Comparison of four mechanisms for request exchange in collaborative transportation

This paper presents frameworks for auction-based and posted price mechanisms for exchanging requests between carriers operating in the same geographical areas. Similar mechanisms have been studied before in a rather isolated manner. In this work, all mechanisms are tested within the same scenario and by comparing their results; the aim is to obtain insights regarding the efficiency gains they offer to the network and the required information for their application. Moreover, this comparison provides a clear idea of what is needed to implement each mechanism and the results to expect if one invests in the mentioned implementation, i.e., how much would the net profit be affected. The mechanisms' main objective is to give carriers the chance to improve their original sets of requests in order to increase their overall efficiency. Due to the nature of routing problems, requests usually have different costs for different carriers, depending on the location of the depots as well as on the requests that have to be serviced. The exchange mechanism should find a new allocation such that carriers obtain requests with higher value (i.e., lower cost) for them. Results obtained here show that individual auction-based mechanisms provide similar results, on average, to centralized auction-based mechanisms, both outperforming posted price mechanisms. Nevertheless, this improvement difference comes at the cost of providing more information (included in the carriers' bids). Readers can use the presented comparison and results to improve decisions taken prior to collaboration, specifically deciding which mechanism is the best option for a specific situation.     

Two-phase heuristic algorithms for full truckloads multi-depot capacitated vehicle routing problem in carrier collaboration

Collaborative transportation, as an emerging new mode, represents one of the major developing trends of transportation systems. Focusing on the full truckloads multi-depot capacitated vehicle routing problem in carrier collaboration, this paper proposes a mathematical programming model and its corresponding graph theory model, with the objective of minimizing empty vehicle movements. A two-phase greedy algorithm is given to solve practical large-scale problems. In the first phase, a set of directed cycles is created to fulfil the transportation orders. In the second phase, chains that are composed of cycles are generated. Furthermore, a set of local search strategies is put forward to improve the initial results. To evaluate the performance of the proposed algorithms, two lower bounds are developed. Finally, computational experiments on various randomly generated problems are conducted. The results show that the proposed methods are effective and the algorithms can provide reasonable solutions within an acceptable computational time.     

基于时间风险的共同配送利益分配模型研究

共同配送联盟伙伴间的利益分配,不仅要考虑联盟伙伴在联盟中的贡献,而且要考虑因延迟集货而给联盟带来的风险因素,体现风险收益。在深入分析现有共同配送利益分配模型的基础上,通过计算延迟集货时间的几率,来衡量联盟伙伴可能对共同配送联盟带来的时间风险,并在利益分配的过程中考虑该风险因素,建立了基于时间风险的Raiffa解利益分配模型。最后通过实例验证了文中模型的有效性和可行性。     

A Game Theoretical Approach for Improving the Operational Efficiencies of Less-than-truckload Carriers Through Load Exchanges

Less-than-truckload (LTL) transportation offers fast, flexible and relatively low-cost transportation services to shippers. In order to cope with the effects of economic recessions, the LTL industry implemented ideas such as reducing excess capacity and increasing revenues through better yield management. In this paper, we extend these initiatives beyond the reach of individual carriers and propose a collaborative framework that facilitates load exchanges to reduce the operational costs. Even though collective solutions are proven to provide benefits to the participants by reducing the inefficiencies using a system-wide perspective, such solutions are often not attainable in real-life as the negotiating parties are seeking to maximize their individual profits rather than the overall profit and also they are unwilling to share confidential information. Therefore, a mechanism that enables collaboration among the carriers should account for the rationality of the individual participants and should require minimal information transfer between participants. Having this in mind, we propose a mechanism that facilities collaboration through a series of load exchange iterations and identifies an equilibrium among selfish carriers with limited information transfer among the participants. Our time-efficient mechanism can handle large instances with thousands of loads as well as provide significant benefits over the non-collaborative management of LTL networks.

     

A review on cost allocation methods in collaborative transportation

Collaboration in transportation between two or more agents is becoming an important approach to find efficient solutions or plans. Efficiency can be measured in, for example, lower cost or more flexibility. An important aspect of the collaboration is to decide on how to share the benefits—for example, cost, profit, or resources. There are many sharing mechanisms or cost allocations proposed in the literature. Some are based on simple proportional rules and others are based on theoretical concepts found in game theory. We provide a survey on cost allocation methods found in the literature on collaborative transportation, including problems on planning, vehicle routing, traveling salesman, distribution, and inventory. A total of 55 scientific articles compose the main part of the survey, most of them published between 2010 and 2015. We identify more than 40 cost allocation methods used in this stream of literature. We describe the theoretical basis for the main methods as well as the cases where they are used. We also report savings from the collaborations when they are based on industrial data. Some directions for future research are discussed.     

