Optimal Toll Design in Second-Best Link-Based Congestion Pricing

2003 ◽  
Vol 1857 (1) ◽  
pp. 85-92 ◽  
Author(s):  
Hai Yang ◽  
Xiaoning Zhang
Keyword(s):  
Cost Minimization ◽  
Real Life ◽  
Research Area ◽  
User Equilibrium ◽  
Mixed Integer ◽  
Continuous Variables ◽  
Second Best ◽  
Pricing Scheme ◽  
General Network ◽  
Selection Of

In the traffic assignment literature, it is well known that a marginal-cost toll is charged on each link to drive a user equilibrium flow pattern toward a system optimum in a general network. Although this principle is theoretically reasonable, it is not practically appealing for many reasons. In real life, a second-best pricing scheme is more attractive, where only a subset of links is subject to toll charge. Previously most studies in the research area of second-best pricing concern the determination of optimal toll levels on predetermined toll links, whereas very little attention has been devoted to the selection of toll locations. The second-best link-based pricing scheme that involves optimal selection of both toll levels and toll locations is described here. Travel cost minimization or social welfare maximization with and without inclusion of the implementation cost of the toll charge is sought in general networks. Optimization models with mixed (integer and continuous) variables are formulated for determining toll levels and toll locations simultaneously. A binary genetic algorithm is used to search optimal toll locations dynamically and a simulated annealing method is used to search optimal toll levels.

A Mathematical Programming-Based Approach for Architecture Selection

2012 ◽  
Author(s):  
Aleksandr A. Kerzhner ◽  
Christiaan J. J. Paredis
Keyword(s):  
Mathematical Programming ◽  
Optimization Technique ◽  
Problem Formulation ◽  
Mixed Integer ◽  
Model Transformations ◽  
Continuous Variables ◽  
Solution Approach ◽  
Computational Support ◽  
Algebraic Constraints ◽  
Selection Of

Modern systems are difficult to design because there are a significant number of potential alternatives to consider. The specification of an alternative includes an architecture (which describes the components and connections of the system) and component sizings (the sizing parameter for each component). In current practice, designers rely mainly on their experience and intuition to select a desired architecture without much computational support and then spend most of their effort on optimizing component sizings. In this paper, an approach for representing an architecture selection as a mixed-integer linear programming optimization is presented; existing solvers are then used to identify promising candidate architectures at early stages of the design process. Mathematical programming is a common optimization technique, but it is rarely used for architecture selection because of the difficulty of manually formulating an architecture selection as a mathematical program. In this paper, the formulation is presented in a modular fashion so that model transformations can be applied to transform a problem formulation that is convenient for designers into the mathematical programming optimization. A modular superstructure representation is used to model the design space; in a superstructure a union of all potential architectures is represented as a set of discrete and continuous variables. Algebraic constraints are added to describe both acceptable variable combinations and system behavior to allow the solver to eliminate clearly poor alternatives and identify promising alternatives. The framework is demonstrated on the selection of an actuation subsystem for a hydraulic excavator, although the solution approach would be similar for most mechanical systems.

scholarly journals Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem

2021 ◽  
Author(s):  
Sayan Surya Shaw ◽  
Shameem Ahmed ◽  
Samir Malakar ◽  
Laura Garcia-Hernandez ◽  
Ajith Abraham ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Real Life ◽  
Class Imbalance ◽  
Ring Theory ◽  
Class Imbalance Problem ◽  
Minority Class ◽  
Swarm Optimization ◽  
Imbalance Problem ◽  
Representative Samples ◽  
Selection Of

AbstractMany real-life datasets are imbalanced in nature, which implies that the number of samples present in one class (minority class) is exceptionally less compared to the number of samples found in the other class (majority class). Hence, if we directly fit these datasets to a standard classifier for training, then it often overlooks the minority class samples while estimating class separating hyperplane(s) and as a result of that it missclassifies the minority class samples. To solve this problem, over the years, many researchers have followed different approaches. However the selection of the true representative samples from the majority class is still considered as an open research problem. A better solution for this problem would be helpful in many applications like fraud detection, disease prediction and text classification. Also, the recent studies show that it needs not only analyzing disproportion between classes, but also other difficulties rooted in the nature of different data and thereby it needs more flexible, self-adaptable, computationally efficient and real-time method for selection of majority class samples without loosing much of important data from it. Keeping this fact in mind, we have proposed a hybrid model constituting Particle Swarm Optimization (PSO), a popular swarm intelligence-based meta-heuristic algorithm, and Ring Theory (RT)-based Evolutionary Algorithm (RTEA), a recently proposed physics-based meta-heuristic algorithm. We have named the algorithm as RT-based PSO or in short RTPSO. RTPSO can select the most representative samples from the majority class as it takes advantage of the efficient exploration and the exploitation phases of its parent algorithms for strengthening the search process. We have used AdaBoost classifier to observe the final classification results of our model. The effectiveness of our proposed method has been evaluated on 15 standard real-life datasets having low to extreme imbalance ratio. The performance of the RTPSO has been compared with PSO, RTEA and other standard undersampling methods. The obtained results demonstrate the superiority of RTPSO over state-of-the-art class imbalance problem-solvers considered here for comparison. The source code of this work is available in https://github.com/Sayansurya/RTPSO_Class_imbalance.

