Modelling of linguistic variables in multicriteria energy policy support

The climate change and the increasing complexity of the energy sector along with the prerequisite for sustainability have broadened the energy policy shaping field by bringing out new challenges. Decision support tools and methods, such as Multicriteria Decision Aid (MCDA), are necessary for energy policy, in the pursuit of appropriate approaches necessary to support the restructuring of the energy sector, concerning patterns of energy extraction, generation, transformation and use, from unsustainable to sustainable forms of development. Papers devoted to the investigation of MCDA models using linguistic variables for energy policy support seem to be not available in the international literature. The scope of this paper is to explore different linguistic representation and computational models in MCDA that are or can be applied to energy policy support and to establish a clear linkage between them. This paper argues that MCDA methodologies with direct computation on linguistic variables can support energy policy frameworks, bridging the gap between energy policy makers thinking, reasoning, representation and computing. Finally, current trends, open questions and prospects in this topic are pointed out.     

ON THE OPTIMAL POLICY FOR COMBINING HARVESTING OF PREDATOR AND PREY

An optimal policy is sought for maximizing present value from the combined harvest of two ecologically dependent species, which would coexist as predator and prey in the absence of harvesting. Harvest rate for each species is assumed proportional to both stock level and effort. For a large class of biological productivity functions, it is established that the optimal equilibrium point in the phase-plane of stock levels must be a saddle-point. For quadratic productivity functions, a combination of analytical reasoning and numerical experiment is used to show that first and second order necessary conditions for optimality are satisfied by a unique approach to equilibrium, which must therefore be optimal. The corresponding control law is given, and an apparent suggestion of two previous authors concerning the policy is shown to be inappropriate. The optimal policy enables an estimate to be made of the true loss of resource value due to a catastrophic fall in stock level.     

On a class of dynamic programming problems whose optimal controls and states are independent of the future

When applying dynamic programming for optimal decision making one usually needs considerable knowledge about the future. This knowledge, e.g. about future functions and parameters, necessary to determine optimal control policies, however, is often not available and thus precludes the application of dynamic programming.In the present paper it is shown that for a certain class of dynamic programming problems the optimal control policy is independent of the future. To illustrate the results an application in inventory control is given and further applications in the theories of economic growth and corporate finance are listed in the references.     

On air traffic flow management with rerouting. Part I: Deterministic case

In this paper a deterministic mixed 0-1 model for the air traffic flow management problem is presented. The model allows for flight cancelation and rerouting, if necessary. It considers several types of objective functions to minimize, namely, the number of flights exceeding a given time delay (that can be zero), separable and non-separable ground holding and air delay costs, penalization of alternative routes to the scheduled one for each flight, time unit delay cost to arrive to the nodes (i.e., air sectors and airports) and penalization for advancing arrival to the nodes over the schedule. The arrival and departure capacity at the airports is obviously considered, as well as the capacity of the different sectors in the airspace, being allowed to vary along the time horizon. So, the model is aimed to help for better decision-making regarding the ground holding and air delays imposed on flights in an air network, on a short term policy for a given time horizon. It is so strong that there is no additional cut appending, nor does it require the execution of the branch-and-bound phase to obtain the optimal solution for the problem in many cases of the testbeds with which we have experimented. In the other cases, the help of the cut identifying and heuristic schemes of the state-of-the art optimization engine of choice is required in order to obtain the solution of the problem, and the branch-and-bound phase is not required either. An extensive computational experience is reported for large-scale instances, some of which have been taken from the literature and some others were coming from industry.     

Optimal Consumption Portfolio and No-Arbitrage with Nonproportional Transaction Costs

In this paper we consider a finite-state financial market with non-proportional transaction cost and bid-ask spreads. The transaction cost consists of two parts: a fixed cost and a proportional cost to the size of transaction. We show that the existence of an optimal consumption policy implies that the market has no strong arbitrage; the opposite, however, is not true, i.e., no strong arbitrage does not imply the existence of an optimal consumption policy. This is in sharp contrast with the case of proportional transaction cost and other cases reported in the literature, where no strong arbitrage is equivalent to the existence of an optimal consumption policy. We also study the relationship between weak arbitrage and strong arbitrage. Different from the market with proportional transaction cost, we find that these two forms of arbitrage are equivalent unless the fixed cost is zero. A necessary and sufficient condition for the existence of an optimal consumption policy is also obtained. Supported by CAS, NSFC, RGC of Hong Kong and NSF under Grant No. DMI-0196084 and DMI-0200306.     

