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

2015 ◽  
Vol 2015 ◽  
pp. 1-21 ◽  
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.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
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.

scholarly journals A Bi-Objective Fuzzy Credibilistic Chance-Constrained Programming Approach for the Hazardous Materials Road-Rail Multimodal Routing Problem under Uncertainty and Sustainability

2019 ◽  
Vol 11 (9) ◽  
pp. 2577 ◽  
Author(s):  
Yan Sun ◽  
Xinya Li ◽  
Xia Liang ◽  
Cevin Zhang
Keyword(s):  
Carbon Dioxide ◽  
Carbon Dioxide Emission ◽  
Hazardous Materials ◽  
Mixed Integer ◽  
Programming Approach ◽  
Population Exposure ◽  
Chance Constrained Programming ◽  
Routing Problem ◽  
Multimodal Transportation ◽  
Conflicting Objectives

Hazardous materials transportation involves extensive risk and cannot be avoided in practice. An advanced routing, however, can help to reduce the risk by planning the best transportation routes for hazardous materials that can make effective tradeoffs between the risk objective and the economic objective. In this study, we explore the hazardous materials routing problem in the road-rail multimodal transportation network with a hub-and-spoke structure, in which the risk is measured by the multiplication of population exposure and the associated volume of hazardous materials, and minimizing the total risk of all the transportation orders of hazardous materials is set as the risk objective. It is difficult to estimate the population exposure exactly during the routing decision-making process, which results in its uncertainty. In this study, we formulate the uncertain population exposure from a fuzzy programming perspective by using triangular fuzzy numbers. Moreover, the carbon dioxide emission constraint is formulated to realize the sustainable transportation of hazardous materials. To optimize the problem under the above framework, we first establish a bi-objective fuzzy mixed integer nonlinear programming model, and then develop a three-stage exact solution strategy that the combines fuzzy credibilistic chance constraint, linearization technique, and the normalized weighting method. Finally, a computational experiment is carried out to verify the feasibility of the proposed method in dealing with the problem. The experimental results indicate that tradeoffs between the two conflicting objectives can be effectively made by using the Pareto frontier to the hazardous materials routing problem. Furthermore, the credibility level and carbon dioxide emission cap significantly influence the hazardous materials routing optimization. Their effects on the optimization result are quantified by using sensitivity analysis, which can draw some useful insights to help decision makers to better organize the hazardous materials road-rail multimodal transportation under uncertainty and sustainability.

A Multi-Period Evacuation Vehicle Routing Problem Model

2014 ◽  
Vol 931-932 ◽  
pp. 578-582
Author(s):  
Sunarin Chanta ◽  
Ornurai Sangsawang
Keyword(s):  
Vehicle Routing ◽  
Public Transportation ◽  
Vehicle Routing Problem ◽  
Programming Model ◽  
High Water ◽  
Mixed Integer ◽  
Routing Problem ◽  
Problem Model ◽  
Proposed Model ◽  
Evacuation Routing

In this paper, we proposed an optimization model that addresses the evacuation routing problem for flood disaster when evacuees trying to move from affected areas to safe places using public transportation. A focus is on the situation of evacuating during high water level when special high vehicles are needed. The objective is to minimize the total traveled distance through evacuation periods where a limited number of vehicles is given. We formulated the problem as a mixed integer programming model based on the capacitated vehicle routing problem with multiple evcuation periods where demand changing by the time. The proposed model has been tested on a real-world case study affected by the severe flooding in Thailand, 2011.

scholarly journals A Synchronous Optimization Model for Multiship Shuttle Tanker Fleet Design and Scheduling Considering Hard Time Window Constraint

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Zhenfeng Jiang ◽  
Dongxu Chen ◽  
Zhongzhen Yang
Keyword(s):  
Time Window ◽  
Programming Model ◽  
Transportation Cost ◽  
Exact Algorithm ◽  
Mixed Integer ◽  
Routing Problem ◽  
Hard Time ◽  
Proposed Model ◽  
Total Transportation Cost ◽  
Time Window Constraints