Routing straddle carriers for the loading operation of containers using a beam search algorithm

This paper discusses how to route straddle carriers during the loading operation of export containers in port container terminals. The objective of the routing is to minimize the total travel distance of straddle carriers in the yard. The routing problem is comprised of the container allocation problem and the carrier routing problem. In the container allocation problem, containers in the yard are divided into multiple classes, each of which will be loaded by a quay crane. The container allocation problem is formulated as a transportation problem. In the carrier routing problem, the sequence of yard-bays that a carrier visits is determined. A beam search algorithm is developed for the carrier routing problem. A numerical experimentation is carried out in order to evaluate the performance of the algorithm.     

Cooperative planning in express carrier networks — An empirical study on the effectiveness of a real-time Decision Support System

For small transportation firms cooperation in a carrier network is a proper mean to overcome the inefficiencies from deadheading. To be successful, such a network has to secure two different but equally important aspects: the partners have to be aware of specific consolidation potentials through order exchange which is an optimization and communication problem, and, the partners have to experience incentives to contribute actively to the network which is the problem of finding a fair cost/profit allocation schema for order exchanges. In this paper we discuss the experience with the development of a Decision Support System for a specific express carrier network. We illustrate how the consolidation potentials in such a network with autonomously planning carriers can be exploited and cost effectiveness can be improved substantially through the use of a suitable distributed Decision Support System if the two success factors awareness and fairness are addressed properly.     

Bilateral slot exchange and co-allocation for liner alliance carriers of containerized maritime logistics

With the maritime logistics technology evolving toward standardization and modernization, container liner shipping has become the most valued mode of transportation in the shipping industry. Liner carriers share resources in the alliance-based mode to achieve synergy. Specifically, slot exchange is a key and effective alliance-based means to enhance competitiveness and operational effectiveness. To this end, this article looks at how to optimize slot capacity allocation within a container liner alliance under the slot exchange mode in the containerized maritime logistics industry. First, we explained the slot exchange concept and its impact on the shipping business. Second, we identified the key factors that may affect slot allocation decisions. Then we established an optimal multi-objective slot exchange allocation model for liner alliances and solved the problem with the NSGA-II algorithm. Finally, taking the cooperation of two carriers on two trans-Pacific routes as an example, we used the model to establish the optimal slot exchange strategy for each carrier on the shipping alliance's routes to verify the practical application value of this model. The results showed that the model proposed in this article can help develop an optimal slot allocation plan, providing a scientific and effective tool for container liner alliances to formulate slot exchange and allocation strategies.     

A two-stage optimization model for road-rail transshipment procurement and truckload synergetic routing

Road-rail multimodal transport has increasing importance in modern cargo transport. This paper presents a novel two-stage model for optimizing auction game strategy and route in container road-rail transshipment. Two critical questions are investigated in the two stages, respectively: (a) In the first stage, what is the best pricing strategy for either large-scale or small-scale carriers in a stochastic bid generation problem? (b) In the second stage, what are the best routes of the container trucks to minimize the total expected profit of the carrier according to its pricing strategy? To deal with the first stage problem, this paper presents an evolutionary game model to capture the long-term strategic behaviors of the carriers and to optimize the choice between historical pricing strategy and combinatorial strategy for each carrier. The equilibrium points of the evolutionary game model and Evolutionary Stability Strategy are derived and discussed. In the second stage, a fleet management problem is presented to determine the routing of the trucks over the whole temporal horizon regarding the transportation price. An A*-based search heuristic is proposed to find the satisfying solution to this difficult problem. To show the efficiency and effectiveness of the proposed method, numerical studies are conducted. The results show that (a) The optimized pricing strategy is impacted by the expected profit rate, the correlation coefficient between different transport ation enterprise carriers, and the probability of winning bids. The equilibrium point of the ordering bidding system depends on whether these critical input parameters exceed a threshold. (b) In the fleet management and route optimization stage, the small-scale carrier has gained about 11.42% more profits than the large-scale carrier.     

