A hub covering network design problem for cargo applications in Turkey

Hub location problems involve locating hub facilities and allocating demand nodes to hubs in order to provide service between origin–destination pairs. In this study, we focus on cargo applications of the hub location problem. Through observations from the Turkish cargo sector, we propose a new mathematical model for the hub location problem that relaxes the complete hub network assumption. Our model minimizes the cost of establishing hubs and hub links, while designing a network that services each origin–destination pair within a time bound. We formulate a single-allocation hub covering model that permits visiting at most three hubs on a route. The model is then applied to the realistic instances of the Turkish network and to the Civil Aeronautics Board data set.     

The planar hub location problem: a probabilistic clustering approach

Given the demand between each origin-destination pair on a network, the planar hub location problem is to locate the multiple hubs anywhere on the plane and to assign the traffic to them so as to minimize the total travelling cost. The trips between any two points can be nonstop (no hubs used) or started by visiting any of the hubs. The travel cost between hubs is discounted with a factor. It is assumed that each point can be served by multiple hubs. We propose a probabilistic clustering method for the planar hub-location problem which is analogous to the method of Iyigun and Ben-Israel (in Operations Research Letters 38, 207–214, 2010; Computational Optmization and Applications, 2013) for the solution of the multi-facility location problem. The proposed method is an iterative probabilistic approach assuming that all trips can be taken with probabilities that depend on the travel costs based on the hub locations. Each hub location is the convex combination of all data points and other hubs. The probabilities are updated at each iteration together with the hub locations. Computations stop when the hub locations stop moving. Fermat-Weber problem and multi-facility location problem are the special cases of the proposed approach.     

航线联盟下航空货运网络枢纽点选址问题研究

本文研究航空联盟下航空货运网络的枢纽点选址问题(HLP),基于枢纽点的数量及位置随机与容量的限制。首先引入航线联盟选择概率函数,确定不同航段上的航线自营运输或外包运输的概率;其次根据选址中心法则,以网络总成本最小化为目标,建立枢纽点选址模型;再次采用改进的免疫混沌遗传算法求解模型;最后,以顺丰航空公司案例进行实例分析。结果表明:1)本文改进的算法较免疫混沌遗传算法并与免疫遗传算法及CPLEX结果对比,发现本文设计的算法有较强的收敛性和计算速度,且计算结果与CPLEX求解器求解结果相差不大;2)枢纽点数量不确定时,枢纽点的位置多集中在东部城市;3)航空公司选择联盟环境可以大大降低运营成本,航空公司为提高自身利润,应考虑加入联盟,从而降低自身成本。     

基于正交试验的乘用车运输网络优化方法

在交通部治理公路超限运输的背景下,本文研究了乘用车物流企业多式联运模式下的网络优化问题,以运输网络总成本最小为目标,考虑物流时效、枢纽节点容量及规模经济效应等因素,构建了基于轴辐式理论的运输网络优化模型,提出了混合智能优化算法。针对多参数多水平的寻优问题,对模型的三个关键输入参数,即枢纽节点数量、枢纽节点容量和规模效应折扣系数,引入正交试验方法,降低求解多参数多水平寻优问题的工作量,为确定各参数合理取值提供了新的途径。研究结果表明:枢纽节点容量、折扣系数与枢纽数量三个输入参数对优化结果的影响具有主次顺序,影响程度依次减弱,而且只有枢纽节点容量与折扣系数对乘用车运输网络总效益的影响起显著作用。采用混合轴辐式的网络结构与多式联运的运输组织模式进行优化后的运输网络,相对于原有“点对点”公路运输网络总成本减少10%,从运营管理与成本控制两方面均可有效应对公路治超带来的风险。     

A multi-objective chance-constrained network optimal model with random fuzzy coefficients and its application to logistics distribution center location problem

The problem of the distribution center is concerned with how to select distribution centers from a potential set in order to minimize the total relevant cost comprising of fixed costs of the distribution center and transport costs, and minimize the transportation time. In this paper, we propose a multi-objective network optimal model with random fuzzy coefficients for the logistics distribution center location problem. Furthermore, we convert the uncertain model into a deterministic one by the probability and possibility measure. Then the spanning tree-based genetic algorithm (st-GA) by the Prüfer number representation is introduced to solve the crisp multiobjective programming. At last, the proposed model and algorithm are applied to the Xinxi Dairy Holdings Limited Company to show the efficiency.     

