scholarly journals The Effect of Nonlinear Charging Function and Line Change Constraints on Electric Bus Scheduling

2021 ◽  
Vol 33 (4) ◽  
pp. 527-538
Author(s):  
Aijia Zhang ◽  
Tiezhu Li ◽  
Ran Tu ◽  
Changyin Dong ◽  
Haibo Chen ◽  
...  
Keyword(s):  
Real World ◽  
Control Method ◽  
Large Deviation ◽  
Programming Model ◽  
Piecewise Linear ◽  
Mixed Integer ◽  
Piecewise Linear Approximation ◽  
Effective Control ◽  
Electric Bus ◽  
Bus Schedule

The recharging plans are a key component of the electric bus schedule. Since the real-world charging function of electric vehicles follows a nonlinear relationship with the charging duration, it is challenging to accurately estimate the charging time. To provide a feasible bus schedule given the nonlinear charging function, this paper proposes a mixed integer programming model with a piecewise linear charging approximation and multi-depot and multi-vehicle type scheduling. The objective of the model is to minimise the total cost of the schedule, which includes the vehicle purchasing cost and operation cost. From a practical point of view, the number of line changes of each bus is also taken as one of the constraints in the optimisation. An improved heuristic algorithm is then proposed to find high-quality solutions of the problem with an efficient computation. Finally, a real-world dataset is used for the case study. The results of using different charging functions indicate a large deviation between the linear charging function and the piecewise linear approximation, which can effectively avoid the infeasible bus schedules. Moreover, the experiments show that the proposed line change constraints can be an effective control method for transit operators.

scholarly journals Design of a Logistics Nonlinear System for a Complex, Multiechelon, Supply Chain Network with Uncertain Demands

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Aaron Guerrero Campanur ◽  
Elias Olivares-Benitez ◽  
Pablo A. Miranda ◽  
Rodolfo Eleazar Perez-Loaiza ◽  
Jose Humberto Ablanedo-Rosas
Keyword(s):  
Supply Chain ◽  
Programming Model ◽  
Piecewise Linear ◽  
Distribution Network ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Piecewise Linear Approximation ◽  
Computational Time ◽  
Local Optimum ◽  
Decision Variables

Industrial systems, such as logistics and supply chain networks, are complex systems because they comprise a big number of interconnected actors and significant nonlinear and stochastic features. This paper analyzes a distribution network design problem for a four-echelon supply chain. The problem is represented as an inventory-location model with uncertain demand and a continuous review inventory policy. The decision variables include location at the intermediate levels and product flows between echelons. The related safety and cyclic inventory levels can be computed from these decision variables. The problem is formulated as a mixed integer nonlinear programming model to find the optimal design of the distribution network. A linearization of the nonlinear model based on a piecewise linear approximation is proposed. The objective function and nonlinear constraints are reformulated as linear formulations, transforming the original nonlinear problem into a mixed integer linear programming model. The proposed approach was tested in 50 instances to compare the nonlinear and linear formulations. The results prove that the proposed linearization outperforms the nonlinear formulation achieving convergence to a better local optimum with shorter computational time. This method provides flexibility to the decision-maker allowing the analysis of scenarios in a shorter time.

scholarly journals Improving schedule adherence based on dynamic signal control and speed guidance in connected bus system

2020 ◽  
Vol 3 (2) ◽  
pp. 79-88
Author(s):  
Mingjie Hao ◽  
Yiming Bie ◽  
Le Zhang ◽  
Chengyuan Mao
Keyword(s):  
Control Method ◽  
Programming Model ◽  
Signal Control ◽  
Signal Timing ◽  
Illustrative Case ◽  
Operating Speed ◽  
Content Type ◽  
Schedule Adherence ◽  
Bus Schedule ◽  
Bus System

Purpose The purpose of this paper is to develop a dynamic control method to improve bus schedule adherence under connected bus system. Design/methodology/approach The authors developed a dynamic programming model that optimally schedules the bus operating speed at road sections and multiple signal timing plans at intersections to improve bus schedule adherence. First, the bus route was partitioned into three types of sections: stop, road and intersection. Then, transit agencies can control buses in real time based on all collected information; i.e. control bus operating speed on road sections and adjust the signal timing plans through signal controllers to improve the schedule adherence in connected bus environment. Finally, bus punctuality at the downstream stop and the saturation degree deviations of intersections were selected as the evaluation criteria in optimizing signal control plans and bus speeds jointly. Findings An illustrative case study by using a bus rapid transit line in Jinan city was performed to verify the proposed model. It revealed that based on the proposed strategy, the objective value could be reduced by 73.7%, which indicated that the punctuality was highly improved but not to incur excessive congestion for other vehicular traffic. Originality/value In this paper, the authors applied speed guidance and the adjustment of the signal control plans for multiple cycles in advance to improve the scheduled stability; furthermore, the proposed control strategy can reduce the effect on private traffics to the utmost extend.

