Use Of Optimization Models Research Articles

Challenges and opportunities of the optimality approach in plant ecology

A meeting was held in Hyytiälä, Finland 10–12 April 2000 to assess critically the current challenges and limitations of the optimality approach in plant ecophysiology and botany. This article summarises the general discussions and views of the participants on the use of optimisation models as tools in plant ecophysiological research. A general framework of the evolutionary optimisation problem is sketched with a review of applications, typically involved with balanced regulation between parallel processes. The usefulness and limitations of the approach are discussed in terms of published examples, with special reference to model testing. We conclude that, regardless of inevitable problems of model formulation, wider application of the optimality approach could provide a step forward in plant ecophysiology. A major role of evolutionary theory in this process is simply the formulation of testable hypotheses, the evaluation of which can lead to important advances in our ecophysiological understanding and predictive ability.

Clone-based modelling for optimal production planning in process industries

This paper outlines the structure of a visually interactive graphical modeller for optimum production planning of process type production systems. Introducing a 'clone-based modelling' approach, the proposed system allows users to build graphical models of a production system with instances of templates of production units through its interactive visual interface, for a 'natural' representation. These 'clones' accept user-defined values for their parameters, expose decision variables, and internally hold parameterized and indexed expressions of their constraints and partial objective functions that represent the contribution of the production unit to the mathematical model of the production system. Without any interaction from the user, clones build this hidden optimization model that will be solved internally by a commercial solver to provide optimum results for all of the solver's decision variables. The proposed 'clone-based modelling' approach allows the continuous use of optimization models without a mathematical programming background, as changes to the graphical model are automatically reflected into the hidden mathematical model.

Space allocation in UK grocery retailing

Space allocation is usually explained through the use of optimization models. This study examines the perceptions of experienced managers in major UK grocery retailers relating to effective space utilization. From the literature, initial telephone survey and interviews, 48 variables relating to the same issue were identified. These then formed the basis of a questionnaire, which was postally administered to managers within the space planning function in six of the UK’s largest grocery retailers. Thirty completed questionnaires were returned. Principal components analysis was used to explore interrelationships among the variables. Of the 48 variables 18 were identified as most important. The 18 variables accounted for over 80 per cent of the total variance. These variables were associated with six underlying components. These six components (and the associated variable scores) were then examined using structural equation modelling. An SEM model was developed with a combined goodness of fit index of 0.85. The developed model is very different from the relations posited in existing space planning systems.

Testing Concepts of Animal Foraging Behavior

Beginning sttudents i1 ecology are almost universally interested in animal behavior, but few have a comfortable grasp of the approach to the study of animal behavior and ecology. Because an approach is essential to understanding in most aspects of the discipline (Alcock 1993), 1 have worked to develop an ecology exercise that exposes students to evolutionary thinking. In the exercise I first teach some theoretical concepts in the form of a graphical model, and then allow the students to apply the concepts by developing a field experiment to test predictions of the model. The approach to the study of animal behavior views behavior as the product of natural selection, and thus asks what behavioral patterns are expected to evolve in an animal, given a set of basic assumptions about the animal. This approach often makes use of optimality models that formulate mathematical solutions to problems in animal behavior by first establishing a set of basic constraints or assumptions about the animal, and then solving to find the behavior that maximizes a relevant component of the animal's fitness. This approach is very useful in the study of behavior because it allows us to understand w7hy an animal behaves the way it does, not just howv it behaves. Thus the approach allows the study of anim.al behavior to be a predictive, rather than descriptive, science.

Adaptation: A Life History Perspective

The use of optimization models to study adaptations is discussed. The optimal value of a trait must maximize fitness, that is, lifetime reproductive success. This means that every trait under optimization must be translated into demographic parameters Although there is no universal measure of fitness, some general rules are given about whether net reproductive rate R or intrinsic population growth rate r should be maximized in given conditions, Models maximizing R are usually simple, and they produce easy-to-interpret predictions. The scope of applicability of models assuming maximization of R broadens if we are satisfied with post hoc explanations of natural patterns (predictions in a weak sense). Optimization models should be verified using comparative methods. With a comparative method, however, optimization models become tools for approaching general questions about selective forces and the resulting patterns, not for studying local adaptations, Hypotheses about local adaptations are always difficult to test, and optimization models seem of little help here.

