A Linear Dynamic Progrmming Approach to Irrigation System Management with Depleting Groundwater

Article Abstract:

A model for measuring the economic benefits of irrigation system development over a depleting aquifer is presented, along with related methodology for detailed long-range farm planning. The paper considers management issues, such as distribution system configuration, drilling policy, area developed for irrigation, and crop production. A Linear Dynamic Programming (LDP) method is developed and applied to derive optimal temporal investments in the use of stock resources and long-term cropping plans. First, parametric linear programming (PLP) is used to maximize periodic profits subject to specified values of state variables related to annual water use and irrigation system capacity. The PLP results are then used in a dynamic programming model to determine the optimal allocation of water and irrigation resources over time. The impact of aquifer depletion on the profitability of furrow and pivot irrigation systems is illustrated for a typical farm situation in the Texas High Plains. Results indicate that the economic benefits of modern water and energy efficient irrigation systems may come from the expansion of current irrigation intensity rather than from an extended period of irrigation when water is initially scarce relative to land. Several conditions are identified where economic depletion may occur before the point of physical exhaustion is reached. (Reprinted by Permission of Publisher.)

Agriculture, Linear programming, Profits, Dynamic programming, Strategic Planning, Resource Management, Profit, Water Supply, Texas

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Cognitive Heuristics and Feedback in a Dynamic Decision Environment

Article Abstract:

Research on cognitive processes in decision making has identified heuristics that often work well but sometimes lead to serious errors. This paper presents an investigation of the performance of heuristics in a complex dynamic setting, characterized by repeated decisions with feedback. There are three components: (1) A simulated task resembling medical decision problems (diagnosis and treatment) is described. (2) Computer models of decision strategies are developed. These include models based on cognitive heuristics as well as benchmark strategies that indicate the limit of the heuristic strategies' performance. The upper benchmark is based on statistical decision theory, the lower one on random trial and error. (3) Selected task characteristics are systematically varied and their influence on performance evaluated in simulation experiments. Results indicate that task characteristics often studied in past research (e.g., symptom diagonosticity, disease base-rates) have less influence on performance relative to feedback-related aspects of the task. These dynamic characteristics are a major determinant of when heuristics perform well or badly. The results also provide insights about the costs and benefits of various cognitive heuristics. In addition, the possible contribution of this research to the design and evaluation of decision aids is considered. (Reprinted by Permission of Publisher.)

Expert systems, Health care information services, Dynamics (Mechanics), Decision theory, Simulation, Medical Advice Systems, Decision Models, Feedback, Decision Making, Bayesian Theory, Dynamics

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Approximation Methods for the Uncapacitated Dynamic Lot Size Problem

Article Abstract:

We provide worst case error bounds for several approximation methods (heuristics, product aggregation, and partitioning of the planning horizon) for the uncapacitated dynamic lot size problem. We propose two managerially oriented heuristics and show that they have a relative worst case error bound equal to two, and develop similar analyses for methods known as the least cost per unit heuristic, the part period balancing heuristic, and an economic order quantity heuristic (expressed in terms of a time supply of demand). We also show how errors introduced by partitioning of the planning horizon in multi-product multi-facility problems are bounded by product set-up costs, and how errors introduced by product aggregation are bounded by set-up costs, holding costs, and demands. The latter results suggest methods for product aggregation that minimize the worst case error bounds. (Reprinted by Permission of Publisher).

Management, Algorithms, Inventory control, Algorithm, Cost, Production, Approximation

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