Tabu search design for difficult forest management optimization problems

2003 ◽  
Vol 33 (6) ◽  
pp. 1126-1133 ◽  
Author(s):  
Evelyn W Richards ◽  
Eldon A Gunn
Keyword(s):  
Forest Management ◽  
Tabu Search ◽  
Planning Process ◽  
Short Term Memory ◽  
Optimization Problems ◽  
Heuristic Method ◽  
Management Problem ◽  
Discounted Cost ◽  
Design Elements ◽  
Search Design

A series of tabu search (TS) methods for solving the stand harvesting and road access optimization problem was developed and evaluated. This challenging forest management problem includes spatial constraints for maximum opening size, adjacency delay (green up), as well as timber-flow targets derived exogenously from a strategic planning process. The base harvest decision unit is the stand, and harvest blocks are created dynamically as adjacent stands are scheduled for treatments. The road network subproblem is solved using a fast heuristic method to select a minimum discounted cost set of road construction projects so that scheduled stands are accessible. The TS methods range from simple ones with fixed tabu tenure to an adaptive search with feedback mechanisms to control tabu tenure and to direct the search near constraint boundaries. It was found that while simple TS algorithms can find feasible solutions to the problem, these may be far from optimal. A good short-term memory strategy, constraint boundaries smoothed using penalty functions, and customized diversification moves were important design elements in the most successful TS algorithm for this problem. This paper points out the necessity to design the TS method carefully, since there are many possible TS designs and the design choices matter.

A New Scatter Search Design for Multiobjective Combinatorial Optimization with an Application to Facility Location

2020 ◽  
Author(s):  
A. D. López-Sánchez ◽  
J. Sánchez-Oro ◽  
M. Laguna
Keyword(s):  
Tabu Search ◽  
Multiobjective Optimization ◽  
Operations Research ◽  
Short Term Memory ◽  
Scatter Search ◽  
Combination Method ◽  
Heuristic Optimization ◽  
Short Term ◽  
Term Memory ◽  
Search Design

Metaheuristic optimization is at the heart of the intersection between computer science and operations research. The INFORMS Journal of Computing has been fundamental in advancing the ideas behind metaheuristic methodologies. Fred Glover’s “Tabu Search—Part I” was published more than 30 years ago in the first volume of the then ORSA Journal on Computing. This article, one of the most cited in the area of heuristic optimization, paved the way for many contributions to the methodology and practice of operations research. As a continuation of this stream of research, we describe a new scatter search design for multiobjective optimization. The design includes a short-term memory tabu search and a path relinking combination method. We show how the strategies and mechanisms within scatter search and tabu search can be combined to produce a highly effective approach to multiobjective optimization.

scholarly journals Design of Flotation Circuits Using Tabu-Search Algorithms: Multispecies, Equipment Design, and Profitability Parameters

Minerals ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 181 ◽  
Author(s):  
Freddy Lucay ◽  
Edelmira Gálvez ◽  
Luis Cisternas
Keyword(s):  
Tabu Search ◽  
Approximate Method ◽  
Short Term Memory ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Adaptive Procedure ◽  
Problem Size ◽  
Combinatorial Optimization Problems

The design of a flotation circuit based on optimization techniques requires a superstructure for representing a set of alternatives, a mathematical model for modeling the alternatives, and an optimization technique for solving the problem. The optimization techniques are classified into exact and approximate methods. The first has been widely used. However, the probability of finding an optimal solution decreases when the problem size increases. Genetic algorithms have been the approximate method used for designing flotation circuits when the studied problems were small. The Tabu-search algorithm (TSA) is an approximate method used for solving combinatorial optimization problems. This algorithm is an adaptive procedure that has the ability to employ many other methods. The TSA uses short-term memory to prevent the algorithm from being trapped in cycles. The TSA has many practical advantages but has not been used for designing flotation circuits. We propose using the TSA for solving the flotation circuit design problem. The TSA implemented in this work applies diversification and intensification strategies: diversification is used for exploring new regions, and intensification for exploring regions close to a good solution. Four cases were analyzed to demonstrate the applicability of the algorithm: different objective function, different mathematical models, and a benchmarking between TSA and Baron solver. The results indicate that the developed algorithm presents the ability to converge to a solution optimal or near optimal for a complex combination of requirements and constraints, whereas other methods do not. TSA and the Baron solver provide similar designs, but TSA is faster. We conclude that the developed TSA could be useful in the design of full-scale concentration circuits.

