Planning for Truck Fleet Size in the Presence of a Common‐carrier Option

Ball, Michael O.; Golden, Bruce L.; Assad, Arjang A.; Bodin, Lawrence

doi:10.1111/j.1540-5915.1983.tb00172.x

Cited by 116 publications

(65 citation statements)

References 11 publications

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“…For example, the numbers of Pareto-optimal paths from the distribution center 0 to the destination nodes 1 ∼ 4 are 2, 3, 5, and 4, respectively. Then a code of [1,4,3,5] means the 1st (1 mod 2), 1st (4 mod 3), 3rd (3 mod 5), and 1st (5 mod 4) of the Pareto paths from o to 1 ∼ 4 for the individual are adopted for transportation. (3) Population Updating Strategy.…”

Section: Problem-solving Methodsmentioning

confidence: 99%

“…Similar to that of ordinary goods, logistic distribution of hazardous materials can be classified as full container load (FCL) [1,2] and less than container load (LCL) [3]. If the transport volume required by a destination is no less than the capacity of one transportation vehicle, there will be no less than one vehicle involved in a logistic job.…”

Section: Introductionmentioning

confidence: 99%

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Path Planning and Vehicle Scheduling Optimization for Logistic Distribution of Hazardous Materials in Full Container Load

Chai

et al. 2017

Discrete Dynamics in Nature and Society

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Mathematical models for path planning and vehicle scheduling for logistic distribution of hazardous materials in full container load (FCL) are established, with their problem-solving methods proposed. First, a two-stage multiobjective optimization algorithm is designed for path planning. In the first stage, pulse algorithm is used to obtain the Pareto paths from the distribution center to each destination. In the second stage, a multiobjective optimization method based on Nondominated Sorting Genetic Algorithm II (NSGA-II) is designed to obtain candidate transport paths. Second, with analysis on the operating process of vehicles with hazardous materials in FCL, the vehicle scheduling problem is converted to Vehicle Routing Problem with Time Windows (VRPTW). A problem-solving method based on estimation of distribution is adopted. A transport timetable for all vehicles based on their transport paths is calculated, with participation of the decision-makers. A visual vehicle scheduling plan is presented for the decision-makers. Last, two examples are used to test the method proposed in this study: distribution of hazardous materials in a small-scale test network and distribution of oil products for sixteen gas stations in the main districts of Lanzhou city. In both examples, our method is used to obtain the path selection and vehicle scheduling plan, proving that validity of our method is verified.

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Section: Problem-solving Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Path Planning and Vehicle Scheduling Optimization for Logistic Distribution of Hazardous Materials in Full Container Load

Chai

et al. 2017

Discrete Dynamics in Nature and Society

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“…One class of network optimization problems considered in the literature is to determine the optimal fleet size and the resulting vehicle routes when external carriers are available. Ball et al [5] consider this problem with a fleet of homogeneous vehicles and discuss some approximate solution strategies. Klincewicz et al [84] look into the case of delivery of goods from a warehouse to local customers.…”

Section: Fleet Composition In Network Optimization Problemsmentioning

confidence: 99%

“…The study considers a single ferry type, but the authors argue that the model and algorithm can be extended to include multiple ferry types with different service characteristics. [21] 1981 Statistical method Fleet sizing Road-based 3 Sim and Templeton [135] 1982 Statistical method Fleet sizing Generic 4 Ball, Golden, Assad and Bodin [5] 1983 Mixed integer programming, constructive heuristics Fleet sizing Road-based 5 Turnquist [148] 1985 Classification of research opportunities in transportation Fleet sizing Generic 6 Turnquist and Jordan [149] 1986 Statistical method Fleet sizing Generic 7 Bookbinder and Reece [14] 1988 Non-linear mixed integer programming …”

Section: Fleet Composition In Network Optimization Problemsmentioning

confidence: 99%

Industrial aspects and literature survey: Fleet composition and routing

Hoff

Andersson

Christiansen

et al. 2010

Computers & Operations Research

293

150

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The purpose of this paper is to describe industrial aspects of combined fleet composition and routing in maritime and road-based transportation, and to present the current status of research in the form of a comprehensive literature review. With a backdrop of industrial aspects, a categorized survey of relevant literature since the first published papers in the 1950's is given. First, the literature review discusses some early seminal and application-oriented papers, presents a classification of problems, and then focuses on a basic definition of combined fleet composition and routing: the fleet size and mix vehicle routing problem. Three basic mathematical formulations from the literature are presented and compared. Further, the literature of extended and related problems is described and categorized. Surveys of application oriented research in road-based and maritime transportation conclude the review. Finally, we contrast the literature with aspects of industrial applications from a critical, but constructive stance. Major issues for future work are suggested.

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“…The vehicle scheduling problem, or the full-truckload problem, are typically cast in relatively simple terms, where a set of vehicles needs to cover a set of loads. Tours may need to cover tasks within a time window, and the length of the tour is typically constrained to the maximum number of hours a driver can spend on the road (see BALL et al, 1981;BODIN et al, 1983;ATKINSON, 1994, for example). This earlier work primarily used tour construction and tour improvement procedures.…”

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confidence: 99%

Adaptive Labeling Algorithms for the Dynamic Assignment Problem

Powell

Snow

Cheung

2000

Transportation Science

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We consider the problem of dynamically routing a driver to cover a sequence of tasks (with no consolidation), using a complex set of driver attributes and operational rules. Our We consider the problem of routing and scheduling a heterogeneous set of drivers to cover a known set of tasks. There is a reward for covering each task, and not all the tasks have to be covered. The reward received can depend on when a task is covered, and the cost of covering a task can be dependent, in an arbitrary way, on the characteristics of the driver. After a driver has finished one task, he may be free to cover another task, although subsequent assignments have to obey hours of service regulations and service commitments on tasks. A driver may or may not have to return home at the end of a shift, and we may be planning tours for the driver for several days into the future. A driver can handle only one task at a time; there is no in-vehicle consolidation. A task might represent a job that has to be performed at a specific location, but our work is motivated by applications where the task involves moving a load of freight from one location to another, thereby adding a spatial element to the problem.The construction of each tour is subject to various constraints, such as government regulations on driver hours and constraints associated with each task. For example, each task has an associated time window and the driver must arrive at the origin and destination of the task between their respective time windows. Other constraints might reflect driver capabilities and load characteristics; for example, the load might require a driver with certain training or a specific type of tractor. Because there are very few hard constraints in real-time operations, assignments that violate stated goals are assessed penalties according to a function that varies according to the constraint violation. Similar penalty functions can also be constructed for other assignment rules and calibrated to simulate the human decision process. This paper presents two algorithms, which are optimization-based heuristics, that solve this type of problem, which we refer to as the dynamic assignment problem. Our work is motivated by the need to solve these problems in a real-time setting. This need imposes three constraints on our solution approach. First, the run times must be exceptionally fast. In real problems, data updates can come in every few seconds, so the algorithm must be able to produce a revised solution very quickly for problems with perhaps 500 drivers. As a result, the algorithm must be amenable to finding new solutions given 50 Transportation Science,

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Planning for Truck Fleet Size in the Presence of a Common‐carrier Option

Cited by 116 publications

References 11 publications

Path Planning and Vehicle Scheduling Optimization for Logistic Distribution of Hazardous Materials in Full Container Load

Path Planning and Vehicle Scheduling Optimization for Logistic Distribution of Hazardous Materials in Full Container Load

Industrial aspects and literature survey: Fleet composition and routing

Adaptive Labeling Algorithms for the Dynamic Assignment Problem

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