scholarly journals Optimising the selection of food items for FFQs using Mixed Integer Linear Programming

2014 ◽  
Vol 18 (1) ◽  
pp. 68-74 ◽  
Author(s):  
Johanna C Gerdessen ◽  
Olga W Souverein ◽  
Pieter van ‘t Veer ◽  
Jeanne HM de Vries
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Selection Process ◽  
Saturated Fat ◽  
Mixed Integer ◽  
Monounsaturated Fat ◽  
Food Items ◽  
Milp Model ◽  
Selection Of

AbstractObjectiveTo support the selection of food items for FFQs in such a way that the amount of information on all relevant nutrients is maximised while the food list is as short as possible.DesignSelection of the most informative food items to be included in FFQs was modelled as a Mixed Integer Linear Programming (MILP) model. The methodology was demonstrated for an FFQ with interest in energy, total protein, total fat, saturated fat, monounsaturated fat, polyunsaturated fat, total carbohydrates, mono- and disaccharides, dietary fibre and potassium.ResultsThe food lists generated by the MILP model have good performance in terms of length, coverage and R2 (explained variance) of all nutrients. MILP-generated food lists were 32–40 % shorter than a benchmark food list, whereas their quality in terms of R2 was similar to that of the benchmark.ConclusionsThe results suggest that the MILP model makes the selection process faster, more standardised and transparent, and is especially helpful in coping with multiple nutrients. The complexity of the method does not increase with increasing number of nutrients. The generated food lists appear either shorter or provide more information than a food list generated without the MILP model.

scholarly journals A Generalized Minimum Cost Flow Model for Multiple Emergency Flow Routing

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jianxun Cui ◽  
Shi An ◽  
Meng Zhao
Keyword(s):  
Flow Model ◽  
Time Window ◽  
Programming Model ◽  
Real Life ◽  
Minimum Cost ◽  
Emergency Evacuation ◽  
Mixed Integer ◽  
Fixed Cost ◽  
Minimum Cost Flow ◽  
Cost Flow

During real-life disasters, that is, earthquakes, floods, terrorist attacks, and other unexpected events, emergency evacuation and rescue are two primary operations that can save the lives and property of the affected population. It is unavoidable that evacuation flow and rescue flow will conflict with each other on the same spatial road network and within the same time window. Therefore, we propose a novel generalized minimum cost flow model to optimize the distribution pattern of these two types of flow on the same network by introducing the conflict cost. The travel time on each link is assumed to be subject to a bureau of public road (BPR) function rather than a fixed cost. Additionally, we integrate contraflow operations into this model to redesign the network shared by those two types of flow. A nonconvex mixed-integer nonlinear programming model with bilinear, fractional, and power components is constructed, and GAMS/BARON is used to solve this programming model. A case study is conducted in the downtown area of Harbin city in China to verify the efficiency of proposed model, and several helpful findings and managerial insights are also presented.

scholarly journals Solving the nuclear dismantling project scheduling problem by combining mixed-integer and constraint programming techniques and metaheuristics

2021 ◽  
Author(s):  
Felix Hübner ◽  
Patrick Gerhards ◽  
Christian Stürck ◽  
Rebekka Volk
Keyword(s):  
Constraint Programming ◽  
Project Scheduling ◽  
Computational Study ◽  
Real Life ◽  
Mixed Integer ◽  
Extensive Literature ◽  
Scheduling Problem ◽  
Multiple Modes ◽  
Project Scheduling Problem ◽  
Optimisation Techniques

AbstractScheduling of megaprojects is very challenging because of typical characteristics, such as expected long project durations, many activities with multiple modes, scarce resources, and investment decisions. Furthermore, each megaproject has additional specific characteristics to be considered. Since the number of nuclear dismantling projects is expected to increase considerably worldwide in the coming decades, we use this type of megaproject as an application case in this paper. Therefore, we consider the specific characteristics of constrained renewable and non-renewable resources, multiple modes, precedence relations with and without no-wait condition, and a cost minimisation objective. To reliably plan at minimum costs considering all relevant characteristics, scheduling methods can be applied. But the extensive literature review conducted did not reveal a scheduling method considering the special characteristics of nuclear dismantling projects. Consequently, we introduce a novel scheduling problem referred to as the nuclear dismantling project scheduling problem. Furthermore, we developed and implemented an effective metaheuristic to obtain feasible schedules for projects with about 300 activities. We tested our approach with real-life data of three different nuclear dismantling projects in Germany. On average, it took less than a second to find an initial feasible solution for our samples. This solution could be further improved using metaheuristic procedures and exact optimisation techniques such as mixed-integer programming and constraint programming. The computational study shows that utilising exact optimisation techniques is beneficial compared to standard metaheuristics. The main result is the development of an initial solution finding procedure and an adaptive large neighbourhood search with iterative destroy and recreate operations that is competitive with state-of-the-art methods of related problems. The described problem and findings can be transferred to other megaprojects.

