scholarly journals A Methodology for Increasing Convergence Speed of Traffic Assignment Algorithms Based on the Use of a Generalised Averaging Function

2020 ◽  
Vol 10 (16) ◽  
pp. 5698
Author(s):  
Marilisa Botte ◽  
Mariano Gallo ◽  
Mario Marinelli ◽  
Luca D’Acierno
Keyword(s):  
Network Design ◽  
Traffic Assignment ◽  
Convergence Speed ◽  
Network Design Problem ◽  
Traffic Flows ◽  
Assignment Problems ◽  
Design Problems ◽  
Method Of Successive Averages ◽  
Parameter Values ◽  
Real Scale

In this paper, we propose a generalisation of the Method of Successive Averages (MSA) for solving traffic assignment problems. The generalisation consists in proposing a different step sequence within the general MSA framework to reduce computing times. The proposed step sequence is based on the modification of the classic 1/k sequence for improving the convergence speed of the algorithm. The reduction in computing times is useful if the assignment problems are subroutines of algorithms for solving Network Design Problems—such algorithms require estimation of the equilibrium traffic flows at each iteration, hence, many thousands of times for real-scale cases. The proposed algorithm is tested with different parameter values and compared with the classic MSA algorithm on a small and on two real-scale networks. A test inside a Network Design Problem is also reported. Numerical results show that the proposed algorithm outperforms the classic MSA with reductions in computing times, reaching up to 79%. Finally, the theoretical properties are studied for stating the convergence of the proposed algorithm.

scholarly journals Multimodal Green Logistics Network Design of Urban Agglomeration with Stochastic Demand

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Jiehui Jiang ◽  
Dezhi Zhang ◽  
Shuangyan Li ◽  
Yajie Liu
Keyword(s):  
Network Design ◽  
Carbon Tax ◽  
Stochastic Demand ◽  
Urban Agglomeration ◽  
Decision Models ◽  
Network Design Problem ◽  
Carbon Taxes ◽  
Green Logistics ◽  
Logistics Network ◽  
Method Of Successive Averages

This study investigates a multimodal green logistics network design problem of urban agglomeration with stochastic demand, in which different logistics authorities among the different cities jointly optimize the logistics node configurations and uniform carbon taxes over logistics transport modes to maximize the total social welfare of urban agglomeration and consider logistics users’ choice behaviors. The users’ choice behaviors are captured by a logit-based stochastic equilibrium model. To describe the game behaviors of logistics authorities in urban agglomeration, the problem is formulated as two nonlinear bilevel programming models, namely, independent and centralized decision models. Next, a quantum-behaved particle swarm optimization (QPSO) embedded with a Method of Successive Averages (MSA) is presented to solve the proposed models. Simulation results show that to achieve the overall optimization layout of the green logistics network in urban agglomeration the logistics authorities should adopt centralized decisions, construct a multimode logistics network, and make a reasonable carbon tax.

Numerical Studies on Reformulation Techniques for Continuous Network Design with Asymmetric User Equilibria

2010 ◽  
Vol 1 (1) ◽  
pp. 52-72 ◽  
Author(s):  
Michael Ferris ◽  
Henry X. Liu
Keyword(s):  
Nonlinear Programming ◽  
Network Design ◽  
Mathematical Program ◽  
Network Design Problem ◽  
Complementarity Constraints ◽  
Nonlinear Programming Problem ◽  
Design Problems ◽  
Nonlinear Complementarity ◽  
Numerical Studies ◽  
Constraints Model

In this article, we aim to find the most effective reformulation techniques to solve the MPCC (mathematical program with complementarity constraints) model that we proposed recently for continuous network design problems under asymmetric user equilibria. The MPCC model is based on a link-node nonlinear complementarity formulation for asymmetric user equilibria. By applying various reformulation techniques for the lower level nonlinear complementarity, the original bilevel formulation can be converted to a single level nonlinear programming problem. We show that certain reformulations are more effective than others to solve the proposed MPCC model. Recommendations are thus provided on how to choose a reformulation of the continuous network design problem that can be solved effectively and/or efficiently.

scholarly journals Case Article—Integrating Network Design Models for a Global Supply Network

2020 ◽  
Author(s):  
Yong Wu
Keyword(s):  
Network Design ◽  
Operations Research ◽  
Optimization Problems ◽  
Integrated Approach ◽  
Complex Problem ◽  
Network Design Problem ◽  
Supply Network ◽  
Assignment Problems ◽  
Design Models ◽  
Holistic Understanding

In typical operations research courses, optimization problems, such as transportation and assignment problems, are frequently discussed and taught as stand-alone problems. An integrated approach may prove to be necessary in order to enable students to have a holistic understanding of a complex problem (e.g., a project). In this paper, a global supply network design problem is presented where the case company can source from multiple “suppliers” using multiple modes of transport (including the use of containers with different capacities), allowing lateral supply between warehouses, etc. As more factors are considered, the problem becomes much more complex than any isolated problem in a typical course. The case was tested in an undergraduate course in Australia, and students found this case challenging but at the same time rewarding once solved.