A Synchronous Optimization for Multiship Shuttle Tanker Fleet Design and Scheduling is solved in the context of development of floating production storage and offloading device (FPSO). In this paper, the shuttle tanker fleet scheduling problem is considered as a vehicle routing problem with hard time window constraints. A mixed integer programming model aiming at minimizing total transportation cost is proposed to model this problem. To solve this model, we propose an exact algorithm based on the column generation and perform numerical experiments. The experiment results show that the proposed model and algorithm can effectively solve the problem.

scholarly journals On the Selective Vehicle Routing Problem

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 771 ◽  
Author(s):  
Cosmin Sabo ◽  
Petrică C. Pop ◽  
Andrei Horvat-Marc
Keyword(s):  
Mathematical Model ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Mixed Integer ◽  
The Novel ◽  
Routing Problem ◽  
Proposed Model ◽  
Benchmark Instances ◽  
Number Of Customers ◽  
Optimal Set

The Generalized Vehicle Routing Problem (GVRP) is an extension of the classical Vehicle Routing Problem (VRP), in which we are looking for an optimal set of delivery or collection routes from a given depot to a number of customers divided into predefined, mutually exclusive, and exhaustive clusters, visiting exactly one customer from each cluster and fulfilling the capacity restrictions. This paper deals with a more generic version of the GVRP, introduced recently and called Selective Vehicle Routing Problem (SVRP). This problem generalizes the GVRP in the sense that the customers are divided into clusters, but they may belong to one or more clusters. The aim of this work is to describe a novel mixed integer programming based mathematical model of the SVRP. To validate the consistency of the novel mathematical model, a comparison between the proposed model and the existing models from literature is performed, on the existing benchmark instances for SVRP and on a set of additional benchmark instances used in the case of GVRP and adapted for SVRP. The proposed model showed better results against the existing models.

Network Design for Shipping Service of Large-Scale Intermodal Liners

2012 ◽  
Vol 2269 (1) ◽  
pp. 42-50 ◽  
Author(s):  
Qiang Meng ◽  
Shuaian Wang ◽  
Zhiyuan Liu
Keyword(s):  
Network Design ◽  
Large Scale ◽  
Programming Model ◽  
Transportation Network ◽  
Liner Shipping ◽  
Maritime Transportation ◽  
Mixed Integer ◽  
Hub And Spoke ◽  
Shipping Company ◽  
Proposed Model

A model was developed for network design of a shipping service for large-scale intermodal liners that captured essential practical issues, including consistency with current services, slot purchasing, inland and maritime transportation, multiple-type containers, and origin-to-destination transit time. The model used a liner shipping hub-and-spoke network to facilitate laden container routing from one port to another. Laden container routing in the inland transportation network was combined with the maritime network by defining a set of candidate export and import ports. Empty container flow is described on the basis of path flow and leg flow in the inland and maritime networks, respectively. The problem of network design for shipping service of an intermodal liner was formulated as a mixed-integer linear programming model. The proposed model was used to design the shipping services for a global liner shipping company.

scholarly journals Synthesis of Sustainable Carbon Negative Eco-Industrial Parks

2021 ◽  
Vol 9 ◽  
Author(s):  
Elizabeth J. Abraham ◽  
Farah Ramadan ◽  
Dhabia M. Al-Mohannadi
Keyword(s):  
Carbon Dioxide ◽  
Circular Economy ◽  
Carbon Dioxide Emissions ◽  
Carbon Capture ◽  
Carbon Dioxide Emission ◽  
Value Added ◽  
Mixed Integer ◽  
Industrial Parks ◽  
And Storage

