Possibility/Necessity-Based Probabilistic Expectation Models for Linear Programming Problems with Discrete Fuzzy Random Variables

This paper considers linear programming problems (LPPs) where the objective functions involve discrete fuzzy random variables (fuzzy set-valued discrete random variables). New decision making models, which are useful in fuzzy stochastic environments, are proposed based on both possibility theory and probability theory. In multi-objective cases, Pareto optimal solutions of the proposed models are newly defined. Computational algorithms for obtaining the Pareto optimal solutions of the proposed models are provided. It is shown that problems involving discrete fuzzy random variables can be transformed into deterministic nonlinear mathematical programming problems which can be solved through a conventional mathematical programming solver under practically reasonable assumptions. A numerical example of agriculture production problems is given to demonstrate the applicability of the proposed models to real-world problems in fuzzy stochastic environments

Insecticidal and repellant activities of Southeast Asia plants towards insect pests: a review

Crops are being damaged by several plant pests. Several strategies have been developed to restrict the damage of cultivated plants by using synthetic pesticides and repellants. However, the use to control these insects is highly discouraged because of their risks on humans. Therefore, several alternatives have been developed from plant extracts to protect crops from plant pests. Accordingly, this review focuses on outlining the insecticidal and repellant activities of Southeast Asia plants towards insect pests. Several extracts of plants from Southeast Asia were investigated to explore their insecticidal and repellant activities. Azadiracha indica (neem) and Piper species were highly considered for their insecticidal and repellant activities compared to other plants. This review also addressed the investigation on extracts of other plant species that were reported to exert insecticidal and repellant activities. Most of the conducted studies have been still in the primarily stage of investigation, lacking a focus on the insecticidal and repellant spectrum and the identification of the active constituents which are responsible for the insecticidal and repellant activity

Land use optimization using the fuzzy mathematical-spatial approach: a case study of Chelgerd watershed, Iran

In recent years, inappropriate land use, urban and industrial development along with different pollutions emanating from it gives rise to loss of natural resources and further leads to destructive floods, soil erosion, sedimentation and other various environmental, economic and social damages. Thus, management and planning are essential for the proper utilization, protection and revival of these resources. This study aimed to develop a mathematical-spatial optimum utilization model using FGP – MOLA in watershed including environmental and economic objectives while considering social issues. The results showed that the proposed model can lead to economic growth to 37% and decreasing the environmental damages to 2.4%. Under optimized condition, the area allocated to dry farming lands will decrease about 12% and gardens will increase about 423% and the other land uses remain unchanged too. In addition to, the results demonstrated the usefulness and efficiency of the proposed fuzzy model due to its flexibility and capability to simultaneously provide both optimum values and location of production resources

Multi-criteria governmental crop planning problem based on an integrated AHP-PROMETHEE approach

The study uses analytic hierarchy process (AHP) and preference ranking organisation method for enrichment evaluations (PROMETHEE) to deal with the crop planning problem as a multi-criteria decision-making problem, for governmental lands in Gaza Strip under two conditions: the normal economy condition and the resistant economy condition. These two conditions are studied from the governmental point of view. The study goal is to rank crops according to some considered criteria. Crops are divided into eight types that include vegetables, fruits, citrus, olives, palms, export crops, field crops and medical and aromatic crops. The developed AHP and PROMETHEE compare crops with respect to seven main criteria, namely; economical, financial, marketing, environmental, technical, political and social criteria. AHP is used to obtain criteria weights to be used as input for PROMETHEE to outrank alternatives. The

Application of the Fuzzy Approach for Agricultural Production Planning in a Watershed, a Case Study of the Atrak Watershed, Iran

Watersheds are large-scale regions where the agricultural production planning is associated with multiple objectives, usually, including economic, social and environmental targets. Uncertainty plays an important role in all agricultural planning because some factors are not fully controllable while some input data or parameters such as demand, resources, costs and objective functions are imprecise. This paper applies fuzzy multi-objective mathematical programming model to the Atrak watershed agricultural development plan. The model focuses on attaining three objectives simultaneously, namely, profit maximization, employment maximization and erosion minimization and these are subjected to 88 constraints. Results of the model indicate that, when compared with the current cropping structure, the implementation of the optimal cropping pattern could increase profit and employment and decrease soil erosion significantly. (Authors' abstract

Abstracts of Invited and Selected Papers and Organized Symposia, WAEA Annual Meetings, San Francisco, California, July 6-8, 2005

Teaching/Communication/Extension/Profession,

Developing collaborative planning support tools for optimised farming in Western Australia

