scholarly journals Two-Level Planning of Customized Bus Routes Based on Uncertainty Theory

2021 ◽  
Vol 13 (20) ◽  
pp. 11418
Author(s):  
Bing Zhang ◽  
Zhishan Zhong ◽  
Zi Sang ◽  
Mingyang Zhang ◽  
Yunqiang Xue
Keyword(s):  
Uncertainty Theory ◽  
Optimization Problem ◽  
Operating Time ◽  
Route Planning ◽  
Theoretical Support ◽  
Level Model ◽  
Upper Level ◽  
Bus Route Planning ◽  
Two Level Model

The optimization problem of customized bus routes is affected by uncertain factors in reality; therefore, this paper introduces uncertainty theory to study the above problem. A two-level planning model that takes the maximum total revenue of the bus company as the upper-level goal and the minimum total travel cost of passengers as the lower-level goal is established, using uncertainty theory to study and solve practical problems with uncertain factors. The genetic algorithm is used to solve the model, and the feasibility of the model is verified through a case study. The research results show that the application of the two-level model of customized bus route planning based on uncertain vehicle operating time established in this paper to customize bus route planning can take into account the travel needs of passengers and high-quality experiences while also bringing benefits to enterprises and achieving a win–win situation. The research in this article provides theoretical support for the optimization of customized bus routes.

scholarly journals Research on Optimization of Customized Bus Routes Based on Uncertainty Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zi Sang ◽  
Bing Zhang ◽  
Yunqiang Xue ◽  
Hongzhi Guan
Keyword(s):  
Optimization Model ◽  
Uncertainty Theory ◽  
Optimization Problem ◽  
Route Planning ◽  
Route Optimization ◽  
Improved Genetic Algorithm ◽  
Optimization Scheme ◽  
Theoretical Support ◽  
Vehicle Operation

In the optimization process of the routes of customized buses, there are numerous uncertainties in the route planning and setting. In this study, the uncertainty theory is introduced into the optimization problem of a customized bus route, and an uncertain customized bus route optimization model is established, which aims at the minimizing the total mileage of vehicle operation. An improved genetic algorithm is used to solve the model, whose feasibility is verified by a case study. The results show that the optimization model based on the uncertainty theory can yield a reasonable customized bus route optimization scheme, and the total mileage reduced from 35.6 kilometers to 32.2 kilometers. This research provides the theoretical support for the optimization of customized bus routes.

scholarly journals Bi-Level Operation Scheduling of Distribution Systems with Multi-Microgrids Considering Uncertainties

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1441
Author(s):  
Saeid Esmaeili ◽  
Amjad Anvari-Moghaddam ◽  
Erfan Azimi ◽  
Alireza Nateghi ◽  
João P. S. Catalão
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Optimization Problem ◽  
Point Of View ◽  
Mixed Integer ◽  
Wholesale Market ◽  
System Operator ◽  
Level Model ◽  
Upper Level ◽  
Operation Scheduling

A bi-level operation scheduling of distribution system operator (DSO) and multi-microgrids (MMGs) considering both the wholesale market and retail market is presented in this paper. To this end, the upper-level optimization problem minimizes the total costs from DSO’s point of view, while the profits of microgrids (MGs) are maximized in the lower-level optimization problem. Besides, a scenario-based stochastic programming framework using the heuristic moment matching (HMM) method is developed to tackle the uncertain nature of the problem. In this regard, the HMM technique is employed to model the scenario matrix with a reduced number of scenarios, which is effectively suitable to achieve the correlations among uncertainties. In order to solve the proposed non-linear bi-level model, Karush–Kuhn–Tucker (KKT) optimality conditions and linearization techniques are employed to transform the bi-level problem into a single-level mixed-integer linear programming (MILP) optimization problem. The effectiveness of the proposed model is demonstrated on a real-test MMG system.

scholarly journals Bi-Level Model for Public Rail Transportation under Incomplete Data

