random yield supply chain with a yield dependent secondary market abstract this paper considers a simple supply chain with one supplier and one retailer where the supplier s production is subject to random yield and the retailer faces uncertain demand there exists a secondary market for acquiring or disposing products by the supplier we study both the centralized and decentralized systems in the decentralized system a no risk sharing contract and a risk sharing minimum commitment contract are analyzed the supply chain with the risk sharing contract is further analyzed with a constant secondary market price and a yield dependent secondary market price we present both the supplier s and the retailer s optimal strategies and provide insights for managers when making decisions under random yield risk and demand uncertainty we find that the secondary market generally has a positive impact on supply chain performance and the actual effect of random yield risk on the supply chain performance depends on cost parameters and supply chain contract settings under certain conditions reducing yield randomness may weaken the double marginalization effect and improve the chain performance from the numerical study we also show that there exists an optimal commitment level for the supply chain

foreign direct investment and employment rights in south eastern europe the dominant neoliberal policy community holds that a reduction in employment rights and social protection is likely to promote economic recovery and growth it has been suggested that investors are likely to shun countries where such rights are strong in contrast radical labour market deregulation is seen as encouraging both local business and multinationals to invest this study explores whether labour market deregulation in south eastern europe has really encouraged multinationals to invest in the region we find that the weakening of important aspects of employment rights under the law appears to detract from rather than encourage foreign direct investment fdi we also show that stronger employment rights are more likely to attract fdi when the host country is located within the european union this finding suggests that the complementarities associated with stronger employment rights and more committed labour may offset the overall deterrent effects of the greater regulation associated with eu membership

a fuzzy goal programming approach to portfolio selection abstract portfolio selection is a usual multiobjective problem this paper will try to deal with the optimum portfolio for a private investor taking into account three criteria return risk and liquidity these objectives in general are not crisp from the point of view of the investor so we will deal with them in fuzzy terms the problem formulation is a goal programming g p one where the goals and the constraints are fuzzy we will apply a fuzzy g p approach to the above problem to obtain a solution then we will offer the investor help in handling the results

continuous review inventory models where random lead time depends on lot size and reserved capacity the processing time of large orders is in many industries longer than that of small orders this renders supply lead times in such settings to be increasing in the order size yet that pattern is not re ected in existing inventory control models especially those allowing for random lead times this work aims at rectifying the situation our setting is an order quantity reorder point model with backordering where the shortage penalty is incurred per unit per unit time the processing time of each unit is random the processing time of a lot is correlated with its size for the case where lead time is proportional to the lot size we obtain a closed form solution that is unlike the classical q r model where lead time is independent of lot size no iterations are required here we also analyze a case where the processing time exhibits economies of scale in the lot size finally we consider a situation where a customer can secure shorter processing times by reserving capacity at the supplier s manufacturing facility 2000 elsevier science b v all rights reserved

asset allocation with distorted beliefs and transaction costs abstract in this paper we study the problem of the optimal portfolio selection with transaction costs for a decision maker who is faced with knightian uncertainty the decision maker s portfolio consists of one risky and one risk free asset and we assume that the transaction costs are proportional to the traded volume of the risky asset the attitude to uncertainty is modeled by the choquet expected utility we derive optimal strategies and bounds of the no transaction region for both optimistic and pessimistic decision makers the no transaction region of a pessimistic investor is narrower and its bounds lie closer to the origin than that of an optimistic trader moreover under the choquet expected utility the structure of the no transaction region is not necessarily a closed interval as it is under the standard expected utility model

a periodic review production planning model with uncertain capacity and uncertain demand optimality of extended myopic policies increasing product complexity manufacturing environment complexity and an increased emphasis on product quality are all factors leading to uncertainties in production processes these uncertainties are in the form of unplanned machine maintenance varying production yields and rework among others in planning for production an adequate model must incorporate these uncertainties into the representation of the production process this paper treats the aggregate planning problem for a single product with random demand and random capacity in the single period problem random capacity does not affect the optimal policy but results in a unimodal nonconvex cost function in the multiple period and infinite horizon settings order up to policies that are dependent on the distribution of capacity are shown to be optimal in spite of a nonconvex cost in the infinite horizon setting an intuitive description of the situation leads to the notion of a class of extended myopic policies requiring the consideration o

