scholarly journals The Shrinking of Beijing and the Rising of Xiong’an: Optimize Population Migration in terms of Transport Service

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Hongzhi Lin
Keyword(s):  
Bilevel Programming ◽  
Central Government ◽  
Programming Model ◽  
Service Level ◽  
User Equilibrium ◽  
Population Migration ◽  
Transport Service ◽  
Level Model ◽  
Upper Level ◽  
Bilevel Programming Model

The population of Beijing has already come to its loading capacity. The China central government plans to build an ideal city named Xiong’an nearby Beijing. The city is expected to work as a carrying hub for noncapital functions of Beijing. The central government does not rush to build before a deliberated urban planning is accomplished. For sustainable development, a difficulty faced by urban planners is that the maximum number of people can be migrated from Beijing to Xiong’an with constraint on level of transport service. This paper developed a specialized bilevel programming model where the upper level is to ensure a predetermined transport service level regarding to population migration, while the lower level is feedback equilibrium between trip generation and traffic assignment. To be more specific, trip is generated by the gravity model, and traffic is assigned by the user equilibrium model. It is well known that the bilevel programming problem is tough and challenging. A try-and-error algorithm is designed for the upper-level model, and a method of successive average (MSA) is developed for the lower-level model. The effectiveness of the model and algorithm is validated by an experimental study using the current transport network between Beijing and Xiong’an. It shows that the methods can be very useful to identify the maximum population migration subject to level of transport service.

scholarly journals Parcel Locker Location Based on a Bilevel Programming Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Gege Yang ◽  
Yin Huang ◽  
Ying Fu ◽  
Biao Huang ◽  
Sishi Sheng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Bilevel Programming ◽  
Consumer Satisfaction ◽  
Programming Model ◽  
Optimal Location ◽  
Network Efficiency ◽  
Demand Distribution ◽  
Level Model ◽  
Upper Level ◽  
Bilevel Programming Model

In order to improve delivery network efficiency and to solve consumer satisfaction problems, parcel locker location optimisation scheme is proposed based on the delivery demand under the e-commerce environment. In this paper, a bilevel programming (BLP) model is established to identify the optimal location for parcel lockers by considering benefits of consumers and logistics planning departments. The upper-level model is to determine the optimal location by minimising the planners’ cost, and the lower one gives an equilibrium demand distribution by minimising the consumers’ pick-up cost. On the special form of constraints, a bilevel genetic algorithm is proposed based on GIS data and a genetic algorithm. Finally, a numerical example is employed to demonstrate the application of the method, which indicates that the model can solve the problem of parcel locker location.

scholarly journals A Simultaneous Solution for Reserve Capacity Maximization and Delay Minimization Problems in Signalized Road Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Ozgur Baskan ◽  
Huseyin Ceylan ◽  
Cenk Ozan
Keyword(s):  
Bilevel Programming ◽  
Road Network ◽  
Programming Model ◽  
Numerical Test ◽  
User Equilibrium ◽  
Reserve Capacity ◽  
Delay Minimization ◽  
Weighted Sum Method ◽  
Capacity Maximization ◽  
Bilevel Programming Model

In this study, we present a bilevel programming model in which upper level is defined as a biobjective problem and the lower level is considered as a stochastic user equilibrium assignment problem. It is clear that the biobjective problem has two objectives: the first maximizes the reserve capacity whereas the second minimizes performance index of a road network. We use a weighted-sum method to determine the Pareto optimal solutions of the biobjective problem by applying normalization approach for making the objective functions dimensionless. Following, a differential evolution based heuristic solution algorithm is introduced to overcome the problem presented by use of biobjective bilevel programming model. The first numerical test is conducted on two-junction network in order to represent the effect of the weighting on the solution of combined reserve capacity maximization and delay minimization problem. Allsop & Charlesworth’s network, which is a widely preferred road network in the literature, is selected for the second numerical application in order to present the applicability of the proposed model on a medium-sized signalized road network. Results support authorities who should usually make a choice between two conflicting issues, namely, reserve capacity maximization and delay minimization.

scholarly journals Bilevel programming model and algorithms for flight gate assignment problem