带固定轴线成本的轴辐式网络设计问题

带固定轴线成本的轴辐式网络设计问题广泛应用于第三方物流、邮政和航空运输等领域. 现有研究主要考虑了枢纽站的节点成本, 本研究则强调合并运输的固定轴线成本. 固定轴线成本的必要性在于：轴辐式网络中的轴线运输需要借助更大型的运输工具, 因此必须支付固定成本. 建立了该问题的混合整数规划模型, 探讨了最优解特征, 并构造了求解问题的拉格朗日松驰算法, 实验显示算法具有非常好的求解效率与求解质量. 同时, 还讨论了一个重要的扩展问题：增加O-D流的绕道约束, 绕道约束常常应用于快递运输和应急物流等领域. 在局部修改原算法的基础上提供了扩展问题的求解方案.     

轴辐式枢纽网络系统的关键枢纽设施识别问题研究

作为轴辐式枢纽网络关键因素的节点,尤其是起到中转作用的枢纽节点是网络稳定运行的重要环节。当这些节点被中断时,将对整个网络产生严重的影响。最直接的表现方式即是网络运行成本的急剧上升。因此本文研究如何识别对网络成本具有决定性影响的关键节点。首先,提出枢纽功能性中断问题和模型,并通过禁忌搜索算法进行求解。最后通过中国航空实例验证模型和算法在实际应用中的有效性。结果显示模型和算法能够有效识别出中国航空网络较重要的关键城市以及相对影响较弱的城市。可以为资源有限情况下,中国航空网络中各城市防御设施的合理分级和部署,为重点保护城市的鉴别提供依据和帮助。     

A fuzzy goal programming and meta heuristic algorithms for solving integrated production: distribution planning problem

Integrated production–distribution planning is one of the most important issues in supply chain management (SCM). We consider a supply chain (SC) network to consist of a manufacturer, with multiple plants, products, distribution centers (DCs), retailers and customers. A multi-objective linear programming problem for integrating production–distribution, which considers various simultaneously conflicting objectives, is developed. The decision maker’s imprecise aspiration levels of goals are incorporated into the model using a fuzzy goal programming approach. Due to complexity of the considered problem we propose three meta-heuristics to tackle the problem. A simple genetic algorithm and a particle swarm optimization (PSO) algorithm with a new fitness function, and an improved hybrid genetic algorithm are developed. In order to show the efficiency of the proposed methods, two classes of problems are considered and their instances are solved using all methods. The obtained results show that the improved hybrid genetic algorithm gives us the best solutions in a reasonable computational time.     

A capacitated hub location problem in freight logistics multimodal networks

In this paper we deal with a capacitated hub location problem arising in a freight logistics context; in particular, we have the need of locating logistics platforms for containers travelling via road and rail. The problem is modelled on a weighed multimodal network. We give a mixed integer linear programming model for the problem, having the goal of minimizing the location and shipping costs. The proposed formulation presents some novel features for modelling capacity bounds that are given both for the candidate hub nodes and the arcs incident to them; further, the containerised origin-destination (\(o-d)\) demand can be split among several platforms and different travelling modes. Note that here the network is not fully connected and only one hub for each \(o-d\) pair is used, serving both to consolidate consignments on less transport connections and as reloading point for a modal change. Results of an extensive computational experimentation performed with randomly generated instances of different size and capacity values are reported. In the test bed designed to validate the proposed model all the instances up to 135 nodes and 20 candidate hubs are optimally solved in few seconds by the commercial solver CPLEX 12.5.     

