINGO软件求解计算方法,显著减少了计算的迭代次数.实例结果表明,利用LINGO可以实现快速准确的决策,从而为物资运输最短时间决策提供了一种有效的决策方法.