scholarly journals The Bike-Sharing Rebalancing Problem Considering Multi-Energy Mixed Fleets and Traffic Restrictions

2020 ◽  
Vol 13 (1) ◽  
pp. 270
Author(s):  
Yongji Jia ◽  
Wang Zeng ◽  
Yanting Xing ◽  
Dong Yang ◽  
Jia Li
Keyword(s):  
Simulated Annealing Algorithm ◽  
Programming Model ◽  
Mixed Integer ◽  
Low Carbon ◽  
Sustainable Transport ◽  
Road Congestion ◽  
Neighborhood Structures ◽  
Bike Sharing ◽  
User Demand ◽  
Annealing Algorithm

Nowadays, as a low-carbon and sustainable transport mode bike-sharing systems are increasingly popular all over the world, as they can reduce road congestion and decrease greenhouse gas emissions. Aiming at the problem of the mismatch of bike supply and user demand, the operators have to transfer bikes from surplus stations to deficiency stations to redistribute them among stations by vehicles. In this paper, we consider a mixed fleet of electric vehicles and internal combustion vehicles as well as the traffic restrictions to the traditional vehicles in some metropolises. The mixed integer programming model is firstly established with the objective of minimizing the total rebalancing cost of the mixed fleet. Then, a simulated annealing algorithm enhanced with variable neighborhood structures is designed and applied to a set of randomly generated test instances. The computational results and sensitivity analysis indicate that the proposed algorithm can effectively reduce the total cost of rebalancing.

scholarly journals Heuristic Algorithms for MapReduce Scheduling Problem with Open-Map Task and Series-Reduce Tasks

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Feifeng Zheng ◽  
Zhaojie Wang ◽  
Yinfeng Xu ◽  
Ming Liu
Keyword(s):  
Numerical Experiments ◽  
Simulated Annealing Algorithm ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Scheduling Model ◽  
Open Map ◽  
Annealing Algorithm ◽  
Mapreduce Scheduling

Based on the classical MapReduce concept, we propose an extended MapReduce scheduling model. In the extended MapReduce scheduling problem, we assumed that each job contains an open-map task (the map task can be divided into multiple unparallel operations) and series-reduce tasks (each reduce task consists of only one operation). Different from the classical MapReduce scheduling problem, we also assume that all the operations cannot be processed in parallel, and the machine settings are unrelated machines. For solving the extended MapReduce scheduling problem, we establish a mixed-integer programming model with the minimum makespan as the objective function. We then propose a genetic algorithm, a simulated annealing algorithm, and an L-F algorithm to solve this problem. Numerical experiments show that L-F algorithm has better performance in solving this problem.

scholarly journals Addressing the Bike Repositioning Problem in Bike Sharing System: A Two-Stage Stochastic Programming Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Qiong Tang ◽  
Zhuo Fu ◽  
Dezhi Zhang ◽  
Hao Guo ◽  
Minyi Li
Keyword(s):  
Stochastic Programming ◽  
Simulated Annealing Algorithm ◽  
Programming Model ◽  
Two Stage ◽  
System A ◽  
Penalty Costs ◽  
Bike Sharing ◽  
Holding Cost ◽  
Stochastic Programming Model ◽  
Annealing Algorithm

In this paper, a bike repositioning problem with stochastic demand is studied. The problem is formulated as a two-stage stochastic programming model to optimize the routing and loading/unloading decisions of the repositioning truck at each station and depot under stochastic demands. The goal of the model is to minimize the expected total sum of the transportation costs, the expected penalty costs at all stations, and the holding cost of the depot. A simulated annealing algorithm is developed to solve the model. Numerical experiments are conducted on a set of instances from 20 to 90 stations to demonstrate the effectiveness of the solution algorithm and the accuracy of the proposed two-stage stochastic model.

scholarly journals OPTIMIZATION OF SHIP SPEED AND FLEET DEPLOYMENT UNDER CARBON EMISSIONS POLICIES FOR CONTAINER SHIPPING

Transport ◽  
2019 ◽  
Vol 34 (3) ◽  
pp. 260-274 ◽  
Author(s):  
Yuwei Xing ◽  
Hualong Yang ◽  
Xuefei Ma ◽  
Yan Zhang
Keyword(s):  
Carbon Emissions ◽  
Simulated Annealing Algorithm ◽  
Programming Model ◽  
Carbon Tax ◽  
Mixed Integer ◽  
Container Shipping ◽  
Nonlinear Programming Model ◽  
Annealing Algorithm ◽  
Ship Speed ◽  
Fleet Deployment

In this paper, under the consideration of two carbon emissions policies, the issues of optimizing ship speed and fleet deployment for container shipping were addressed. A mixed-integer nonlinear programming model of ship speed and fleet deployment was established with the objective of minimising total weekly operating costs. A simulated annealing algorithm was proposed to solve the problem. An empirical analysis was conducted with the data selected from the benchmark suite. The applicability and effectiveness of the established model and its algorithm are verified by the results. According to the results, two policies of the cap-and-trade programme and the carbon tax can better optimize the results of the ship speed and fleet deployment problem to achieve the goal of reducing carbon emissions. The research remarks in this paper will provide a solution for container shipping companies to make optimized decisions under carbon emissions policies.

