运输线路优化问题的最小路集算法

运输是物流的基本功能,有效地协调运输时间和运输成本两者间的关系,使其保持经济性和可靠性的均衡状态,是企业选择运输方式时必须考虑的重要因素.本文针对最大流量和最短路径的可靠性问题,引入最小路集算法,为描述运输线路正常运行的可靠程度,提供一种新的计算方法.     

工件的运输和继列分批加工协作排序问题

近年来,工件的运输和加工协作排序问题在物流和供应链管理领域得到广泛关注. 讨论了先用 $\ m$ 台车辆将工件从等待区域运输到继列分批处理机处, 再进行分批加工的协作排序问题, 加工一批工件需要支付一定的费用, 目标为最小化工件的总完工时间与批的加工费用之和. 在工件的加工时间都相等的情况下, 如果工件运输方案确定, 给出了多项式时间的动态规划算法; 如果工件运输方案不确定, 证明了该问题是{\, NP}-难的, 给出了车辆返回时间 $\ t=0$ 时, 最差性能比等于 $\ 2-\frac{1}{m}$ 的近似算法.     

具有两种运输方式选择的物流排序问题

本文研究了单机环境下,有两种运输方式可供选择的集成生产和运输的排序问题。有多个工件需要在一台机器上进行加工,工件生产完后需要分批运到客户处。有两种运输方式,普通运输和特快运输可供选择。制造商需要安排工件的加工顺序,选择合适的运输方式和出发时间,以极小化相应的时间目标与运输费用的加权和。研究了排序理论中主要的两个目标函数,分析了问题的复杂性,对于这些问题给出了它们的最优算法。     

双渠道双目标的多期供应链网络均衡问题研究

以变分不等式为工具建立双渠道双目标的多期供应链网络均衡模型.在传统实体交易和电子网上交易的双渠道环境下,考虑时间因素与库存变量的影响,提出由制造商、零售商和需求市场组成的三层多期供应链网络均衡问题,其中制造商和零售商均追求经济利润最大化与风险最小化的双重目标.分别对各系统成员的独立决策行为及其相互作用进行分析,得到各层决策成员的均衡条件并给出相应经济解释,推导与双渠道双目标的多期供应链网络均衡问题等价的有限维变分不等式模型.在证明其解在一定的假设条件下存在的基础上,构造数值算例以验证模型的正确性与合理性.     

对称集值向量拟均衡问题解集的稳定性

本文研究了对称集值向量拟均衡问题解集的稳定性.证明了在约束映射满足一定连续性与目标映射是锥-恰当拟凸的集值映射条件下,对称集值向量拟均衡问题的解集是稳定的,还证明了每个对称集值向量拟均衡问题的解集至少存在一个本质连通区.     

一类具有非线性目标函数的运输问题

在作物资调运计划时,有时会遇到这样的问题,在完成调运计划的条件下,允许某些收、发点在一定范围内增加收、发量,目标是这个调运计划的平均运输费用最低,这类问题可以归结为下列具有非线性目标函数的运输问题。     

广义强向量拟均衡问题解集的通有稳定性

研究广义强向量拟均衡问题(GSVQEP)解集的通有稳定性.在约束集值映射满足一定连续与目标映射是锥-真拟凸的集值映射条件下,证明了广义强向量拟均衡问题构成的空间M中,在Baire分类意义下,广义强向量拟均衡问题解集是通有稳定的,且给出了空间M中对每个广义强向量拟均衡问题的解集至少存在一个本质连通区.     

带运输时间和自动机的流水作业排序问题的复杂性

研究目标函数为使最大完工时间达到最小的三台机器情况下的流水作业排序问题, 同一工件在某台机器上完工后和在下一台机器上开始加工这段时间内, 存在称为运输时间的时间间隔, 所有的运输工作均由自动机来完成, 自动机在同一时间内最多运输一个工件, 文章研究该问题及其特殊情况下的复杂性.     