the structure of periodic review policies in the presence of random yield this work provides a comprehensive analysis of a general periodic review production inventory model with random variable yield existence of an order point whose value does not depend on yield being random is proved in the single period case without specifying the yield model and using a very general cost structure when yield is a random multiple of lot size the nonorder up to optimal policy is characterized for a finite horizon model the finite horizon value functions are shown to converge to the solution of an infinite horizon functional equation and the infinite horizon order point is shown to be no smaller than when yield is certain

optimal inventory policy with supply uncertainty and demand cancellation we consider a periodic review model where the firm manages its inventory under supply uncertainty and demand cancellation we show that because of supply uncertainty the optimal inventory policy has the structure of re order point type that is we order if the initial inventory falls below this re order point otherwise we do not order this is in contrast to the work of yuan and cheung 2003 who prove the optimality of an order up to policy in the absence of supply uncertainty we also investigate the impact of supply uncertainty and demand cancellation on the performance of the supply chain using our model we are able to quantify the importance of reducing the variance of either the distribution of yield or the distribution of demand cancellation the single multiple periods and the infinite horizon models are studied

ad revenue optimization in live broadcasting in live broadcasting the break lengths available for commercials are not always fixed and known in advance e g strategic and injury time outs are of variable duration in live sports transmissions broadcasters actively manage their advertising revenue by jointly optimizing sales and scheduling policies we characterize the optimal dynamic schedule in a simplified setting that incorporates stochastic break durations and advertisement lengths of 15 and 30 seconds the optimal policy is a greedy look ahead rule that accounts for the remaining number of breaks in this setting there is no value to perfect information at the scheduling stage and hence knowing the duration of all breaks would not change the schedule we present heuristics to help solve scheduling problems of even greater complexity the performance of these heuristics under various scenarios is tested by running simulations calibrated using industry data the simple greedy heuristic is shown to perform well except when revenues are con

a multi period fuzzy portfolio optimization model with minimum transaction lots abstract in this paper we consider a multi period fuzzy portfolio optimization problem with minimum transaction lots based on possibility theory we formulate a mean semivariance portfolio selection model with the objectives of maximizing the terminal wealth and minimizing the cumulative risk over the whole investment horizon in the proposed model we take the return risk transaction costs diversification degree cardinality constraint and minimum transaction lots into consideration to reflect investor s aspiration levels for the two objectives a fuzzy decision technique is employed to transform the proposed model into a single objective mixed integer nonlinear programming problem then we design a genetic algorithm for solution finally we give an empirical application in chinese stock markets to demonstrate the idea of our model and the effectiveness of the designed algorithm

a periodic review lot sizing problem with random yields disruptions and inventory capacity this paper examines a periodic reviewed lot sizing problem with random yields disruptions and limited inventory capacity to characterise the continuous production an additive random yield model is considered rather than a multiplicative one disruptions cause breakdowns to production inventory capacity is included since the production has to be shut down when the inventory buffer is full both disruptions and shutdowns lead to a start up cost and a stochastic lead time to recover the production these compound factors of uncertainty are encountered in practical planning decisions in process industries we review the existing random yield models which are then compared with the additive model with a linear production cost the additive model has an order up to policy to be optimal disruptions deteriorate the expected actual production quantity and the fill rate dramatically even though the optimal order up to level increases compared with the cases of no disruption considering inventory capacity makes the problem to be a non convex dynamic programming problem numerical analysis shows that the performance is dramatically deteriorated when the inventory capacity is rather tight which indicates the importance of selecting a proper inventory capacity to reduce the negative impacts and avoid redundant investment on capacity moreover the start up cost plays an important role in determining the level of inventory capacity

the risk averse newsvendor problem with random capacity abstract we study the effect of capacity uncertainty on the inventory decisions of a risk averse newsvendor we consider two well known risk criteria namely value at risk var included as a constraint and conditional value at risk cvar for the risk neutral newsvendor we find that the optimal order quantity is not affected by the capacity uncertainty however this result does not hold for the risk averse newsvendor problem specifically we find that capacity uncertainty decreases the order quantity under the cvar criterion under the var constraint capacity uncertainty leads to an order decrease for low confidence levels but to an order increase for high confidence levels this implies that the risk criterion should be carefully selected as it has an important effect on inventory decisions this is shown for the newsvendor problem but is also likely to hold for other inventory control problems that future research can address

