Stabilizing management of fluctuating resources

We consider a resource management problem in which the management objective is to minimize fluctuations in resource stocks. Stabilizing management policies constitute the designing of memoryless state feedback control strategies for a discrete time resource model which contains unknown but bounded fluctuations. We also show that the problem of maximizing sustainable yield in an uncertain fishery can be considered as the problem of stabilizing of the stock level.The paper corresponds to an invited talk at the 14th Symposium on Operations Research, Ulm, September 6–8, 1989.The support by Yrjö Jahnsson Foundation is gratefully acknowledged.This work was supported by NSF and AFOSR under grant ECS 8602524.     

OPTIMAL RECOVERY OF A SHARED RESOURCE STOCK: A DIFFERENTIAL GAME MODEL WITH EFFICIENT MEMORY EQUILIBRIA

We consider an optimal two-country management of depleted transboundary renewable resources. The management problem is modelled as a differential game, in which memory strategies are used. The countries negotiate an agreement among Pareto efficient harvesting programs. They monitor the evolution of the agreement, and they memorize deviations from the agreement in the past. If the agreement is observed by the countries, they continue cooperation. If one of the countries breaches the contract, then both countries continue in a noncooperative management mode for the rest of the game. This noncooperative option is called a threat policy. The credibility of the threats is guaranteed by their equilibrium property. Transfer or side payments are studied as a particular cooperative management program. Transfer payments allow one country to buy out the other from the fishery for the purpose of eliminating the inefficiency caused by the joint access to the resources. It is shown that efficient equilibria can be reached in a class of resource management games, which allow the use of memory strategies. In particular, continuous time transfer payments (e.g., a share of the harvest) should be used instead of a once-and-for-all transfer payment.     

Use of stochastic control theory to model a forest management system

A forest management problem due to Hellman has been modelled as a stochastic control problem with one state variable (inventory level) and one control variable (consumption rate of wood by the factories). The stochastic process governing the evolution of the inventory level is transformed into an Itô stoachastic differential equation by approximating the compound Poisson process of wood arrivals into the depot as a Wiener process. The resulting stochastic control problem is solved by using the Hamilton-Jacobi-Bellman equation of stochastic dynamic programming. Two numerical examples illustrate the results.     

Stochastic modelling and optimization for environmental management

In modelling and managing complex environmental systems, inherent uncertainties of all relevant natural processes are to be taken into consideration. In the present paper diverse stochastic modelling and optimization approaches for handling such problems (primarily in the field of water quality analysis and control) are highlighted, drawing on the findings of case studies and real-world applications.     

Harvesting in a pelagic fishery: The case of Northern Chile

This paper analyses the pelagic fishery of Northern Chile, estimating harvesting functions that contribute to understand why rather poor incentives to exit may predominate in pelagic fisheries, despite scarcer fish stocks. Our results show that pervessel catch's stock sensitivity (the catchtobiomass elasticity value) varies negatively with stock levels. Stock levels preceding a marked fall into biological overfishing would have been associated to biomass elasticities lower than the unitary value. This suggests that during catch bonanza periods, catchperunitofeffort would fail to detect a rapidly declining stock trend, increasing the risk of fishing collapse. Moreover, external economies in search efforts would have reduced the incentives to exit, particularly for the smaller vessels in our sample. Finally, we find evidence of either constant or increasing marginal returns in the use of pervessel fishing effort, which suggests that inefficiency in production has resulted from direct restrictions upon fishing effort. Overall, our findings provide consistent evidence that enhances the necessity of more efficient regulations upon harvesting in pelagic fisheries.     

Optimal timing of interventions in fishery resource and pest management

Assuming that a fish population follows the continuous logistic growth or the discrete Beverton-Holt model, several optimal impulsive harvesting policies for the maximum stock level of the fish at the end of a fishing season are investigated under the condition of fixed intensity and frequency of impulsive harvesting. The optimal impulsive harvesting moments for all cases considered are given analytically and the related numerical simulations are also provided. Furthermore, the methods employed can also be used to investigate the optimal timing of chemical control in pest management. Our results confirm that the optimal timing of pesticide applications such that the density of the pest population is minimal at any time during a planting season or the average of density of the pest population over the planting season is minimal is the beginning of the planting season. In practice, the results can be used to guide the fisherman to manage fisheries and guide farmers to control pests.     

