Domiciling Truck Drivers More Strategically in a Transportation Network

The authors propose a method to better domicile truck drivers in a relay-point highway transportation network to obtain better solutions for the truck driver domiciling and sourcing problem. The authors exploit characteristics of the truckload driver routing problem over a transportation network and introduce a new approach to domicile, source, and route truck drivers while more inclusively considering performance and cost measures related to the driver, transportation carrier, and customer. Driver domicile and relay-point locations are exploited to balance driver pay and recruiting costs and driving time. A mixed integer quadratic program will determine where driver domiciles are located to base drivers, source drivers, route drivers, etc. while considering key costs related to transporting truckload freight over long distances. A method to improve driver domicile locations is introduced to enhance driving jobs and driver sourcing, but not at the expense of the transportation carrier and customer. A numerical experiment will be conducted.

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Predictively Coordinated Vehicle Acceleration and Lane Selection Using Mixed Integer Programming

Volume 1: Advances in Control Design Methods; Advances in Nonlinear Control; Advances in Robotics; Assistive and Rehabilitation Robotics; Automotive Dynamics and Emerging Powertrain Technologies; Automotive Systems; Bio Engineering Applications; Bio-Mechatronics and Physical Human Robot Interaction; Biomedical and Neural Systems; Biomedical and Neural Systems Modeling, Diagnostics, and Healthcare ◽

10.1115/dscc2018-9177 ◽

2018 ◽

Cited By ~ 5

Author(s):

R. Austin Dollar ◽

Ardalan Vahidi

Keyword(s):

Autonomous Vehicle ◽

Mixed Integer ◽

Quadratic Program ◽

Vehicle To Vehicle ◽

Trip Time ◽

Induced Travel ◽

Vehicle Acceleration ◽

Vehicle Technology ◽

Rule Based Approach ◽

Integer Quadratic Program

Autonomous vehicle technology provides the means to optimize motion planning beyond human capacity. In particular, the problem of navigating multi-lane traffic optimally for trip time, energy efficiency, and collision avoidance presents challenges beyond those of single-lane roadways. For example, the host vehicle must simultaneously track multiple obstacles, the drivable region is non-convex, and automated vehicles must obey social expectations. Furthermore, reactive decision-making may result in becoming stuck in an undesirable traffic position. This paper presents a fundamental approach to these problems using model predictive control with a mixed integer quadratic program at its core. Lateral and longitudinal movements are coordinated to avoid collisions, track a velocity and lane, and minimize acceleration. Vehicle-to-vehicle connectivity provides a preview of surrounding vehicles’ motion. Simulation results show a 79% reduction in congestion-induced travel time and an 80% decrease in congestion-induced fuel consumption compared to a rule-based approach.

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Modeling the Multicommodity Multimodal Routing Problem with Schedule-Based Services and Carbon Dioxide Emission Costs

Mathematical Problems in Engineering ◽

10.1155/2015/406218 ◽

2015 ◽

Vol 2015 ◽

pp. 1-21 ◽

Cited By ~ 14

Author(s):

Yan Sun ◽

Maoxiang Lang

Keyword(s):

Carbon Dioxide ◽

Carbon Dioxide Emissions ◽

Transportation Network ◽

Linearization Method ◽

Mixed Integer ◽

Multicommodity Flow ◽

Routing Problem ◽

Multimodal Transportation ◽

Proposed Model ◽

Freight Routing

We explore a freight routing problem wherein the aim is to assign optimal routes to move commodities through a multimodal transportation network. This problem belongs to the operational level of service network planning. The following formulation characteristics will be comprehensively considered: (1) multicommodity flow routing; (2) a capacitated multimodal transportation network with schedule-based rail services and time-flexible road services; (3) carbon dioxide emissions consideration; and (4) a generalized costs optimum oriented to customer demands. The specific planning of freight routing is thus defined as a capacitated time-sensitive multicommodity multimodal generalized shortest path problem. To solve this problem systematically, we first establish a node-arc-based mixed integer nonlinear programming model that combines the above formulation characteristics in a comprehensive manner. Then, we develop a linearization method to transform the proposed model into a linear one. Finally, a computational experiment from the Chinese inland container export business is presented to demonstrate the feasibility of the model and linearization method. The computational results indicate that implementing the proposed model and linearization method in the mathematical programming software Lingo can effectively solve the large-scale practical multicommodity multimodal transportation routing problem.