Adaptive granular local search heuristic for a dynamic vehicle routing problem

The advance of communication and information technologies based on satellite and wireless networks have allowed transportation companies to benefit from real-time information for dynamic vehicle routing with time windows. During daily operations, we consider the case in which customers can place requests such that their demand and location are stochastic variables. The time windows at customer locations can be violated although lateness costs are incurred. The objective is to define a set of vehicle routes which are dynamically updated to accommodate new customers in order to maximize the expected profit. This is the difference between the total revenue and the sum of lateness costs and costs associated with the total distance traveled. The solution approach makes use of a new constructive heuristic that scatters vehicles in the service area and an adaptive granular local search procedure. The strategies of letting a vehicle wait, positioning a vehicle in a region where customers are likely to appear, and diverting a vehicle away from its current destination are integrated within a granular local search heuristic. The performance of the proposed approach is assessed in test problems based on real-life Brazilian transportation companies.     

A model with a solution algorithm for the cash transportation vehicle routing and scheduling problem

Cash transportation vehicle routing and scheduling are essential for security carriers to minimize their operating costs and ensure safe cash conveyance. In real operations, to increase cash conveyance safety, there must be significant variation in daily cash transportation vehicle routes and schedules, making such vehicle routes and schedules difficult to formulate. However, for convenient planning purposes, security carriers normally plan such routes and schedules based on personal experience, without considering variations in routes and schedules from a system perspective. As a result, the obtained routes and schedules are neither safe nor efficient for transporting cash. In this study, a model is developed where the time–space network technique is utilized to formulate the potential movements of cash transportation vehicles among all demand points in the dimensions of time and space. This model incorporates a new concept of similarity of time and space for routing and scheduling, which is expected to help security carriers formulate more flexible routing and scheduling strategies. This is helpful to reduce the risk of robbery. Mathematically, the model is formulated as an integer multiple-commodity network flow problem. A solution algorithm, based on a problem decomposition/collapsing technique, coupled with the use of a mathematical programming software, is developed to efficiently solve the problem. The case study results show that our model and solution algorithm could be useful references for security carriers in actual practice.     

Closed Bernoulli Production Lines: Analysis, Continuous Improvement, and Leanness

In closed production lines, each part is placed on a carrier at the input of the first machine and is removed from the carrier at the output of the last machine. The first machine is starved if no carriers are available, and the last machine is blocked if the empty carrier buffer is full. The number of carriers in the system is S and the capacity of the empty carrier buffer is N ₀. Under the assumption that the machines obey the Bernoulli reliability model, this paper provides methods for determining if a pair (N ₀, S) impedes the open line performance and, if it does, develops techniques for improvement with respect to S and N ₀. In addition, bottlenecks in closed lines are discussed, and an approach to selecting the smallest N ₀ and S, which result in no impediment, is described.     

多车场物流协同运输调度问题研究

物流协同运输是现代物流模式的发展趋势,而利润则是驱动物流协同的关键因素。针对多车场物流协同运输中的调度问题,基于汽车配件物流运输的相关约束,建立了由利润驱动的配送任务模型。通过模糊聚类建立基于集货中心点的遗传种群,通过遗传算法的交叉、变异操作进行迭代进化,在一定的迭代次数内得到一个最优解。通过一个实例说明了该模型的求解结果可以使得不同物流企业的运输车辆之间发生协同的同时达到增加物流企业运输利润的目标,从而验证了该模型的正确性和合理性。     

共享汽车系统空车调度的鲁棒优化模型

面向共享汽车系统的运营商与潜在用户,针对实现最大利润的空车调度问题,同时考虑乘客需求信息的不确定性对调度过程的影响,利用基于可调决策规则的鲁棒优化方法进行建模与求解.在共享汽车系统中,乘客的出行需求是不确定的,给出相应的不确定集合描述,将乘客的出行需求限制在一定的区间内,并灵活限制时间上的乘客需求之和,以减小模型的保守性.在此基础上引入可调决策规则,使得空车调度的策略可以根据已实现的需求进行调整,提出空车调度的鲁棒优化模型及其可解的线性规划形式.仿真实验利用真实的滴滴订单信息模拟用户使用共享汽车出行的需求,展示该模型所提出的空车调度策略(相较于确定性模型)会投入更多的费用在空车调度上,使运营商在平均意义和最差情况下均获得更大的利润并满足更多的乘客需求,表明所提出模型的鲁棒性和实用性.     