scholarly journals Study on Optimization of Active Control Schemes for Considering Transient Processes in the Case of Pipeline Leakage

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1692
Author(s):  
Wan Zhang ◽  
Ruihao Shen ◽  
Ning Xu ◽  
Haoran Zhang ◽  
Yongtu Liang
Keyword(s):  
Active Control ◽  
Programming Model ◽  
High Reliability ◽  
Control Measures ◽  
Transient Processes ◽  
Mixed Integer ◽  
Effective Control ◽  
Hydraulic Systems ◽  
Control Plan ◽  
Control Schemes

Pipeline leakage of crude oil, refined oil or other petroleum derivatives can cause serious damage to the environment, soil, and more importantly, pose a serious threat to personal safety. The losses can be minimized to a degree by active control. Therefore, timely and effective control measures should be taken to minimize the leak volume whenever a pipeline leaks. However, the complexity of pipeline hydraulic systems makes it difficult to optimize control schemes for pipeline hydraulic devices under leak conditions, and existing studies rarely consider complex transient processes. This paper aims to establish a mixed integer linear programming model considering transient processes, hydraulic constraints, equipment constraints and flow constraints, and develop a detailed control scheme of the devices by the branch and bound algorithm. Moreover, it is the objective of the model to figure out the most optimal control plan to minimize the leakage. Experiments on a real-world liquid pipeline have proved the practicability and high reliability of the model.

scholarly journals Mixed-Integer Linear Programming Model by Linear Approximation for a Strike Package-to-Target Assignment Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Heungseob Kim
Keyword(s):  
Linear Programming ◽  
Linear Approximation ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Programming Model ◽  
Mixed Integer ◽  
Piecewise Linear Approximation ◽  
Heterogeneous Vehicle Routing Problem ◽  
Target Assignment ◽  
Milp Model

This study deals with an aircraft-to-target assignment (ATA) problem considering the modern air operation environment, such as the strike package concept, multiple targets for a sortie, and the strike packages’ survivability. For the ATA problem, this study introduces a novel mathematical model in which a heterogeneous vehicle routing problem (HVRP) and a weapon-to-target assignment (WTA) problem are conceptually integrated. The HVRP generates the flight routes for strike packages because this study confirms that the survivability of a strike package depends on the path, and the WTA problem evaluates the likelihood of successful target destruction of assigned weapons. Although the first version of the model is developed as a mixed-integer nonlinear programming (MINLP) model, this study attempts to convert it to a mixed-integer linear programming (MILP) model using the logarithmic transformation and piecewise linear approximation methods. For an ATA problem, this activity could provide an opportunity to use the excellent existing algorithms for searching the optimal solution of LP models. To maximize the operational effectiveness, the MILP model simultaneously determines the following for each strike package: (a) composition type, (b) targets, (c) flight route, (d) types, and (e) quantity of weapons for each target.

scholarly journals Sparse Poisson regression via mixed-integer optimization

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249916
Author(s):  
Hiroki Saishu ◽  
Kota Kudo ◽  
Yuichi Takano
Keyword(s):  
Poisson Regression ◽  
Likelihood Function ◽  
Piecewise Linear ◽  
Low Noise ◽  
Computer Hardware ◽  
Mixed Integer ◽  
Piecewise Linear Approximation ◽  
Integer Optimization ◽  
Mixed Integer Optimization ◽  
Log Likelihood

We present a mixed-integer optimization (MIO) approach to sparse Poisson regression. The MIO approach to sparse linear regression was first proposed in the 1970s, but has recently received renewed attention due to advances in optimization algorithms and computer hardware. In contrast to many sparse estimation algorithms, the MIO approach has the advantage of finding the best subset of explanatory variables with respect to various criterion functions. In this paper, we focus on a sparse Poisson regression that maximizes the weighted sum of the log-likelihood function and the L2-regularization term. For this problem, we derive a mixed-integer quadratic optimization (MIQO) formulation by applying a piecewise-linear approximation to the log-likelihood function. Optimization software can solve this MIQO problem to optimality. Moreover, we propose two methods for selecting a limited number of tangent lines effective for piecewise-linear approximations. We assess the efficacy of our method through computational experiments using synthetic and real-world datasets. Our methods provide better log-likelihood values than do conventional greedy algorithms in selecting tangent lines. In addition, our MIQO formulation delivers better out-of-sample prediction performance than do forward stepwise selection and L1-regularized estimation, especially in low-noise situations.

scholarly journals Speed Optimization for Container Ship Fleet Deployment Considering Fuel Consumption