2020 ◽  
Vol 124 (1281) ◽  
pp. 1667-1682
Author(s):  
J. Lin ◽  
X. Ding ◽  
H. Li ◽  
J. Zhou
Keyword(s):  
Bilevel Programming ◽  
Programming Model ◽  
Eastern China ◽  
The Other ◽  
Adaptive Genetic Algorithm ◽  
Transfer Time ◽  
Objective Functions ◽  
Upper Level ◽  
Bilevel Programming Model ◽  
Bilevel Optimisation

ABSTRACTConsidering the decision-making requirements of airport, airlines and passengers, a bilevel programming model which contains two parts was proposed in this paper. One part is to improve the utilization of gates of the airport (upper level), so the objective function of the upper level to the minimum overall variance of slack time between two consecutive air crafts at the same gate. The other part looks at maximize the airline revenue and passengers more conveniently and comfortably (lower level). The lower level has two objective functions — the minimum passenger transfer failure and the minimum passenger average transfer time, respectively. According to the latest data of an airport in Eastern China, the adaptive genetic algorithm is used to solve the above-mentioned bilevel optimisation problems. The numerical experiment shows that the model not only reduces the variance of the relaxation time, but also optimises the flight gate allocation and achieves the initial goal.

scholarly journals Optimizing Train Timetable Based on Departure Time Preference of Passengers for High-Speed Rails

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Zhipeng Huang ◽  
Huimin Niu ◽  
Ruhu Gao ◽  
Haoyu Fan ◽  
Chenglin Liu
Keyword(s):  
Bilevel Programming ◽  
High Speed ◽  
Service Providers ◽  
Programming Model ◽  
Operating Cost ◽  
User Equilibrium ◽  
Optimal Operation ◽  
High Speed Railway ◽  
Train Timetable ◽  
Bilevel Programming Model

Passengers would like to choose the most suitable train based on their travel preferences, expenses, and train timetable in the high-speed railway corridor. Meanwhile, the railway department will constantly adjust the train timetable according to the distribution of passenger flows during a day to achieve the optimal operation cost and energy consumption saving plan. The question is how to meet the differential travel needs of passengers and achieve sustainable goals of service providers. Therefore, it is necessary to design a demand-oriented and environment-friendly high-speed railway timetable. This paper formulates the optimization of train timetable for a given high-speed railway corridor, which is based on the interests of both passengers and transportation department. In particular, a traveling time-space network with virtual departure arc is constructed to analyze generalized travel costs of passengers of each origin-destination (OD), and bilevel programming model is used to optimize the problem. The upper integer programming model regards the minimization of the operating cost, which is simplified to the minimum traveling time of total trains, as the goal. The lower level is a user equilibrium model which arranges each OD passenger flow to different trains. A general advanced metaheuristic algorithm embedded with the Frank–Wolfe method is designed to implement the bilevel programming model. Finally, a real-world numerical experiment is conducted to verify the effectiveness of both the model and the algorithm.

FUZZY BILEVEL PROGRAMMING: MULTI-OBJECTIVE AND MULTI-FOLLOWER WITH SHARED VARIABLES

2008 ◽  
Vol 16 (supp02) ◽  
pp. 105-133 ◽  
Author(s):  
GUANGQUAN ZHANG ◽  
JIE LU ◽  
YA GAO
Keyword(s):  
Bilevel Programming ◽  
Optimization Problems ◽  
Programming Model ◽  
Hierarchical Optimization ◽  
Multi Objective ◽  
Decision Variables ◽  
Shared Variables ◽  
Upper Level ◽  
Bilevel Programming Model ◽  
Multiple Followers

Bilevel programming deals with hierarchical optimization problems in which the leader at the upper level attempts to optimize his or her objectives, but subject to a set of constraints and the follower's reactions. Typical bilevel programming considers one leader one follower situation and supposes each of them has only one objective. In real world situations, multiple followers may be involved and they may be with different relationships such as sharing decision variables or not, sharing objectives or not. Therefore, the leader's decision will be affected not only by those followers' reactions but also by their relationships. In addition, any of the leader and/or these followers may have multiple conflict objectives that should be optimized simultaneously. Furthermore, the parameters of a bilevel programming model may be described by uncertain values. This paper addresses all these three issues as a whole by particularly focusing on the situation of sharing decision variables among followers. It first proposes a set of fuzzy multi-objective multi-follower bilevel programming (FMMBP) models to describe the complex issue. It then presents an approximation branch-and-bound algorithm to solve the FMMBP problems. Finally, two examples illustrate the proposed models and algorithm.

