Heuristic “Optimization”: Why, When, and How to Use It

Zanakis, Stelios H.; Evans, James R.

doi:10.1287/inte.11.5.84

Cited by 119 publications

(43 citation statements)

References 29 publications

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“…Heuristic programming includes procedures that can reduce search in problem-solving activities, obtain solutions to problems within a limited computing time, and provide good (not necessarily optimal) solutions to difficult problems easily and quickly (Zanakis and Evans 1981). It may overcome many limitations of mathematical techniques in solving problems of forest-management scheduling (Elwood and Rose 1990).…”

Section: The Modeling Toolmentioning

confidence: 99%

Forest planning in an Oregon case study: Defining the problem and attempting to meet goals with a spatial-analysis technique

Bettinger

Johnson

Sessions

1996

Environmental Management

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Five major management goals were identified for the upper Grande Ronde River Basin on the Wallowa-Whitman National Forest in northeastern Oregon: to produce high-quality fish habitat, to maintain elk habitat, to restore and maintain forest conditions within the natural range of viability, and to contribute to community economic stability. From the broad goals, specific goals for stream temperature, habitat effectiveness index (HEI), habitat corridors, maintenance of land in late or old seral stages, and a nondeclining even flow of timber were selected. A case study was undertaken in a small watershed that is under typical societal constraints to determine whether one decision-support tool, SNAP II+, could evaluate the selected goals in a single planning exercise. Three riparian management strategies and two forest road scenarios were used. The exclusion of harvest and road-building from riparian zones in order to increase habitat protection decreased harvest levels and net present value but maintained preactivity stream temperatures. Other resources were generally maintained within prescribed management levels. Although the technique has limitations (e.g., it does not account for riparian zones in calculations of forage and cover for HEI, and it can use the maximum but not minimum acreage goal for some resources), it shows promise for evaluating management tradeoffs in watershed analysis.

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Section: The Modeling Toolmentioning

confidence: 99%

Forest planning in an Oregon case study: Defining the problem and attempting to meet goals with a spatial-analysis technique

Bettinger

Johnson

Sessions

1996

Environmental Management

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“…Although the database designer may experiment with different physical designs, a set of heuristics is available in the DSS in pursuit of very good (many times close to optimal) physical designs. Heuristic procedures are known for providing good solutions [10,33]; such procedures are reported in [15,17] for specific database problems. Two types of heuristics (actually five heuristics in all) are included in the DSS.…”

Section: Bmentioning

confidence: 99%

An interactive DSS tool for physical database design

Palvia

1991

Information Sciences

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Abstract:The design of efficient physical databases is a complex activity involving the consideration of a large number of factors. Because of the complexity, mathematical programming approaches seeking to optimize the physical database have to make many simplifying assumptions; therefore, their applicability is limited. Further, the database designer may want to experiment with design preferences and features not considered by the mathematical optimization approaches. In order to effectively design the physical database, this article describes an interactive DSS tool, which aides the database designer in this task. The database design is accomplished in the context of a high-level abstract model which is capable of being implemented in a variety of DBMSs and file systems. Because of this generic nature of the abstract model, the utility of the DSS tool is enhanced. The interactive tool not only lets the designer experiment with his own designs, but also provides several heuristic optimization procedures to enable the generation of many good designs. The heuristic designs may be used for final physical database design as well as for further experimentation. The paper also includes examples of how the physical design selected using the abstract model and the interactive tool may be implemented on several DBMSs and file systems. Article: INTRODUCTIONDatabase design is a challenging and complex activity involving two phases: logical design and physical design. Logical design involves the development of a logical data structure (LDS) for the task domain; and physical design is concerned with developing storage structures for placing data on secondary storage, given a specific LDS. In this paper, we focus on the physical design task and assume that the LDS is given to us based on prior design activity (as in Figure 1, adapted from [5]). Prior works have generally dealt with and developed design/optimization models for specific aspects of physical database design. These works include: index selection [1,14,17], file structuring and models of file organization [18,30], and record segmentation and structuring [15,17,23,24]. Some of these models are reviewed in [29]. Quoting from [19]: "While the attention to individual design problems results in elegant solutions, it is quite plausible that those individual solutions will have to be perturbed when the total database is put together."

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“…The general application of heuristic procedures has been discussed in [11] and [34]; heuristic procedures have also been applied in the file design arena [16] [18]. For demonstration purposes, the physical data base design model chosen here is an abstraction of a generic model; thus, the results are applicable to different logical models and commercial DBMSs.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Optimization of Physical Data Bases: Using a Generic and Abstract Design Model*

Palvia

1988

Decision Sciences

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Abstract:Designing efficient physical data bases is a complex activity, involving the consideration of a large number of factors. Mathematical programming-based optimization models for physical design make many simplifying assumptions; thus, their applicability is limited. In this article, we show that heuristic algorithms can be successfully used in the development of very good, physical data base designs. Two heuristic optimization algorithms are proposed in the contest of a genetic and abstract model for physical design. One algorithm is based on generic principles of heuristic optimization. The other is based on capturing and using problemspecific information in the heuristics. The goodness of the algorithms is demonstrated over a wide range of problems and factor values. Subject Areas: Heuristics, Management Information Systems, and Simulation. Article:INTRODUCTION Data base design is a challenging activity and involves two phases: logical and physical design. Logical design involves the development of a logical data structure (LDS) for the task domain. Physical design is concerned with developing structures for placing data on secondary storage, given a specific LDS. While both phases require significant effort, the physical data base design phase is the focus of this article. The main concern in physical data base design is the efficiency of the physical design, which can be measured in a number of ways, such as storage requirement, response time and total system cost.Prior works have generally dealt with and developed design/optimization models for specific aspects of physical data base design. These works include index selection While the attention to individual design problems results in elegant solutions, it is quite plausible that those individual solutions will have to be perturbed when the total data base is put together. [20, p. 217] Thus, the need for comprehensive design models (which deal with the entire data base design problem rather than parts of it) cannot be overstated. Comprehensive physical design models fall under two categories: (1) those specific to and (2) those generic and independent of any particular logical data model and/or commercial DBMS. The first category offers the advantage of direct implementation on a particular DBMS, but is difficult to convert to another implementation. The second category allows more flexibility at the expense of added conversion requirements for a particular implementation. Optimization of physical design in the first category is reported in [29] for the hierarchical data model and in [12]

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Heuristic “Optimization”: Why, When, and How to Use It

Cited by 119 publications

References 29 publications

Forest planning in an Oregon case study: Defining the problem and attempting to meet goals with a spatial-analysis technique

Forest planning in an Oregon case study: Defining the problem and attempting to meet goals with a spatial-analysis technique

An interactive DSS tool for physical database design

Heuristic Optimization of Physical Data Bases: Using a Generic and Abstract Design Model*

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