Estimation of mean and variance of net present value with certain and uncertain project life: a multiperiod CAPM approach

A multiperiod capital asset pricing model has the ability to consider risk by incorporating correlation between project and market parameters. This paper presents expressions for mean and variance of net present value of a project, in a multiperiod capital asset pricing model context, for the cases of certain and uncertain project lives. Both cases consider two types of cash flows, independent and correlated over time. The effects of (i) uncertainty in project life, (ii) correlation amongst subsequent cash flows, and (iii) the elasticity of expectations on the estimates of mean and variance of net present value have been studied through a numerical example.     

Schumann,a modeling framework for supply chain management under uncertainty

We present a modeling framework for the optimization of a manufacturing, assembly and distribution (MAD) supply chain planning problem under uncertainty in product demand and component supplying cost and delivery time, mainly. The automotive sector has been chosen as the pilot area for this type of multiperiod multiproduct multilevel problem, but the approach has a far more reaching application. A deterministic treatment of the problem provides unsatisfactory results. We use a 2-stage scenario analysis based on a partial recourse approach, where MAD supply chain policy can be implemented for a given set of initial time periods, such that the solution for the other periods does not need to be anticipated and, then, it depends on the scenario to occur. In any case, it takes into consideration all the given scenarios. Very useful schemes are used for modeling balance equations and multiperiod linking constraints. A dual approach splitting variable scheme is been used for dealing with the implementable time periods related variables, via a redundant circular linking representation.     

CORO,a modeling and an algorithmic framework for oil supply,transformation and distribution optimization under uncertainty

We present a modeling framework for the optimization of a multiperiod Supply, Transformation and Distribution (STD) scheduling problem under uncertainty on the product demand, spot supply cost and spot selling price. The Hydrocarbon and Chemical sector has been chosen as the pilot area, but the approach has a far more reaching application. A deterministic treatment of the problem provides unsatisfactory results. We use a 2-stage scenario analysis based on a partial recourse approach, where the STD policy can be implemented for a given set of initial time periods, such that the solution for the other periods does not need to be anticipated and, then, it depends on the scenario to occur. In any case, it takes into consideration all the given scenarios. Novel schemes are presented for modeling multiperiod linking constraints, such that they are satisfied through the scenario tree; they are modeled by using a splitting variable scheme, via a reduntant circular linking representation.     

A multiperiod model for production planning and design in a multiproduct batch environment

A general multiperiod model to optimize simultaneously production planning and design decisions applied to multiproduct batch plants is proposed. This model includes deterministic seasonal variations of costs, prices, demands and supplies. The overall problem is formulated as a mixed-integer linear programming model by applying appropriate linearizations of non-linear terms. The performance criterion is to maximize the net present value of the profit, which comprises sales, investment, inventories, waste disposal and resources costs, and a penalty term accounting for late deliveries. A noteworthy feature of this approach is the selection of unit dimensions from the available discrete sizes, following the usual procurement policy in this area. The model simultaneously calculates the plant structure (parallel units in every stage, and allocation of intermediate storage tanks), and unit sizes, as well as the production planning decisions in each period (stocks of both product and raw materials, production plans, policies of sales and procurement, etc.).     

International portfolio management with affine policies

While dynamic decision making has traditionally been represented as scenario trees, these may become severely intractable and difficult to compute with an increasing number of time periods. We present an alternative tractable approach to multiperiod international portfolio optimization based on an affine dependence between the decision variables and the past returns. Because local asset and currency returns are modeled separately, the original model is non-linear and non-convex. With the aid of robust optimization techniques, however, we develop a tractable semidefinite programming formulation of our model, where the uncertain returns are contained in an ellipsoidal uncertainty set. We add to our formulation the minimization of the worst case value-at-risk and show the close relationship with robust optimization. Numerical results demonstrate the potential gains from considering a dynamic multiperiod setting relative to a single stage approach.     

An MINLP retrofit approach for improving the flexibility of heat exchanger networks

A mixed integer nonlinear programming (MINLP) model for the retrofit of heat exchanger networks (HENs) in order to improve their flexibility is presented in this paper. As stream flowrates and inlet temperatures and/or heat transfer coefficients are allowed to vary within either specified ranges or discrete sets, a multiperiod hyperstructure network representation is developed based on critical operating conditions (i.e. periods of operation) that limit the network's flexibility. This multiperiod hyperstructure includes all possible network configurations. Structural modifications, such as new stream matches, exchanger reassignments, splitting and mixing of streams are explicitly modeled either considering one-to-one or one-to-many assignment of heat exchangers to stream matches. Energy recovery and utility consumption are not predetermined but are optimized as part of a total annualized cost along with the structural modification cost in the objective function. Thus, trade-offs between operating and retrofit investment costs to improve the flexibility of a HEN are accounted for. The resulting large scale MINLP is solved with the application of the Generalized Benders Decomposition. The proposed multiperiod retrofit model can be included in a general framework to improve the operability of heat exchanger networks.     

