Reserve Capacity of Mixed Urban Road Networks, Network Configuration and Signal Settings

The authors formulate the transportation mixed network design problem (MNDP) as a mixed-integer bi-level mathematical problem, based on the concept of reserve capacity. The upper level goal is to maximize the reserve capacity by signal settings at intersections, determine street direction and increase street capacities via addition of lanes. The lower level problem is a deterministic user equilibrium traffic assignment problem to minimize the user travel time. The model being non-convex, meta-heuristic methods are used to solve the problem. A hybridization of genetic algorithm with simulated annealing and a bee algorithm are proposed. Numerical examples are illustrated to verify the effectiveness of the proposed model and the algorithms.

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Optimizing capacity of signalized road network with reversible lanes

Transport ◽

10.3846/16484142.2014.994227 ◽

2015 ◽

Vol 33 (1) ◽

pp. 1-11 ◽

Cited By ~ 6

Author(s):

Jian Wang ◽

Wei Deng

Keyword(s):

Traffic Flow ◽

Road Network ◽

Flow Direction ◽

Network Capacity ◽

User Equilibrium ◽

Network Design Problem ◽

Mixed Integer ◽

Mixed Network ◽

Level Problem ◽

Upper Level

This paper studies the network capacity problem on signalized road network with reversible lanes. A Mixed Network Design Problem (MDNP) is formulated to describe the problem where the upper-level problem is a mixed integer non-linear program designed to maximize the network capacity by optimizing the input parameters (e.g. the signal splits, circles, reassigned number of lanes and O–D demands), while the lower-level problem is the common Deterministic User Equilibrium (DUE) assignment problem formulated to model the drivers’ route choices. According to whether one way strategy is permitted in practice, two strategies for implementing reversible roadway are considered. In the first strategy, not all lanes are reversible and the reversible roadways always hold its ability to accommodate the two-way traffic flow. In the second strategy, one-way road is allowed, which means that all the lanes are reversible and could be assigned to one flow direction if the traffic flow in both directions is severally unsymmetrical. Genetic Algorithm (GA) is detailedly presented to solve the bi-level network capacity problem. The application of the proposed method on a numerical example denotes that Strategy 2 can make more use of the physical capacity of key links (signal controlled links), thus, the corresponding network capacity outperforms it is of Strategy 1 considerably.

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Charging station planning based on the accumulation prospect theory and dynamic user equilibrium

Complex & Intelligent Systems ◽

10.1007/s40747-021-00414-w ◽

2021 ◽

Author(s):

Qiu Heting ◽

Dou Shuihai ◽

Shang Huayan ◽

Zhang Jun

Keyword(s):

Travel Time ◽

User Equilibrium ◽

Network Design Problem ◽

Mixed Integer ◽

Traffic Equilibrium ◽

Charging Station ◽

Level Model ◽

Upper Level ◽

Generalized Cost ◽

Range Limitation

AbstractThis study investigates the electric vehicle (EV) traffic equilibrium and optimal deployment of charging locations subject to range limitation. The problem is similar to a network design problem with traffic equilibrium, which is characterized by a bilevel model structure. The upper level objective is to optimally locate charging stations such that the total generalized cost of all users is minimized, where the user’s generalized cost includes two parts, travel time and energy consumption. The total generalized cost is a measure of the total societal cost. The lower level model seeks traffic equilibrium, in which travelers minimize their individual generalized cost. All the utilized paths have identical generalized cost while satisfying the range limitation constraint. In particular, we use origin-based flows to maintain the range limitation constraint at the path level without path enumeration. To obtain the global solution, the optimality condition of the lower level model is added to the upper level problem resulting in a single level model. The nonlinear travel time function is approximated by piecewise linear functions, enabling the problem to be formulated as a mixed integer linear program. We use a modest-sized network to analyze the model and illustrate that it can determine the optimal charging station locations in a planning context while factoring the EV users’ individual path choice behaviours.

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Optimal Decision-Making Strategy of an Electric Vehicle Aggregator in Short-Term Electricity Markets

Energies ◽

10.3390/en11092413 ◽

2018 ◽

Vol 11 (9) ◽

pp. 2413 ◽

Cited By ~ 14

Author(s):

Homa Rashidizadeh-Kermani ◽

Hamid Najafi ◽

Amjad Anvari-Moghaddam ◽

Josep Guerrero

Keyword(s):

