A multiple criteria decision making approach to manure management systems in the Netherlands

The intensification of livestock operations in the last few decades has resulted in an increased social concern over the environmental impacts of livestock operations and thus making appropriate manure management decisions increasingly important. A socially acceptable manure management system that simultaneously achieves the pressing environmental objectives while balancing the socio-economic welfare of farmers and society at large is needed. Manure management decisions involve a number of decision makers with different and conflicting views of what is acceptable in the context of sustainable development. This paper developed a decision-making tool based on a multiple criteria decision making (MCDM) approach to address the manure management problems in the Netherlands. This paper has demonstrated the application of compromise programming and goal programming to evaluate key trade-offs between socio-economic benefits and environmental sustainability of manure management systems while taking decision makers’ conflicting views of the different criteria into account. The proposed methodology is a useful tool in assisting decision makers and policy makers in designing policies that enhance the introduction of economically, socially and environmentally sustainable manure management systems.     

Computational bounds for elevator control policies by large scale linear programming

We computationally assess policies for the elevator control problem by a new column-generation approach for the linear programming method for discounted infinite-horizon Markov decision problems. By analyzing the optimality of given actions in given states, we were able to provably improve the well-known nearest-neighbor policy. Moreover, with the method we could identify an optimal parking policy. This approach can be used to detect and resolve weaknesses in particular policies for Markov decision problems.     

OPTIMAL HARVESTING OF FISH POPULATIONS WITH NON-STATIONARY STOCK-RECRUITMENT RELATIONSHIPS

Optimal harvesting policies are commonly derived by assuming a time-invariant relationship between some productivity measure and the resource variable under control. Yet, long-term trends in the environment appear to induce persistent changes in spawning success in several fish stocks. I show that when predictable trends in environmental effects are incorporated into stock-recruitment models, optimal policies respond to changing environmental conditions in a way that depends very much on the management objective. When the goal is to maximize expected discounted yield, resulting risk-neutral policies computed for a model of a cyclic iteroparous population respond by continuously adjusting optimal spawning targets in phase with the environmental cycle: escapements are raised when favorable conditions are anticipated and they are lowered when poor environments are expected. These feedback responses reinforce recruitment fluctuations and lead to a sequence of boom and bust periods in the fishery. Policies shift diametrically when a risk-averse objective is pursued such as maximization of the expected sum of discounted logarithms of catches. Optimal escapements closely parallel fluctuations in population abundance, with harvest rates and catches much less variable than in the risk-neutral policy. Harvest rates respond in a compensatory way to changes in population abundance, anticipated environmental conditions, and expected strength of incoming year-classes. Depending on the specific model used, a constant harvest rate strategy may perform nearly as well as the optimal. Analytical results are provided that characterize risk-neutral optimal policies for stochastic delay-difference population models. Results show that knowledge of current environmental conditions can be used to construct harvest policies which are nearly as good as those “optimal” ones based on long-term environmental forecasts.     

Power to youth: Designing democracy for long-term well-being

Democratic processes may not take the welfare of future generations sufficiently into account and thus may not achieve sustainability. We introduce rejection/support rewards (RSRs) and show that a dual democratic mechanism–RSRs and elections–can achieve sustainability. RSRs stipulate that incumbents who are not re-elected, but obtain the majority support among young voters, receive a particular monetary or non-monetary reward. Such rejection/support rewards induce politicians to undertake long-term beneficial policies, but may invite excessive reward-seeking. We identify optimal RSRs under different informational circumstances.     

OPTIMAL-SUSTAINABLE MANAGEMENT OF MULTI-SPECIES FISHERIES: LESSONS FROM A PREDATOR-PREY MODEL

ABSTRACT. In this paper we consider the meaning of sustainable resource management in multi-dimensional resources. Based on the principle of intergenerational fairness, we define fisheries management as sustainable if it does not lead to a decline in the net present value of the fishery. If sustainability, or intergenerational fairness, were held as an obligation by fishery managers, then the traditional present-value maximization objective would be constrained. Using numerical solutions to a simple predator-prey model, we explore how the optimal-sustainable management of this fishery would differ from management that seeks to maximize the present value of the benefits. General lessons regarding the meaning of sustainable fishery management are discussed.     