Introducing a Design Framework for a Multi-Modal Public Transportation System, Focusing on Mixed-Fleet Bike-Sharing Systems

2018 ◽  
Vol 2672 (36) ◽  
pp. 103-115
Author(s):  
Mehrnaz Ghamami ◽  
MohammadHossein (Sam) Shojaei
Keyword(s):  
Public Transportation ◽  
Simulated Annealing Algorithm ◽  
Mixed Integer ◽  
Small Communities ◽  
Extended Range ◽  
Bike Sharing ◽  
Public Transportation System ◽  
Annealing Algorithm ◽  
Nonlinear Objective Function ◽  
Travel Distances

Bike-sharing is increasingly becoming more popular. Electric bikes as an emerging transportation technology have extended range and are less physically demanding, compared with regular bicycles, thus they can be incorporated into regular bike-sharing systems to attract more users. This study aims at capturing the users’ preference, while considering investors’ limitations and societal cost and benefits of each mode. The problem is defined as a mixed-integer non-liner problem, with nonlinear objective function and constraints. Because of the computationally challenging nature of the problem, a metaheuristic algorithm based on simulated annealing algorithm is proposed for its solution. The performance of the algorithm is tested in this study and convergence patterns are observed. The main findings of this study which are derived from the hypothetical numerical example, include but are not limited to: (1) the most popular public modes are bus and pedelec, because these two modes (bus and pedelec) are less expensive and have the ability to traverse longer distances in comparison to similar modes (i.e., e-scooter/car and bike), and (2) for small communities with short travel distances (feasible within the ranges of active modes), users would not choose fuel-consuming modes, and thus their choice is insensitive to fuel cost.

scholarly journals A Path-Based Selection Solution Approach for the Low Carbon Vehicle Routing Problem with a Time-Window Constraint

2020 ◽  
Vol 10 (4) ◽  
pp. 1489 ◽  
Author(s):  
Xianlong Ge ◽  
Xiaobo Ge ◽  
Weixin Wang
Keyword(s):  
Time Window ◽  
Programming Model ◽  
Model Simulation ◽  
Path Selection ◽  
Urban Traffic ◽  
Mixed Integer ◽  
Low Carbon ◽  
Gradual Improvement ◽  
Solution Approach ◽  
Urban Distribution

Due to the gradual improvement of urban traffic network construction and the increasing number of optional paths between any two points, how to optimize a vehicle travel path in a multi-path road network and then improve the efficiency of urban distribution has become a difficult problem for logistics companies. For this purpose, a mixed-integer mathematical programming model with a time window based on multiple paths for urban distribution in a multi-path environment is established and its exact solution solved using software CPLEX. Additionally, in order to test the application and feasibility of the model, simulation experiments were performed on the four parameters of time, distance, cost, and fuel consumption. Furthermore, using Jingdong (JD), the main urban area in Chongqing, as an example, the experimental results reveal that an algorithm that considers the path selection can significantly improve the efficiency of urban distribution in metropolitan areas with complex road structures.

scholarly journals Optimizing the Location of Virtual Stations in Free-Floating Bike-Sharing Systems with the User Demand during Morning and Evening Rush Hours

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Zhuo Sun ◽  
Yiming Li ◽  
Yi Zuo
Keyword(s):  
Local Governments ◽  
Optimization Problem ◽  
Clustering Algorithm ◽  
Mixed Integer ◽  
Optimized Design ◽  
Bike Sharing ◽  
User Demand ◽  
Location Assignment ◽  
Milp Model ◽  
The Cost

In recent years, free-floating bike-sharing systems (FFBSSs) have been considerably developed in China. As there is no requirement to construct bike stations, this system can substantially reduce the cost when compared to the traditional bike-sharing systems. However, FFBSSs have also become a critical cause of parking disorder, especially during the morning and evening rush hours. To address this issue, the local governments stipulated that FFBSSs are required to deploy virtual stations near public transit stations and major establishments. Therefore, the location assignment of virtual stations is sufficiently considered in the FFBSSs, which is required to solve the parking disorder and satisfy the user demand, simultaneously. The purpose of this study is to optimize the location assignment of virtual stations that can meet the growing demand of users by analyzing the usage data of their shared bikes. This optimization problem is generally formulated as a mixed-integer linear programming (MILP) model to maximize the user demand. As an alternative solution, this article proposes a clustering algorithm, which can solve this problem in real time. The experimental results demonstrate that the MILP model and the proposed method are superior to the K-means method. Our method not only provides a solution for maximizing the user demand but also gives an optimized design scheme of the FFBSSs that represents the characteristics of virtual stations.

Aircraft sequencing and scheduling in TMAs under wind direction uncertainties

2020 ◽  
Vol 124 (1282) ◽  
pp. 1896-1912
Author(s):  
R.K. Cecen ◽  
C. Cetek ◽  
O. Kaya
Keyword(s):  
Stochastic Model ◽  
Wind Direction ◽  
Simulated Annealing Algorithm ◽  
Programming Model ◽  
Air Traffic ◽  
Mixed Integer ◽  
Scheduling Problems ◽  
Traffic Demand ◽  
Proposed Model ◽  
Sequencing And Scheduling

ABSTRACTAircraft sequencing and scheduling within terminal airspaces has become more complicated due to increased air traffic demand and airspace complexity. A stochastic mixed-integer linear programming model is proposed to handle aircraft sequencing and scheduling problems using the simulated annealing algorithm. The proposed model allows for proper aircraft sequencing considering wind direction uncertainties, which are critical in the decision-making process. The proposed model aims to minimise total aircraft delay for a runway airport serving mixed operations. To test the stochastic model, an appropriate number of scenarios were generated for different air traffic demand rates. The results indicate that the stochastic model reduces the total aircraft delay considerably when compared with the deterministic approach.

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