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Mixed Integer Quadratic Program trajectory generation for a quadrotor with a cable-suspended payload

2015 IEEE International Conference on Robotics and Automation (ICRA) ◽

10.1109/icra.2015.7139492 ◽

2015 ◽

Cited By ~ 52

Author(s):

Sarah Tang ◽

Vijay Kumar

Keyword(s):

Trajectory Generation ◽

Mixed Integer ◽

Quadratic Program ◽

Integer Quadratic Program

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Application of mixed integer quadratic program to shortest vector problems

JSIAM Letters ◽

10.14495/jsiaml.9.65 ◽

2017 ◽

Vol 9 (0) ◽

pp. 65-68

Author(s):

Keiji Kimura ◽

Hayato Waki ◽

Masaya Yasuda

Keyword(s):

Mixed Integer ◽

Quadratic Program ◽

Integer Quadratic Program

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A Simulated Annealing Heuristic for the Capacitated Green Vehicle Routing Problem

Mathematical Problems in Engineering ◽

10.1155/2019/2358258 ◽

2019 ◽

Vol 2019 ◽

pp. 1-18 ◽

Cited By ~ 5

Author(s):

Nur Mayke Eka Normasari ◽

Vincent F. Yu ◽

Candra Bachtiyar ◽

Sukoyo

Keyword(s):

Sensitivity Analysis ◽

Simulated Annealing ◽

Numerical Experiment ◽

Vehicle Routing ◽

Vehicle Routing Problem ◽

Mixed Integer ◽

Mixed Integer Linear Program ◽

Routing Problem ◽

Total Distance ◽

Number Of Customers

This research studies the capacitated green vehicle routing problem (CGVRP), which is an extension of the green vehicle routing problem (GVRP), characterized by the purpose of harmonizing environmental and economic costs by implementing effective routes to meet any environmental concerns while fulfilling customer demand. We formulate the mathematical model of the CGVRP and propose a simulated annealing (SA) heuristic for its solution in which the CGVRP is set up as a mixed integer linear program (MILP). The objective of the CGVRP is to minimize the total distance traveled by an alternative fuel vehicle (AFV). This research conducts a numerical experiment and sensitivity analysis. The results of the numerical experiment show that the SA algorithm is capable of obtaining good CGVRP solutions within a reasonable amount of time, and the sensitivity analysis demonstrates that the total distance is dependent on the number of customers and the vehicle driving range.

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Mixed-integer quadratic program trajectory generation for heterogeneous quadrotor teams

2012 IEEE International Conference on Robotics and Automation ◽

10.1109/icra.2012.6225009 ◽

2012 ◽

Cited By ~ 104

Author(s):

Daniel Mellinger ◽

Alex Kushleyev ◽

Vijay Kumar

Keyword(s):

Trajectory Generation ◽

Mixed Integer ◽

Quadratic Program ◽

Integer Quadratic Program

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Green and Reliable Freight Routing Problem in the Road-Rail Intermodal Transportation Network with Uncertain Parameters: A Fuzzy Goal Programming Approach

Journal of Advanced Transportation ◽

10.1155/2020/7570686 ◽

2020 ◽

Vol 2020 ◽

pp. 1-21 ◽

Cited By ~ 6

Author(s):

Yan Sun

Keyword(s):

Transportation Network ◽

Mixed Integer ◽

Programming Approach ◽

Uncertain Parameters ◽

Intermodal Transportation ◽

Road Transportation ◽

Routing Problem ◽

The Road ◽

Green Routing ◽

Freight Routing

In this study, the author focuses on modeling and optimizing a freight routing problem in a road-rail intermodal transportation network that combines the hub-and-spoke and point-to-point structures. The operations of road transportation are time flexible, while rail transportation has fixed departure times. The reliability of the routing is improved by modeling the uncertainty of the road-rail intermodal transportation network. Parameters that are influenced by the real-time status of the network, including capacities, travel times, loading and unloading times, and container trains’ fixed departure times, are considered uncertain in the routing decision-making. Based on fuzzy set theory, triangular fuzzy numbers are employed to formulate the uncertain parameters as well as resulting uncertain variables. Green routing is also discussed by treating the minimization of carbon dioxide emissions as an objective. First of all, a multiobjective fuzzy mixed integer nonlinear programming model is established for the specific reliable and green routing problem. Then, defuzzification, linearization, and weighted sum method are implemented to present a crisp linear model whose global optimum solutions can be effectively obtained by the exact solution algorithm run by mathematical programming software. Finally, a numerical case is given to demonstrate how the proposed methods work. In the case, sensitivity analysis is adopted to reveal the effects of uncertainty on the routing optimization. Fuzzy simulation is then performed to help decision makers to select the best crisp route plan by determining the best confidence level shown in the fuzzy chance constraints.

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