diversification under supply uncertainty supply chain management is becoming an increasingly important issue especially when in most industries the cost of materials purchased comprises 40 60 of the total sales revenue despite the benefits cited for single sourcing in the popular literature there is enough evidence of industries having two three sources for most parts in this paper we address the operational issue of quantity allocation between two uncertain suppliers and its effects on the inventory policies of the buyer based on the type of delivery contract a buyer has with the suppliers we suggest three models for the supply process model i is a one delivery contract with all of the order quantity delivered either in the current period with probability or in the next period with probability 1 model ii is also a one delivery contract with a random fraction of the order quantity delivered in the current period the portion of the order quantity not delivered is cancelled model iii is similar to model ii with the remaining quantity delivered in the next period we derive the optimal ordering policies that minimize the total ordering holding and penalty costs with backlogging we show that the optimal ordering policy in period n for each of these models is as follows for x a n order nothing for vi n x a n use only one supplier and for x vi n order from both suppliers for the limiting case in the single period version of model i we derive conditions under which one would continue ordering from one or the other or both suppliers for model ii we give sufficient conditions for not using the second more expensive supplier when the demand and yield distributions have some special form for the single period version of models ii and iii with equal marginal ordering costs we show that the optimal order quantities follow a ratio rule when demand is exponential and yields are either normal or gamma distributed

inventory control in a fluctuating demand environment we present an inventory model where the demand rate varies with an underlying state of the world variable this variable can represent economic fluctuations or stages in the product life cycle for example we derive some basic characteristics of optimal policies and develop algorithms for computing them in addition we show that certain monotonicity patterns in the problem data are reflected in the optimal policies

diversification under yield randomness in inventory models abstract in this paper we consider the case of random yield and diversification in two different inventory models it is assumed that two sources suppliers exist who ship an amount which is a random function of the amount requisitioned since they may charge different prices and their reliability in terms of the variance of the yield random variable may be different diversification may be more desirable than using a single supplier we analyze this problem for both the eoq model and the newsboy model analytic results for the optimal order quantities and the minimum cost are obtained for the eoq model convexity of the objective function is also discussed we develop the necessary conditions for the newsboy model and propose an approximate solution technique the concavity of the expected profit function is shown numerical examples are provided for both models

a note on coordination in decentralized assembly systems with uncertain component yields abstract gurnani and gerchak h gurnani y gerchak coordination in decentralized assembly systems with uncertain component yields european journal of operational research 176 2007 1559 1576 study coordination of a decentralized assembly system in which the demand of the assembler is deterministic and the component yields are random they present incentive alignment control mechanisms under which system coordination is achieved in this note we extend gurnani and gerchak s model to the case of positive salvage value and n asymmetric suppliers and show that the shortage penalty contract which can coordinate gurnani and gerchak s model no longer coordinates the extended model furthermore we present a new kind of contract surplus subsidy contract to coordinate the extended model and prove that the profit of the supply chain under coordination can be arbitrarily divided between the component suppliers and the assembler

optimality of myopic policies for dynamic lot sizing problems in serial production lines with random yields and autoregressive demand we study lot size policies in a serial multistage manufacturing inventory system with two key generalizations namely 1 random yields at each production stage and 2 an autoregressive demand process previous research shows that the optimal policies in models with random yields even in models with a single installation lack the familiar order up to structure and are not myopic thus dynamic programming algorithms are needed to compute optimal policies and one encounters the curse of dimensionality this is exacerbated here by the need to expand the size and dimension of the state space to accommodate the autoregressive demand feature nevertheless although our model is more complex we prove that there is an optimal policy with the order up to feature and more importantly that the optimal policy is myopic this avoids the computational burden of dynamic programming our results depend on two assumptions concerning the stochastic yield namely that the expected yield at a work station is proportional to the lot size and the distribution of the deviation of the yield from its mean does not depend on the lot size we introduce the concept of echelon like variables a generalization of clark and scarf s classical concept of echelon variables to derive the structure of optimal policies furthermore we show that the same kind of policy is optimal for several criteria infinite horizon discounted cost infinite horizon long run average cost and finite horizon discounted cost with the appropriate choice of the salvage value function

