Applying simulated annealing using different methods for the neighborhood search in forest planning problems

2014 ◽  
Vol 233 (3) ◽  
pp. 700-710 ◽  
Author(s):  
Paulo Borges ◽  
Tron Eid ◽  
Even Bergseng
Keyword(s):  
Simulated Annealing ◽  
Neighborhood Search ◽  
Forest Planning ◽  
Planning Problems

scholarly journals Performance of four meta-heuristics to solve a forestry production planning problem

2020 ◽  
Vol 12 ◽  
pp. 1-5
Author(s):  
Emanuelly Canabrava Magalhães ◽  
Carlos Alberto Araújo Júnior ◽  
Francisco Conesa Roca ◽  
Mylla Vyctória Coutinho Sousa
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Net Present Value ◽  
Clonal Selection ◽  
Computational Effort ◽  
Neighborhood Search ◽  
Forest Planning ◽  
Planning Problem ◽  
Clonal Selection Algorithm ◽  
Forest Production

The use of artificial intelligence as a tool to aid in the planning of forest production has gained more and more space. Highlighting the metaheuristics, due to the ability to generate optimal solutions for a given optimization problem in a short time, without great computational effort. The present study aims to evaluate the performance of the metaheuristics Genetic Algorithm, Simulated Annealing, Variable Neighborhood Search and Clonal Selection Algorithm applied in a model of regulation of forest production. It was considered a planning horizon of 16 years, in which the model aims to maximize the Net Present Value (NPV), having as restrictions age of cut between 5 and 7 years and minimum and maximum logging demand of 140,000 and 160,000 m3, respectively. Different combinations of configurations were considered for each of the metaheuristics, 30-second processing time and 30 replicates for each configuration, all processing being performed in MeP - Metaheuristics for forest Planning software. The Simulated Annealing metaheuristic obtained the best results when compared to the others, reaching the minimum and maximum demand demanded in all tested configurations, in contrast, the Genetic Algorithm was the one with the worst performance. Thus, the capacity to use metaheuristics as a tool for forest planning is observed.

scholarly journals Improved Simulated Annealing Based Network Model for E-Recycling Reverse Logistics Decisions under Uncertainty

2018 ◽  
Vol 2018 ◽  
pp. 1-17
Author(s):  
Lei Wang ◽  
Mark Goh ◽  
Ronggui Ding ◽  
Vikas Kumar Mishra
Keyword(s):  
Simulated Annealing ◽  
Reverse Logistics ◽  
Electronic Waste ◽  
Neighborhood Search ◽  
Generation Process ◽  
Candidate Solution ◽  
Novel Approach ◽  
Numerical Experimentation ◽  
The Government ◽  
Quantitative Importance

Electronic waste recycle (e-recycling) is gaining increasing importance due to greater environmental concerns, legislation, and corporate social responsibility. A novel approach is explored for designing the e-recycling reverse logistics network (RLN) under uncertainty. The goal is to obtain a solution, i.e., increasing the storage capacity of the logistics node, to achieve optimal or near-optimal profit under the collection requirement set by the government and the investment from the enterprise. The approach comprises two parts: a matrix-based simulation model of RLN formed for the uncertainty of demand and reverse logistics collection which calculates the profit under a given candidate solution and simulated annealing (SA) algorithm that is tailored to generating solution using the output of RLN model. To increase the efficiency of the SA algorithm, network static analysis is proposed for getting the quantitative importance of each node in RLN, including the static network generation process and index design. Accordingly, the quantitative importance is applied to increase the likelihood of generating a better candidate solution in the neighborhood search of SA. Numerical experimentation is conducted to validate the RLN model as well as the efficiency of the improved SA.

scholarly journals ANALISA PERBANDINGAN METODE SIMULATED ANNEALING DAN LARGE NEIGHBORHOOD SEARCH UNTUK MEMECAHKAN MASALAH LOKASI DAN RUTE KENDARAAN DUA ESELON