Bankruptcy prevention in multiperiod Markowitz optimization problem

A multiperiod version of the Markowitz optimization problem is considered by assuming that the financial market model is discrete with respect to time and the number of scenarios. Basing on the optimal strategy of securities trading, we find the upper bound of the expected final portfolio value which does not lead to bankruptcy.     

A Method for Single Facility Multiple Products Scheduling

The single facility multiple products scheduling problem is formulated into a multiperiod mathematical programming model with zero-one variables. An algorithm to solve the scheduling problem by using the concept of state vectors of dynamic programming is described. An example of application of the model and the algorithm is also presented. It has been found that a suitable selection of a state vector reduces greatly the dimensionality of the problem     

Optimal asset allocation for a general portfolio of life insurance policies

Asset liability matching remains an important topic in life insurance research. The objective of this paper is to find an optimal asset allocation for a general portfolio of life insurance policies. Using a multi-asset model to investigate the optimal asset allocation of life insurance reserves, this study obtains formulae for the first two moments of the accumulated asset value. These formulae enable the analysis of portfolio problems and a first approximation of optimal investment strategies. This research provides a new perspective for solving both single-period and multiperiod asset allocation problems in application to life insurance policies. The authors obtain an efficient frontier in the case of single-period method; for the multiperiod method, the optimal asset allocation strategies can differ considerably for different portfolio structures.     

Calculating risk neutral probabilities and optimal portfolio policies in a dynamic investment model with downside risk control

This paper presents a method for solving multiperiod investment models with downside risk control characterized by the portfolio’s worst outcome. The stochastic programming problem is decomposed into two subproblems: a nonlinear optimization model identifying the optimal terminal wealth distribution and a stochastic linear programming model replicating the identified optimal portfolio wealth. The replicating portfolio coincides with the optimal solution to the investor’s problem if the market is frictionless. The multiperiod stochastic linear programming model tests for the absence of arbitrage opportunities and its dual feasible solutions generate all risk neutral probability measures. When there are constraints such as liquidity or position requirements, the method yields approximate portfolio policies by minimizing the initial cost of the replication portfolio. A numerical example illustrates the difference between the replicating result and the optimal unconstrained portfolio.     

Exact and hybrid methods for the multiperiod field service routing problem

This article deals with a particular class of routing problem, consisting of the planning and routing of technicians in the field. This problem has been identified as a multiperiod, multidepot uncapacitated vehicle routing problem with specific constraints that we call the multiperiod field service routing problem (MPFSRP). We propose a set covering formulation of the problem for the column generation technique and we develop an exact branch and price solution method for small-sized instances. We also propose several heuristic versions for larger instances. We present the results of experiments on realistic data adapted from an industrial application.     

On the Use of Pseudo Shadow Prices in a Multiperiod Formulation of the Production Planning Problem

A multiperiod linear programming model of the production planning problem is used to argue for a different costing, profit and resource-evaluation procedure in manufacturing. This procedure is based on the concept of ‘pseudo shadow prices’, and it avoids many of the pitfalls associated with the more traditional accounting methodologies. Conclusions are drawn which question the profit objectives used by many firms, as well as by those in academia.     

Estimation and status prediction in a discrete mover‐stayer model with covariate effects on stayer's probability

A discrete‐time mover‐stayer (MS) model is an extension of a discrete‐time Markov chain, which assumes a simple form of population heterogeneity. The individuals in the population are either stayers, who never leave their initial states or movers who move according to a Markov chain. We, in turn, propose an extension of the MS model by specifying the stayer's probability as a logistic function of an individual's covariates. Such extension has been recently discussed for a continuous time MS but has not been considered before for a discrete time one. This extension allows for an in‐sample classification of subjects who never left their initial states into stayers or movers. The parameters of an extended MS model are estimated using the expectation‐maximization algorithm. A novel bootstrap procedure is proposed for out of sample validation of the in‐sample classification. The bootstrap procedure is also applied to validate the in‐sample classification with respect to a more general dichotomy than the MS one. The developed methods are illustrated with the data set on installment loans. But they can be applied more broadly in credit risk area, where prediction of creditworthiness of a loan borrower or lessee is of major interest.     

均值—动态VaR多阶段投资组合优化研究

将动态风险度量方法运用到多阶段投资组合中,提出了具有交易成本和交易量限制的均值—动态VaR多阶段投资组合模型,并运用自创算法——离散近似迭代法求解.方法的基本思路为:首先,将模型中的连续型状态变量离散化,并将上述模型转化多阶段赋权有向图,然后,运用极大代数求出起点至终点的最长路程,即获得模型的一个可行解;最后,以该可行解为基础,继续迭代直到前后两个可行解非常接近.证明了该方法的收敛性,并以一个具体的算例,验证了该算法可以较快地计算出不同终期财富所对应的最优投资策略.     