基于两阶段启发式算法的物流配送选址-路径问题研究

为了解决配送中心选址与带时间窗的多中心车辆路径优化组合决策问题,利用双层规划法建立了配送中心选址与车辆路径安排的多目标整数规划模型,针对该模型的特点,采用两阶段启发式算法进行了求解。首先,通过基于聚集度的启发式算法对客户进行分类,确定了备选配送中心的服务范围;然后,基于双层规划法,以配送中心选址成本最小作为上层规划目标,以车辆配送成本最小作为下层规划目标,建立了多目标整数规划模型;最后,利用改进的蚁群算法进行了求解。通过分析实例数据和Barreto Benchmark算例的实验结果,验证了该模型的有效性和可行性。     

Impact analysis of maritime cabotage legislations on liner hub-and-spoke shipping network design

Maritime cabotage is a legislation published by a particular coastal country, which is used to conduct the cargo transportation between its two domestic ports. This paper proposes a two-phase mathematical programming model to formulate the liner hub-and-spoke shipping network design problem subject to the maritime cabotage legislations, i.e., the hub location and feeder allocation problem for phase I and the ship route design with ship fleet deployment problem for phase II. The problem in phase I is formulated as a mixed-integer linear programming model. By developing a hub port expanding technique, the problem in phase II is formulated as a vehicle routing problem with pickup and delivery. A Lagrangian relaxation based solution method is proposed to solve it. Numerical implementations based on the Asia–Europe–Oceania shipping services are carried out to account for the impact analysis of the maritime cabotage legislations on liner hub-and-spoke shipping network design problem.     

多目标应急物流中心选址的鲁棒优化模型

针对重大突发事件的应急物资救援,研究了应急物流中心的选址及应急物资的调运问题。利用离散的情景集合描述受灾点应急物资需求的不确定性以及应急物资运输成本和运输时间的不确定性,同时考虑应急救援成本和应急救援时间两个目标,建立了多目标应急物流中心选址的确定型模型和鲁棒优化模型。为将多目标问题转化为单目标问题,利用成本单目标和时间单目标的最优结果将多目标转化为相对值再加权处理,该方法既可消除多个目标之间的单位及数量级差异,还可以根据问题的数据变化进行动态调整。以提供应急物资救援服务的设施作为编码,设计了一种通用的混合蛙跳算法。为检验模型和算法的有效性,设计了一个多情景的算例,结果表明两个模型和算法具备良好的可行性和有效性,且鲁棒优化模型能较好地保持对各种不确定性的抗干扰能力;最后,讨论分析了成本偏好权重和鲁棒约束系数的影响,结果表明可根据成本偏好权重的取值范围来区分各种应急救援阶段,体现不同救援阶段的救援要求及特征,并给出了成本偏好权重和鲁棒约束系数的取值建议。     

Implementation of scatter search for multi-objective optimization: a comparative study

Interest in the design of efficient meta-heuristics for the application to combinatorial optimization problems is growing rapidly. The optimal design of water distribution networks is an important optimization problem which consists of finding the best way of conveying water from the sources to the users, thus satisfying their requirements. The efficient design of looped networks is a much more complex problem than the design of branched ones, but their greater reliability can compensate for the increase in cost when closing some loops. Mathematically, this is a non-linear optimization problem, constrained to a combinatorial space, since the diameters are discrete and it has a very large number of local solutions. Many works have dealt with the minimization of the cost of the network but few have considered their cost and reliability simultaneously. The aim of this paper is to evaluate the performance of an implementation of Scatter Search in a multi-objective formulation of this problem. Results obtained in three benchmark networks show that the method here proposed performs accurately well in comparison with other multi-objective approaches also implemented.     

Location of hubs in a competitive environment

We offer a formulation that locates hubs on a network in a competitive environment; that is, customer capture is sought, which happens whenever the location of a new hub results in a reduction of the current cost (time, distance) needed by the traffic that goes from the specified origin to the specified destination. The formulation presented here reduces the number of variables and constraints as compared to existing covering models. This model is suited for both air passenger and cargo transportation. In this model, each origin–destination flow can go through either one or two hubs, and each demand point can be assigned to more than a hub, depending on the different destinations of its traffic. Links (“spokes”) have no capacity limit. Computational experience is provided.     