WEAK AND STRONG SUSTAINABILITY,ENVIRONMENTAL CONSERVATION AND ECONOMIC GROWTH

ABSTRACT. To investigate the role of explicit and implicit assumptions in different models of weak and strong sustain‐ability, the Solow/Hartwick model of intergenerational equity with nonrenewable resources is gradually extended to include renewable resources, endogenous technical progress, and stock pollution. This reveals the fundamental role of endogenous technical progress for sustainable development, the inconsistency of implicit sustainability assumptions in various models, as well as the existence of a Hartwick rule for Daly's steady‐state economy. Moreover, it shows that the concepts of Solow sustainability and strong sustainability coincide as a special case of weak sustainability. The latter integrates economic and environmental concerns and aims at maintaining the welfare potential of an economy over time. It does not rule out economic growth by assumption. Rather, the analysis shows that environmental conservation and economic growth can be compatible with each other, without jeopardizing social welfare. Finally, the analysis shows that the discussion of sustain‐ability models cannot be restricted to the explicit differences that are usually pointed out by their authors and commentators. Rather, implicit assumptions must be made explicit.     

报酬函数及转移速率族均非一致有界的连续时间折扣马氏决策规划

本文首次在报酬函数及转移速率族均非一致有界的条件下，对可数状态空间，可地动集的连续时间折扣马氏决策规划进行研究，文中引入一类新的无界报酬函数，在一类新的马氏策略中，讨论了最优策略的存在性及春结构，除证明了在有界报酬和一致有界转移速率族下成立的主要结果外，本文还得到一些重要结论。     

Including sustainability criteria into inventory models

Research on sustainability performance has considerably enriched operations management literature in recent years. However, work with quantitative models is still scarce. This paper contributes by revisiting classical inventory methods taking sustainability concerns into account. We believe that reducing all aspects of sustainable development to a single objective is not desirable. We thus reformulate the classical economic order quantity model as a multiobjective problem. We refer to this model as the sustainable order quantity model. Then, a multi-echelon extension of the sustainable order quantity model is studied. For both models, the set of efficient solutions (Pareto optimal solutions) is analytically characterized. These results are used to provide some insights about the effectiveness of different regulatory policies to control carbon emissions. We also use an interactive procedure that allows the decision maker to quickly identify the best option among these solutions. The proposed interactive procedure is a new combination of multi-criteria decision analysis techniques.     

RISK-BASED MULTIATTRIBUTE DECISION-MAKING IN PROPERTY AND WATERSHED MANAGEMENT

ABSTRACT. Determining best management systems for properties and evaluating their sustainability at the watershed scale are useful and important aspects of integrated watershed management. Multiattribute decision-making (MADM) is very useful for modeling the selection of best management systems for properties in a watershed. This paper reviews four MADM approaches including utility theory, surrogate worth tradeoff, free iterative search and stochastic dominance with respect to a function (SDWF). Emphasis is on determining how the first three methods could be used to determine the best (most preferred) combinations of attributes and associated management systems for a property. An application of the expected utility method with risk neutral preferences is presented in which farmer's preferences for five attributes are used to rank five farming systems for an agricultural watershed in Missouri. A framework is presented for assessing the sustainability of the best management systems for all properties in a watershed and the cost-effectiveness of policies for enhancing sustainable resource management at the watershed scale.     

Nonstationary Markov decision problems with converging parameters

This paper considers the solution of Markov decision problems whose parameters can be obtained only via approximating schemes, or where it is computationally preferable to approximate the parameters, rather than employing exact algorithms for their computation.Various models are presented in which this situation occurs. Furthermore, it is shown that a modified value-iteration method may be employed, both for the discounted version and for the undiscounted version of the model, in order to solve the optimality equation and to find optimal policies. In both cases, the convergence rate is determined.As a side result, we characterize the asymptotic behavior of backward products of a geometrically convergent sequence of Markov matrices.     

