Buyer’s response to a temporary price reduction incorporating freight costs

In this paper, we characterize the buyer’s response to a temporary price reduction. Although there have been many studies that have considered the above problem, most of those studies assume that the buyer orders FOB (free on board) destination and that the freight charges are included in the supplier’s unit price. Our model thus becomes applicable for a buyer whose strategy is to include transportation costs in their purchase decisions. The buyer may want direct control on his inbound logistics costs. The company may have outsourced its logistics function and as a result is charged for freight as invoiced by the public motor carrier. In some cases, the supplier may only allow for orders that are FOB origin. Our model allows for less-than-truckload as well as truckload rates. Freight cost for a LTL shipment is modeled using tariffs set by public carriers in practice. These tariffs generally involve 6–7 breakpoints in terms of the weight of the shipment. Another complication in practice is that the shipper/buyer has an option to over-declare the weight of the shipment.     

快递企业航空货运网络的构建

快递业竞争激烈,构建高效合理的航空货运网络是快递企业提高竞争力的重要手段。“枢纽—辐射”式航空货运网络是整合航空快递资源、提高航空快递资源利用效率、提高快递企业竞争力的有效模式。本文以降低航空快递网络成本、加快航空快递处理时间为目标,从航空快递网络枢纽的选取、指派关系的确定、枢纽个数的选择三个方面研究了航空快递网络模型建立问题,选用遗传算法求解不同枢纽个数下航空快递网络的运输成本,并据此进行枢纽的选取,运用重力模型法进行指派关系的确定,在此基础上运用超效率DEA模型确定枢纽个数。接着,以包含17个节点的顺丰航空快递网络的规划为例,对本文所提出的模型和算法进行了验证,验证结果证实了模型的合理性。本文的研究为快递企业构建航空货运网络提供了科学实用的方法,该方法的使用可以降低航空货运成本,提高效率,从而提高快递企业的竞争力。     

A metaheuristic for stochastic service network design

This paper considers the time-dependent service network design problem with stochastic demand represented by scenarios. To our knowledge, this is the first attempt to address real life-size instances of this problem. The model integrates the balancing of empty vehicles, the cost of handling freight in intermediate terminals, the costs associated with moving freight using the selected services, and the penalty costs of not being able to deliver freight. A metaheuristic is presented and computational results are reported on a set of large new problem instances.     

Balancing fleet size and repositioning costs in LTL trucking

This paper develops an optimization modeling approach for analyzing the trade-off between the cost of a larger fleet of tractors and the cost of repositioning tractors for a trucking company operating a consolidation network, such as a less-than-truckload (LTL) company. Specifically, we analyze the value of using extra tractor repositioning moves (in addition to the ones required to balance resources throughout the network) to reduce the fixed costs of owning or leasing a tractor fleet during a planning horizon. We develop network flow optimization models, some with side constraints and nonlinear objective functions, using event-based, time-expanded networks to determine appropriate fleet sizes and extra repositioning moves under different repositioning strategies, and we compare the optimal costs of the strategies. For repositioning costs, two different cost schemes are explored: one linear and one nonlinear. Computational experiments using real data from a national LTL carrier compare the total system costs obtained with four different strategies and show that extra repositioning may indeed enable fleet size reductions and concomitant cost savings.     

An Algorithmic Framework for Routeing LTL Shipments

One way of reducing the unit cost of shipment by a motor carrier is to consolidate many LTL (less-than-truck-load) shipments. Explicit information on the route structure is a prerequisite to the understanding of the costs of the services provided and to the planning of the allocation of the equipment and personnel among the terminals. In this paper, an approach for routeing the LTL shipments is presented and is applied to a hypothetical network. Solution results of this example show that the algorithm offers an efficient and reasonably accurate method for the routeing of LTL shipments via the intermediate terminals.     

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.     

The hub location and network design problem with fixed and variable arc costs: formulation and dual-based solution heuristic

Many air, less-than-truck load and intermodal transportation and telecommunication networks incorporate hubs in an effort to reduce total cost. These hubs function as make bulk/break bulk or consolidation/deconsolidation centres. In this paper, a new hub location and network design formulation is presented that considers the fixed costs of establishing the hubs and the arcs in the network, and the variable costs associated with the demands on the arcs. The problem is formulated as a mixed integer programming problem embedding a multi-commodity flow model. The formulation can be transformed into some previously modelled hub network design problems. We develop a dual-based heuristic that exploits the multi-commodity flow problem structure embedded in the formulation. The test results indicate that the heuristic is an effective way to solve this computationally complex problem.     