随机需求下考虑半成品库存的多周期生产决策优化

为了更好地应对需求的不确定性,在需求实现之前,企业既可以生产成品直接满足需求,亦可生产部分半成品,在观察到实际需求之后短时间内迅速完成剩余生产环节以满足需求。未加工的半成品和未售出的成品可用于满足后续周期的需求。作为一种提高生产灵活性的手段,分阶段生产的方式会产生更高的成本。企业需要在成本和灵活性之间作出权衡,优化生产决策。模型通过动态规划的方法,研究需求不确定情况下考虑半成品库存的多周期生产决策问题,通过分析目标函数以及最优值函数的结构性质,推导出最优的多周期生产策略为修正的目标库存策略,并且分析了不同参数对最优策略的影响。     

Global saddle points of nonlinear augmented Lagrangian functions

We notice that the results for the existence of global (local) saddle points of augmented Lagrangian functions in the literature were only sufficient conditions of some special types of augmented Lagrangian. In this paper, we introduce a general class of nonlinear augmented Lagrangian functions for constrained optimization problem. In two different cases, we present sufficient and necessary conditions for the existence of global saddle points. Moreover, as corollaries of the two results above, we not only obtain sufficient and necessary conditions for the existence of global saddle points of some special types of augmented Lagrangian functions mentioned in the literature, but also give some weaker sufficient conditions than the ones in the literature. Compared with our recent work (Wang et al. in Math Oper Res 38:740–760, 2013), the nonlinear augmented Lagrangian functions in this paper are more general and the results in this paper are original. We show that some examples (such as improved barrier augmented Lagrangian) satisfy the assumptions of this paper, but not available in Wang et al. (2013).     

Optimization of a Multiperiod Resource Allocation Model

In this paper we examine multiperiod resource allocation problems, such as allocating a given marketing budget among T periods. The return functions of each period are assumed to be concave functions of the effective effort variable, which is composed of the expenditures in all previous periods and the present one. Assuming that the effect of an amount spent in period t is decreasing by a fixed rate in successive periods, necessary and sufficient conditions for a non-boundary optimal policy are derived. Under these conditions the optimal policy which maximizes total returns is obtained.     

货币政策、市场状态对中国股市微观流动性影响的实证分析

本文研究了我国股市微观流动性的影响因素。在假定所有经济变量之间相互影响、内生决定的情况下,建立VAR系统,通过脉冲响应函数、方差分解的方法分别讨论了市场收益、波动及货币政策对股市微观流动性的影响。实证结果表明,货币政策对股市流动性并无显著影响,货币政策的松紧、"宏观流动性"的增强或减弱并不必然导致股市微观流动性的增强或减弱;市场的波动对股市流动性也无显著的影响效应,但市场的收益状态却决定着股市微观流动性的强弱。     

Unified theory of augmented Lagrangian methods for constrained global optimization

We classify in this paper different augmented Lagrangian functions into three unified classes. Based on two unified formulations, we construct, respectively, two convergent augmented Lagrangian methods that do not require the global solvability of the Lagrangian relaxation and whose global convergence properties do not require the boundedness of the multiplier sequence and any constraint qualification. In particular, when the sequence of iteration points does not converge, we give a sufficient and necessary condition for the convergence of the objective value of the iteration points. We further derive two multiplier algorithms which require the same convergence condition and possess the same properties as the proposed convergent augmented Lagrangian methods. The existence of a global saddle point is crucial to guarantee the success of a dual search. We generalize in the second half of this paper the existence theorems for a global saddle point in the literature under the framework of the unified classes of augmented Lagrangian functions.     

Constrained Extremum Problems and Image Space Analysis—Part III: Generalized Systems

In Part I, sufficient and necessary optimality conditions and the image regularity conditions of constrained scalar and vector extremum problems are reviewed for Image Space Analysis. Part II presents the main feature of the duality and penalization of constrained scalar and vector extremum problems by virtue of Image Space Analysis. In the light, as said in Part I and Part II, to describe the state of Image Space Analysis for constrained optimization, and to stress that it allows us to unify and generalize the several topics of Optimization, in this Part III, we continue to give an exhaustive literature review on separation functions, gap functions and error bounds for generalized systems. Part III also throws light on some research gaps and concludes with the scope of future research in this area.     

The continuous resupply policy for deteriorating items with stock-dependent observable demand in a two-warehouse and two-echelon supply chain

This paper expands previous work on stock-dependent demand for a retailer with a two-warehouse (OW/RW) situation to the case of deteriorating items and where the retailer seeks to obtain the integrated optimal distribution policy from collaboration with a supplier. Motivated by practical applications and recent literature, a policy is considered whereby products in good order from the retailer’s back-room (RW) are frequently transferred to its capacitated main store OW. Because the demand depends on the stock of good products in the OW, the aim is to keep this stock at its full capacity with products in good condition, and this can be done for as long as the RW stock of good products is positive. A firm’s objective function is the Net Present Value (NPV) of the firm’s future cash-flows. The profit functions are developed for both this continuous resupply policy and the commonly used policy in the OW/RW literature. Numerical examples are included and have been solved with grid search methods. The examples illustrate the benefits of adopting the continuous resupply policy, and also collaboration between the retailer and the wholesaler. Moreover, it is shown how these benefits can be shared by small adjustments to the product’s unit price between the firms.     