Decision Making ◽

Electric Vehicle ◽

Electricity Markets ◽

Strong Duality ◽

Test System ◽

Optimal Decision ◽

Measurement Tool ◽

Proposed Model ◽

Level Problem ◽

Upper Level

This paper proposes the problem of decision making of an electric vehicle (EV) aggregator in a competitive market in the presence of different uncertain resources. In the proposed model, a bi-level problem is formulated where, in the upper-level, the objective of the aggregator is to maximize its expected profit through its interactions and, in the lower-level, the EV owners minimize their payments. Therefore, the objectives of the upper and the lower-level are contrary. To solve the obtained nonlinear bi-level program, Karush-Kuhn-Tucker (KKT) optimality conditions and strong duality are applied to transform the initial problem into a linear single-level problem. Moreover, to deal with various uncertainties, including market prices, EVs charge/discharge demands and the prices offered by rivals, a risk measurement tool is incorporated into the problem. The proposed model is finally applied to a test system and its effectiveness is evaluated. Simulation results show that the proposed approach has the potential to offer significant benefits to the aggregator and EV owners for better decision-making in an uncertain environment. During different situations, it is observed that with increasing risk-aversion factor, as the aggregator tries to hedge against volatilities, its purchases from day-ahead and negative balancing markets decreases significantly. However, the participation of EV aggregator in the positive balancing market increases accordingly to make more profit.

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A Bilevel Multiobjective Optimisation Approach for Solving the Evacuation Location Assignment Problem

Advances in Civil Engineering ◽

10.1155/2019/6052931 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Ahmed W. A. Hammad

Keyword(s):

Assignment Problem ◽

Programming Model ◽

Decision Makers ◽

Mixed Integer ◽

Multiobjective Optimisation ◽

Proposed Model ◽

Level Problem ◽

Location Assignment ◽

Optimisation Model

In this paper, a bilevel multiobjective optimisation model is proposed to solve the evacuation location assignment problem. The model incorporates the two decision-makers’ spaces, namely, urban planners and evacuees. In order to solve the proposed problem, it is first reformulated into a single-level problem using the Karush–Kuhn–Tucker conditions. Next, the problem is linearised into a mixed-integer linear programming model and solved using an off-the-shelf solver. A case study is examined to showcase the applicability of the proposed model, which is solved using single-objective and multiobjective lexicographic optimisation approaches. The model provides planners with an ability to determine the best locations for placement of shelters in such a way that the evacuees’ traffic assignment on the existing network is optimised.

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A new planning model to support logistics service providers in selecting mode, route, and terminal location

Polish Maritime Research ◽

10.2478/pomr-2013-0028 ◽

2013 ◽

Vol 20 (Special-Issue) ◽

pp. 67-73 ◽

Cited By ~ 3

Author(s):

Nathan Huynh ◽

Fateme Fotuhi

Keyword(s):

Design Problem ◽

Service Providers ◽

Network Design Problem ◽

Mixed Integer ◽

Planning Model ◽

Mixed Integer Linear Program ◽

Logistics Service ◽

Logistics Service Providers ◽

Proposed Model ◽

Different Types

Abstract In this paper, we address thefreight network design problem. A mixed integer linear program is formulated to help logistics service providers jointlyselect the best terminal locations among a set of candidate locations, shipping modes, and route for shipping different types of commodities. The developed model isapplied to two different networksto show its applicability. Results obtained from CPLEX for the case studiesare presented, and the benefit of the proposed model is discussed

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Topological Effects and Performance Optimization in Transportation Continuous Network Design

Mathematical Problems in Engineering ◽

10.1155/2014/490483 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Jianjun Wu ◽

Xin Guo ◽

Huijun Sun ◽

Bo Wang

Keyword(s):

Network Design ◽

Performance Optimization ◽

Transportation Network ◽

Small World ◽

User Equilibrium ◽

Solution Approach ◽

Proposed Model ◽

Upper Level ◽

And Performance ◽

Bilevel Transportation

Because of the limitation of budget, in the planning of road works, increased efforts should be made on links that are more critical to the whole traffic system. Therefore, it would be helpful to model and evaluate the vulnerability and reliability of the transportation network when the network design is processing. This paper proposes a bilevel transportation network design model, in which the upper level is to minimize the performance of the network under the given budgets, while the lower level is a typical user equilibrium assignment problem. A new solution approach based on particle swarm optimization (PSO) method is presented. The topological effects on the performance of transportation networks are studied with the consideration of three typical networks, regular lattice, random graph, and small-world network. Numerical examples and simulations are presented to demonstrate the proposed model.