Use of optimization models in river basin water quality planning

A literature review on water quality management optimization models is presented. They include linear and non-linear approaches, deterministic and stochastic methods, as well as multi-criteria decision techniques. Two river basin decision support systems, DESERT and STREAMPLAN developed recently at the International Institute of Applied Systems Analysis, (IIASA) are outlined. The usage of these tools is illustrated by results obtained for three river basins located in Central and Eastern European countries. Examples incorporate least-cost DO policies, regional strategies to reduce nutrient emissions with special regard to the control of inland sea eutrophication, multi-criteria decision making and the sequential usage of optimization and simulation models to assess the sensitivity of a particular policy in terms of water quality and economic impacts. Major features of available optimization models are classified. Reasons of their rare real life applications and associated barriers are evaluated, primarily the nature of legislation and related institutional structures. Finally, a future outlook is presented.

Use of optimization models in river basin water quality planning

A literature review on water quality management optimization models is presented. They include linear and non-linear approaches, deterministic and stochastic methods, as well as multi-criteria decision techniques. Two river basin decision support systems, DESERT and STREAMPLAN developed recently at the International Institute of Applied Systems Analysis, (IIASA) are outlined. The usage of these tools is illustrated by results obtained for three river basins located in Central and Eastern European countries. Examples incorporate least-cost DO policies, regional strategies to reduce nutrient emissions with special regard to the control of inland sea eutrophication, multi-criteria decision making and the sequential usage of optimization and simulation models to assess the sensitivity of a particular policy in terms of water quality and economic impacts. Major features of available optimization models are classified. Reasons of their rare real life applications and associated barriers are evaluated, primarily the nature of legislation and related institutional structures. Finally, a future outlook is presented.

A note on optimal algorithms for reserve site selection

A recent note by Underhill ( Biol. Conserv., 70, 85-7, 1994) points out the need for the use of optimization models and a closer working relationship with mathematicians for the solution of biological management problems such as the reserve site selection problem. In this note we give the mathematical formulation of what he terms ‘the more realistic’ version of the reserve selection problem, namely, the problem of maximizing the number of species preserved given a fixed budget for reserve sites. We also discuss some straight-forward data reduction schemes which may reduce the solution time for these problems when they are solved using general off-the-shelf optimization code as mentioned by Underhill.

On the use of optimization models for portfolio selection: A review and some computational results

Portfolio theory deals with the question of how to allocate resources among several competing alternatives (stocks, bonds), many of which have an unknown outcome. In this paper we provide an overview of different portfolio models with emphasis on the corresponding optimization problems. For the classical Markowitz mean-variance model we present computational results, applying a dual algorithm for constrained optimization.

How (not) to test an optimality model

The controversy over the use of optimality models in the investigation of adaptation is long-standing. Nonetheless, little or no attention has been paid in this debate to the most important question to be asked about such models: how should the test of an optimality model be structured if the local optimality of the trait is to be assessed? Here we answer this question and describe how such a test can contribute to a test of adaptationism.

Optimality Models and the Test of Adaptationism

The use of optimality models in the investigation of adaptation remains controversial. Critics charge that advocates of the optimality approach assume that the traits they analyze are optimal. Advocates of the approach deny this but admit to assuming that the traits have adaptive explanations. This controversy is part of the ongoing debate about adaptationism. We believe that this controversy remains unresolved in part because of ambiguity in the definition of adaptationism. In this article, we clarify the thesis of adaptationism, show how the structure of optimality models relates to that thesis, and describe how the thesis of adaptationism is testable. In addition, we describe the types of analyses that are essential to a test of an optimality model if the optimality of the trait is to be assessed and if assessments of the success of specific models are to contribute to a test of adaptationism. These analyses allow one to distinguish between the hypothesis that natural selection has had some influence or an important influence on a trait and the hypothesis that the trait is optimal. At present, to our knowledge, there are only two sets of studies in evolutionary biology in which this critical distinction has been made.

