scholarly journals Emergency Resource Location and Allocation in Traffic Contingency Plan for Sports Mega-Event

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ling Shen ◽  
Jian Lu ◽  
Ling Deng ◽  
Manman Li
Keyword(s):  
Programming Model ◽  
Mixed Integer ◽  
Resource Location ◽  
Contingency Plan ◽  
Winter Olympics ◽  
The Road ◽  
Material Resources ◽  
Real Coded Genetic Algorithm ◽  
Mega Event ◽  
Rescue Time

In view of the transactional and textual features on issue handling in mega-event traffic contingency plan, this paper gives a quantitative method for emergency resources location and allocation. Given that the requirement on safeguards in the sports mega-events is temporary and stringent, we first divide the facilities into temporary emergency facilities and fixed emergency facilities and the resources into material resources and human resources. Considering the uncertainty of emergency incidents, we then construct a mixed integer linear programming model. To solve this model, the bisection method is used to import the material quantity placed in each emergency facility, and the shortest path algorithm is used to import the rescue time matrix. Considering the slowness of convergence rate when the road network is large, a modified matrix real-coded genetic algorithm is designed with the crossover operator based on a greedy algorithm. The application of the model and algorithms is validated by the case based on 2022 Beijing Winter Olympics. Sensitivity analysis of some important parameters is also conducted to provide insights for traffic emergency resources management in sports mega-event.

scholarly journals Resilience-Based Optimization of Postdisaster Restoration Strategy for Road Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xinhua Mao ◽  
Jibiao Zhou ◽  
Changwei Yuan ◽  
Dan Liu
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Network Performance ◽  
Network Flows ◽  
Programming Model ◽  
Mixed Integer ◽  
Total System ◽  
Network Resilience ◽  
The Road ◽  
Restoration Strategies

This work proposes a framework for the optimization of postdisaster road network restoration strategies from a perspective of resilience. The network performance is evaluated by the total system travel time (TSTT). After the implementation of a postdisaster restoration schedule, the network flows in a certain period of days are on a disequilibrium state; thus, a link-based day-to-day traffic assignment model is employed to compute TSTT and simulate the traffic evolution. Two indicators are developed to assess the road network resilience, i.e., the resilience of performance loss and the resilience of recovery rapidity. The former is calculated based on TSTT, and the latter is computed according to the restoration makespan. Then, we formulate the restoration optimization problem as a resilience-based bi-objective mixed integer programming model aiming to maximize the network resilience. Due to the NP-hardness of the model, a genetic algorithm is developed to solve the model. Finally, a case study is conducted to demonstrate the effectiveness of the proposed method. The effects of key parameters including the number of work crews, travelers’ sensitivity to travel time, availability of budget, and decision makers’ preference on the values of the two objectives are investigated as well.

An optimization model for annual harvest planning

2004 ◽  
Vol 34 (8) ◽  
pp. 1747-1754 ◽  
Author(s):  
Jenny Karlsson ◽  
Mikael Rönnqvist ◽  
Johan Bergström
Keyword(s):  
Programming Model ◽  
Mixed Integer ◽  
Maintenance Cost ◽  
Road Maintenance ◽  
Continuous Variables ◽  
Home Base ◽  
The Road ◽  
Wood Processing ◽  
Production Capacities ◽  
And Storage

The problem we consider is annual harvesting planning from the perspective of Swedish forest companies. The main decisions deal with which areas to harvest during an annual period so that the wood-processing facilities receive the required amount of assortments. Each area has a specific size and composition of assortments, and the choice of harvesting areas affects the production level of different assortments. We need to decide which harvest team to use for each area, considering that each team has different skills, home base, and production capacities. Also, the weather and road conditions vary during the year. Some roads cannot be used during certain time periods and others should be avoided. The road maintenance cost varies during the year. Also, some areas cannot be harvested during certain periods. Overall decisions about transportation and storage are also included. In this paper, we develop a mixed integer programming model for the problem. There are binary variables associated with harvesting, allocation of teams, and road-opening decisions. The other decisions are represented by continuous variables. We solve this problem directly with CPLEX 8.1 within a practical solution time limit. Computational results from a major Swedish forest company are presented.

Analysis on the Penetration Level of Wind Farm Considering Transient Stability Constraint and Uncertainty of Wind Power

2020 ◽  
Vol 13 (7) ◽  
pp. 1078-1086
Author(s):  
Bai Hao ◽  
Huang Andi ◽  
Zhou Changcheng
Keyword(s):  
Wind Power ◽  
Energy Function ◽  
Wind Farm ◽  
Transient Stability ◽  
Programming Model ◽  
Forecast Error ◽  
Credibility Theory ◽  
Mixed Integer ◽  
Stability Constraint ◽  
Level Problem

Background: The penetration level of a wind farm with transient stability constraint and static security constraint has been a key problem in wind power applications. Objective: The study explores maximum penetration level problem of wind considering transient stability constraint and uncertainty of wind power out, based on credibility theory and corrected energy function method. Methods: According to the corrected energy function, the transient stability constraint of the power grid is transferred to the penetration level problem of a wind farm. Wind speed forecast error is handled as a fuzzy variable to express the uncertainty of wind farm output. Then this paper builds a fuzzy chance-constrained model to calculate wind farm penetration level. To avoid inefficient fuzzy simulation, the model is simplified to a mixed integer linear programming model. Results: The results validate the proposed model and investigate the influence of grid-connection node, wind turbine characteristic, fuzzy reliability index, and transient stability index on wind farm penetration level. Conclusion: The result shows that the model proposed in this study can consider the uncertainty of wind power out and establish a quantitative transient stability constraint to determine the wind farm penetration level with a certain fuzzy confidence level.

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

scholarly journals New general mixed-integer linear programming model for mobile workforce management

2021 ◽  
Author(s):  
András Éles ◽  
István Heckl ◽  
Heriberto Cabezas
Keyword(s):  
Programming Model ◽  
Time Windows ◽  
Mutual Exclusion ◽  
Parallel Execution ◽  
Mixed Integer ◽  
Management Problem ◽  
Routing Problem ◽  
Workforce Management ◽  
Computational Performance ◽  
Wide Range

AbstractA mathematical model is introduced to solve a mobile workforce management problem. In such a problem there are a number of tasks to be executed at different locations by various teams. For example, when an electricity utility company has to deal with planned system upgrades and damages caused by storms. The aim is to determine the schedule of the teams in such a way that the overall cost is minimal. The mobile workforce management problem involves scheduling. The following questions should be answered: when to perform a task, how to route vehicles—the vehicle routing problem—and the order the sites should be visited and by which teams. These problems are already complex in themselves. This paper proposes an integrated mathematical programming model formulation, which, by the assignment of its binary variables, can be easily included in heuristic algorithmic frameworks. In the problem specification, a wide range of parameters can be set. This includes absolute and expected time windows for tasks, packing and unpacking in case of team movement, resource utilization, relations between tasks such as precedence, mutual exclusion or parallel execution, and team-dependent travelling and execution times and costs. To make the model able to solve larger problems, an algorithmic framework is also implemented which can be used to find heuristic solutions in acceptable time. This latter solution method can be used as an alternative. Computational performance is examined through a series of test cases in which the most important factors are scaled.

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