1. Introduction
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. General background Personal computers, computer networks, large databases, color graphics and
computer-based models are among the technological developments which stimulate interest in the use of computers to support decision-making. Such uses are called
‘decision support systems’ DSS. Decision support systems are used as aids to enhance the effectiveness of the decision-making process in organizations. Cur-
rently, many organizations operating in various business domains utilize DSS. Aquaculture is one of the fields that are looked upon as a potential application
domain for advanced information technologies, including decision support systems.
The importance of aquaculture stems from the fact that worldwide demand for quality fish protein is increasing dramatically, while in the meantime, the natural
fisheries are near their maximum sustainable yield MSY levels and are in the process of depletion. In addition, when fish are compared to alternative sources of
protein such as terrestrial livestock, the following can be deduced:
1. Fish have better feed conversion ratios. 2. Fish protein is better quality, as it is associated with a low content of calories,
a low content of saturated fatty acids, and a high content of polyunsaturated fatty acids V-3. This is a healthier diet with respect to its contribution in
preventing cancer, heart diseases, vascular diseases and rheumatic diseases. The implications of such an increase in demand are twofold. First, the increase
in demand has put tremendous pressure on limited resources, such as land and labor, required for aquaculture development. As a result, in many regions, aquacul-
ture is moving towards more intensive operations taking advantage of the improved production technologies. Moreover, the decision-making process is further compli-
cated because of the dynamic and stochastic nature of the biological, physical and economic environments, thereby emphasizing the need for developing decision aids
such as DSS for better management of aquacultural facilities.
Second, more sincere effort is dedicated towards aquaculture development as an alternative source of high quality cheap protein, particularly for developing coun-
tries where protein shortages already exist. Such effort is led by local governments as well as several international organizations that are involved in planning and
financing aquaculture development in various regions of the world. The World Bank, the Food and Agriculture Organization of the United Nations, and the
United States Agency for International Development are just a few examples of such organizations. Due to the wide range of issues involved in regional aquacul-
ture development and the inherent complexities of these issues, the development and use of decision aids such as DSS as well as the application of new information
technology techniques are inevitable.
During the last decade, a tremendous increase in the number of decision support systems DSS applications is reported in the literature Eom and Lee, 1990,
thereby acknowledging DSS as a useful technology that can improve decision-mak- ing in organizations. Moreover, while DSS applications are increasingly used in the
effective management of natural resources as seen in recent conferences ASAE, 1993; WASWC, 1995, aquaculture has not yet had its share. However, with the
rapid increase in the number of models developed for aquaculture, together with their associated data requirements, interest is rising in the development of inte-
grated DSS that pool models and data into one easy to use computer system suitable for practical applications, either operational or strategic Leung, 1993.
In that respect, Muench et al. 1986 developed a commercial computerized package for managing pond data Pond Manager© and evaluating optimal har-
vesting strategies Harvest Optimizer© for pond aquaculture. This marks the beginning of a simple aquaculture decision support system. More recently, Ernst et
al. 1993 developed a DSS for finfish aquaculture that is entirely concerned with operational level issues. The DSS consists of an integrated set of computerized
tools, including mathematical programming and logical algorithms, simulation models, expert systems and graphical user interface that provide applied and
integrated expertise in fish genetics, biology and culture, aquatic chemistry, engi- neering, and ecosystem processes. On the other hand, in regard to DSS targeted for
strategic and planning purposes in aquaculture, none have been reported in the literature until this point in time.
The application of multi-level and multi-objective analysis in aquaculture is very few. In fact, Sylvia and Anderson 1993 is the only study to date using such an
analytical framework. They developed a bio-economic model for developing infor- mation for private and public salmon aquaculture policy strategies when environ-
mental issues are important. The two levels of analysis refer to the two actors — salmon producers and policy makers. While the producers are assumed to maximize
profits, the public policy makers are faced with four policy objectives including revenue, benthic quality, profits, and tax revenues. The policy instruments in their
study include the number of allowable sites and an effluent tax.
On the other hand, multiple criteria decision-making MCDM techniques are already in use in agriculture on an operational as well as on a strategic level. With
regard to operational models, Romero and Rehman 1989 provided a comprehen- sive survey of agricultural MCDM models. However, only a few models are
developed for strategic purposes. One of the early models developed in that respect is the multi-regional, single time period linear goal programming model by Bazaraa
and Bouzaher 1981 for agricultural planning in a developing economy. The economic aspects are addressed through the incorporation of several objectives as
opposed to the conventional approaches which stress maximization of ‘economic welfare’, defined as efficiency through maximum social product without considering
income distribution. The policy objectives considered are regional employment, foreign exchange expenditures, and regional demand satisfaction. Regional employ-
ment reflects the emphasis on income distribution while regional demand satisfac- tion reflects the importance of limiting foreign trade deficits and providing basic
nutrition for the population. The constraints considered are for land, labor, water, machinery, fertilizer, and capital resources. The decisions suggested by the solution
include acreage allocated to different crops, sequences of crop rotations, fertilizer levels, and transportation and distribution of crops and livestock among regions.
The model is illustrated using data from the agricultural sector in Egypt.
Tapia 1990 developed a multiple objective linear programming model for agricultural planning. The model seeks to determine which types of crops to plant,
in what months of the year and in what different ratios for a given farm in order to maximize net total income, to maximize total yield per unit land area, to
maximize land usage, and to minimize production costs. The constraints considered are land constraints, vegetable family constraints indicating that no vegetable types
belonging to the same family can be planted in immediate succession, seasonality constraints as not all vegetables can grow in certain months, and market availabil-
ity constraints which ensure a significant harvest of vegetables belonging to a specific family. The model is solved as a multi-objective mathematical programming
formulation.
Tabucanon 1990 suggested MCDM as a framework for an analytical inquiry into policy issues on food commodities. The main motivation behind his choice lies
in the various criteria inherent in food policy issues as they relate to socio-economic and political goals. Tabucanon overviewed MCDM, its process, techniques and
applications as it relates to food policy analysis.
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. Objecti6es The main objective of this work is to design and implement an aquacultural
development decision support system ADDSS that aids the decision maker or the planner in making choices regarding the planning and development of the aquacul-
ture industry in a given region. Given the inherent multiple objective nature of aquaculture planning, and due to the lack of multiple criteria decision-making
MCDM models for regional aquacultural development, it is also the objective of this work to develop such a model. The applicability of the system is then
demonstrated through a case study from Egypt.
2. Methods
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