Buffer allocation for a class of nonlinear stochastic knapsack problems

In this paper, we examine a class of nonlinear, stochastic knapsack problems which occur in manufacturing, facility or other network design applications.Series, merge-and-split topologies of series-parallelM/M/1/K andM/M/C/K queueing networks with an overall buffer constraint bound are examined. Bounds on the objective function are proposed and a sensitivity analysis is utilized to quantify the effects of buffer variations on network performance measures.     

A Game Theoretical Multiple Resource Interaction Approach to Resource Allocation in an Air Campaign

In this paper we propose a multiple resource interaction model in a game-theoretical framework to solve resource allocation problems in theater level military campaigns. An air raid campaign using SEAD aircraft and bombers against an enemy target defended by air defense units is considered as the basic platform. Conditions for the existence of saddle point in pure strategies is proved and explicit feedback strategies are obtained for a simplified model with linear attrition function limited by resource availability. An illustrative example demonstrates the key features.     

资源分配的多目标模糊优选动态规划分析法

本尝试应用多目标模糊优选动态规划分析法求解具有多个量纲不一的定量评价目标,或既有定量目标又有定性目标的有限资源分配问题。     

Feedback Nash equilibria in a problem of optimal fishery management

The paper deals with a problem of optimal management of a common-property fishery, modelled as a two-player differential game. Under nonclassical assumptions on harvest rates and utilities, a feedback Nash equilibrium is determined, using a bionomic equilibrium concept. Later on, this assumption is relaxed and a feedback Nash equilibrium is established under minimal hypotheses.     

Generalized Surrogate Problem Methodology for Online Stochastic Discrete Optimization

We consider stochastic discrete optimization problems where the decision variables are nonnegative integers and propose a generalized surrogate problem methodology that modifies and extends previous work in Ref. 1. Our approach is based on an online control scheme which transforms the problem into a surrogate continuous optimization problem and proceeds to solve the latter using standard gradient-based approaches while simultaneously updating both the actual and surrogate system states. In contrast to Ref. 1, the proposed methodology applies to arbitrary constraint sets. It is shown that, under certain conditions, the solution of the original problem is recovered from the optimal surrogate state. Applications of this approach include solutions to multicommodity resource allocation problems; in these problems, exploiting the convergence speed of the method, one can overcome the obstacle posed by the presence of local optima.     

Online Surrogate Problem Methodology for Stochastic Discrete Resource Allocation Problems

We consider stochastic discrete optimization problems where the decision variables are nonnegative integers. We propose and analyze an online control scheme which transforms the problem into a surrogate continuous optimization problem and proceeds to solve the latter using standard gradient-based approaches, while simultaneously updating both the actual and surrogate system states. It is shown that the solution of the original problem is recovered as an element of the discrete state neighborhood of the optimal surrogate state. For the special case of separable cost functions, we show that this methodology becomes particularly efficient. Finally, convergence of the proposed algorithm is established under standard technical conditions; numerical results are included in the paper to illustrate the fast convergence of this approach.     

A STOCHASTIC FEEDBACK MODEL FOR OPTIMAL MANAGEMENT OF RENEWABLE RESOURCES

Analytical expressions for optimal harvest of a renewable resource stock which is subject to a stochastic process are found. These expressions give the optimal harvest as an explicit feedback control law. All relations in the model, including the stochastic process, may be arbitrary functions of the state variable (stock). The objective function, however, is at most a quadratic function in the control variable (yield). A quadratic objective function includes the cases of downward sloping demand and increasing marginal costs which are the most common sources for nonlinearities in the economic part of the model. When it is assumed that there is a moratorium on harvest for stock sizes below a certain level (biological barrier), it is shown that the barrier requirements influence the optimal harvest paths throughout.     

基于多维风险视角的政府卫生保健资源配置决策行为研究

研究在疾病风险和医学治疗风险同时共存的情形下,政府卫生保健资源的优先配置行为决策问题.当存在治疗风险(患病风险)时,分析病人患病的不确定性(治疗的不确定性)对政府卫生保健资源的配置效应,同时给出配置更多的卫生资源到更高风险病人群体的社会规划者的风险偏好条件.当这两类风险是局部的或正象限依赖的风险时,研究两种来源的风险对政府卫生保健资源的配置的联合影响.将之前学者提出的卫生保健资源的配置模型扩展到两类风险共存的情形,同时对于不确定下的卫生保健资源配置决策问题提供新的见解.     

Solution for a dynamic bargaining problem with an application to resource management

Nash's bargaining solution for dynamic,N-player problems is considered. Computational methods for finding solution candidates to bargaining problems are presented and some sufficient conditions for bargaining solutions are established. The theory and computation are illustrated by a resource management example.