2020 ◽  
Vol 4 (1) ◽  
pp. 35-46
Author(s):  
Winarno (Universitas Singaperbangsa Karawang) ◽  
A. A. N. Perwira Redi (Universitas Pertamina)
Keyword(s):  
Simulated Annealing ◽  
Computational Time ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Large Neighborhood ◽  
Location Routing ◽  
Node Removal ◽  
Better Than

AbstractTwo-echelon location routing problem (2E-LRP) is a problem that considers distribution problem in a two-level / echelon transport system. The first echelon considers trips from a main depot to a set of selected satellite. The second echelon considers routes to serve customers from the selected satellite. This study proposes two metaheuristics algorithms to solve 2E-LRP: Simulated Annealing (SA) and Large Neighborhood Search (LNS) heuristics. The neighborhood / operator moves of both algorithms are modified specifically to solve 2E-LRP. The proposed SA uses swap, insert, and reverse operators. Meanwhile the proposed LNS uses four destructive operator (random route removal, worst removal, route removal, related node removal, not related node removal) and two constructive operator (greedy insertion and modived greedy insertion). Previously known dataset is used to test the performance of the both algorithms. Numerical experiment results show that SA performs better than LNS. The objective function value for SA and LNS are 176.125 and 181.478, respectively. Besides, the average computational time of SA and LNS are 119.02s and 352.17s, respectively.AbstrakPermasalahan penentuan lokasi fasilitas sekaligus rute kendaraan dengan mempertimbangkan sistem transportasi dua eselon juga dikenal dengan two-echelon location routing problem (2E-LRP) atau masalah lokasi dan rute kendaraan dua eselon (MLRKDE). Pada eselon pertama keputusan yang perlu diambil adalah penentuan lokasi fasilitas (diistilahkan satelit) dan rute kendaraan dari depo ke lokasi satelit terpilih. Pada eselon kedua dilakukan penentuan rute kendaraan dari satelit ke masing-masing pelanggan mempertimbangan jumlah permintaan dan kapasitas kendaraan. Dalam penelitian ini dikembangkan dua algoritma metaheuristik yaitu Simulated Annealing (SA) dan Large Neighborhood Search (LNS). Operator yang digunakan kedua algoritma tersebut didesain khusus untuk permasalahan MLRKDE. Algoritma SA menggunakan operator swap, insert, dan reverse. Algoritma LNS menggunakan operator perusakan (random route removal, worst removal, route removal, related node removal, dan not related node removal) dan perbaikan (greedy insertion dan modified greedy insertion). Benchmark data dari penelitian sebelumnya digunakan untuk menguji performa kedua algoritma tersebut. Hasil eksperimen menunjukkan bahwa performa algoritma SA lebih baik daripada LNS. Rata-rata nilai fungsi objektif dari SA dan LNS adalah 176.125 dan 181.478. Waktu rata-rata komputasi algoritma SA and LNS pada permasalahan ini adalah 119.02 dan 352.17 detik.

scholarly journals A Knowledge-Based Simulated Annealing Algorithm to Multiple Satellites Mission Planning Problems

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Da-Wei Jin ◽  
Li-Ning Xing
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Mission Planning ◽  
Knowledge Based ◽  
Combination Optimization ◽  
Annealing Algorithm ◽  
The Given ◽  
Planning Problems ◽  
Annealing Method

The multiple satellites mission planning is a complex combination optimization problem. A knowledge-based simulated annealing algorithm is proposed to the multiple satellites mission planning problems. The experimental results suggest that the proposed algorithm is effective to the given problem. The knowledge-based simulated annealing method will provide a useful reference for the improvement of existing optimization approaches.

scholarly journals TUNING OF THE METAHEURISTIC VARIABLE NEIGHBORHOOD SEARCH FOR A FOREST PLANNING PROBLEM

CERNE ◽  
2018 ◽  
Vol 24 (3) ◽  
pp. 259-268
Author(s):  
Carlos Alberto Araújo Júnior ◽  
João Batista Mendes ◽  
Adriana Leandra de Assis ◽  
Christian Dias Cabacinha ◽  
Jonathan James Stocks ◽  
...  
Keyword(s):  
Variable Neighborhood Search ◽  
Neighborhood Search ◽  
Forest Planning ◽  
Planning Problem