the measurement and control of trading costs 1990 the measurement and control of trading costs financial analysts journal vol 46 no 6 pp 73 80

stochastic network programming for financial planning problems several financial planning problems are posed as dynamic generalized network models with stochastic parameters examples include asset allocation for portfolio selection international cash management and programmed trading arbitrage despite the large size of the resulting stochastic programs the network structure can be exploited within the solution strategy giving rise to efficient implementations empirical results are presented indicating the benefits of the stochastic network approach for the asset allocation case

agriculture aid and economic growth in africa how can foreign aid to agriculture support economic growth in africa this paper constructs a geographically indexed applied general equilibrium model that considers pathways through which aid might affect growth and structural transformation of labor markets in the context of soil nutrient variation minimum subsistence consumption requirements domestic transport costs labor mobility and constraints to self financing of agricultural inputs using plausible parameters the model is presented for uganda as an illustrative case three stylized scenarios demonstrate the potential economy wide impacts of both soil nutrient loss and replenishment and how foreign aid can be targeted to support agricultural inputs that boost rural productivity and shift labor to boost real wages one simulation shows how a temporary program of targeted official development assistance oda for agriculture could generate contrary to traditional dutch disease concerns an expansion in the primary tradable sector and positive permanent productivity and welfare effects leading to a steady decline in the need for complementary oda for budget support

environmental safety stock the impacts of regulatory and voluntary control policies on production planning inventory control and environmental performance abstract this paper analyzes the impacts of different pollution control policies on a firm s decisions of production planning and inventory control based on a stochastic model with both demand and environmental uncertainties we derive the optimal policies of production planning and inventory control under both regulatory and voluntary pollution control approaches and investigate their operational and environmental effects we establish that the conventional wisdom which suggests that reduction of environmental waste at the end of a production process also decreases the stock and throughput levels of a production system is not necessarily true rather a regulatory environmental standard that limits the total amount of waste may induce the firm to raise its planned stock level which would lead to a higher expected amount of environmental wastes before the standard is enforced as well as environmental risks at other stages of the production process the additional planned stock level which is termed environmental safety stock can be reversed by using the voluntary control approach that provides the firm with the flexibility to occasionally exceed the environmental standard we also conduct numerical experiments to analyze the effects of different values of model parameters under different control approaches the analytical results provide new insights to the impacts of a firm s production and inventory decisions on the natural environment as well as to the choices of pollution control approaches by decision makers in both the private and public sectors

a simple algorithm for optimal portfolio selection with fixed transaction costs the general optimal portfolio selection problem with fixed transaction costs is a complex mathematical programming problem however by placing reasonable restrictions on the variance covariance matrix of returns it is possible to simplify the solution of the problem specifically if the structure of returns between securities is such that the pairwise correlation coefficients are approximately the same a fairly simple algorithm which requires little computational effort can be employed this method can also be extended to the case where changes in the information set necessitate a revision of an existing portfolio

inventory models with random yield in a random environment abstract we consider a single item inventory model which is observed periodically in a randomly changing environment all model parameters are specified by the state of the environment which is assumed to be a time homogeneous markov chain yield is random due to the random capacity of the vendor i e a given order is fully received if the order quantity is less than this capacity otherwise the quantity received is equal to the available capacity the problem is analyzed in single multiple and infinite periods and it is shown that in all cases the optimal policy is the well known base stock policy where the optimal order up to level depends on the state of the environment the results are compared with the solutions of the certain yield model when there is infinite capacity we show that the order up to levels are equal in the single period case however in multiple and infinite periods we order the same or more if the yield is random

portfolio rebalancing model with transaction costs based on fuzzy decision theory the fuzzy set is one of the powerful tools used to describe an uncertain environment as well as quantifying any potential return and risk portfolio liquidity is taken into account and a linear programming model for portfolio rebalancing with transaction costs is proposed the level of return that an investor might aspire to the risk and the liquidity of portfolio are vague in an uncertain financial environment considering them as fuzzy numbers we propose a portfolio rebalancing model with transaction costs based on fuzzy decision theory an example is given to illustrate the behavior of the proposed model using real data from the shanghai stock exchange