scholarly journals Penyelesaian Masalah TSP Pada Rute Kunjungan ATM Dengan Pendekatan Heuristik (Tabu Search)

2017 ◽  
Vol 2 (1) ◽  
pp. 13
Author(s):  
Jhon Pontas Simbolon ◽  
Muhammad Zarlis
Keyword(s):  
Tabu Search ◽  
Minimum Distance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Heuristic Method ◽  
Search Process ◽  
Local Optimum ◽  
Heuristic Methods ◽  
Exact Methods

Determination of optimum route is a problem that can be found in a variety of activities. Principal of the problem is how to organize the trip so the distance is the minimum distance that the optimum is best found on a map or graph. There are many algorithms available to solve them. Algorithm is divided into two parts, the exact methods and heuristic methods. Heuristic method is considered the best method because it can work quickly. Tabu search is a heuristic method that is often used in solving optimization problems. The algorithm works by improving a solution by using memory to avoid that the search process does not get stuck at a local optimum value by rejecting new solutions that may be in memory (taboo) so that the new solution will be more dispersed. The author will implement a tabu search algorithm to provide a better alternative solution to solve the problems of the effectiveness of the distribution of charging money at the ATM machine.

scholarly journals TABU SEARCH APPROACH FOR TYPE 2 PROBLEMS OF ASSEMBLY LINE BALACING

2011 ◽  
Vol 14 (2) ◽  
pp. 22-28
Author(s):  
Hung Vo Duong
Keyword(s):  
Problem Solving ◽  
Tabu Search ◽  
Assembly Line ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Heuristic Method ◽  
Tabu Search Algorithm ◽  
Combinatorial Optimization Problems

In this research, Tabu search algorithm, a heuristic method for solving combinatorial optimization problems, has been applied for type 2 problems of assembly line balancing. For type 2 problems, two methodologies are developed for problem solving. Method 1 is direct solving for type 2 problems, and method 2 gives solving through type 1 problems. As such, Tabu search algorithm for type 1 problem is employed for problem solving at second stage. The success of this research points out empty workstations (unnecessary) to reduce investment cost and operational costs. Moreover, the range of cycle time and number of workststions are provided for selection.

scholarly journals Simulating the Effects of Intensifying Silviculture on Desired Species Yields across a Broad Environmental Gradient

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 755
Author(s):  
Eric B. Searle ◽  
F. Wayne Bell ◽  
Guy R. Larocque ◽  
Mathieu Fortin ◽  
Jennifer Dacosta ◽  
...  
Keyword(s):  
Climate Change ◽  
Forest Management ◽  
Planning Process ◽  
Mechanistic Model ◽  
Natural Disturbance ◽  
Fine Tuning ◽  
Mechanistic Models ◽  
Paradigm Shifts ◽  
Ecological Processes ◽  
Forest Modelling

In the past two decades, forest management has undergone major paradigm shifts that are challenging the current forest modelling architecture. New silvicultural systems, guidelines for natural disturbance emulation, a desire to enhance structural complexity, major advances in successional theory, and climate change have all highlighted the limitations of current empirical models in covering this range of conditions. Mechanistic models, which focus on modelling underlying ecological processes rather than specific forest conditions, have the potential to meet these new paradigm shifts in a consistent framework, thereby streamlining the planning process. Here we use the NEBIE (a silvicultural intervention scale that classifies management intensities as natural, extensive, basic, intensive, and elite) plot network, from across Ontario, Canada, to examine the applicability of a mechanistic model, ZELIG-CFS (a version of the ZELIG tree growth model developed by the Canadian Forest Service), to simulate yields and species compositions. As silvicultural intensity increased, overall yield generally increased. Species compositions met the desired outcomes when specific silvicultural treatments were implemented and otherwise generally moved from more shade-intolerant to more shade-tolerant species through time. Our results indicated that a mechanistic model can simulate complex stands across a range of forest types and silvicultural systems while accounting for climate change. Finally, we highlight the need to improve the modelling of regeneration processes in ZELIG-CFS to better represent regeneration dynamics in plantations. While fine-tuning is needed, mechanistic models present an option to incorporate adaptive complexity into modelling forest management outcomes.

scholarly journals Аn example of different AHP structuring in a forest management problem