Hybrid column generation and large neighborhood search for the dial-a-ride problem

Demographic change towards an ever aging population entails an increasing demand for specialized transportation systems to complement the traditional public means of transportation. Typically, users place transportation requests, specifying a pickup and a drop off location and a fleet of minibuses or taxis is used to serve these requests. The underlying optimization problem can be modeled as a dial-a-ride problem. In the dial-a-ride problem considered in this paper, total routing costs are minimized while respecting time window, maximum user ride time, maximum route duration, and vehicle capacity restrictions. We propose a hybrid column generation and large neighborhood search algorithm and compare different hybridization strategies on a set of benchmark instances from the literature.     

Development of a maritime transportation planning support system for car carriers based on genetic algorithm

Recently, the port logistics market is rapidly expanding, along with the active maritime trade. To adjust to this trend and gain a competitive advantage, competition among shipping companies at home and abroad has intensified, and many efforts are being made for the improvement of customer services and cost saving. In particular, car carriers transporting more than 80% of total car import/export volume must quickly make efforts to reduce transportation costs. Much research has been conducted to improve the efficiency of maritime transportation, but studies on car carriers, which are given relatively less importance, have been lacking. The car carrier’s transportation planning is similar to the vehicle routing problem, but it is much more complicated in that cars and cargo are prepared at different points in time, and cargo can be loaded not only at the departing port but also at other ports. Therefore, in an effort to solve the problem, this study has developed a meta-heuristic algorithm based on a genetic algorithm, and we have succeeded in developing a maritime transportation planning support system with the algorithm, thus making it possible to prepare various alternatives, evaluate them, and consequently support user’s decision making.     

Fuzzy cost-profit tradeoff model for locating a vehicle inspection station considering regional constraints

Facility location allocation （FLA） is one of the important issues in the logistics and transportation fields. In practice, since customer demands, allocations, and even locations of customers and facilities are usually changing, the FLA problem features uncertainty. To account for this uncertainty, some researchers have addressed the fuzzy profit and cost issues of FLA. However, a decision-maker needs to reach a specific profit, minimizing the cost to target customers. To handle this issue it is essential to propose an effective fuzzy cost-profit tradeoff approach of FLA. Moreover, some regional constraints can greatly influence FLA. By taking a vehicle inspection station as a typical automotive service enterprise example, and combined with the credibility measure of fuzzy set theory, this work presents new fuzzy cost-profit tradeoff FLA models with regional constraints. A hybrid algorithm integrating fuzzy simulation and genetic algorithms （GA） is proposed to solve the proposed models. Some numerical examples are given to illustrate the proposed models and the effectiveness of the proposed algorithm.     

模糊合作对策区间Shapley值法的改进及应用

模糊合作对策的收益分配是个复杂问题,受到合作方的风险承担、合作努力、市场竞争、创新贡献和资源投入等因素的影响,而且不同因素有着不同的重要性。运用区间Shapley值法对模糊合作对策的收益进行初步分配。通过将AHP-GEM法和模糊综合评价法相结合,引入收益分配的综合修正因子,对区间Shapley值法进行改进,建立了模糊合作对策利益分配的改进模型。以制造业和物流业联盟为例,说明了改进模型的实用性和可行性。     

Autonomic resource provisioning for multilayer cloud applications with K-nearest neighbor resource scaling and priority-based resource allocation

Providing a pool of various resources and services to customers on the Internet in exchanging money has made cloud computing as one of the most popular technologies. Management of the provided resources and services at the lowest cost and maximum profit is a crucial issue for cloud providers. Thus, cloud providers proceed to auto-scale the computing resources according to the users' requests in order to minimize the operational costs. Therefore, the required time and costs to scale-up and down computing resources are considered as one of the major limits of scaling which has made this issue an important challenge in cloud computing. In this paper, a new approach is proposed based on MAPE-K loop to auto-scale the resources for multilayered cloud applications. K-nearest neighbor (K-NN) algorithm is used to analyze and label virtual machines and statistical methods are used to make scaling decision. In addition, a resource allocation algorithm is proposed to allocate requests on the resources. Results of the simulation revealed that the proposed approach results in operational costs reduction, as well as improving the resource utilization, response time, and profit.