Optimization in computerized energy modelling for Tanzania

An overview of energy planning research is presented on implementation of the LEAP model for Tanzania through the use of optimization models in combination with a forecasting program. Two models were developed in the research. The first determines the optimum mix of energy resources at minimum cost. The second seeks the optimum number of end-use biomass devices and hectares of land to be afforested to minimize the woodfuel deficit. The second model was tested using data for Dar es Salaam only. The output of a forecasting program was fed into optimization models the outputs of which were entered into LEAP. The LEAP model is run using optimal and non-optimal inputs for comparison. The results prove the hypothesis that energy planning with the LEAP model is more effective if the model is complemented by optimization models.

Integrated Logistics Management, Total Cost Analysis and Optimization Modelling

States that total logistics costs in many companies can be reduced by as much as 10 per cent as a result of integrated planning, which can have an enormous impact on a company′s profitability, especially in a competitive environment characterized by small profit margins. Integrated logistics planning will see expanded use of optimization models over the next few years in an attempt to minimize total logistics costs; many applications will be standardized, such as distribution centre (DC) locations and will lead to less standardized ones, linking financial and logistics decision‐making features or marketing and logistics planning for profit maximization.

Aggregation and Disaggregation Techniques and Methodology in Optimization

A fundamental issue in the use of optimization models is the tradeoff between the level of detail and the ease of using and solving the model. Aggregation and disaggregation techniques have proven to be valuable tools for manipulating data and determining the appropriate policies to employ for this tradeoff. Furthermore, aggregation and disaggregation techniques offer promise for solving large-scale optimization models, supply a set of promising methodologies for studying the underlying structure of both univariate and multivariate data sets, and provide a set of tools for manipulating data for different levels of decision makers. In this paper, we develop a general framework for aggregation and disaggregation methodology, survey previous work regarding aggregation and disaggregation techniques for optimization problems, illuminate the appropriate role of aggregation and disaggregation methodology for optimization applications, and propose future research directions.

IAHS/AGU symposium on groundwater contamination

Papers presented at a two-day jointly sponsored IAHS/AGU symposium on groundwater contamination are briefly summarized. This international symposium was held 11–12 May, 1989, in Baltimore, Maryland. Presentations encompassed recent research developments in three general areas: abiotic and biotic processes governing contaminant transport; aquifer rehabilitation; and the influence of agricultural practices and nonpoint sources on aquifer quality. Contributions offered an interesting mixture of theoretical, mathematical, laboratory, and field studies. In the first session, transport processes explored ranged from dispersion and fingering to nonequilibrium sorption, metals complexation, and bacteria migration. The use of optimization modeling in the design of remediation strategies was the focus of another session. Here theoretical studies were presented alongside case histories of aquifer rehabilitation. In a final session, a number of models for agricultural management were described. These presentations were complemented by case studies of actual aquifer degradation resulting from land-use and management practices.

Adaptive walks on behavioural landscapes and the evolution of optimal behaviour by natural selection

One of the main challenges to the adaptationist programme in general and to the use of optimality models in behavioural and evolutionary ecology in particular is that natural selection need not optimise fitness. This challenge is addressed by considering the evolution of optimal patch choice by natural selection. The behavioural model is based on a state variable approach in which a strategy consists of a sequence denoting the patch to be visited as a function of the organism's state and time. The optimal strategy maximises expected terminal reproduction. The fitnesses of alternative strategies are computed by iteration of the associated equations for fitness; this characterises the adaptive behavioural landscape. There may be enormous numbers of strategies that have near optimal fitnesses. A population model is used to connect frequencies of behavioural types from one generation to the next. Theories on adaptive walks on fitness landscapes are considered in the context of behaviour. The main result is that within the context of optimality arguments at selective equilibrium, sub-optimal behaviours can persist. General implications for research in behavioural ecology, including tests of behavioural theories, are discussed.