scholarly journals Modeling the Optimal Maintenance Scheduling Strategy for Bridge Networks

2020 ◽  
Vol 10 (2) ◽  
pp. 498
Author(s):  
Xinhua Mao ◽  
Xiandong Jiang ◽  
Changwei Yuan ◽  
Jibiao Zhou
Keyword(s):  
Programming Model ◽  
User Equilibrium ◽  
Maintenance Scheduling ◽  
Traffic Demand ◽  
Scheduling Strategy ◽  
Bridge Networks ◽  
Traffic Delays ◽  
Optimal Maintenance ◽  
Level Model ◽  
Upper Level

An optimal maintenance scheduling strategy for bridge networks can generate an efficient allocation of resources with budget limits and mitigate the perturbations caused by maintenance activities to the traffic flows. This research formulates the optimal maintenance scheduling problem as a bi-level programming model. The upper-level model is a multi-objective nonlinear programming model, which minimizes the total traffic delays during the maintenance period and maximizes the number of bridges to be maintained subject to the budget limit and the number of crews. In the lower-level, the users’ route choice following the upper-level decision is simulated using a modified user equilibrium model. Then, the proposed bi-level model is transformed into an equivalent single-level model that is solved by the simulated annealing algorithm. Finally, the model and algorithm are tested using a highway bridge network. The results show that the proposed method has an advantage in saving maintenance costs, reducing traffic delays, minimizing makespan compared with two empirical maintenance strategies. The sensitivity analysis reveals that traffic demand, number of crews, availability of budget, and decision maker’s preference all have significant effects on the optimal maintenance scheduling scheme for bridges including time sequence and job sequence.

scholarly journals Optimizing Bus Frequencies under Uncertain Demand: Case Study of the Transit Network in a Developing City

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Zhengfeng Huang ◽  
Gang Ren ◽  
Haixu Liu
Keyword(s):  
Travel Time ◽  
Programming Model ◽  
Uncertain Demand ◽  
Transit Assignment ◽  
Transit System ◽  
Transit Network ◽  
Passenger Demand ◽  
Level Model ◽  
Upper Level ◽  
Bilevel Programming Model

Various factors can make predicting bus passenger demand uncertain. In this study, a bilevel programming model for optimizing bus frequencies based on uncertain bus passenger demand is formulated. There are two terms constituting the upper-level objective. The first is transit network cost, consisting of the passengers’ expected travel time and operating costs, and the second is transit network robustness performance, indicated by the variance in passenger travel time. The second term reflects the risk aversion of decision maker, and it can make the most uncertain demand be met by the bus operation with the optimal transit frequency. With transit link’s proportional flow eigenvalues (mean and covariance) obtained from the lower-level model, the upper-level objective is formulated by the analytical method. In the lower-level model, the above two eigenvalues are calculated by analyzing the propagation of mean transit trips and their variation in the optimal strategy transit assignment process. The genetic algorithm (GA) used to solve the model is tested in an example network. Finally, the model is applied to determining optimal bus frequencies in the city of Liupanshui, China. The total cost of the transit system in Liupanshui can be reduced by about 6% via this method.

scholarly journals A Bilevel Programming Model and Algorithm for the Static Bike Repositioning Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Qiong Tang ◽  
Zhuo Fu ◽  
Meng Qiu
Keyword(s):  
Bilevel Programming ◽  
Programming Model ◽  
Iterated Local Search ◽  
Transportation Mode ◽  
Bike Sharing ◽  
Loading And Unloading ◽  
Upper Level ◽  
Route Cost ◽  
Bilevel Programming Model ◽  
Transportation Route

In this paper, by taking the outsourcing transportation mode into account, a bilevel programming model is proposed to formulate the static bike repositioning (SBR) problem, which can be used to determine the number of bikes loaded and unloaded at each station and the optimal truck routes in bike sharing systems (BSS). The upper-level BSS providers determine the optimal loading and unloading quantities at stations to minimize the total penalties. The lower-level truck owner pursues the minimum transportation route cost. An iterated local search and tabu search are developed to solve the model. Computational tests on a set of instances from 20 to 200 bikes demonstrate the effectiveness of the model and algorithms proposed, together with some insightful findings.

Sign in / Sign up

Export Citation Format

Share Document