We revisit an approximate stochastic dynamic programming method that we proposed earlier for the optimization of multireservoir problems. The method exploited the convexity properties of the value function to sample the reservoir level space based on the local curvature of the value function, which is estimated by the difference between a lower and an upper bounds (error bound). Unlike the previous approach in which the state space is exhaustively partitioned into full dimensional simplices whose vertices formed a discrete grid over which the value function was approximated, here we propose instead a new randomized approach for selecting the grid points from a small number of randomly sampled simplices from which an error bound is estimated. Results of numerical experiments on three literature test problems and simulated midterm reservoir optimization problems illustrate the advantages of the randomized approach which can solve models of higher dimensions than with the exhaustive approach.

This paper presents a novel approach for approximate stochastic dynamic programming (ASDP) over a continuous state space when the optimization phase has a near-convex structure. The approach entails a simplicial partitioning of the state space. Bounds on the true value function are used to refine the partition. We also provide analytic formulae for the computation of the expectation of the value function in the "uni-basin" case where natural inflows are strongly correlated. The approach is experimented on several configurations of hydro-energy systems. It is also tested against actual industrial data. (C) 2017 Elsevier B.V. All rights reserved.

ABSTRACTThe objective of this paper is to present a dynamic analysis of the relationship between environmental (EP) and financial performance (FP). More precisely, we have analysed this relationship by considering the measurement of EP lagged by one, two, and three periods. The introduction of lagged variables at both an aggregate and non‐aggregate level, aims to capture the effects of EP on FP over time. Our results show that the aggregate measure of the lagged EP has a persistent positive effect on FP, extended over three years. This effect appears to be more marked for large size companies, for companies with low risk levels and for those spending less on investment. Results for non‐aggregate measurements reveal an asymmetric relationship between FP and KLD's concerns score, of which the impact is negative and persistent, and between FP and KLD's strengths score, where the effect is positive and limited to one year. Copyright © 2015 John Wiley & Sons, Ltd and ERP Environment

We present a new approach for adaptive approximation of the value function in stochastic dynamic programming. Under convexity assumptions, our method is based on a simplicial partition of the state space. Bounds on the value function provide guidance as to where refinement should be done, if at all. Thus, the method allows for a trade-off between solution time and accuracy. The proposed scheme is experimented in the particular context of hydroelectric production across multiple reservoirs.

Different factors explaining divergent results on the relationship between corporate Social Performance (SP) and Financial Performance (FP) can be found in the academic literature. The main objective of this chapter is to test the impact of these factors on these divergent results. It also aims to assess the intensity of the sensitivity of this relationship to these factors considered individually or in combination. The results of our experimental research show that the estimated relationship depends on the methodological choice. More specifically, the relationship varies according to the measurement of the SP, the measurement of FP and the chosen sample. This relationship is neither stable nor necessarily linear, as many relevant academic works in the literature assume. This work concentrates on the knowledge gained from this literature and suggests lines of reflection to better understand the studied relationship in a field which is still evolving.

Socially responsible mutual funds, also known as socially responsible invested funds, are one of the main instruments of Socially Responsible Investment (SRI).The term "fund" is used to refer to a ready-made financial product where investor's money is pooled into a portfolio and a fund/investment manager decides which shares to buy.Therefore, this financial product is attractive for passive investors without a high degree of financial knowledge.Nevertheless, investment tools aimed at assisting the investors in their selection of socially responsible companies which serve best their social and environmental values are rather scare and this lack of tools assisting investors in SRI is even more important when we refer to socially responsible mutual funds.The aim of this paper is to assist individual passive investors in their investment decisions providing them with a ranking of mutual funds adjusted to their social, environmental and ethical particular preferences.The proposed approach is illustrated with a real US equity mutual funds' ranking example.

There is a continuing debate in the Corporate Social Responsibility literature as to whether and how firms' social performance (SP) affects their financial performance (FP). Theoretical arguments as well as empirical measurements point to somewhat contradictory results. Most of the empirical work is predicated on rigid conventional models, expressing constant or strictly monotonic marginal returns in the assumed SP-FP relationship. This paper revisits this relationship from a global perspective, relaxing the range of admissible models. A non-monotonic framework incorporating contextual factors is proposed. Five models are tested over a common 17 years horizon. They yield consistent significant estimates and concur on the existence of such a relationship although the latter has evolved over time. They support the notion of a complex SP-FP impact.

Reservoir systems operations problems are in essence stochastic because of the uncertain nature of natural inflows. This leads to very large stochastic models that may not be easy to handle numerically. In this paper, we revisit the decomposition method developed by Rockafellar and Wets (Math Oper Res 119–147, 1991) by proposing new heuristics to initialize and dynamically adjust the penalty parameter of the augmented Lagrangian function on which this method is based. The heuristics are tested on multi-reservoir problems generated randomly and compared with the traditional strategy of setting the penalty parameter to a fixed value.

ABSTRACTSocially responsible investors pursue financial as well as nonfinancial goals. Whereas the role of financial criteria in investment decisions is well understood, much less is known on the influence of social responsibility considerations. This work seeks to integrate both dimensions within a data envelopment analysis framework consistent with second‐order stochastic dominance efficiency. We compare the performance of conventional versus socially responsible mutual funds on an empirical data set. Our data do not support the conjecture that conventional mutual funds exhibit superior overall performance. Copyright © 2013 John Wiley & Sons, Ltd.

We present a method for exact computation of the expected value of a non-separable, piecewise linear function of two variables. We assume a stochastic model of two random variables obtained by linear combination of two independent principal components having gamma distributions with integer shape parameters. The method can be implemented so the computational effort is proportional to the number of piecewise affine pieces times the product of the shape parameters of the gamma distributions. It can be used for fast computation of expected values in stochastic dynamic programming.