A system dynamics sustainable business model for Petroleos Mexicanos (Pemex): case based on the Global Reporting Initiative

We propose a System Dynamics model to explain the implementation and development of business sustainable policies at Petroleos Mexicanos (Pemex). The model is built using information reported by this company on the Global Reporting Initiative website. The simulation model incorporates the participation and expectations of internal and external stakeholders in defining sustainability (as perceived by the organization) and implementing its initiatives. Three variables (leadership, stakeholder motivation and external factors), identified as leverage points in the model, are used as means to improve the organization's journey towards achieving sustainability. The results of the simulation model seem to indicate that by increasing leadership activity and levels of stakeholder motivation, the journey towards sustainability can be greatly improved, and that external economic factors do not have a significant impact on sustainability achievement. These findings are pertinent for organizations currently in the process of implementing sustainable policies and strategies.     

自治单种群模型及其最优捕获策略

本文用一种新的方法,讨论了单种群生物资源的捕获优化问题.分别以单位时间最大可持续捕获量和单位时间最大净利润为管理目标,得到一类自治单种群捕获模型的最优捕获策略,所得结果包括了文献中研究过的几乎所有自治单种群捕获模型的相应研究结果.     

Ecological-economic modelling for the sustainable management of biodiversity

Terrestrial and marine biodiversity provides the basis for both ecosystems functioning and numerous commodities or services that underpin human well-being. From several decades, alarming trends have been reported worldwide for both biodiversity and ecosystem services. Therefore the sustainable management of biodiversity requires a double viewpoint balancing ecological conservation with the welfare of human societies. Understanding the underlying trade-offs, synergies and interactions imposes the development of interdisciplinary researches and methods. In that respect, bio-economic or ecological economic modeling is likely to play a major role. The present paper intends to elicit the key features, strengths and challenges of bio-economic approaches especially in mathematical and computational terms. It first recall the main bio-economic methods, models and decisional instruments used in these types of analyses. Then the paper shows to what extent bio-economic sustainability lies between equilibrium, viability and optimality mathematical frameworks. It ends up by identifying new major challenges among which the operationalization of ecosystem based management, the precautionary principle and the implementation of governance are especially important.     

Compactness of the space of non-randomized policies in countable-state sequential decision processes

For sequential decision processes with countable state spaces, we prove compactness of the set of strategic measures corresponding to nonrandomized policies. For the Borel state case, this set may not be compact (Piunovskiy, Optimal control of random sequences in problems with constraints. Kluwer, Boston, p. 170, 1997) in spite of compactness of the set of strategic measures corresponding to all policies (Schäl, On dynamic programming: compactness of the space of policies. Stoch Processes Appl 3(4):345–364, 1975b; Balder, On compactness of the space of policies in stochastic dynamic programming. Stoch Processes Appl 32(1):141–150, 1989). We use the compactness result from this paper to show the existence of optimal policies for countable-state constrained optimization of expected discounted and nonpositive rewards, when the optimality is considered within the class of nonrandomized policies. This paper also studies the convergence of a value-iteration algorithm for such constrained problems.     

On the application of mathematical models in maintenance

This paper may be seen as an appeal to maintenance modellers to work with maintenance engineers and managers on real problems. Such collaboration is essential if maintenance modelling is to be accepted within the engineering community. It is also particularly important in the design and building of maintenance management information systems if such systems are to be used to manage and operate maintenance policy in the new millennium. In this context, developing areas of maintenance modelling are discussed, namely: inspection maintenance; condition based maintenance; maintenance for multi-component systems; and maintenance management information systems. Some new models relating to capital replacement are also considered. Thus, we are concerned with the mathematical modelling of maintenance rather than with management processes relating to maintenance. Discussion of maintenance management information systems is included because of their importance in providing data for mathematical modelling and in implementing model-based maintenance policy.     

Reduction of total-cost and average-cost MDPs with weakly continuous transition probabilities to discounted MDPs

This note describes sufficient conditions under which total-cost and average-cost Markov decision processes (MDPs) with general state and action spaces, and with weakly continuous transition probabilities, can be reduced to discounted MDPs. For undiscounted problems, these reductions imply the validity of optimality equations and the existence of stationary optimal policies. The reductions also provide methods for computing optimal policies. The results are applied to a capacitated inventory control problem with fixed costs and lost sales.     

