scholarly journals A Green Demand-Responsive Airport Shuttle Service Problem with Time-Varying Speeds

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Ming Wei ◽  
Binbin Jing ◽  
Jian Yin ◽  
Yang Zang
Keyword(s):  
Time Windows ◽  
Operating Cost ◽  
Model Performance ◽  
Path Selection ◽  
Mixed Integer ◽  
Time Varying ◽  
Alternative Fuel Vehicles ◽  
Nsga Ii ◽  
The Difference ◽  
On The Road

This study proposes a multiobjective mixed integer linear programming (MOMILP) model for a demand-responsive airport shuttle service. The approach aims to assign a set of alternative fuel vehicles (AFVs) located at different depots to visit each demand point within the specified time and transport all of them to the airport. The proposed model effectively captures the interactions between path selection and environmental protection. Moreover, users with flexible pick-up time windows, the time-varying speed of vehicles on the road network, and the limited fuel for the route duration are also fully considered in this model. The work aims at simultaneously minimizing the operating cost, vehicle fuel consumption, and CO2 emissions. Since this task is an NP-hard problem, a heuristic-based nondominated sorting genetic algorithm (NSGA-II) is also presented to find Pareto optimal solutions in a reasonable amount of time. Finally, a real-world example is provided to illustrate the proposed methodology. The results demonstrate that the model not only selects an optimal depot for each AFV but also determines its route and timetable plan. A sensitivity analysis is also given to assess the effect of early/late arrival penalty weights and the number of AFVs on the model performance, and the difference in quality between the proposed and traditional models is compared.

scholarly journals Multigraph Modeling for Urban Distribution of Emergency Commodities with Semisoft Time Windows under Uncertainty

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hamid Tikani ◽  
Mostafa Setak ◽  
Darya Abbasi
Keyword(s):  
High Performance ◽  
Transportation Network ◽  
Time Windows ◽  
Path Selection ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Total Weighted Completion Time ◽  
Nsga Ii ◽  
Proposed Model ◽  
Demand Node

In this paper, we studied a stochastic bi-objective mathematical model for effective and reliable rescue operations in multigraph network. The problem is addressed by a two-stage stochastic nonlinear mixed-integer program where the reliability of routes is explicitly traded-off with total weighted completion time. The underlying transportation network is able to keep a group of multiattribute parallel arcs between every pair of nodes. By this, the proposed model should consider the routing decision in logistic planning along with the path selection in an uncertain condition. The first stage of the model concerns with the vehicle routing decisions which is not involved with random parameters; besides, the second stage of the model involves with the departure time at each demand node and path finding decisions after observation of random vectors in the first stage considering a finite number of scenarios. To efficiently solve the presented model, an enhanced nondominated sorting genetic algorithm II (NSGA-II) is proposed. The effectiveness of the introduced method is then evaluated by conducting several numerical examples. The results implied the high performance of our method in comparison to the standard NSGA-II. In further analyses, we investigated the beneficiary of using multigraph setting and showed the applicability of the proposed model using a real transportation case.

scholarly journals Time-Dependent Electric Vehicle Routing Problem with Time Windows and Path Flexibility

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Li Wang ◽  
Shuai Gao ◽  
Kai Wang ◽  
Tong Li ◽  
Lin Li ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Electric Vehicle ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Optimal Path ◽  
Travel Speed ◽  
Path Selection ◽  
Time Dependent ◽  
Mixed Integer ◽  
Routing Problem