Optimal number and location of storage hubs and biogas production reactors in farmlands with allocation of multiple feedstocks

This paper focuses on the problem for designing a logistics system for bio-methane gas (BMG) production. In practice, farm residues such as crop residue, wood residue, and livestock manure are used in reactors as reactants to generate BMG. A multi-residue, multi-hub, multi-reactor location-allocation model is developed to design the logistics of BMG production system. Both the hubs’ and reactors’ locations, and the residue's distribution plan are investigated to minimize the total construction and logistical cost. The costs of construction, transportation, feedstocks and labor are taken into consideration to reflect the lifecycle cost of the entire undertaking. In this paper, a mixed-integer nonlinear problem is proposed to simulate a BMG production supply chain. In addition to the optimal solution methods, a search-based heuristic was also proposed to determine the locations of hubs and reactors for large instances and along with a proper allocation of residues that are transported from the farms to the hubs to the reactors. Several numerical examples are tested to evaluate the performance of the heuristic as well.     

考虑订单合并和货物转运的多式联运路径优化研究

为了获得运输的规模经济效应,本文研究了一种考虑订单合并和货物转运的零担多式联运路径优化问题。首先,以总运输成本为目标函数,以网络中的运输工具容量、可以提供的运输工具最大数量、运输工具服务的关闭时间以及订单时间窗为约束,构建混合整数规划模型,在模型中允许多个订单进行合并运输并考虑运输过程中的转运成本。其次,由于多式联运路径优化问题是典型的NP-hard问题,为了快速求解该模型,开发了一种可以快速为该问题提供近似最优解和下界的列生成启发式算法。最后,生成并测试了大量算例,结果表明所开发的列生成启发式算法可以在较短的时间内提供高质量的近似最优解。文章所构建的模型和开发的列生成启发式算法可以为零担自营多式联运物流企业提供高效的决策支持。     

A biased random-key genetic algorithm for the tree of hubs location problem

Hubs are facilities used to treat and dispatch resources in a transportation network. The objective of Hub Location Problems (HLP) is to locate a set of hubs in a network and route resources from origins to destinations such that the total cost of attending all demands is minimized. In this paper, we investigate a particular HLP, called the Tree of Hubs Location Problem in which hubs are connected by means of a tree and the overall network infrastructure relies on a spanning tree. This problem is particularly interesting when the total cost of building the hub backbone is high. We propose a biased random key genetic algorithm for solving the tree of hubs location problem. Computational results show that the proposed heuristic is robust and effective to this problem. The method was able to improve best known solutions of two benchmark instances used in the experiments.     

The capacitated p-hub median problem with integral constraints: An application to a Chinese air cargo network

Consolidation at hubs in a pure hub-and-spoke network eliminates partial center-to-center direct loads, resulting in savings in transportation costs. In this research, we propose a general capacitated p-hub median model, with economies of scale and integral constraints on the paths. This model requires the selection of a specific p among a set of candidate hubs so that the total cost on the resulting pure capacitated hub-and-spoke network is minimized while simultaneously meeting origin–destination demands, operational capacity and singular path constraints. We explored the problem structure and developed a genetic algorithm using the path for encoding. This algorithm is capable of determining local optimality within less than 0.1% of the Lagrangian relaxation lower bounds on our Chinese air cargo network testing case and has reasonable computational times. The study showed that designating airports with high pickups or deliveries as hubs resulted in a high percentage of origin–destination pairs (ODs) in direct deliveries. Furthermore, the more hubs there are, the higher the direct share and the less likely for double rehandles. Sensitivity analysis on the discount rate showed that the economies of scale on trunk lines of hub-and-spoke networks may have a substantial impact on both the operating costs and the route patterns.     

Strategic planning in LTL logistics – increasing the capacity utilization of trucks

A “less than truckload” (LTL) network organises the transport of small shipping volumes by truck between given depots. To be cost-efficient it is necessary to bundle and unbundle goods on their way, using depots as so-called hubs. Our aim is to develop a strategic plan which is cost-optimal for given average shipping volumes. We consider transshipment and transport costs; to give a realistic estimate of the economies of scale, we charge each truck on a specific route equally, whether it is full or (nearly) empty.Real-sized problems become too hard for standard solvers so that we develop a combination of heuristic strategies (which can, in the end, be combined with solvers like CPLEX). We consider the problem in two flavours: MAPIT requires to transport unsplit goods from one depot to another, using at most two intermediate depots as hubs. IO-MAPIT furthermore considers the circulation of trucks.     

A mathematical programming model for integrating production and procurement transport decisions

In this paper, we propose a new mathematical programming model for integrating production and procurement transport planning decisions in manufacturing systems in a unique optimization model. This problem was introduced conceptually and dubbed as MRP IV by Díaz-Madroñero et al. (2012) to extend the current MRP (material requirement planning) systems. This proposal simultaneously considers material, production resources capacities and procurement transport planning decisions with different shipping modes (such as full-truckload, less-than-truckload and milk-run) in the supply chain to avoid suboptimal results, which are usually generated due to sequential and independent plans. We considered an industrial automobile company to validate the proposed model using real world data. The results obtained by the MRP IV proposed model, in terms of total planning costs and transport efficiency indicators, are better than those obtained in the current heuristic procedures followed in the company under study.     

