scholarly journals Evacuation Network Optimization Model with Lane-Based Reversal and Routing

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xing Zhao ◽  
Zhao-yan Feng ◽  
Yan Li ◽  
Antoine Bernard
Keyword(s):  
Emergency Response ◽  
Network Optimization ◽  
Optimization Model ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Single Arc ◽  
Annealing Algorithm ◽  
Upper Level ◽  
Reversal Design ◽  
Lane Based

Sometimes, the evacuation measure may seem to be the best choice as an emergency response. To enable an efficiency evacuation, a network optimization model which integrates lane-based reversal design and routing with intersection crossing conflict elimination for evacuation is constructed. The proposed bilevel model minimizes the total evacuation time to leave the evacuation zone. A tabu search algorithm is applied to find an optimal lane reversal plan in the upper-level. The lower-level utilizes a simulated annealing algorithm to get two types of “a single arc for an intersection approach” and “multiple arcs for an intersection approach” lane-based route plans with intersection crossing conflict elimination. An experiment of a nine-intersection evacuation zone illustrates the validity of the model and the algorithm. A field case with network topology of Jianye District around the Nanjing Olympics Sports Center is studied to show the applicability of this algorithm.

scholarly journals Scheduling Optimization of Sugarcane Harvest Using Simulated Annealing Algorithm

2018 ◽  
Vol 5 (2) ◽  
pp. 138-147
Author(s):  
Eka Nur Afifah ◽  
Alamsyah Alamsyah ◽  
Endang Sugiharti
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Planning Process ◽  
Search Algorithm ◽  
Raw Material ◽  
Local Search Algorithm ◽  
Harvest Scheduling ◽  
Actual Result ◽  
Material Supply ◽  
Annealing Algorithm

Scheduling is one of the important part in production planning process. One of the factor that influence the smooth production process is raw material supply. Sugarcane supply as the main raw material in the making of sugar is the most important componen. The algorithm that used in this study was Simulated Annealing (SA) algorithm. SA apability to accept the bad or no better solution within certain time distinguist it from another local search algorithm. Aim of this study was to implement the SA algorithm in scheduling the sugarcane harvest process so that the amount of sugarcane harvest not so differ from mill capacity of the factory. Data used in this study were 60 data from sugarcane farms that ready to cut and mill capacity 1660 tons. Sugarcane harvest process in 19 days producing 33043,76 tons used SA algorithm and 27089,47 tons from factory actual result. Based on few experiments, obtained sugarcane harvest average by SA algorithm was 1651,63 tons per day and factory actual result was 1354,47 tons. Result of harvest scheduling used SA algorithm showed not so differ average from mill capacity of factory. Truck uses scheduling by SA algorithm showed average 119 trucks per day while from factory actual result was 156 trucks. With the same harvest time, SA algorithm result was greater  and the amount of used truck less than actual result of factory. Thus, can be concluded SA algorithm can make the scheduling of sugarcane harvest become more optimall compared to other methods applied by the factory nowdays.

scholarly journals Fault Localization Based on Hybrid Genetic Simulated Annealing Algorithm

2021 ◽  
Vol 28 (2) ◽  
pp. 101-109
Keyword(s):  
Simulated Annealing ◽  
Software Testing ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Fault Localization ◽  
Good Effect ◽  
Test Case ◽  
Genetic Simulated Annealing Algorithm ◽  
Starting Point ◽  
Annealing Algorithm

Software testing is an important stage in the software development process, which is the key to ensure software quality and improve software reliability. Software fault localization is the most important part of software testing. In this paper, the fault localization problem is modeled as a combinatorial optimization problem, using the function call path as a starting point. A heuristic search algorithm based on hybrid genetic simulated annealing algorithm is used to locate software defects. Experimental results show that the fault localization method, which combines genetic algorithm, simulated annealing algorithm and function correlation analysis method, has a good effect on single fault localization and multi-fault localization. It greatly reduces the requirement of test case coverage and the burden of the testers, and improves the effect of fault localization.

scholarly journals A Stone Resource Assignment Model under the Fuzzy Environment

2012 ◽  
Vol 2012 ◽  
pp. 1-26 ◽  
Author(s):  
Liming Yao ◽  
Jiuping Xu ◽  
Feng Guo
Keyword(s):  
Simulated Annealing Algorithm ◽  
Optimization Method ◽  
Resource Assignment ◽  
Possibility Measure ◽  
Fuzzy Environment ◽  
Assignment Model ◽  
Simulation Based ◽  
Annealing Algorithm ◽  
Upper Level

This paper proposes a bilevel multiobjective optimization model with fuzzy coefficients to tackle a stone resource assignment problem with the aim of decreasing dust and waste water emissions. On the upper level, the local government wants to assign a reasonable exploitation amount to each stone plant so as to minimize total emissions and maximize employment and economic profit. On the lower level, stone plants must reasonably assign stone resources to produce different stone products under the exploitation constraint. To deal with inherent uncertainties, the object functions and constraints are defuzzified using a possibility measure. A fuzzy simulation-based improved simulated annealing algorithm (FS-ISA) is designed to search for the Pareto optimal solutions. Finally, a case study is presented to demonstrate the practicality and efficiency of the model. Results and a comparison analysis are presented to highlight the performance of the optimization method, which proves to be very efficient compared with other algorithms.