With energy and environmental issues becoming increasingly prominent, electric vehicles (EVs) have become the important transportation means in the logistics distribution. In the real-world urban road network, there often exist multiple paths between any two locations (depot, customer, and charging station) since the time-dependent travel times. That is, the travel speed of an EV on each path may be different during different time periods, and thus, this paper explicitly considers path selection between two locations in the time-dependent electric vehicle routing problem with time windows, denoted as path flexibility. Therefore, the integrated decision-making should include not only the routing plan but also the path selection, and the interested problem of this paper is a time-dependent electric vehicle routing problem with time windows and path flexibility (TDEVRP-PF). In order to determine the optimal path between any two locations, an optimization model is established with the goal of minimizing the distance and the battery energy consumption associated with travel speed and cargo load. On the basis of the optimal path model, a 0-1 mixed-integer programming model is then formulated to minimize the total travel distance. Hereinafter, an improved version of the variable neighborhood search (VNS) algorithm is utilized to solve the proposed models, in which multithreading technique is adopted to improve the solution efficiency significantly. Ultimately, several numerical experiments are carried out to test the performance of VNS with a view to the conclusion that the improved VNS is effective in finding high-quality distribution schemes consisted of the distribution routes, traveling paths, and charging plans, which are of practical significance to select and arrange EVs for logistics enterprises.

scholarly journals Simultaneous Optimization of the Liner Shipping Route and Ship Schedule Designs with Time Windows

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xi Jiang ◽  
Haijun Mao ◽  
Hao Zhang
Keyword(s):  
Time Window ◽  
Programming Model ◽  
Time Windows ◽  
Operating Cost ◽  
Optimal Solution ◽  
Liner Shipping ◽  
Simultaneous Optimization ◽  
Mixed Integer ◽  
Time Window Constraints ◽  
Call Sequence

This paper proposes to address the problem of the simultaneous optimization of the liner shipping route and ship schedule designs by incorporating port time windows. A mathematical programming model was developed to minimize the carrier’s total operating cost by simultaneously optimizing the port call sequence, ship arrival time per port of call, and sailing speed per shipping leg under port time window constraints. In view of its structure, the nonlinear nonconvex optimization model is further transformed into a mixed-integer linear programming model that can be efficiently solved by extant solvers to provide a global optimal solution. The results of the numerical experiments performed using a real-world case study indicated that the proposed model performs significantly better than the models that handle the design problems separately. The results also showed that different time windows will affect the optimal port call sequence. Moreover, port time windows, bunker price, and port efficiency all affect the total operating cost of the designed shipping route.

scholarly journals An Optimization Model for Demand-Responsive Feeder Transit Services Based on Ride-Sharing Car

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 370 ◽  
Author(s):  
Bo Sun ◽  
Ming Wei ◽  
Wei Wu
Keyword(s):  
Time Windows ◽  
Model Performance ◽  
Optimal Solution ◽  
Walking Distance ◽  
Ride Sharing ◽  
Chongqing City ◽  
Feeder Transit ◽  
Transit Services ◽  
Integrated Operation ◽  
The Difference

Ride-sharing (RS) plays an important role in saving energy and alleviating traffic pressure. The vehicles in the demand-responsive feeder transit services (DRT) are generally not ride-sharing cars. Therefore, we proposed an optimal DRT model based on the ride-sharing car, which aimed at assigning a set of vehicles, starting at origin locations and ending at destination locations with their service time windows, to transport passengers of all demand points to the transportation hub (i.e., railway, metro, airport, etc.). The proposed model offered an integrated operation of pedestrian guidance (from unvisited demand points to visited ones) and transit routing (from visited ones to the transportation hub). The objective was to simultaneously minimize weighted passenger walking and riding time. A two-stage heuristic algorithm based on a genetic algorithm (GA) was adopted to solve the problem. The methodology was tested with a case study in Chongqing City, China. The results showed that the model could select optimal pick-up locations and also determine the best pedestrian and route plan. Validation and analysis were also carried out to assess the effect of maximum walking distance and the number of share cars on the model performance, and the difference in quality between the heuristic and optimal solution was also compared.

scholarly journals Risk Analysis and Route Optimization of Dangerous Goods Transportation Based on the Empirical Path Set

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Haixing Wang ◽  
Qianqian Liang
Keyword(s):  
Risk Analysis ◽  
Path Selection ◽  
Route Optimization ◽  
Time Varying ◽  
Road Transportation ◽  
The Road ◽  
Dangerous Goods ◽  
Comprehensive Method ◽  
On The Road ◽  
Population And Environment