Solving the Laser Cutting Path Problem Using Population-Based Simulated Annealing with Adaptive Large Neighborhood Search

2020 ◽  
Vol 833 ◽  
pp. 29-34 ◽  
Author(s):  
Makbul Hajad ◽  
Viboon Tangwarodomnukun ◽  
Chaiya Dumkum ◽  
Chorkaew Jaturanonda
Keyword(s):  
Simulated Annealing ◽  
Laser Cutting ◽  
Computing Time ◽  
Population Based ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Adaptive Large Neighborhood Search ◽  
Large Neighborhood ◽  
Generalized Traveling Salesman Problem ◽  
Cutting Path

This paper presents an alternative algorithm for solving the laser cutting path problem which was modeled as Generalized Traveling Salesman Problem (GTSP). The objective is to minimize the traveling distance of laser cutting of all profiles in a given layout, where a laser beam makes a single visit and then does the complete cut of individual profile in an optimum sequence. This study proposed a hybrid method combining population-based simulated annealing (SA) with an adaptive large neighborhood search (ALNS) algorithm to solve the cutting path problem. Recombination procedures were executed alternately using swap, reversion, insertion and removal-insertion through a fitness proportionate selection mechanism. In order to reduce the computing time and maintain the solution quality, the 35% proportion of population were executed in each iteration using the cultural algorithm selection method. The results revealed that the algorithm can solve several ranges of problem size with an acceptable percentage of error compared to the best known solution.

Operational Planning Using Mixed Integer Programming

1988 ◽  
Vol 64 (6) ◽  
pp. 485-488 ◽  
Author(s):  
H. Douglas Walker ◽  
Stephen W. Preiss
Keyword(s):  
Planning Process ◽  
Cost Savings ◽  
Mathematical Structure ◽  
Timber Harvesting ◽  
Operational Planning ◽  
Mixed Integer ◽  
Forest Planning ◽  
Planning Problem ◽  
Process Data ◽  
Planning Problems

A mathematical model was constructed and used to help plan five-year timber harvesting and delivery activities from an industrially managed public forest in Ontario. Harvest systems, harvest levels, and wood flows from compartments within the forest to various mills and delivery points were scheduled to minimize costs. The mathematical structure of the model may suggest applications to related forest planning problems. The model was useful in addressing the planning problem, and model results were used within the company's planning process. Data accuracy problems precluded assessing definitively the expected cost savings resulting from model use.

scholarly journals Integer linear programming outperforms simulated annealing for solving conservation planning problems

2019 ◽  
Author(s):  
Richard Schuster ◽  
Jeffrey O. Hanson ◽  
Matt Strimas-Mackey ◽  
Joseph R. Bennett
Keyword(s):  
Linear Programming ◽  
Simulated Annealing ◽  
Integer Linear Programming ◽  
Conservation Planning ◽  
Practical Advantage ◽  
Processing Times ◽  
Conservation Plans ◽  
The Cost ◽  
Planning Problems

AbstractThe resources available for conserving biodiversity are limited, and so protected areas need to be established in places that will achieve objectives for minimal cost. Two of the main algorithms for solving systematic conservation planning problems are Simulated Annealing (SA) and Integer linear programming (ILP). Using a case study in British Columbia, Canada, we compare the cost-effectiveness and processing times of SA versus ILP using both commercial and open-source algorithms. Plans for expanding protected area systems based on ILP algorithms were 12 to 30% cheaper than plans using SA. The best ILP solver we examined was on average 1071 times faster than the SA algorithm tested. The performance advantages of ILP solvers were also observed when we aimed for spatially compact solutions by including a boundary penalty. One practical advantage of using ILP over SA is that the analysis does not require calibration, saving even more time. Given the performance of ILP solvers, they can be used to generate conservation plans in real-time during stakeholder meetings and can facilitate rapid sensitivity analysis, and contribute to a more transparent, inclusive, and defensible decision-making process.

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