A Hybrid Lagrangian Relaxation and Tabu Search Method for Interdependent-Choice Network Design Problems

2011 ◽  
pp. 294-324 ◽  
Author(s):  
Chi Xie ◽  
Mark A. Turnquist ◽  
S. Travis Waller
Keyword(s):  
Tabu Search ◽  
Network Design ◽  
Lagrangian Relaxation ◽  
Optimization Problems ◽  
Search Method ◽  
Network Design Problem ◽  
Design Problems ◽  
Network Design Problems ◽  
Tabu Search Method ◽  
Algorithmic Logic

Hybridization offers a promising approach in designing and developing improved metaheuristic methods for a variety of complex combinatorial optimization problems. This chapter presents a hybrid Lagrangian relaxation and tabu search method for a class of discrete network design problems with complex interdependent-choice constraints. This method takes advantage of Lagrangian relaxation for problem decomposition and complexity reduction while its algorithmic logic is designed based on the principles of tabu search. The algorithmic advance and solution performance of the method are illustrated by implementing it for solving a network design problem with lane reversal and crossing elimination strategies, arising from urban evacuation planning.

scholarly journals Optimization of Process Design Problems Using Differential Evolution Algorithm

2016 ◽  
Vol 15 (2) ◽  
pp. 89
Author(s):  
A.M. Gujarathia ◽  
G. Vakili-Nezhaad ◽  
M. Vatani
Keyword(s):  
Differential Evolution ◽  
Network Design ◽  
Design Problem ◽  
Differential Evolution Algorithm ◽  
Network Design Problem ◽  
Design Problems ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Lle Data ◽  
Reactor Network

 A modified differential evolution algorithm (MDE) has been used for solving different process related design problems (namely calculation of the NRTL and Two-Suffix Margules activity coefficient models parameters in 20 ternary extraction systems including different ionic liquids and reactor network design problem). The obtained results, in terms of root mean square deviations (rmsd) for these models are satisfactory, with the overall values of 0.0023 and 0.0170 for 169 tie-lines for NRTL and Two-Suffix Margules models, respectively. The results showed that the MDE algorithm results in better solutions compared to the previous work based on genetic algorithm (GA) for correlating liquid-liquid equilibrium (LLE) data in these systems. MDE also outperformed DE algorithm when tested on reactor network design problem with respect to convergence and speed. 

Numerical Studies on Reformulation Techniques for Continuous Network Design with Asymmetric User Equilibria

2012 ◽  
pp. 138-157
Author(s):  
Xuegang (Jeff) Ban ◽  
Michael Ferris ◽  
Henry X. Liu
Keyword(s):  
Nonlinear Programming ◽  
Network Design ◽  
Network Design Problem ◽  
Complementarity Constraints ◽  
Nonlinear Programming Problem ◽  
Design Problems ◽  
Single Level ◽  
Nonlinear Complementarity ◽  
Numerical Studies ◽  
Constraints Model

In this article, we aim to find the most effective reformulation techniques to solve the MPCC (mathematical program with complementarity constraints) model that we proposed recently for continuous network design problems under asymmetric user equilibria. The MPCC model is based on a link-node nonlinear complementarity formulation for asymmetric user equilibria. By applying various reformulation techniques for the lower level nonlinear complementarity, the original bilevel formulation can be converted to a single level nonlinear programming problem. We show that certain reformulations are more effective than others to solve the proposed MPCC model. Recommendations are thus provided on how to choose a reformulation of the continuous network design problem that can be solved effectively and/or efficiently.

scholarly journals The Reversible Lane Network Design Problem (RL-NDP) for Smart Cities with Automated Traffic

2020 ◽  
Vol 12 (3) ◽  
pp. 1226 ◽  
Author(s):  
Lígia Conceição ◽  
Gonçalo Homem de Almeida Correia ◽  
José Pedro Tavares
Keyword(s):  
Network Design ◽  
Design Problem ◽  
Traffic Assignment ◽  
Smart Cities ◽  
User Equilibrium ◽  
Sustainable Transportation ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Travel Times ◽  
The City

With automated vehicles (AVs), reversible lanes could be a sustainable transportation solution once there is vehicle-to-infrastructure connectivity informing AVs about the lane configuration changes. This paper introduced the reversible lane network design problem (RL-NDP), formulated in mixed-integer non-linear mathematical programming—both the traffic assignment and the reversible lane decisions were embedded. The model was applied on an hourly basis in the case study of the city of Delft, the Netherlands. Reversible lanes are examined under no traffic equilibrium (former paths are maintained); user-equilibrium (UE) assignment (AVs decide their own paths); and system-optimum (SO) traffic assignment (AVs are forced to follow SO paths). We found out that reversible lanes reduce congested roads, total travel times, and delays up to 36%, 9%, and 22%, respectively. The SO scenario was revealed to be beneficial in reducing the total travel time and congested roads in peak hours, whereas UE is equally optimal in the remaining hours. A dual-scenario mixing SO and UE throughout the day reduced congested roads, total travel times, and delay up to 40%, 8%, and 19%, respectively, yet increased 1% in travel distance. The spatial analysis suggested a substantial lane variability in the suburbs, yet a strong presence of reversible lanes in the city center.

scholarly journals Visualising the Operation of Evolutionary Algorithms Optimising Water Distribution Network Design Problems

10.29007/j62b ◽  
2018 ◽  
Author(s):  
David Walker ◽  
Matthew Craven
Keyword(s):  
Evolutionary Algorithms ◽  
Network Design ◽  
Water Distribution ◽  
Distribution Network ◽  
Water Distribution Network ◽  
Network Design Problem ◽  
Complex Nature ◽  
Design Problems ◽  
Distribution Network Design ◽  
Water Distribution Network Design

Multi-objective evolutionary algorithms (MOEAs) are well known for their ability to optimise the water distribution network design problem. However, their complex nature often restricts their use to algorithm experts. A method is proposed for visualising algorithm performance that will enable an engineer to compare different optimisers and select the best optimisation approach. Results show that the convergence and preservation of diversity can be shown in a simple visualisation that does not rely on in-depth MOEA experience.

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