Based on the empirical path set generated from the track data of dangerous goods vehicles, we present a new method for the risk analysis and path optimization of dangerous goods transportation. First of all, by exploring the travel rules of dangerous goods transport vehicles hidden in the track data, combined with the path set generation algorithm, the method of determining the empirical path set of dangerous goods transport is studied. Secondly, based on the empirical path set, mainly considering the travel rules of vehicles and people on the road, as well as the distribution of population and environment-sensitive areas along the road, a dual objective path selection model is proposed to comprehensively measure the risk and cost of road transportation under time-varying conditions. On this basis, given the principle of avoiding high-risk transportation paths, a comprehensive method of integrating multiple algorithms is proposed to solve the model. Finally, taking a road network as an example, the practicability and effectiveness of the proposed method are verified. The method proposed takes both practicability and safety into account. Based on the experience path set, considering the time-varying characteristics, the decision-maker could choose the appropriate transportation path of dangerous goods according to different preferences, so as to better solve the problem of path selection for dangerous goods transportation.

scholarly journals Multi-Objective Optimal Scheduling Method for a Grid-Connected Redundant Residential Microgrid

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 296 ◽  
Author(s):  
Weiliang Liu ◽  
Changliang Liu ◽  
Yongjun Lin ◽  
Kang Bai ◽  
Liangyu Ma ◽  
...  
Keyword(s):  
Cooling System ◽  
Storage System ◽  
Operating Cost ◽  
Energy Storage System ◽  
Comfort Level ◽  
Optimal Scheduling ◽  
Mixed Integer ◽  
Nsga Ii ◽  
Multi Objective ◽  
Scheduling Method

Optimal scheduling of a redundant residential microgrid (RR-microgrid) could yield economical savings and reduce the emission of pollutants while ensuring the comfort level of users. This paper proposes a novel multi-objective optimal scheduling method for a grid-connected RR-microgrid in which the heating/cooling system of the RR-microgrid is treated as a virtual energy storage system (VESS). An optimization model for grid-connected RR-microgrid scheduling is established based on mixed-integer nonlinear programming (MINLP), which takes the operating cost (OC), thermal comfort level (TCL), and pollution emission (PE) as the optimization objectives. The non-dominate sorting genetic algorithm II (NSGA-II) is employed to search the Pareto front and the best scheduling scheme is determined by the analytic hierarchy process (AHP) method. In a case study, two kinds of heating/cooling systems, the radiant floor heating/cooling system (RFHCS) and the convection heating/cooling system (CHCS) are investigated for the RR-microgrid. respectively, and the feasibility and validity of the scheduling method are ascertained.

scholarly journals Location-Routing Optimization When Renting Social Vehicles in a Two-Stage E-Waste Recycling Network

2021 ◽  
Vol 13 (21) ◽  
pp. 11879
Author(s):  
Feifeng Zheng ◽  
Zhiyu Sun ◽  
Ming Liu
Keyword(s):  
Programming Model ◽  
Time Windows ◽  
Operating Cost ◽  
Waste Recycling ◽  
Mixed Integer ◽  
Improved Genetic Algorithm ◽  
Location Selection ◽  
Location Routing ◽  
Recycling Networks ◽  
Low Efficiency

E-waste recycling has been a hot topic in recent years. The low efficiency and high-operation cost of recycling make it more important to build perfect e-waste recycling networks. To hedge against the limitation of vehicle resources being often neglected in existing research, we propose a mixed integer linear programming model of e-waste recycling by renting idle social vehicles. In the model, both decisions made on the location selection of recycling sites and vehicle routings satisfying all of the demand nodes over the network within time windows are required to minimize the total operating cost. An improved genetic algorithm and heuristic algorithm are designed to solve the model, and numerical experiments are produced to demonstrate the effectiveness of the proposed model and algorithms.

scholarly journals Optimizing Open-Pit Truck Route Based on Minimization of Time-Varying Transport Energy Consumption