带运输时间和自动机的法流水作业排序问题的复杂性

研究目标函数为使最大完工时间达到最小的三台机器情况下的流水作业排序问题,同一工件在某台机器上完工后和在下一台机器上开始加工这段时间内,存在称为运输时间的时间间隔,所有的运输工作均由自动机来完成,自动机在同一时间内最多运输一个工件,文章研究该问题及其特殊情况下的复杂性.     

群体决策问题的一种最优均衡解

本文引进了群体决策问题的一个最优解概念—s~*-最优均衡解,s~*-最优均衡解可以作为群体决策问题的一种解,它的实际意义是为所有的决策者找到一个最优解。我们证明了求解s~*-最优均衡解等价于求解一个相应的单目标优化问题,且在一定条件下s~*-最优均衡解总是存在的。我们也讨论按比例分配的s~*-最优均衡解问题。本文为解决群体决策问题提供了一种新的途径。     

Pareto optimal solutions of a cost–time trade-off bulk transportation problem

A cost–time trade-off bulk transportation problem with the objectives to minimize the total cost and duration of bulk transportation without according priorities to them is considered. The entire requirement of each destination is to be met from one source only; however a source can supply to any number of destinations subject to the availability of the commodity at it. Two new algorithms are provided to obtain the set of Pareto optimal solutions of this problem. This work extends and generalizes the work related to single-objective and prioritized two-objective bulk transportation problems done in the past while providing flexibility in decision making.     

循环取货模式下的多车场协同运输问题研究

分析循环取货模式和协同运输问题的关联性,提出了供需节点分离下的多车场一体化协同运输路线优化问题,考虑运输价值和运输成本,引入节点-弧流量的概念,通过比较流量大小确定节点集合,构建了问题的多供应点、多需求点运输模型.考虑取货的单向性和送货的闭合性,构造了求解模型的两阶段算法,运用动态规划的递推解法确定取货最优路线,然后基于余弦定理的几何法求解出发点和返回点不相同的送货路径优化问题,最后通过算例分析,说明了模型的合理性和算法的有效性.     

A Stochastic Bottleneck Transportation Problem

In this paper, a stochastic bottleneck transportation problem, which aims at minimizing the transportation time target subject to a chance constraint, is formulated and an algorithm based on a parametric programming approach is developed to solve it. Further, assuming the transportation costs to be deterministic, a trade-off analysis between the transportation time target and the total cost is given. In addition, methods are developed which give the whole spectrum of optimal solutions to the problems mentioned above. The algorithms are illustrated by numerical examples. The computational complexity of the algorithms is also discussed.     

An algorithm for a fuzzy transportation problem to select a new type of coal for a steel manufacturing unit

Manufacturing of steel involves thermal energy intensive processes with coal as the major input. Energy generated is a direct function of ash content of coal and as such it weighs very high as regards the choice of coal. In this paper, we study a multiobjective transportation problem to introduce a new type of coal in a steel manufacturing unit in India. The use of new type of coal serves three non-prioritized objectives, viz. minimization of the total freight cost, the transportation time and the ratio of ash content to the production of hot metal. It has been observed from the past data that the supply and demand points have shown fluctuations around their estimated values because of changing economic conditions. To deal with uncertainties of supply and demand parameters, we transform the past data pertaining to the amount of supply of the ith supply point and the amount of demand of the jth demand point using level (λ,ρ) interval-valued fuzzy numbers. We use a linear ranking function to defuzzify the fuzzy transportation problem. A transportation algorithm is developed to find the non-dominated solutions for the defuzzified problem. The application of the algorithm is illustrated by numerical examples constructed from the data provided by the manufacturing unit.      