Growing climate change concerns in recent years have led to an increased need for carbon dioxide emission reduction. This can be achieved by implementing the concept of circular economy, which promotes the practice of resource conservation, emission minimization, and the maintenance of sustainable revenue streams. A considerable amount of carbon dioxide emissions is a consequence of stationary sources from industrial processes. These emissions can be reduced using carbon capture utilization and storage (CCUS) or reduced at source by using emission free renewable resources. The method developed within this work uses mixed integer linear programming (MILP) to design sustainable clusters that convert seawater (including waste brine), air, and waste carbon dioxide emissions to value-added products with sunlight as the main energy source. In this way, circular economy is employed to minimize fresh resource consumption and maximize material reuse. The potential of this work is demonstrated through a case study, which shows that an industrial park may be profitable while adhering to strict emission and material constraints.

scholarly journals A Multivariate Grey Prediction Model Using Neural Networks with Application to Carbon Dioxide Emissions Forecasting

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yu-Jing Chiu ◽  
Yi-Chung Hu ◽  
Peng Jiang ◽  
Jingci Xie ◽  
Yen-Wei Ken
Keyword(s):  
Carbon Dioxide ◽  
Prediction Model ◽  
Co2 Emissions ◽  
Carbon Dioxide Emissions ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Proposed Model ◽  
Predicted Values ◽  
Carbon Dioxide Co2 ◽  
Grey Relational

The forecast of carbon dioxide (CO2) emissions has played a significant role in drawing up energy development policies for individual countries. Since data about CO2 emissions are often limited and do not conform to the usual statistical assumptions, this study attempts to develop a novel multivariate grey prediction model (MGPM) for CO2 emissions. Compared with other MGPMs, the proposed model has several distinctive features. First, both feature selection and residual modification are considered to improve prediction accuracy. For the former, grey relational analysis is used to filter out the irrelevant features that have weaker relevance with CO2 emissions. For the latter, predicted values obtained from the proposed MGPM are further adjusted by establishing a neural-network-based residual model. Prediction accuracies of the proposed MGPM were verified using real CO2 emission cases. Experimental results demonstrated that the proposed MGPM performed well compared with other MGPMs considered.

Domiciling Truck Drivers More Strategically in a Transportation Network

2018 ◽  
pp. 846-865
Author(s):  
Kerry Melton ◽  
Sandeep Parepally
Keyword(s):  
Numerical Experiment ◽  
Transportation Network ◽  
Truck Drivers ◽  
Mixed Integer ◽  
Quadratic Program ◽  
Routing Problem ◽  
New Approach ◽  
Driving Time ◽  
Highway Transportation ◽  
Integer Quadratic Program

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.

An incentive mechanism-based negotiation model for green supply chain networks

2020 ◽  
pp. 014233122092981
Author(s):  
Fang Yu ◽  
Chun Zhang ◽  
Yongsheng Yang
Keyword(s):  
Carbon Dioxide ◽  
Supply Chain ◽  
Carbon Dioxide Emissions ◽  
Green Supply Chain ◽  
Supply Chain Network ◽  
Supply Chain Networks ◽  
Environmental Attributes ◽  
Proposed Model ◽  
Total Profit ◽  
Negotiation Mechanism

This research aims to prompt agents to improve their strategies initiatively in order to decrease carbon dioxide emissions and enhance green factors during production and consumption processes. An incentive negotiation mechanism is proposed for agents in supply chains to improve their strategies. Multiple items, multiple attributes, and multiple echelons are involved in the proposed model. In addition, this research takes both the commerce and the environmental attributes into account. The environmental attributes were transformed into rewards or penalty by setting reward factors or penalty factors, and were taken into account during the calculation of the profits. The simulation results show that the proposed model was feasible to solve the complex negotiation problems, and had a good performance. The green factors of agents in the green supply chain network are increased when the agents have low initial green factors. Moreover, the proposed model can effectively reduce the carbon dioxide emissions as well. The proposed model can be seen as a “win–win” solution from the perspective of both business and environmental protection. The total profit of the green supply chain network is improved, and the harm to the environment is decreased as well.

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