In this paper, the problem of path planning for a ground search unit looking for an object of unknown location is considered. As in the classical optimal searcher path problem, the probability of finding the search object is the main criterion of optimality and the search unit is constrained by the environment topology that influences its choices for a navigable path as well as its detection capabilities. This paper proposes an extension to the classical optimal searcher path problem in discrete time and space by integrating inter-region visibility as an additional criterion. This new formulation allows a refinement in the discretization of the space in which a ground search unit evolves. A general mixed-integer programming model is proposed, and experimental results with a moving object in grid environments are discussed.

This paper proposes several concepts of efficient solutions for multicriteria decision problems under uncertainty. We show how alternative notions of efficiency may be grounded on different decision ‘contexts’, depending on what is known about the Decision Maker's (DM) preference structure and probabilistic anticipations. We define efficient sets arising naturally from polar decision contexts. We investigate these sets from the points of view of their relative inclusions and point out some particular subsets which may be especially relevant to some decision situations.

Several notions of efficiency are conceivable for the multiobjective stochastic linear programming problem. In this paper, assuming that the problem’s randomness can be described by discrete scenarios with known probabilities and that decision makers’ preferences, although unknown, can be represented by a class of utility functions, we examine a set of strongly efficient solutions, the unanimous solutions. We state inclusion relations between this and other classes of efficient solutions (admissible and advocated solutions) previously studied. Under plausible assumptions about decision makers’ risk attitudes, we examine how candidates for unanimity can be generated and then tested.

We analyze the computation of optimal and approximately optimal policies for a discrete-time model of a single reservoir whose discharges generate hydroelectric power. Inflows in successive periods are random variables. Revenue from hydroelectric production is represented by a piecewise linear function. We use the special structure of optimal policies, together with piecewise affine approximations of the optimal return functions at each stage of dynamic programming, to decrease the computational effort by an order of magnitude compared with ordinary value iteration. The method is then used to obtain easily computable lower and upper bounds on the value function of an optimal policy, and a policy whose value function is between the bounds.

We present a specialized policy iteration method for the computation of optimal and approximately optimal policies for a discrete-time model of a single reservoir whose discharges generate hydroelectric power. The model is described in (Lamond et al., 1995) and (Drouin et al., 1996), where the special structure of optimal policies is given and an approximate value iteration method is presented, using piecewise affine approximations of the optimal return functions. Here, we present a finite method for computing an optimal policy in O(n3) arithmetic operations, where n is the number of states in the associated Markov decision process, and a finite method for computing a lower bound on the optimal value function in O(m2n) where m is the number of nodes of the piecewise affine approximation.

Several concepts of distributional efficiency are proposed for the Multiobjective Stochastic Linear Programming (MSLP) problem, in contexts where the probability distribution of random parameters is known and the decision maker (DM) has an unknown multi-attribute utility function belonging to a given glass U. We present a general efficient set, the U-admissible solutions, and two subsets, the U-unanimous and U-advocated solutions, the latter being particularly relevant to the case of a single DM. We show how advocated solutions can be generated and/or tested when U is the class of non-decreasing additive concave functions.

Data Envelopment Analysis (DEA) is an approach to assess the relative efficiency of organizations using multiple inputs to produce multiple outputs. This assessment is made from the standpoint most favourable to each organization. If an organization is not well enveloped, in the sense that it is not comparable to a sufficient number of other organizations (called referents), DEA may understate inefficiency. A lower bound on the efficiency measure may be obtained by requiring that the organization being evaluated be compared with at least k non-redundant referents. For any feasible choice of k, the procedure proposed here selects the most favourable set of referents, and guarantees a greatest lower bound on the efficiency measure, thus usefully complementing the information provided by conventional DEA.

In most of the approaches to the Multiobjective Stochastic Linear Programming problem that have been proposed in the literature, the notion of quality of a solution is not adequately defined. We reconsider this problem from a decision point of view, in contexts where either the decision maker's preference structure cannot be described by a utility function, or where this structure is expressed by an unknown non-decreasing utility function and the probability distribution of the random parameters is unknown. We define a fundamental set of ‘pointwise admissible’ solutions, as well as several subsets of particular interest. We discuss the relevance of these various pointwise efficient sets, their interrelations, and their practical identification.

This paper proposes a methodology for evaluating and selecting R&D projects in a collective decision setting, especially useful at sectorial and national levels. It consists of two major phases: Evaluation and Selection. The evaluation process repeatedly uses mathematical programming models to determine the “relative values” of a given R&D project from the viewpoint of the other R&D projects. The selection process of R&D projects is based on these “relative values” and is done through a model-based outranking method. The salient features of the methodology developed are its ability to (1) permit the evaluation of an R&D project from the viewpoint of the other R&D projects without at first imposing a uniform evaluation scheme, and (2) maximize the level of consensus as to which projects should not be retained in the R&D Program being funded, thus minimizing the level of possible resentment in those organizations or departments whose projects are not included in the R&D Program. Also discussed in this paper is an application of the methodology to evaluate and select major R&D projects in the iron and steel industry of Turkey.

Under conditions of chronic exchange rate overshooting and mildly segmented capital markets, optimal currency denomination decision rules for international debt financing are derived for risk-neutral and risk-averse borrowers. For the latter, an inter-temporal expected utility framework yields the risk-adjusted cost of foreign debt, which allows for the pricing of currency cross-hedging effects in multi-currency debt portfolios, artificial currency unit-denominated debt instruments as well as currency swaps.