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 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 Threshold Accepting Heuristic for Fair Flow Optimization in Wireless Mesh Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jarosław Hurkała ◽  
Tomasz Śliwiński
Keyword(s):  
Wireless Mesh Networks ◽  
Simulated Annealing Algorithm ◽  
Mesh Networks ◽  
Difficult Problem ◽  
Weighted Averaging ◽  
Mixed Integer ◽  
Threshold Accepting ◽  
Wireless Mesh ◽  
Annealing Algorithm ◽  
Better Than

Developing effective, fairness-preserving optimization algorithms is of considerable importance in systems which serve many users. In this paper we show the results of the threshold accepting procedure applied to extremely difficult problem of fair resource allocation in wireless mesh networks (WMN). The fairness is modeled by allowing preferences with regard to distribution of Internet traffic between network participants. As aggregation operator we utilize weighted ordered weighted averaging (WOWA). In the underlaying optimization problem, the physical medium properties cause strong interference among simultaneously operating node devices, leading to nonlinearities in the mixed-integer pricing subproblem. That is where the threshold accepting procedure is applied. We show that, the threshold accepting heuristic performs much better than the widely utilized simulated annealing algorithm.

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 DP-TABU: an algorithm to solve single-depot multi-line vehicle scheduling problem

2021 ◽  
Author(s):  
Zhao Xinchao ◽  
Sun Hao ◽  
Lu Juan ◽  
Li Zhiyu
Keyword(s):  
Public Transportation ◽  
Performance Optimization ◽  
Resource Sharing ◽  
Simulated Annealing Algorithm ◽  
Single Line ◽  
Scheduling Problem ◽  
Object A ◽  
Vehicle Operation ◽  
Service Object ◽  
Annealing Algorithm

AbstractA DP-TABU algorithm is proposed which can effectively solve the multi-line scheduling problem of single Deport (SD-ML-VSP). The multi-line regional coordinated dispatch of the single-line deport of the bus is to solve the problems of idle low-peak vehicles and insufficient peak capacity in single-line scheduling. The capacity of multiple lines at the same station is adjusted to realize resource sharing such as timetables, vehicles, and drivers. Shared capacity such as bus departure intervals and bus schedules. Taking the regional scheduling of multiple lines at the same station as the service object, a vehicle operation planning model based on the objective of optimal public transportation resources (minimum bus and driver costs) is established to optimize the vehicle dispatching mode of multiple lines. We applied this algorithm to the three lines S105, S107, and S159 of Zhengzhou Public Transport Corporation, and the results proved that the algorithm is effective. Through comparison with manual scheduling and simulated annealing algorithm, the advantages of DP-TABU algorithm in performance optimization and robustness are further verified.

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.

scholarly journals A Mathematical Model and a Simulated Annealing Algorithm for Balancing Multi-manned Assembly Line Problem with Sequence-Dependent Setup Time

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Wucheng Yang ◽  
Wenming Cheng
Keyword(s):  
Simulated Annealing ◽  
Assembly Line ◽  
Simulated Annealing Algorithm ◽  
Assembly Line Balancing ◽  
Setup Time ◽  
Mixed Integer ◽  
Sequence Dependent ◽  
Sequence Dependent Setup Time ◽  
Annealing Algorithm ◽  
Sequence Dependent Setup

Multi-manned assembly lines have been widely applied to the industrial production, especially for large-sized products such as cars, buses, and trucks, in which more than one operator in the same station simultaneously performs different tasks in parallel. This study deals with a multi-manned assembly line balancing problem by simultaneously considering the forward and backward sequence-dependent setup time (MALBPS). A mixed-integer programming is established to characterize the problem. Besides, a simulated annealing algorithm is also proposed to solve it. To validate the performance of the proposed approaches, a set of benchmark instances are tested and the lower bound of the proposed problem is also given. The results demonstrated that the proposed algorithm is quite effective to solve the problem.

Modeling and solution of maximal covering problem considering gradual coverage with variable radius over multi-periods

2018 ◽  
Vol 52 (4-5) ◽  
pp. 1245-1260 ◽  
Author(s):  
Alireza Eydi ◽  
Javad Mohebi
Keyword(s):  
Simulated Annealing ◽  
Facility Location ◽  
Location Problem ◽  
Simulated Annealing Algorithm ◽  
Mixed Integer ◽  
Multiple Time ◽  
Variable Radius ◽  
Maximal Covering ◽  
Annealing Algorithm ◽  
Time Periods

Facility location is a critical component of strategic planning for public and private firms. Due to high cost of facility location, making decisions for such a problem has become an important issue which have gained a large deal of attention from researchers. This study examined the gradual maximal covering location problem with variable radius over multiple time periods. In gradual covering location problem, it is assumed that full coverage is replaced by a coverage function, so that increasing the distance from the facility decreases the amount of demand coverage. In variable radius covering problems, however, each facility is considered to have a fixed cost along with a variable cost which has a direct impact on the coverage radius. In real-world problems, since demand may change over time, necessitating relocation of the facilities, the problem can be formulated over multiple time periods. In this study, a mixed integer programming model was presented in which not only facility capacity was considered, but also two objectives were followed: coverage maximization and relocation cost minimization. A metaheuristic algorithm was presented to solve the maximal covering location problem. A simulated annealing algorithm was proposed, with its results presented. Computational results and comparisons demonstrated good performance of the simulated annealing algorithm.

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