Study on multi-objective optimization of flow allocation in a multi-commodity stochastic-flow network with unreliable nodes

A long distance transportation problem was abstracted to a resource flow allocation problem upon a stochastic-flow network with unreliable nodes. The objectives were the probability that transmission was successful and transportation cost. In order to solve constructed model, a multi-objective genetic algorithm was propounded. Tested by examples, the algorithm well solved the flow allocation problem in a stochastic-flow network.     

Time-dependent discrete road network design with both tactical and strategic decisions

This paper aims to model and investigate the discrete urban road network design problem, using a multi-objective time-dependent decision-making approach. Given a base network made up with two-way links, candidate link expansion projects, and candidate link construction projects, the problem determines the optimal combination of one-way and two-way links, the optimal selection of capacity expansion projects, and the optimal lane allocations on two-way links over a dual time scale. The problem considers both the total travel time and the total CO emissions as the two objective function measures. The problem is modelled using a time-dependent approach that considers a planning horizon of multiple years and both morning and evening peaks. Under this approach, the model allows determining the sequence of link construction, the expansion projects over a predetermined planning horizon, the configuration of street orientations, and the lane allocations for morning and evening peaks in each year of the planning horizon. This model is formulated as a mixed-integer programming problem with mathematical equilibrium constraints. In this regard, two multi-objective metaheuristics, including a modified non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective B-cell algorithm, are proposed to solve the above-mentioned problem. Computational results for various test networks are also presented in this paper.     

A competitive hub location and pricing problem

We formulate and solve a new hub location and pricing problem, describing a situation in which an existing transportation company operates a hub and spoke network, and a new company wants to enter into the same market, using an incomplete hub and spoke network. The entrant maximizes its profit by choosing the best hub locations and network topology and applying optimal pricing, considering that the existing company applies mill pricing. Customers’ behavior is modeled using a logit discrete choice model. We solve instances derived from the CAB dataset using a genetic algorithm and a closed expression for the optimal pricing. Our model confirms that, in competitive settings, seeking the largest market share is dominated by profit maximization. We also describe some conditions under which it is not convenient for the entrant to enter the market.     

A cooperative swarm intelligence algorithm for multi-objective discrete optimization with application to the knapsack problem

We propose a novel cooperative swarm intelligence algorithm to solve multi-objective discrete optimization problems (MODP). Our algorithm combines a firefly algorithm (FA) and a particle swarm optimization (PSO). Basically, we address three main points: the effect of FA and PSO cooperation on the exploration of the search space, the discretization of the two algorithms using a transfer function, and finally, the use of the epsilon dominance relation to manage the size of the external archive and to guarantee the convergence and the diversity of Pareto optimal solutions.We compared the results of our algorithm with the results of five well-known meta-heuristics on nine multi-objective knapsack problem benchmarks. The experiments show clearly the ability of our algorithm to provide a better spread of solutions with a better convergence behavior.     

Meta-heuristic algorithms for wafer sorting scheduling problems

Wafer sorting is usually regarded as the most critical stage in the whole wafer probing process. This paper discusses the wafer sorting scheduling problem (WSSP) with total setup time minimization as the primary criterion and the minimization of the number of machines used as the secondary criterion. Although the need to consider multiple criteria in real-world WSSPs is widely recognized, the present study is the first attempt to investigate this argument with setups consideration. In view of the strongly NP-hard nature of this problem, three meta-heuristic algorithms—an ant colony system algorithm, a Genetic algorithm, and a Tabu search algorithm are proposed. The proposed meta-heuristics are empirically evaluated by 480 simulation instances based on the characteristics of a real wafer testing shop-floor and found to be very effective in terms of finding good quality solutions.     

多类型需求流的截流选址问题研究和算法实现

本文研究了网络上固定的O-D对上存在不同类型的需求流的选址问题。在基本截流模型的基础上提出了多类型需求流多目标截流选址问题的模型,将模型转化为多目标模糊规划问题,运用混合遗传算法求解模型,最后给出了算例,并与分支定界法相比,证明了混合遗传算法可以有效的求解此模型。