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.

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 SIMULATED ANNEALING ALGORITHM FOR FEATURE SELECTION

2015 ◽  
Vol 15 (2) ◽  
pp. 6471-6479
Author(s):  
Francisca Rosario ◽  
Dr. K. Thangadurai
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Weather Data ◽  
Computational Technique ◽  
Slow Cooling ◽  
Solution Quality ◽  
Data Set ◽  
Annealing Algorithm

In the process of physical annealing, a solid is heated until all particles randomly arrange themselves forming the liquid state. A slow cooling process is then used to crystallize the liquid. This process is known as simulated annealing. Simulated annealing is stochastic computational technique that searches for global optimum solutions in optimization problems. The main goal here is to give the algorithm more time in the search space exploration by accepting moves, which may degrade the solution quality, with some probability depending on a parameter called temperature. In this discussion the simulated annealing algorithm is implemented in pest and weather data set for feature selection and it reduces the dimension of the attributes through specified iterations.

scholarly journals Near-optimal state assignment in a finite state machine

2021 ◽  
Vol 3 (1) ◽  
pp. 9-16
Author(s):  
Reinaldo Da Silva Ribeiro ◽  
Rafael Lima de Carvalho ◽  
Tiago Da Silva Almeida
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Systems Design ◽  
The State ◽  
Solution Quality ◽  
State Assignment ◽  
Finite State ◽  
Annealing Algorithm ◽  
The Impact

In this research, the application of the Simulated Annealing algorithm to solve the state assignment problem in finite state machines is investigated. The state assignment is a classic NP-Complete problem in digital systems design and impacts directly on both area and power costs as well as on the design time. The solutions found in the literature uses population-based methods that consume additional computer resources. The Simulated Annealing algorithm has been chosen because it does not use populations while seeking a solution. Therefore, the objective of this research is to evaluate the impact on the quality of the solution when using the Simulated Annealing approach. The proposed solution is evaluated using the LGSynth89 benchmark and compared with other approaches in the state-of-the-art. The experimental simulations point out an average loss in solution quality of 14.29%, while an average processing performance of 58.67%. The results indicate that it is possible to have few quality losses with a significant increase in processing performance.

Study on Hybrid Genetic Simulated Annealing Algorithm for Multi-Vehicle and Multi-Cargo Loading Problem

2010 ◽  
Vol 171-172 ◽  
pp. 167-170 ◽  
Author(s):  
Xiao Bo Wang ◽  
Jin Ying Sun ◽  
Chun Yu Ren
Keyword(s):  
Simulated Annealing ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Solution Quality ◽  
Genetic Simulated Annealing Algorithm ◽  
Algorithm Construct ◽  
Loading Problem ◽  
Annealing Algorithm ◽  
Crossover And Mutation

This paper studies multi-vehicle and multi-cargo loading problem under the limited loading capacity. Hybrid genetic simulated annealing algorithm is used to get the optimization solution. Firstly, adopt hybrid coding so as to make the problem more succinctly. On the basis of cubage-weight balance algorithm, construct initial solution to improve the feasibility. Adopt the improved non-uniform mutation so as to enhance local search ability of chromosomes. Secondly, through utilizing Boltzmann mechanism of simulated annealing algorithm, control crossover and mutation operation of genetic algorithm, search efficiency so as to improve the solution quality of algorithm. Finally, the example can be shown that the above model and algorithm is effective and can provide for large-scale ideas to solve practical problems.

Modeling and solution of maximal covering problem considering gradual coverage with variable radius over multi-periods

2018 ◽  
Vol 52 (4-5) ◽  
pp. 1245-1260 ◽  
Author(s):  
Alireza Eydi ◽  
Javad Mohebi
Keyword(s):  
Simulated Annealing ◽  
Facility Location ◽  
Location Problem ◽  
Simulated Annealing Algorithm ◽  
Mixed Integer ◽  
Multiple Time ◽  
Variable Radius ◽  
Maximal Covering ◽  
Annealing Algorithm ◽  
Time Periods

Facility location is a critical component of strategic planning for public and private firms. Due to high cost of facility location, making decisions for such a problem has become an important issue which have gained a large deal of attention from researchers. This study examined the gradual maximal covering location problem with variable radius over multiple time periods. In gradual covering location problem, it is assumed that full coverage is replaced by a coverage function, so that increasing the distance from the facility decreases the amount of demand coverage. In variable radius covering problems, however, each facility is considered to have a fixed cost along with a variable cost which has a direct impact on the coverage radius. In real-world problems, since demand may change over time, necessitating relocation of the facilities, the problem can be formulated over multiple time periods. In this study, a mixed integer programming model was presented in which not only facility capacity was considered, but also two objectives were followed: coverage maximization and relocation cost minimization. A metaheuristic algorithm was presented to solve the maximal covering location problem. A simulated annealing algorithm was proposed, with its results presented. Computational results and comparisons demonstrated good performance of the simulated annealing algorithm.

scholarly journals Simulated Annealing Algorithm Combined with Chaos for Task Allocation in Real-Time Distributed Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Wenbo Wu ◽  
Jiahong Liang ◽  
Xinyu Yao ◽  
Baohong Liu
Keyword(s):  
Simulated Annealing ◽  
Distributed Systems ◽  
Real Time ◽  
Task Allocation ◽  
Simulated Annealing Algorithm ◽  
Solution Space ◽  
Solution Quality ◽  
Adaptive Schemes ◽  
Adaptive Simulated Annealing ◽  
Annealing Algorithm

This paper addresses the problem of task allocation in real-time distributed systems with the goal of maximizing the system reliability, which has been shown to be NP-hard. We take account of the deadline constraint to formulate this problem and then propose an algorithm called chaotic adaptive simulated annealing (XASA) to solve the problem. Firstly, XASA begins with chaotic optimization which takes a chaotic walk in the solution space and generates several local minima; secondly XASA improves SA algorithm via several adaptive schemes and continues to search the optimal based on the results of chaotic optimization. The effectiveness of XASA is evaluated by comparing with traditional SA algorithm and improved SA algorithm. The results show that XASA can achieve a satisfactory performance of speedup without loss of solution quality.

A Metaheuristic for the Pickup and Delivery Problem with Time Windows

2003 ◽  
Vol 12 (02) ◽  
pp. 173-186 ◽  
Author(s):  
Haibing Li ◽  
Andrew Lim
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Time Windows ◽  
Pickup And Delivery ◽  
Data Sets ◽  
Search Procedure ◽  
Delivery Problem ◽  
Pickup And Delivery Problem ◽  
Annealing Algorithm ◽  
Problem Instances

In this paper, we propose a metaheuristic to solve the pickup and delivery problem with time windows. Our approach is a tabu-embedded simulated annealing algorithm which restarts a search procedure from the current best solution after several non-improving search iterations. The computational experiments on the six newly-generated different data sets marked our algorithm as the first approach to solve large multiple-vehicle PDPTW problem instances with various distribution properties.

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