2021 ◽  
Vol 13 (9) ◽  
pp. 5242
Author(s):  
Chao-Feng Gao ◽  
Zhi-Hua Hu
Keyword(s):  
Energy Consumption ◽  
Approximation Algorithm ◽  
Fuel Consumption ◽  
Programming Model ◽  
Mixed Integer ◽  
Piecewise Linear Approximation ◽  
Container Ship ◽  
Trade Offs ◽  
Speed Optimization ◽  
Fleet Deployment

In recent years, low energy consumption has become the common choice of economic development in the world. In order to control energy consumption, shipping line speed optimization has become strategically important. to reduce fuel consumption, this study optimizes the container ship fleet deployment problem by adopting the strategy of adjusting each leg of each route’s sailing speed. To calculate fuel consumption more accurately, both sailing speed and the ship's payload are considered. A multi-objective mixed integer nonlinear programming model is established to optimize the allocation of liner routes with multiple ship types on multiple routes. A linear outer-approximation algorithm and an improved piecewise linear approximation algorithm are used for linearization. If segments of an interval increase, the results will be more accurate but will take more time to compute. As fuel prices increase, to make trade-offs among economic and environmental considerations, the shipping company is adopting the “adding ship and slow down its speed” strategy, which verifies the validity and applicability of the established model.

scholarly journals New Algorithm to Solve Mixed Integer Quadratically Constrained Quadratic Programming Problems Using Piecewise Linear Approximation

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 198
Author(s):  
Loay Alkhalifa ◽  
Hans Mittelmann
Keyword(s):  
Quadratic Programming ◽  
Linear Approximation ◽  
Piecewise Linear ◽  
Linear Optimization ◽  
Nonlinear Problems ◽  
Computational Experiments ◽  
Mixed Integer ◽  
Piecewise Linear Approximation ◽  
Quadratically Constrained Quadratic Programming ◽  
Quadratically Constrained

Techniques and methods of linear optimization underwent a significant improvement in the 20th century which led to the development of reliable mixed integer linear programming (MILP) solvers. It would be useful if these solvers could handle mixed integer nonlinear programming (MINLP) problems. Piecewise linear approximation (PLA) is one of most popular methods used to transform nonlinear problems into linear ones. This paper will introduce PLA with brief a background and literature review, followed by describing our contribution before presenting the results of computational experiments and our findings. The goals of this paper are (a) improving PLA models by using nonuniform domain partitioning, and (b) proposing an idea of applying PLA partially on MINLP problems, making them easier to handle. The computational experiments were done using quadratically constrained quadratic programming (QCQP) and MIQCQP and they showed that problems under PLA with nonuniform partition resulted in more accurate solutions and required less time compared to PLA with uniform partition.

Designing a drone assisted sample collection and testing system during epidemic outbreaks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sayan Chakraborty ◽  
Raviarun Arumugaraj Nadar ◽  
Aviral Tiwari
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Clinical Sample ◽  
Diagnostic System ◽  
Mixed Integer ◽  
Effective Control ◽  
Sample Collection ◽  
Two Phase ◽  
Content Type ◽  
Urban City

Purpose A major component in managing pandemic outbreaks involves testing the suspected individuals and isolating them to avoid transmission in the community. This requires setting up testing centres for diagnosis of the infected individuals, which usually involves movement of either patient from their residence to the testing centre or personnel visiting the patient, thus aggregating the risk of transmission to localities and testing centres. The purpose of this paper is to investigate and minimize such movements by developing a drone assisted sample collection and diagnostic system. Design/methodology/approach Effective control of an epidemic outbreak calls for a rapid response and involves testing suspected individuals and isolating them to avoid transmission in the community. This paper presents the problem in a two-phase manner by locating sample collection centres while assigning neighbourhoods to these collection centres and thereafter, assigning collection centres to nearest testing centres. To solve the mathematical model, this study develops a mixed-integer linear programming model and propose an integrated genetic algorithm with a local search-based approach (GA-LS) to solve the problem. Findings Proposed approach is demonstrated as a case problem in an Indian urban city named Kolkata. Computational results show that the integrated GA-LS approach is capable of producing good quality solutions within a short span of time, which aids to the practicality in the circumstance of a pandemic. Social implications The COVID-19 pandemic has shown that the large-scale outbreak of a transmissible disease may require a restriction of movement to take control of the exponential transmission. This paper proposes a system for the location of clinical sample collection centres in such a way that drones can be used for the transportation of samples from the neighbourhood to the testing centres. Originality/value Epidemic outbreaks have been a reason behind a major number of deaths across the world. The present study addresses the critical issue of identifying locations of temporary sample collection centres for drone assisted testing in major cities, which is by its nature unique and has not been considered by any other previous literature. The findings of this study will be of particular interest to the policy-makers to build a more robust epidemic resistance.