Zero-sum Markov games and worst-case optimal control of queueing systems

Zero-sum stochastic games model situations where two persons, called players, control some dynamic system, and both have opposite objectives. One player wishes typically to minimize a cost which has to be paid to the other player. Such a game may also be used to model problems with a single controller who has only partial information on the system: the dynamic of the system may depend on some parameter that is unknown to the controller, and may vary in time in an unpredictable way. A worst-case criterion may be considered, where the unknown parameter is assumed to be chosen by nature (called player 1), and the objective of the controller (player 2) is then to design a policy that guarantees the best performance under worst-case behaviour of nature. The purpose of this paper is to present a survey of stochastic games in queues, where both tools and applications are considered. The first part is devoted to the tools. We present some existing tools for solving finite horizon and infinite horizon discounted Markov games with unbounded cost, and develop new ones that are typically applicable in queueing problems. We then present some new tools and theory of expected average cost stochastic games with unbounded cost. In the second part of the paper we present a survey on existing results on worst-case control of queues, and illustrate the structural properties of best policies of the controller, worst-case policies of nature, and of the value function. Using the theory developed in the first part of the paper, we extend some of the above results, which were known to hold for finite horizon costs or for the discounted cost, to the expected average cost.     

Enhancing case-based reasoning for personnel rostering with selected tabu search concepts

In this paper, we investigate the advantages of using case-based reasoning (CBR) to solve personnel rostering problems. Constraints for personnel rostering problems are commonly categorized as either ‘hard’ or ‘soft’. Hard constraints are those that must be satisfied and a roster that violates none of these constraints is considered to be ‘feasible’. Soft constraints are more flexible and are often used to measure roster quality in terms of staff satisfaction. We introduce a method for repairing hard constraint violations using CBR. CBR is an artificial intelligence paradigm whereby new problems are solved by considering the solutions to previous similar problems. A history of hard constraint violations and their corresponding repairs, which is captured from human rostering experts, is stored and used to solve similar violations in new rosters. The soft constraints are not defined explicitly. Their treatment is captured implicitly during the repair of hard constraint violations. The knowledge in the case-base is combined with selected tabu search concepts in a hybrid meta-heuristic algorithm. Experiments on real-world data from a UK hospital are presented. The results show that CBR can guide a meta-heuristic algorithm towards feasible solutions with high staff satisfaction, without the need to explicitly define soft constraint objectives.     

需求可延期满足的报童模型研究

针对毋须“第一时间”发送的物流需求,商家应设计、提供与此相匹配的服务,基于此运作管理背景,本文以典型问题“可延期发送的货运代理商最优订舱”为例,构建“需求可延期满足的报童模型”。在系统回顾“报童模型”研究的基础上,分析其与基本报童模型的异同及求解难点,讨论解的存在性及唯一性,探索将其转化为基本报童模型的途径,构建“报童-波尔查诺二分迭代” 求解算法,使用训练与测试数据集双重数值模拟验证算法的正确性和有效性。本文重要的价值在于提出了一种新的需求管理运营模式:针对于可延期满足的随机需求,通过提供补偿的方式实现“需求库存化”,需求和供给都得以充分利用,实现供需双方共赢,提升社会效益。     

A comparison of average and discounted cost models for the dynamic lot size inventory problem

It is a common practice in the inventory literature to use average cost models as approximations to the theoretically correct discounted cost models. An average cost model minimizes the average undiscounted cost per period, while a discounted cost model minimizes the total discounted cost over the problem horizon. This paper attempts to answer an important question: How good are the results (the total discounted costs) for the average cost models compared to those for the discounted cost models? This question has been conclusively answered for the simplest inventory model where the demand rate and other parameters are assumed to remain constant in time. This paper addresses this issue for the first time for the case where demand rates are allowed to be nonstationary in time.A discounted cost model has been developed in the paper to carry out this comparison. It is shown that a simple dynamic programming algorithm can be used to find optimal order policies for the discounted cost model.The effect of the varying interest rates and other parameters on the relative performance of the average cost model has been studied by developing an insightful analysis and also by doing a computational study. The results show that, while the average cost model can cost as much as about 26% more than the discounted cost model in extreme cases, this increase is not significant for the parameter values in the range of the common interest.