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Guangwei Liu ◽  
Senlin Chai
Keyword(s):  
Energy Consumption ◽  
Programming Model ◽  
Resistance Coefficient ◽  
Engineering Problem ◽  
Open Pit ◽  
Mixed Integer ◽  
Time Varying ◽  
Improved Genetic Algorithm ◽  
Truck Routing ◽  
On The Road

This paper addresses a special truck routing optimization problem in open-pit mines based on the minimization of time-varying transport energy consumption. A mixed-integer programming model is formulated to clearly describe the engineering problem, and a series of constraints are deduced to strengthen the model. To ensure that the model has time-varying characteristics, a method to estimate time-varying parameters is proposed by using pattern recognition and trend surface estimation. This time-varying resistance coefficient is mainly used to describe the process of road damage caused by frequent rolling of heavy trucks on the road surface. At the same time, in order to make the truck routing converge to the optimal energy consumption solution quickly, some definitions and properties are then provided based on stochastic theory, and a strategy to improve the computational efficiency of the model is proposed using these properties. Finally, an improved genetic algorithm is designed to solve the model. The results of experiments show that the proposed algorithm is effective and efficient.

scholarly journals New general mixed-integer linear programming model for mobile workforce management

2021 ◽  
Author(s):  
András Éles ◽  
István Heckl ◽  
Heriberto Cabezas
Keyword(s):  
Programming Model ◽  
Time Windows ◽  
Mutual Exclusion ◽  
Parallel Execution ◽  
Mixed Integer ◽  
Management Problem ◽  
Routing Problem ◽  
Workforce Management ◽  
Computational Performance ◽  
Wide Range

AbstractA mathematical model is introduced to solve a mobile workforce management problem. In such a problem there are a number of tasks to be executed at different locations by various teams. For example, when an electricity utility company has to deal with planned system upgrades and damages caused by storms. The aim is to determine the schedule of the teams in such a way that the overall cost is minimal. The mobile workforce management problem involves scheduling. The following questions should be answered: when to perform a task, how to route vehicles—the vehicle routing problem—and the order the sites should be visited and by which teams. These problems are already complex in themselves. This paper proposes an integrated mathematical programming model formulation, which, by the assignment of its binary variables, can be easily included in heuristic algorithmic frameworks. In the problem specification, a wide range of parameters can be set. This includes absolute and expected time windows for tasks, packing and unpacking in case of team movement, resource utilization, relations between tasks such as precedence, mutual exclusion or parallel execution, and team-dependent travelling and execution times and costs. To make the model able to solve larger problems, an algorithmic framework is also implemented which can be used to find heuristic solutions in acceptable time. This latter solution method can be used as an alternative. Computational performance is examined through a series of test cases in which the most important factors are scaled.

Spatiotemporal Object Database Approach to Dynamic Segmentation

2003 ◽  
Vol 1836 (1) ◽  
pp. 118-125 ◽  
Author(s):  
Bo Huang ◽  
Li Yao
Keyword(s):  
Query Language ◽  
Time Varying ◽  
Data Types ◽  
The Road ◽  
Dynamic Segmentation ◽  
Management Group ◽  
Object Database ◽  
On The Road ◽  
Temporal Dimensions ◽  
Spatiotemporal Queries

Dynamic segmentation is viewed as one of the most important functions of geographic information systems for transportation applications. Although the road network and associated events (e.g., pavement material, traffic volume, incidents) can be referenced to both space and time, the spatial and temporal dimensions have not been well integrated. Modeling space-varying, time-varying, and space-time-varying events in dynamic segmentation by using an object database approach that is in line with the Object Database Management Group standard is explored. A mechanism called parametric polymorphism is used to lift conventional data types to spatial, temporal, and spatiotemporal types for maintaining knowledge about events that could change spatially, temporally, and spatiotemporally along linear features. An associated object query language, DS-OQL, was designed to support the formulation of spatial, temporal, and spatiotemporal queries on the road and event information.

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