2017 ◽  
Vol 17 (3) ◽  
pp. 75-91 ◽  
Author(s):  
Kristina Pavlova ◽  
Todor Stoilov ◽  
Krasimira Stoilova
Keyword(s):  
Incomplete Data ◽  
Optimization Problem ◽  
Transportation Network ◽  
Flow Distribution ◽  
Rail Transport ◽  
Rail Transportation ◽  
Maximal Flow ◽  
Level Model ◽  
Upper Level ◽  
Transportation Flows

Abstract The increase of the utilization of public rail transportations is searched in directions for redistribution of the passenger travels between rail and bus transportation. The rail transport benefits by redistribution of the transportation flows on paths, predominantly supported by rails. The redistribution of the transportation is formalized by bi-level optimization problem. The upper level optimization estimates the maximal flow, which can be transported through a transportation network, supported both by bus and rail transports. The lower level optimization gives priority to the rail transport by decreasing the costs of flow distribution, using rail transport. This bi-level optimization problem was applied for the case of optimization of the rail exploitation in Bulgaria, defining priorities in transportation of the National transport scheme.

Bi-Level Model for Design of Transit Short-Turning Service Considering Bus Crowding

2017 ◽  
Vol 2649 (1) ◽  
pp. 52-60 ◽  
Author(s):  
Xiyu Yang ◽  
Yuxiong Ji ◽  
Yuchuan Du ◽  
H. Michael Zhang
Keyword(s):  
Optimal Design ◽  
Logit Model ◽  
Operational Cost ◽  
Time Cost ◽  
Total Cost ◽  
Proposed Model ◽  
Level Model ◽  
Upper Level ◽  
Vehicle Travel Time

A bi-level model was developed to design the short-turning strategy on a bus route. The upper-level model aimed at minimizing the total cost, including operational cost, passengers’ waiting time cost, and in-vehicle travel time cost. The lower-level model was a logit model to capture the service choices of passengers. The effects of bus crowding and seat availability were considered explicitly in the proposed model. An algorithm was developed to determine the frequencies of different services and the turnback points of the short-turning service. A case study demonstrates the superiority of the proposed model over alternative models. Sensitivity of the optimal design to seat capacity was also investigated.

scholarly journals Fault Diagnosis with False and/or Missing Alarms in Distribution Systems with Distributed Generators

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2579 ◽  
Author(s):  
Guang Shen ◽  
Yong Zhang ◽  
Haifeng Qiu ◽  
Chongyu Wang ◽  
Fushuan Wen ◽  
...  
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Fault Location ◽  
Distributed Generators ◽  
Comprehensive Method ◽  
Level Model ◽  
Diagnosis Model ◽  
Simulation Results ◽  
Upper Level ◽  
Two Level Model

A comprehensive method is presented in this work to locate faults in distribution systems with distributed generators (DGs). A two-level model is developed for this purpose with both telecommunication and telemetering data employed, so as to make good use of fused information for attaining a more credible optimization solution under scenarios with alarm distortions of feeder terminal units (FTUs) or loss during communication. First, at the upper level, an analytic model is developed to search all potential faulted sections/candidates based on the telecommunication data. Then, on the lower level, a model is presented using the telemetering data to identify the most likely fault location from the candidates provided by the upper model. The essential features of the two-level diagnosis model are demonstrated through a number of case studies. Simulation results have shown that the proposed approach is capable of not only locating the faulted section(s) in a distribution system with DGs but also identifying false and/or missing alarms.

Research on Charging and Discharging Dispatching Strategy for Electric Vehicles

2015 ◽  
Vol 8 (1) ◽  
pp. 176-182 ◽  
Author(s):  
Wang Yong ◽  
Bian Haihong ◽  
Wang Chunning
Keyword(s):  
Decision Maker ◽  
Electric Vehicles ◽  
Optimization Model ◽  
Optimization Problem ◽  
Peak Load ◽  
Optimal Schedule ◽  
Negative Effects ◽  
Load Shifting ◽  
Level Model ◽  
Upper Level