Traffic Signal Priority Control Strategy for Connected Emergency Vehicles with Dilemma Zone Protection for Freight Vehicles

2021 ◽  
pp. 036119812110391
Author(s):  
Debashis Das ◽  
Niraj Vasant Altekar ◽  
K. Larry Head ◽  
Faisal Saleem
Keyword(s):  
Control Method ◽  
Programming Model ◽  
Negative Impact ◽  
Optimal Schedule ◽  
Mixed Integer ◽  
Emergency Vehicle ◽  
Test Bed ◽  
Dilemma Zone ◽  
Emergency Vehicles ◽  
Freight Vehicles

This paper presents an emergency vehicle priority control system based on connected vehicle technology, called MMITSS priority. Traditional preemption does not consider the effect of the current traffic situation, such as the presence of a freight vehicle in the dilemma zone, on an opposing movement and can have a significant negative impact on the minor movements of vehicles. A mixed integer linear programming model is developed which can consider the priority requests from multiple emergency vehicles and dilemma zone requests from freight vehicles that could be trapped in the dilemma zone. The optimization model provides an optimal schedule that minimizes the total weighted priority request delays and dilemma zone request, as well as some flexibility to adapt to other vehicles in real time. The flexible implementation of the optimal signal timing schedule is designed to improve the mobility of the non-emergency vehicles. The approach has been tested and evaluated using microscopic traffic simulation. The simulation experiments show that the proposed priority control method is able to improve the travel time of the vehicles on the minor street while ensuring safe passage of the freight vehicle at the dilemma zone without significantly delaying the emergency vehicles. The method is implemented at the Maricopa County SMARTDrive ProgramSM test bed in Anthem, Arizona.

scholarly journals A mixed integer programming model for a continuous move transportation problem with service constraints

2017 ◽  
Vol 7 (13) ◽  
Author(s):  
J. Fabián López
Keyword(s):  
Vehicle Routing ◽  
Real World ◽  
Large Scale ◽  
Programming Model ◽  
Time Windows ◽  
Mixed Integer ◽  
Pickup And Delivery ◽  
Np Hard ◽  
Routing Problem ◽  
Service Constraints

Key words: Genetic algorithms, logistics routing, metaheuristics, scheduling, time windowsAbstract. We consider a Pickup and Delivery Vehicle Routing Problem (PDP) commonly encountered in real-world logistics operations. The problem involves a set of practical complications that have received little attention in the vehicle routing literature. In this problem, there are multiple vehicle types available to cover a set of pickup and delivery requests, each of which has pickup time windows and delivery time windows. Transportation orders and vehicle types must satisfy a set of compatibility constraints that specify which orders cannot be covered by which vehicle types. In addition we include some dock servicecapacity constraints as is required on common real world operations. This problem requires to be attended on large scale instances (orders ≥ 500), (vehicles ≥ 150). As a generalization of the traveling salesman problem, clearly this problem is NP-hard. The exact algorithms are too slow for large scale instances. The PDP-TWDS is both a packing problem (assign order tovehicles), and a routing problem (find the best route for each vehicle). We propose to solve the problem in three stages. The first stage constructs initials solutions at aggregate level relaxing some constraints on the original problem. The other two stages imposes time windows and dock service constraints. Our results are favorable finding good quality solutions in relatively short computational times.Palabras claves. Algoritmos genéticos, logística de ruteo, metahurística, programación, ventana de horarioResumen. En la solución de problemas combinatorios, es importante evaluar el costobeneficio entre la obtención de soluciones de alta calidad en detrimento de los recursos computacionales requeridos. El problema planteado es para el ruteo de un vehículo con entrega y recolección de producto y con restricciones de ventana de horario. En la práctica, dicho problema requiere ser atendido con instancias de gran escala (nodos ≥100). Existe un fuerte porcentaje de ventanas de horario activas (≥90%) y con factores de amplitud ≥75%. El  problema es NP-hard y por tal motivo la aplicación de un método de solución exacta para resolverlo en la práctica, está limitado por el tiempo requerido para la actividad de ruteo. Se propone un algoritmo genético especializado, el cual ofrece soluciones de buena calidad (% de optimalidad aceptables) y en tiempos de ejecución computacional que hacen útil su aplicación en la práctica de la logística. Para comprobar la eficacia de la propuesta algorítmica se desarrolla un diseño experimental el cual hará uso de las soluciones óptimas obtenidas mediante un algoritmo de ramificación y corte sin límite de tiempo. Los resultados son favorables.

Sign in / Sign up

Export Citation Format

Share Document