Waggling conical pendulums and visualization in mechanics

Students often find mechanics a difficult area to grasp. This paper describes an equation of motion for a waggling conical pendulum. A wide range of pendulum dynamics can be simulated with this model. The equation of motion is embedded in a graphical user interface (GUI) for its numerical solution in MATLAB. This allows a student's focus to be on the influence of different parameters on the pendulums dynamics. The simulation tool can be used as a dynamics demonstrator in a lecture or as an educational tool driven by the imagination of the student. By way of demonstration, the simulation tool has been applied to two damped pendulums and an inverted damped pendulum. The model has also been used to simulate resonance and has shown that there is a wide range of behaviour possible depending on the type of forcing applied. Finally, a forced conical pendulum as a system for harnessing wave energy is considered.     

AN APPLICATION OF MULTI‐CRITERIA DECISION MAKING INCORPORATING STOCHASTIC PRODUCTION FRONTIERS: A CASE STUDY OF ORGANIC COFFEE PRODUCTION IN KONA,HAWAII

Abstract In this paper, we develop a land use allocation model to search for the optimal ratio of organic (nonchemical) and conventional (chemical‐use) farming acreage. The idea is to incorporate stochastic production frontiers (SPFs) to a multi‐criteria decision making (MCDM) model as technological constraints. The objectives of this model are to maximize net returns, minimize chemical inputs, and optimize organic inputs given environmental and ecological concerns. The compromise solutions suggest the desirable ratio of organic and conventional farming acreage and targetable operations for each farming system to improve regional welfare. This method was applied to the Kona coffee belt, Hawaii, and the analysis determined the optimal proportion of organic and conventional Kona coffee farming fields as 0.265 to 0.735 in terms of optimizing community benefits or regional welfare.     

An infinite horizon mathematical programming model of a multicohort single species fishery

Fishery policy evaluation should take account of the initial state of the fishery and the population dynamics of the fish stock. Although multicohort bioeconomic fishery policy evaluation models have been developed, the results from these models depend on the choice of planning period and the desired state of the stock at the end of this period. In this paper it is noted that these limitations can be overcome by evaluating fishery policy over an infinite time horizon, and a mixed integer programming (MIP) model is developed for carrying out this form of analysis in a multicohort single species fishery. This new MIP model allows policies to be evaluated over an infinite horizon by incorporating results from a steady state fishery model into a multiperiod framework. The use of this MIP model in determining policies for reaching and maintaining a steady state is illustrated.     

Controlled predatory pricing in a multiperiod Stackelberg game: an MPEC approach

We analyze a multiperiod oligopolistic market where each period is a Stackelberg game between a leader firm and multiple follower firms. The leader chooses his production level first, taking into account the reaction of the followers. Then, the follower firms decide their production levels after observing the leader’s decision. The difference between the proposed model and other models discussed in literature is that the leader firm has the power to force the follower firms out of business by preventing them from achieving a target sales level in a given time period. The leader firm has an incentive to lower the market prices possibly lower than the Stackelberg equilibrium in order to push the followers to sell less and eventually go out of business. Intentionally lowering the market prices to force competitors to fail is known as predatory pricing, and is illegal under antitrust laws since it negatively affects consumer welfare. In this work, we show that there exists a predatory pricing strategy where the market price is above the average cost and consumer welfare is preserved. We develop a mixed integer nonlinear problem (MINLP) that models the multiperiod Stackelberg game. The MINLP problem is transformed to a mixed integer linear problem (MILP) by using binary variables and piecewise linearization. A cutting plane algorithm is used to solve the resulting MILP. The results show that firms can engage in predatory pricing even if the average market price is forced to remain higher than the average cost. Furthermore, we show that in order to protect the consumers, antitrust laws can control predatory pricing by setting rules on consumer welfare.     

Polyhedral risk measures in electricity portfolio optimization

We compare different multiperiod risk measures taken from the class of polyhedral risk measures with respect to the effect they show when used in the objective of a stochastic program. For this purpose, simulation results of a stochastic programming model for optimizing the electricity portfolio of a German municipal power utility are presented and analyzed. This model aims to minimize risk and expected overall cost simultaneously. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

On the noniterative multiproduct multiperiod production planning method

Bahl and Zionts [H.C. Bahl, S. Zionts, A noniterative multiproduct multiperiod production planning method, Operations Research Letters 1 (1982) 219-221] formulated a problem for planning multiproduct multiperiod production on a single facility. They developed a column-minima noniterative method and claimed that it gave an optimal solution. We show that the claim is incorrect.