The popularity of electric vehicles may lead to negative effects on the power system if the charging procedures of plug-in electric vehicles (PEVs) are uncoordinated. In order to solve the problem, the hierarchical and zonal dispatching architecture and a new bi-level optimization model are respectively presented for the charging/discharging schedules of the PEVs. The upper level model is devoted to minimizing the system load variance so as to implement peak load shifting by optimizing the dispatching plan of all periods for each electric vehicle aggregator (EVA), and the lower one is aimed at tracing the dispatching scheme determined by the upper decision-maker through presenting an optimal schedule of charging and discharging for electric vehicles in the charging areas. Two highly efficient commercial solvers, AMPL/IPOPT and AMPL/CPLEX respectively, are employed to solve the developed optimization problem. Finally, the testing IEEE system consisting of 5 agents and 30 nodes is adopted to illustrate the characteristics of the model and solving method presented in this paper.

Bi-level Game Model for Interaction between Arctic and Traditional Routes

2018 ◽  
Vol 72 (2) ◽  
pp. 269-289 ◽  
Author(s):  
Yangjun Wang ◽  
Ren Zhang
Keyword(s):  
Nash Equilibrium Problem ◽  
Reduced Gradient Method ◽  
Proposed Model ◽  
Arctic Shipping ◽  
Reduced Gradient ◽  
Level Model ◽  
Upper Level ◽  
Path Assignment ◽  
Speed Adjustment

This paper proposes a bi-level model from the perspective of game theory to describe the effect of the rise of Arctic shipping routes on traditional routes and their response. The upper-level model demonstrates the competition between shipping companies that maximise their own profits via speed adjustment, which can be presented as a generalised Nash equilibrium problem and is solved by the generalised reduced-gradient method. The lower-level model illustrates the response of customers who reassign their demands with an elastic total demand, which is presented as a logit-type multi-path assignment problem and is solved by the iterative balancing method. A case study is used to examine the rationality of the proposed model and algorithm.

scholarly journals DETERMINATION OF OPTIMAL WORK START TIME

Transport ◽  
2007 ◽  
Vol 22 (1) ◽  
pp. 45-49 ◽  
Author(s):  
Shu-Guang Li
Keyword(s):  
Travel Time ◽  
Travel Cost ◽  
Decision Variable ◽  
Stochastic Dynamic ◽  
Start Time ◽  
Departure Time ◽  
Level Model ◽  
Upper Level ◽  
Two Level Model

The question is: whether the system total travel cost and travel time are reduced by adjusting the work start time or not? This paper proposes the two‐level model for answering the question; the upper‐level minimizes the system travel cost and travel time by using the work start time as a decision variable, the lower‐level models the stochastic dynamic simultaneous route/departure time equilibrium problem. Finally, numerical results of a small network are provided to illustrate the behavior of the model.

Understanding Resilience Optimization Architectures With an Optimization Problem Repository

2021 ◽  
Author(s):  
Daniel Hulse ◽  
Hongyang Zhang ◽  
Christopher Hoyle
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Health Management ◽  
Single Case ◽  
Decomposition Strategies ◽  
Prevention Model ◽  
System Resilience ◽  
Pandemic Response ◽  
Upper Level

Abstract Optimizing a system’s resilience can be challenging, especially when it involves considering both the inherent resilience of a robust design and the active resilience of a health management system to a set of computationally-expensive hazard simulations. While prior work has developed specialized architectures to effectively and efficiently solve combined design and resilience optimization problems, the comparison of these architectures has been limited to a single case study. To further study resilience optimization formulations, this work develops a problem repository which includes previously-developed resilience optimization problems and additional problems presented in this work: a notional system resilience model, a pandemic response model, and a cooling tank hazard prevention model. This work then uses models in the repository at large to understand the characteristics of resilience optimization problems and study the applicability of optimization architectures and decomposition strategies. Based on the comparisons in the repository, applying an optimization architecture effectively requires understanding the alignment and coupling relationships between the design and resilience models, as well as the efficiency characteristics of the algorithms. While alignment determines the necessity of a surrogate of resilience cost in the upper-level design problem, coupling determines the overall applicability of a sequential, alternating, or bilevel structure. Additionally, the application of decomposition strategies is dependent on there being limited interactions between variable sets, which often does not hold when a resilience policy is parameterized in terms of actions to take in hazardous model states rather than specific given scenarios.

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