Land-use (farm) planning is a highly complex and dynamic process. A land-use plan can be optimal at one point in time, but its currency can change quickly due to the dynamic nature of the variables driving the land-use decision-making process. These include external drivers such as weather and produce markets, that also interact with the biophysical interactions and management activities of crop production.The active environment of an annual farm planning process can be envisioned as being cone-like. At the beginning of the sowing year, the number of options open to the manager is huge, although uncertainty is high due to the inability to foresee future weather and market conditions. As the production year reveals itself, the uncertainties around weather and markets become more certain, as does the impact of weather and management activities on future production levels. This restricts the number of alternative management options available to the farm manager. Moreover, every decision made, such as crop type sown in a paddock, will constrains the range of management activities possible in that paddock for the rest of the growing season.This research has developed a prototype Land-use Decision Support System (LUDSS) to aid farm managers in their tactical farm management decision making. The prototype applies an innovative approach that mimics the way in which a farm manager and/or consultant would search for optimal solutions at a whole-farm level. This model captured the range of possible management activities available to the manager and the impact that both external (to the farm) and internal drivers have on crop production and the environment. It also captured the risk and uncertainty found in the decision space.The developed prototype is based on a Multiple Objective Decision-making (MODM) - á Posteriori approach incorporating an Exhaustive Search method. The objective set used for the model is: maximising profit and minimising environmental impact. Pareto optimisation theory was chosen as the method to select the optimal solution and a Monte Carlo simulator is integrated into the prototype to incorporate the dynamic nature of the farm decision making process. The prototype has a user-friendly front and back end to allow farmers to input data, drive the application and extract information easily

Optimal Cultivation Pattern to Increase Revenue and Reduce Water Use: Application of Linear Programming to Arjan Plain in Fars Province

Because the available water resources of the Arjan plain region in Iran do not fully meet the watering requirements for plants in farmlands, the crops suffer from water stress, a situation that causes them to wilt. The aim of this study is to develop a water resources planning model that helps decision-makers determine an appropriate cultivation pattern, optimize the exploitation from surface water resources, and specify the method of allocating water across different farm crops to minimize the detrimental effects of water shortage. Through investigating various models of water resources planning and properties along with the governing conditions for each of these models, the linear programming model was selected as a suitable option due to its simplicity and practical applicability to water resource allocation planning. The model was run for a five-year period by considering gradual variations through the determination of the most appropriate exploitation pattern from the available water resources (surface and groundwater). Results reveal that the negative water balance can be improved gradually as positive, where it will reach +20 million m3 per year in 2040 from the current deficit of 236 million m3 with an 8% increased net profit

Optimal Cultivation Pattern to Increase Revenue and Reduce Water Use: Application of Linear Programming to Arjan Plain in Fars Province

Because the available water resources of the Arjan plain region in Iran do not fully meet the watering requirements for plants in farmlands, the crops suffer from water stress, a situation that causes them to wilt. The aim of this study is to develop a water resources planning model that helps decision-makers determine an appropriate cultivation pattern, optimize the exploitation from surface water resources, and specify the method of allocating water across different farm crops to minimize the detrimental effects of water shortage. Through investigating various models of water resources planning and properties along with the governing conditions for each of these models, the linear programming model was selected as a suitable option due to its simplicity and practical applicability to water resource allocation planning. The model was run for a five-year period by considering gradual variations through the determination of the most appropriate exploitation pattern from the available water resources (surface and groundwater). Results reveal that the negative water balance can be improved gradually as positive, where it will reach +20 million m3 per year in 2040 from the current deficit of 236 million m3 with an 8% increased net profit

Integrated optimization of sustainable supply chains and transportation networks for multi technology bio-based production:A decision support system based on fuzzy ε-constraint method

Developing and employing effective design methodologies can significantly improve the economic and environmental viability of renewable production processes. This study contributes by presenting a novel bi-level decision support system (DSS) to aid modelling and optimization of multi technology, multi product supply chains and co-modal transportation networks for biomass based (bio-based) production combining two multi-objective mathematical models. Considering the supply chain configuration optimized by the first level of the DSS, in the second level, the transportation network is designed specifying the most appropriate transportation mode and related transportation option under transfer station availability limitations. A hybrid solution methodology that integrates fuzzy set theory and ε-constraint method is proposed. This methodology handles the system specific uncertainties addressing the economic and environmental sustainability aspects by capturing trade-offs between conflicting objectives in the same framework. To explore the viability of the proposed models and solution methodology, a regional supply chain and transportation network is designed using the entire West Midlands (WM) region of the UK as a testing ground. Additionally, scenario and sensitivity analyses are conducted to provide further insights into design and optimization of the biomass based supply chains