Capacitated location model with online demand pooling in a multi-channel supply chain

This paper presents a location model that assigns online demands to the capacitated regional warehouses currently serving in-store demands in a multi-channel supply chain. The model explicitly considers the trade-off between the risk pooling effect and the transportation cost in a two-echelon inventory/logistics system. Keeping the delivery network of the in-store demands unchanged, the model aims to minimize the transportation cost, inventory cost, and fixed handling cost in the system when assigning the online demands. We formulate the assignment problem as a non-linear integer programming model. Lagrangian relaxation based procedures are proposed to solve the model, both the general case and an important special case. Numerical experiments show the efficiency of our algorithms. Furthermore, we find that because of the pooling effect the variance of in-store demands currently served by a warehouse is an important parameter of the warehouse when it is considered as a candidate for supplying online demands. Highly uncertain in-store demands, as well as low transportation cost per unit, can make a warehouse appealing. We illustrate with numerical examples the trade-off between the pooling effect and the transportation cost in the assignment problem. We also evaluate the cost savings between the policy derived from the model, which integrates the transportation cost with the pooling effect, and the commonly used policy, which is based only on the transportation cost. Results show that the derived policy can reduce 1.5–7.5% cost in average and in many instances the percentage of cost savings is more than 10%.     

Efficient algorithms for the 2-Way Multi Modal Shortest Path Problem

We consider the 2-Way Multi Modal Shortest Path Problem (2WMMSPP). Its goal is to find two multi modal paths with total minimal cost, an outgoing path and a return path. The main difficulty lies in the fact that if a private car or bicycle is used during the outgoing path, it has to be picked up during the return path. The shortest return path is typically not equal to the shortest outgoing path as traffic conditions and timetables of public transportation vary throughout the day. In this paper we propose an efficient algorithm based on bi-directional search and provide experimental results on a realistic multi modal transportation network.     

Capacity allocation problem with random demands for the rail container carrier

In this paper, we consider the formulation and heuristic algorithm for the capacity allocation problem with random demands in the rail container transportation. The problem is formulated as the stochastic integer programming model taking into account matches in supply and demand of rail container transportation. A heuristic algorithm for the stochastic integer programming model is proposed. The solution to the model is found by maximizing the expected total profit over the possible control decisions under the uncertainty of demands. Finally, we give numerical experiments to demonstrate the efficiency of the heuristic algorithm.     

Environmentally conscious optimization of supply chain networks

The optimization of supply chain structures considering both economic and environmental performances is nowadays an important research topic. However, enterprises are commonly faced with the competing issues of reduced cost, improved customer service and increased environmental factors as a multi-faceted trade-off problem when designing supply chains. Hence, this paper proposes an environmentally conscious optimization model of a supply chain network with a broader and more comprehensive objective function that considers not just the transportation costs, but also the costs for the amount of greenhouse gas emissions, fuel consumption, transportation times, noise and road roughness. The paper sheds light on the trade-offs between various parameters such as vehicle speed, fuel, time, emissions, noise and their total cost, and offers managerial insights on economies of environmentally conscious supply chain optimization. An integer non-linear programming model is developed to help decision makers find the optimal solution under mentioned considerations. The proposed model is validated through the solution of an example, where its applicability to supply chain problems is demonstrated for managerial insights.     

基于质量控制的供应链计划问题研究

为了研究基于质量控制的供应链计划问题,采用了优化建模的方法,分析了易腐物品生命周期函数的选用及拟合原则,建立起了质量衰减成本与时间之间的关系,考虑了涉及交通及天气情况的运输风险,最终建立了优化模型,以实现供应链运行总成本最小。最后,提出了动态规划的求解方法,并通过仿真实验证明了算法的有效性。     

A bi-criteria hybrid metaheuristic for analysing an integrated automotive supply chain

The automotive industry is one of the most important manufacturing sectors in the world due to its economic impact and technological complexities. While supply chain performance can have a dramatic impact on the automotive industry, there are multiple, often conflicting objectives that typically are used to optimize performance. We model the trade-off between cost and service level, and present a bi-criteria heuristic optimization methodology for a two-stage, integrated automotive supply chain. Our problem contains sequence-dependent setups on parallel machines and auxiliary resource assignments. We minimize the total cost of setups, inventory holding, and transportation costs, and the maximum percentage of outsourced parts per customer, simultaneously. We use our proposed method to solve a set of problem instances that are based on industrial data. Our proposed method generates approximate Pareto (efficient) solutions in a timely manner for use in practice.