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.

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 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.

An evaluation of the simulated annealing algorithm for solving the area-restricted harvest-scheduling model against optimal benchmarks

2005 ◽  
Vol 35 (10) ◽  
pp. 2500-2509 ◽  
Author(s):  
Kevin A Crowe ◽  
J D Nelson
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Computing Time ◽  
Multiple Time ◽  
Harvest Scheduling ◽  
Problem Size ◽  
Solution Quality ◽  
Annealing Algorithm ◽  
Objective Function Values ◽  
Problem Instances

A common approach for incorporating opening constraints into harvest scheduling is through the area-restricted model. This model is used to select which stands to include in each opening while simultaneously determining an optimal harvest schedule over multiple time periods. In this paper we use optimal benchmarks from a range of harvest scheduling problem instances to test a metaheuristic algorithm, simulated annealing, that is commonly used to solve these problems. Performance of the simulated annealing algorithm was assessed over a range of problem attributes such as the number of forest polygons, age-class distribution, and opening size. In total, 29 problem instances were used, ranging in size from 1269 to 36 270 binary decision variables. Overall, the mean objective function values found with simulated annealing ranged from approximately 87% to 99% of the optima after 30 min of computing time, and a moderate downward trend of the relationship between problem size and solution quality was observed.

THE SIMULATED ANNEALING GLOBAL SEARCH ALGORITHM APPLIED TO THE CRYSTALLOGRAPHY OF SURFACES BY LEED

1999 ◽  
Vol 06 (05) ◽  
pp. 651-661 ◽  
Author(s):  
V. B. NASCIMENTO ◽  
V. E. DE CARVALHO ◽  
C. M. C. DE CASTILHO ◽  
E. A. SOARES ◽  
C. BITTENCOURT ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Surface Structure ◽  
Structure Determination ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Global Search ◽  
Structural Parameter ◽  
Original Algorithm ◽  
R Factor ◽  
Annealing Algorithm

Surface structure determination by Low Energy Electron Diffraction (LEED) is based on a comparison between experimentally measured and theoretically calculated intensity versus energy I(V) curves for the diffracted beams. The level of agreement between these, for different structural models, is quantified using a correlation function, the so-called R factor. Minimizing this factor allows one to choose the best structure for which the theoretical simulations are computed. Surface structure determination thus requires an exhaustive search of structural parameter space in order to minimize the R factor. This minimization is usually performed by the use of directed search methods, although they have serious limitations, most notably their inability to distinguish between false and real structures corresponding to local and global R factor minima. In this work we present the implementation of a global search method based on the simulated annealing algorithm, as suggested earlier by Rous, using the Van Hove and Tong standard LEED code and the results of its application to the determination of the structure of the Ag(111) and CdTe(110) surfaces. Two different R factors, RP and R1, have been employed in the structural searches, and the statistical topographies of these two factors were studied. We have also implemented a variation of the simulated annealing algorithm (Fast Simulated Annealing) and applied it to these same two systems. Some preliminary results obtained with this algorithm were used to compare its performance with the original algorithm proposed by Rous.

Sign in / Sign up

Export Citation Format

Share Document