2016 ◽  
pp. 127-137
Author(s):  
Milena Lakicevic ◽  
Bojan Srdjevic ◽  
Ivaylo Velichkov ◽  
Zorica Srdjevic
Keyword(s):  
Decision Making ◽  
Forest Management ◽  
Analytic Hierarchy Process ◽  
Evaluation Procedure ◽  
Decision Making Process ◽  
Management Problem ◽  
Consistency Check ◽  
Analytic Hierarchy ◽  
Hierarchy Process

The paper investigates how different hierarchy structuring in analytic hierarchy process (AHP) may affect the final results in the decision-making process. This problem is analyzed in a case study of the Rila monastery forest stands in Bulgaria. There were three similar and mutually overlapped hierarchies defined. A decision maker evaluated all of them and after analyzing final results and consistency performance, he selected and revised the most appropriate hierarchy structure. Consistency check assisted in detecting the judgments which have strongly violated evaluation procedure. These mistakes are interpreted as a consequence of a large number of required pair-wise comparisons. The paper emphases the importance of properly defining hierarchy structure and recommends using consistency analysis as a guide and not as a directive for the revision of judgments.

scholarly journals An improved arithmetic optimization algorithm with forced switching mechanism for global optimization problems

2022 ◽  
Vol 19 (1) ◽  
pp. 473-512
Author(s):  
Rong Zheng ◽  
◽  
Heming Jia ◽  
Laith Abualigah ◽  
Qingxin Liu ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Heuristic Method ◽  
Population Diversity ◽  
Test Functions ◽  
Design Problems ◽  
Local Optima ◽  
Switching Mechanism ◽  
Engineering Design Problems

<abstract> <p>Arithmetic optimization algorithm (AOA) is a newly proposed meta-heuristic method which is inspired by the arithmetic operators in mathematics. However, the AOA has the weaknesses of insufficient exploration capability and is likely to fall into local optima. To improve the searching quality of original AOA, this paper presents an improved AOA (IAOA) integrated with proposed forced switching mechanism (FSM). The enhanced algorithm uses the random math optimizer probability (<italic>RMOP</italic>) to increase the population diversity for better global search. And then the forced switching mechanism is introduced into the AOA to help the search agents jump out of the local optima. When the search agents cannot find better positions within a certain number of iterations, the proposed FSM will make them conduct the exploratory behavior. Thus the cases of being trapped into local optima can be avoided effectively. The proposed IAOA is extensively tested by twenty-three classical benchmark functions and ten CEC2020 test functions and compared with the AOA and other well-known optimization algorithms. The experimental results show that the proposed algorithm is superior to other comparative algorithms on most of the test functions. Furthermore, the test results of two training problems of multi-layer perceptron (MLP) and three classical engineering design problems also indicate that the proposed IAOA is highly effective when dealing with real-world problems.</p> </abstract>

scholarly journals Integrated Offline and Online Decision Making under Uncertainty

2021 ◽  
Vol 70 ◽  
pp. 77-117
Author(s):  
Allegra De Filippo ◽  
Michele Lombardi ◽  
Michela Milano
Keyword(s):  
Optimization Problems ◽  
Greedy Heuristic ◽  
Management Problem ◽  
Quality Improvements ◽  
Solution Quality ◽  
Convex Optimization Problem ◽  
Routing Problem ◽  
Multi Stage ◽  
The Cost ◽  
Parametric Convex Optimization

This paper considers multi-stage optimization problems under uncertainty that involve distinct offline and online phases. In particular it addresses the issue of integrating these phases to show how the two are often interrelated in real-world applications. Our methods are applicable under two (fairly general) conditions: 1) the uncertainty is exogenous; 2) it is possible to define a greedy heuristic for the online phase that can be modeled as a parametric convex optimization problem. We start with a baseline composed by a two-stage offline approach paired with the online greedy heuristic. We then propose multiple methods to tighten the offline/online integration, leading to significant quality improvements, at the cost of an increased computation effort either in the offline or the online phase. Overall, our methods provide multiple options to balance the solution quality/time trade-off, suiting a variety of practical application scenarios. To test our methods, we ground our approaches on two real cases studies with both offline and online decisions: an energy management problem with uncertain renewable generation and demand, and a vehicle routing problem with uncertain travel times. The application domains feature respectively continuous and discrete decisions. An extensive analysis of the experimental results shows that indeed offline/online integration may lead to substantial benefits.

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