Numerically Optimizing the Distribution of the Infrared Radiative Energy on a Surface of a Thermoplastic Sheet Surface

2018 ◽  
Vol 140 (10) ◽  
Author(s):  
Kahina Bachir Cherif ◽  
Djamal Rebaine ◽  
Fouad Erchiqui ◽  
Issouf Fofana ◽  
Nabil Nahas
Keyword(s):  
Simulated Annealing Algorithm ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Numerical Study ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Radiative Energy ◽  
Sheet Surface ◽  
Integer Linear Programming Problem ◽  
Annealing Algorithm

This paper addresses the problem of distributing uniformly the energy flux intercepted by a thermoplastic sheet surface during the infrared radiation. To do so, we discretized this problem and then formulated it as an integer linear programming problem, for which we applied two meta-heuristic algorithms namely the simulated annealing algorithm (SA) and harmony search algorithm (HSA), in order to minimize the corresponding objective function. The results produced by the numerical study we conducted on the performance of both algorithms are presented and discussed.

Study on Mechanical Bearing Capacity with Tabu Search Algorithm for Multi-Vehicle and Multi-Cargo Loading Problem

2013 ◽  
Vol 345 ◽  
pp. 3-6
Author(s):  
Chun Yu Ren
Keyword(s):  
Tabu Search ◽  
Bearing Capacity ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Tabu Search Algorithm ◽  
Search Efficiency ◽  
Neighborhood Structure ◽  
Loading Problem ◽  
Annealing Algorithm

This paper studies multi-vehicle and multi-cargo loading problem under the limited mechanical bearing capacity. Tabu search algorithm is an algorithm based on neighborhood search. According to the features of the problem, the essay centered the construct initial solution to construct neighborhood structure. Firstly, for the operation, 1-move and 2-opt were applied. Secondly, through utilizing Boltzmann mechanism of simulated annealing algorithm, it can also fasten the speed of convergence, and boost the search efficiency. Finally, the good performance of this algorithm can be proved by experiment calculation and the mechanical engineering examples.

Logistics Distribution Routing Optimization Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 1740-1743 ◽  
Author(s):  
Zhang Chun Hua ◽  
Hua Xin ◽  
Zhang Wei
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Memory Function ◽  
Memory Device ◽  
Routing Problem ◽  
Logistics Distribution ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

Logistics distribution involves preparing goods in the distribution center or logistics node for most reasonable delivery according to the requirements of customers. Genetic algorithm is a random global search algorithm based on the principle of natural evolution. It can be a good solution to optimize the distribution routes. This paper combines genetic algorithm and the simulated annealing algorithm, to which memory device is added, in order to avoid best result losing in the crossover operator of the genetic algorithm. The experimental results show that a memory function with this genetic simulated annealing algorithm in solving the logistics distribution routing problem, can not only get a higher qualified solution, but can also significantly reduce the evolutionary generation that algorithm requires, and obtain solution to the problem in less time.

scholarly journals Research on Flexible Equipment Maintenance Process Scheduling Problems Based on HNSGSA Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Weixing Song ◽  
Jianshe Kang ◽  
Jingjing Wu ◽  
Hui Jia ◽  
Huiqiang Chang
Keyword(s):  
Simulated Annealing ◽  
Optimization Model ◽  
Simulated Annealing Algorithm ◽  
Equipment Maintenance ◽  
Scheduling Problems ◽  
Process Scheduling ◽  
Nsga Ii ◽  
Maintenance Work ◽  
Annealing Algorithm ◽  
Maintenance Process

The characteristics of military equipment maintenance work are analyzed. According to the actual needs of the army, the optimization objective is designed, and a multiobjective flexible maintenance process optimization model is built based on the maintenance business organization process. Combining the advantages of NSGA-II algorithm and the simulated annealing algorithm, this paper proposes a novel improved HNSGSA algorithm, of which algorithm flow is detailed. In accordance with the requirements of the optimization model, this paper also specifically designs the coding methods of the process sequence, the equipment selection and the process scheduling, and the corresponding cross mutation method. The feasibility of the built model is verified by the actual data of maintenance business. And, the superiority, accuracy, and effectiveness of the proposed algorithm are further validated by the comparison with the NSGA-II algorithm and the simulated annealing algorithm, providing a scientific reference for the army to carry out equipment maintenance.

scholarly journals A NOVEL HYBRIDIZED ALGORITHM FOR REDUCTION OF REAL POWER LOSS

2017 ◽  
Vol 5 (11) ◽  
pp. 316-324
Author(s):  
K. Lenin
Keyword(s):  
Simulated Annealing ◽  
Power Loss ◽  
Simulated Annealing Algorithm ◽  
Reactive Power ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Real Power ◽  
Annealing Algorithm ◽  
Gravitational Search ◽  
The Individual

This paper proposes Hybridization of Gravitational Search algorithm with Simulated Annealing algorithm (HGS) for solving optimal reactive power problem. Individual position modernize strategy in Gravitational Search Algorithm (GSA) may cause damage to the individual position and also the local search capability of GSA is very weak. The new HGS algorithm introduced the idea of Simulated Annealing (SA) into Gravitational Search Algorithm (GSA), which took the Metropolis-principle-based individual position modernize strategy to perk up the particle moves, & after the operation of gravitation, Simulated Annealing operation has been applied to the optimal individual. In order to evaluate the efficiency of the proposed Hybridization of Gravitational Search algorithm with Simulated Annealing algorithm (HGS), it has been tested on standard IEEE 118 & practical 191 bus test systems and compared to the standard reported algorithms. Simulation results show that HGS is superior to other algorithms in reducing the real power loss and voltage profiles also within the limits.

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