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ROAD NETWORK RESERVE CAPACITY WITH STOCHASTIC USER EQUILIBRIUM

Transport ◽

10.3846/16484142.2015.1020870 ◽

2015 ◽

Vol 30 (1) ◽

pp. 103-116 ◽

Cited By ~ 9

Author(s):

Jian Wang ◽

Wei Deng ◽

Jinbao Zhao

Keyword(s):

Road Network ◽

User Equilibrium ◽

Reserve Capacity ◽

Stochastic User Equilibrium ◽

Strong Assumption ◽

Solution Algorithms ◽

Proposed Model ◽

Network Investment ◽

The Difference

To relax the strong assumption associated with User Equilibrium (UE) in the previous research of network reserve capacity conducted by Gao and Song (2002), this paper assumes that the drivers all make route choices based on Stochastic User Equilibrium (SUE) principle. Similarly, two bi-level programs are formulated to study the network reserve capacity with SUE problem. The first bi-level program is developed to maximize the network reserve capacity by optimizing signal settings while the traffic demands are reassigned by SUE model. The second program extends the research with Continuous Network Design (CND) problem to find the maximum possible increase in reserve capacity through optimizing allocation of network investment. Two methods, i.e. the sensitivity analysis-based method and Genetic Algorithm (GA), are detailed formulated to solve the bi-level reserve capacity problem. Application of the proposed model and its solution algorithms on two numerical examples find that the network reserve capacity does not always increase with improved quality of drivers’ information. Besides, CND can not only help to increase network reserve capacity, but also can help to make more use of physical capacity of road network at Deterministic User Equilibrium (DUE) condition, thus reduces the difference of reserve capacity between the assumptions of SUE and DUE.

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Optimal Operation of the Campus Microgrid considering the Resource Uncertainty and Demand Response Schemes

Mathematical Problems in Engineering ◽

10.1155/2021/5569701 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Hafiz Abd ul Muqeet ◽

Hafiz Mudassir Munir ◽

Aftab Ahmad ◽

Intisar Ali Sajjad ◽

Guang-Jun Jiang ◽

...

Keyword(s):

Power Systems ◽

Demand Response ◽

Solar Irradiance ◽

Distribution System ◽

Energy Demand ◽

Mathematical Problem ◽

Optimal Solution ◽

Optimal Operation ◽

Mixed Integer ◽

Proposed Model

Present power systems face problems such as rising energy charges and greenhouse gas (GHG) releases. These problems may be assuaged by participating distributed generators (DGs) and demand response (DR) policies in the distribution system (DS). The main focus of this paper is to propose an energy management system (EMS) approach for campus microgrid (µG). For this purpose, a Pakistani university has been investigated and an optimal solution has been proposed. Conventionally, it contains electricity from the national grid only as a supply to fulfil the energy demand. Under the proposed setup, it contains campus owned nondispatchable DGs such as solar photovoltaic (PV) panels and microturbines (MTs) as dispatchable sources. To overcome the random nature of solar irradiance, station battery has been integrated as energy storage. The subsequent nonlinear mathematical problem has been scheduled by mixed-integer nonlinear programming (MINLP) in MATLAB for saving energy cost and battery aging cost. The framework has been validated under deterministic and stochastic environments. Among random parameters, solar irradiance and load have been taken into consideration. Case studies have been carried out considering the demand response strategies to analyze the proposed model. The obtained results show that optimal management and scheduling of storage in the presence of DGs mutually benefit by minimizing consumption cost (customer) and grid load (utility) which show the efficacy of the proposed model. The results obtained are compared to the existing literature and a significant cost reduction is found.

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BI-LEVEL PROGRAMMING MODEL AND ALGORITHMS FOR STOCHASTIC NETWORK WITH ELASTIC DEMAND

Transport ◽

10.3846/16484142.2015.1021835 ◽

2015 ◽

Vol 30 (1) ◽

pp. 117-128 ◽

Cited By ~ 5

Author(s):

Xiang Zhang ◽

Hao Wang ◽

Wei Wang

Keyword(s):

Travel Demand ◽

Programming Model ◽

Choice Model ◽

Consumer Surplus ◽

User Equilibrium ◽

Network Design Problem ◽

Elastic Demand ◽

Stochastic User Equilibrium ◽

Proposed Model ◽

Level Model

Based on a state-of-the-art review of the Road Network Design Problem (RNDP), this paper proposes a bi-level programming model for the RNDP as well as algorithms for it. In the lower level of the proposed model, the elastic-demand Stochastic User Equilibrium (SUE) model is adopted to coincide well with characteristics of users behavior, and additionally, the parameter calibration method for the model is developed based on the Logit path choice model. In the upper level of the proposed model, the consumer surplus is maximized to improve the social benefit of a network in consideration of the travel demand, the construction cost, the off-gas emissions and the saturation level. The algorithm for the lower-level model is developed based on the descent iteration method, Dijkstra’s algorithm and linear search technology. A modified Genetic Algorithm (GA) is developed as the algorithm for the whole bi-level model, which takes designed elitist selection operator, adaptive cross operator, mutation operator and niche technology into consideration. The proposed model and algorithms are applied to a numerical example. The results demonstrate the validity and efficiency of the model and algorithms, which shows a bright prospect of the application in RNDP.

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