Design of hydrogen supply chains under demand uncertainty – a case study of passenger transport in Germany

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Anton Ochoa Bique ◽  
Leonardo K. K. Maia ◽  
Ignacio E. Grossmann ◽  
Edwin Zondervan
Keyword(s):  
Stochastic Optimization ◽  
Demand Uncertainty ◽  
Programming Model ◽  
Cost Minimization ◽  
Water Electrolysis ◽  
Scenario Tree ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Strong Impact ◽  
Hydrogen Supply

Abstract A strategy for the design of a hydrogen supply chain (HSC) network in Germany incorporating the uncertainty in the hydrogen demand is proposed. Based on univariate sensitivity analysis, uncertainty in hydrogen demand has a very strong impact on the overall system costs. Therefore we consider a scenario tree for a stochastic mixed integer linear programming model that incorporates the uncertainty in the hydrogen demand. The model consists of two configurations, which are analyzed and compared to each other according to production types: water electrolysis versus steam methane reforming. Each configuration has a cost minimization target. The concept of value of stochastic solution (VSS) is used to evaluate the stochastic optimization results and compare them to their deterministic counterpart. The VSS of each configuration shows significant benefits of a stochastic optimization approach for the model presented in this study, corresponding up to 26% of infrastructure investments savings.

An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and its Applications to Mechanical Design

1993 ◽  
Author(s):  
B. K. Kannan ◽  
Steven N. Kramer
Keyword(s):  
Lagrange Multiplier ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Continuous Optimization ◽  
Mixed Integer ◽  
Continuous Variables ◽  
Augmented Lagrange Multiplier

Abstract An algorithm for solving nonlinear optimization problems involving discrete, integer, zero-one and continuous variables is presented. The augmented Lagrange multiplier method combined with Powell’s method and Fletcher & Reeves Conjugate Gradient method are used to solve the optimization problem where penalties are imposed on the constraints for integer / discrete violations. The use of zero-one variables as a tool for conceptual design optimization is also described with an example. Several case studies have been presented to illustrate the practical use of this algorithm. The results obtained are compared with those obtained by the Branch and Bound algorithm. Also, a comparison is made between the use of Powell’s method (zeroth order) and the Conjugate Gradient method (first order) in the solution of these mixed variable optimization problems.

scholarly journals Extending QGroundControl for Automated Mission Planning of UAVs

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2339 ◽  
Author(s):  
Cristian Ramirez-Atencia ◽  
David Camacho
Keyword(s):  
Flight Control ◽  
Low Cost ◽  
Real Life ◽  
Mission Planning ◽  
Human Computer Interface ◽  
Simulation Environment ◽  
Test Bed ◽  
Multiple Vehicles ◽  
Operator Workload ◽  
Selection Of

Unmanned Aerial Vehicles (UAVs) have become very popular in the last decade due to some advantages such as strong terrain adaptation, low cost, zero casualties, and so on. One of the most interesting advances in this field is the automation of mission planning (task allocation) and real-time replanning, which are highly useful to increase the autonomy of the vehicle and reduce the operator workload. These automated mission planning and replanning systems require a Human Computer Interface (HCI) that facilitates the visualization and selection of plans that will be executed by the vehicles. In addition, most missions should be assessed before their real-life execution. This paper extends QGroundControl, an open-source simulation environment for flight control of multiple vehicles, by adding a mission designer that permits the operator to build complex missions with tasks and other scenario items; an interface for automated mission planning and replanning, which works as a test bed for different algorithms, and a Decision Support System (DSS) that helps the operator in the selection of the plan. In this work, a complete guide of these systems and some practical use cases are provided

Multi-Period Virtual Cell Formation: A New Methodology Based on Non-Linear Mixed-Integer Programming Model

2013 ◽  
Vol 278-280 ◽  
pp. 2210-2217
Author(s):  
Jie Lv ◽  
Jing Yuan ◽  
Wen Min Han
Keyword(s):  
Cell Size ◽  
Production Cost ◽  
Dynamic Condition ◽  
Cell Formation ◽  
Real Life ◽  
Mixed Integer ◽  
Virtual Cell ◽  
Work In Process ◽  
Non Linear ◽  
Internal Production

This paper researched on multi-period dynamic virtual cell formation problem, and filled the gap on objectives of previous literatures. A developed 0-1 non-linear mixed-integer mathematical model was proposed, it incorporates real-life parameters like alternative routings, operation sequence, duplicate machine, processing time and machine capacity. The advantage of the model is to embed the function relationship between cell size and internal production cost in the model, thus the effects of set-up cost, work-in-process inventory cost, coordination cost and inventory handling cost on VCMS are acted on the model. Finally a numerical example solved by Lingo11.0 software package was presented to verify the model and related discussion was made. The results show that the cell size changes as time in dynamic condition and different scenarios of internal production cost can obtain different cell configurations.

Sign in / Sign up

Export Citation Format

Share Document