On Markovian decision programming with recursive reward functions

In this paper, the infinite horizon Markovian decision programming with recursive reward functions is discussed. We show that Bellman's optimal principle is applicable for our model. Then, a sufficient and necessary condition for a policy to be optimal is given. For the stationary case, an iteration algorithm for finding a stationary optimal policy is designed. The algorithm is a generalization of Howard's [7] and Iwamoto's [3] algorithms.This research was supported by the National Natural Science Foundation of China.     

Policy iteration for robust nonstationary Markov decision processes

Policy iteration is a well-studied algorithm for solving stationary Markov decision processes (MDPs). It has also been extended to robust stationary MDPs. For robust nonstationary MDPs, however, an “as is” execution of this algorithm is not possible because it would call for an infinite amount of computation in each iteration. We therefore present a policy iteration algorithm for robust nonstationary MDPs, which performs finitely implementable approximate variants of policy evaluation and policy improvement in each iteration. We prove that the sequence of cost-to-go functions produced by this algorithm monotonically converges pointwise to the optimal cost-to-go function; the policies generated converge subsequentially to an optimal policy.     

A modified homogeneous algorithm for function minimization

Recently, an algorithm for function minimization was presented, based upon an homogeneous, rather than upon a quadratic, model. Numerical experiments with this algorithm indicated that it rapidly minimizes the standard test functions available in the literature. Although it was proved that the algorithm produces function values which continually descend, no proof of convergence was supplied.In this paper, the homogeneous algorithm is modified primarily by replacing the cubic interpolation routine by Armijo's step size rule. Although not quite as fast as the original version on the standard test functions, this modified form has the advantage that a proof of convergence follows from a general theorem of Polak.     

An EM-Like Algorithm for Semi- and Nonparametric Estimation in Multivariate Mixtures

We propose an algorithm for nonparametric estimation for finite mixtures of multivariate random vectors that strongly resembles a true EM algorithm. The vectors are assumed to have independent coordinates conditional upon knowing from which mixture component they come, but otherwise their density functions are completely unspecified. Sometimes, the density functions may be partially specified by Euclidean parameters, a case we call semiparametric. Our algorithm is much more flexible and easily applicable than existing algorithms in the literature; it can be extended to any number of mixture components and any number of vector coordinates of the multivariate observations. Thus it may be applied even in situations where the model is not identifiable, so care is called for when using it in situations for which identifiability is difficult to establish conclusively. Our algorithm yields much smaller mean integrated squared errors than an alternative algorithm in a simulation study. In another example using a real dataset, it provides new insights that extend previous analyses. Finally, we present two different variations of our algorithm, one stochastic and one deterministic, and find anecdotal evidence that there is not a great deal of difference between the performance of these two variants. The computer code and data used in this article are available online.     

Periodic review lost-sales inventory models with compound Poisson demand and constant lead times of any length

In almost all literature on inventory models with lost sales and periodic reviews the lead time is assumed to be either an integer multiple of or less than the review period. In a lot of practical settings such restrictions are not satisfied. We develop new models allowing constant lead times of any length when demand is compound Poisson. Besides an optimal policy, we consider pure and restricted base-stock policies under new lead time and cost circumstances. Based on our numerical results we conclude that the latter policy, which imposes a restriction on the maximum order size, performs almost as well as the optimal policy. We also propose an approximation procedure to determine the base-stock levels for both policies with closed-form expressions.     

Controlled jump processes

Finite and infinite planning horizon Markov decision problems are formulated for a class of jump processes with general state and action spaces and controls which are measurable functions on the time axis taking values in an appropriate metrizable vector space. For the finite horizon problem, the maximum expected reward is the unique solution, which exists, of a certain differential equation and is a strongly continuous function in the space of upper semi-continuous functions. A necessary and sufficient condition is provided for an admissible control to be optimal, and a sufficient condition is provided for the existence of a measurable optimal policy. For the infinite horizon problem, the maximum expected total reward is the fixed point of a certain operator on the space of upper semi-continuous functions. A stationary policy is optimal over all measurable policies in the transient and discounted cases as well as, with certain added conditions, in the positive and negative cases.     

Dynamic Programming and Inventory Problems

After an introductory discussion of the usefulness of the technique of dynamic programming in solving practical problems of multi-stage decision processes, the paper describes its application to inventory problems. In particular, the effect of allowing the number of decision stages to increase indefinitely is investigated, and it is shown that under certain realistic conditions this situation can be dealt with. It appears to be generally true that the average cost per period will converge, for an optimal policy, as the number of periods considered increases indefinitely, and that it is feasible to search for the policy which minimizes this long-term average cost. The paper concludes with a specific example, in which it is shown that only eight iterations were necessary to find a reasonable approximation to the optimal re-order policy.     

WEAK CONVERGENCE OF HENSTOCK INTEGRABLE SEQUENCES

Some relationships between pointwise and weak convergence of a sequence of Henstock integrable functions are studied, In particular it is provided an example of a sequence of Henstock integrable functions whose pointwise limit is different from the weak one. By introducing an asymptotic version of the Henstock equiintegrability notion it is given a necessary and sufficient condition in order that a pointwisely convergent sequence of Henstock integrable functions is weakly convergent to its pointwise limit.