Modeling to Generate Recreational Alternatives

Water‐resource managers are often faced with rife task of balancing recreational activities in an attempt to maximize the overall recreational benefits from a river system. Available recreation‐use functions for white‐water rafting, boating, and fishing as a function of river flow are used to estimate benefits. This paper focuses on the use of optimization models to generate alternatives (MGA) that maximize total recreational activities. Monthly river‐flow scenarios that produce different mixes of the three recreational activities, yet have essentially the same value of total recreational benefits, were sought. Four MGA methods were evaluated with data for the New River Gorge National River in West Virginia. Two of the MGA methods were based on linear programming, and two were based on dynamic programming. For this study, the MGA methods based on dynamic programming were easier to use and modify. Examples of the types of alternative flow strategies generated for the New River Gorge are presented.

Evolutionary optimization and neural network models of behavior

One of the main challenges to the adaptionist program in general and the use of optimization models in behavioral and evolutionary ecology, in particular, is that organisms are so constrained by ontogeny and phylogeny that they may not be able to attain optimal solutions, however those are defined. This paper responds to the challenge through the comparison of optimality and neural network models for the behavior of an individual polychaete worm. The evolutionary optimization model is used to compute behaviors (movement in and out of a tube) that maximize a measure of Darwinian fitness based on individual survival and reproduction. The neural network involves motor, sensory, energetic reserve and clock neuronal groups. Ontogeny of the neural network is the change of connections of a single individual in response to its experiences in the environment. Evolution of the neural network is the natural selection of initial values of connections between groups and learning rules for changing connections. Taken together, these can be viewed as "design parameters". The best neural networks have fitnesses between 85% and 99% of the fitness of the evolutionary optimization model. More complicated models for polychaete worms are discussed. Formulation of a neural network model for host acceptance decisions by tephritid fruit flies leads to predictions about the neurobiology of the flies. The general conclusion is that neural networks appear to be sufficiently rich and plastic that even weak evolution of design parameters may be sufficient for organisms to achieve behaviors that give fitnesses close to the evolutionary optimal fitness, particularly if the behaviors are relatively simple.

Mathematical models for decision support

It is quite an onerous task to edit the proceedings of a two week long institute with learned contributors from many parts of the world. All the same, the editorial team has found the process of refereeing and reviewing the contributions worthwhile and completing the volume has proven to be a satisfying task. In setting up the institute we had considered models and methods taken from a number of different disciplines. As a result the whole institute - preparing for it, attending it and editing the proceedings - proved to be an intense learning experience for us. Here I speak on behalf of the committee and the editorial team. By the time the institute took place, the papers were delivered and the delegates exchanged their views, the structure of the topics covered and their relative positioning appeared in a different light. In editing the volume I felt compelled to introduce a new structure in grouping the papers. The contents of this volume are organised in eight main sections set out below: 1 . Abstracts. 2. Review Paper. 3. Models with Multiple Criteria and Single or Multiple Decision Makers. 4. Use of Optimisation Models as Decision Support Tools. 5. Role of Information Systems in Decision Making: Database and Model Management Issues. 6. Methods of Artificial Intelligence in Decision Making: Intelligent Knowledge Based Systems. 7. Representation of Uncertainty in Mathematical Models and Knowledge Based Systems. 8. Mathematical Basis for Constructing Models and Model Validation.

Mathematical models for decision support

It is quite an onerous task to edit the proceedings of a two week long institute with learned contributors from many parts of the world. All the same, the editorial team has found the process of refereeing and reviewing the contributions worthwhile and completing the volume has proven to be a satisfying task. In setting up the institute we had considered models and methods taken from a number of different disciplines. As a result the whole institute - preparing for it, attending it and editing the proceedings - proved to be an intense learning experience for us. Here I speak on behalf of the committee and the editorial team. By the time the institute took place, the papers were delivered and the delegates exchanged their views, the structure of the topics covered and their relative positioning appeared in a different light. In editing the volume I felt compelled to introduce a new structure in grouping the papers. The contents of this volume are organised in eight main sections set out below: 1 . Abstracts. 2. Review Paper. 3. Models with Multiple Criteria and Single or Multiple Decision Makers. 4. Use of Optimisation Models as Decision Support Tools. 5. Role of Information Systems in Decision Making: Database and Model Management Issues. 6. Methods of Artificial Intelligence in Decision Making: Intelligent Knowledge Based Systems. 7. Representation of Uncertainty in Mathematical Models and Knowledge Based Systems. 8. Mathematical Basis for Constructing Models and Model Validation.