A variable neighbourhood search algorithm for the constrained task allocation problem

A variable neighbourhood search algorithm that employs new neighbourhoods is proposed for solving a task allocation problem whose main characteristics are: (i) each task requires a certain amount of resources and each processor has a capacity constraint which limits the total resource of the tasks that are assigned to it; (ii) the cost of solution includes fixed costs when using processors, task assignment costs, and communication costs between tasks assigned to different processors. A computational study shows that the algorithm performs well in terms of time and solution quality relative to other local search procedures that have been proposed.     

第三方库存-路线问题的模型及混合遗传算法设计

根据第三方库存-路线问题的特点,以车辆租赁费用和运行费用之和为目标函数,不限制客户每次的配送量小于车辆容量,建立了满载运输和非满载运输混合的整数规划模型.针对第三方库存-路线问题的复杂性,本文设计嵌入禁忌搜索的遗传算法来同时决策库存和路线问题.首先对配送间隔进行编码,然后用禁忌搜索法计算每天需要配送的车辆路线问题.最后与其下界值进行比较,结果表明该算法是一个有效的算法,不但第三方能取得较低的运营总成本和较高的车辆利用率,而且也能为客户节约库存空间.     

A bi-objective genetic approach for the selection of sugarcane varieties to comply with environmental and economic requirements

The selection of sugarcane varieties is an important problem faced by companies in Brazil that exploit sugarcane harvest for energy production. In the light of current concerns regarding the reduction of environmental damage and the efficiency of the production system, research into this problem is called for. In this context the authors begin by outlining the sugarcane variety selection problem in accordance with technical constraints with the purpose of minimizing collection and transport costs and maximizing energy balance obtained from residues of the sugarcane harvest. They then present a previously developed model for the problem within bi-objective binary linear programming and study its computational complexity. Fundamentally, this paper is devoted to the application of a bi-objective genetic heuristic to the question addressed. A computational experiment, performed by resorting to a test set including real and semi-randomly generated instances, is then reported. The results prove the high quality of the heuristic in terms of solution quality, besides computing time. For these reasons, this will be an appropriate tool to help sugarcane company managers to plan their producing activities.     

Mean-chance model for portfolio selection based on uncertain measure

This paper discusses a portfolio selection problem in which security returns are given by experts’ evaluations instead of historical data. A factor method for evaluating security returns based on experts’ judgment is proposed and a mean-chance model for optimal portfolio selection is developed taking transaction costs and investors’ preference on diversification and investment limitations on certain securities into account. The factor method of evaluation can make good use of experts’ knowledge on the effects of economic environment and the companies’ unique characteristics on security returns and incorporate the contemporary relationship of security returns in the portfolio. The use of chance of portfolio return failing to reach the threshold can help investors easily tell their tolerance toward risk and thus facilitate a decision making. To solve the proposed nonlinear programming problem, a genetic algorithm is provided. To illustrate the application of the proposed method, a numerical example is also presented.     

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.     

Routing models and solution procedures for regional less-than-truckload operations

Less-Than-Truckload (LTL) carriers are required on a daily basis to solve Intra-Group Line-Haul (IGLH) problems. IGLH problems require the determination of routes to service required pickups and deliveries (i.e., 28-foot trailers) at End-Of-Line (EOL) terminals. The objective is to minimize total costs, given that tractors are able to simultaneously transport two trailers and that all pickups and deliveries must be accomplished. In this paper, an approximate IGLH solution approach is presented. Given pickup and delivery requirements together with relevant distance data, a matching network is constructed in which nodes correspond to sets of pickups and deliveries and links to routes. A minimum weight non-bipartite matching algorithm is solved over this network and the result is an IGLH solution. This solution is improved by again applying a minimum weight matching algorithm, this time to a matching network in which nodes correspond to routes and links to improved routes. Finally, the routes are sequenced so as to achieve balance at each EOL terminal (i.e., empty trailers must be delivered or picked up as necessary to ensure that each EOL terminal has the same number of pickups and deliveries) and to minimize the inventory of empty trailers. The new IGLH solution procedure is tested on randomly generated data and on data provided by a large LTL carrier. Computational tests show that near-optimal solutions are generated rapidly.     

一种解决集装箱港口拖车调度的混合策略

集装箱码头作为集装箱船舶与其它运输节点之间的枢纽,在不断增长的全球贸易中起着极其重要的作用.由于多种因素影响着集装箱码头的服务效率,从而使其成为了商界和学术界研究的热点.本文针对集装箱码头中的拖车调度问题,对于当某辆拖车完成一项作业任务后,如何去寻找下一个装卸任务的问题,提出了一个多因素的在线调度策略,该策略在针对我国大多数港口的现有布局和信息现状的基础上,综合考虑了拖车空驶和距离平衡多种因素,建立了综合评价函数,并应用评价搜索和离散事件仿真方法对评价函数进行了优化计算.在相关理论分析基础上,通过比较本文的调度策略和港口传统调度策略,我们认为本文的调度策略在缩短船舶停留时间和减少拖车空驶上具有十分明显的效果.