a possibilistic mean semivariance entropy model for multi period portfolio selection with transaction costs abstract this paper deals with a multi period portfolio selection problem with fuzzy returns a possibilistic mean semivariance entropy model for multi period portfolio selection is presented by taking into account four criteria viz return risk transaction cost and diversification degree of portfolio in the proposed model the return level is quantified by the possibilistic mean value of return the risk level is characterized by the lower possibilistic semivariance of return and the diversification degree of portfolio is measured by the originally presented possibilistic entropy furthermore a hybrid intelligent algorithm is designed to obtain the optimal portfolio strategy finally the comparison analysis between the possibilistic entropy model and the proportion entropy model is provided by two numerical examples to illustrate the efficiency of the proposed approaches and the designed algorithm

macro and micro perspectives of growth and poverty in africa this article reviews trends in poverty economic policies and growth in a sample of african countries during the 1990s drawing on the better household data now available experiences have varied some countries have seen sharp drops in income poverty whereas others have witnessed marked increases in some countries overall economic growth has been pro poor and in others not but the aggregate numbers hide systematic distributional effects taking both macro and micro perspectives of growth and poverty in africa the article draws four key conclusions first economic policy reforms improving macroeconomic balances and liberalizing markets appear conducive to reducing poverty second market connectedness is crucial to enable participation in the gains from economic growth some regions and households by virtue of their remoteness were left behind when growth picked up third education and access to land emerge as key private endowments to help households benefit from new economic opportunities finally rainfall variations and ill health have profound effects on poverty outcomes underscoring the significance of social risk management in poverty reduction strategies in africa

an inventory model where customer demand is dependent on a stochastic price process abstract we investigate the optimal inventory operations of a firm selling an item whose price is driven by an exogenous stochastic price process which consequently impacts customer arrivals between ordering cycles this case is typical for retailers that operate in different currencies or trade products consisting of commodities or components whose prices are subject to market fluctuations we assume that there is a stochastic input price process for the inventory item which determines purchase and selling prices according to a general selling price function customers arrive according to a doubly stochastic poisson process that is modulated by stochastic input prices we analyze optimal ordering decisions for both backorder and lost sale cases we show that under certain conditions a price dependent base stock policy is optimal our analysis is then extended to a price modulated compound poisson demand case and the case with fixed ordering cost where a price dependent s s policy is optimal we present a numerical study on the sensitivity of optimal profit to various parameters of the operational setting and stochastic price process such as price volatility customer sensitivity to price changes etc we then make a comparison with a corresponding discrete time benchmark model that ignores within period price fluctuations and present the optimality gap when using the benchmark model as an approximation

roads out of poverty assessing the links between aid public investment growth and poverty reduction the authors develop a macroeconomic framework that captures links between aid public investment growth and poverty public investment is disaggregated into education infrastructure and health and affects both aggregate supply and demand dutch disease effects are captured by accounting for changes in the relative price of domestic goods the authors assess the impact of policy shocks on poverty by linking the model to a household survey they calibrate the model for ethiopia and simulate the changes in the allocation of aid and public investment the authors also calculate the amount by which foreign aid should increase to reach the poverty targets of the millennium development goals

halving global poverty the goal of central importance to this paper is to cut in half the proportion of people living below 1 a day from around 30 percent of the developing world s population in 1990 to 15 percent by 2015 based on population projections in 1990 this would correspond to around one billion people exiting poverty this paper begins by discussing poverty trends on a global scale where the poor are located in the world and how their numbers have been changing over time it then discusses the relationship of economic growth and income distribution to poverty reduction finally it suggests an evidence based agenda for poverty reduction in the developing world full text available at official url

analysis of an inventory system under backorder correlated deterministic demand and geometric supply process abstract in this article we propose a single item periodic review model that investigates the effects of changes in the demand process that occur after stockout realizations we investigate a system where the demands in successive periods are deterministic but affected by the backorder realizations in order to capture the effects of changes in the demand process we use a geometric type supply availability we analytically derive the necessary components for obtaining profit related performance measures and provide computational analysis

reliability evaluation and adjustment of supply chain network design with demand fluctuations this study focuses on supply chain network design problems by considering economies of scale and demand fluctuations a reliability evaluation method is developed to evaluate the performance of plants under demand fluctuations in addition two mathematical programming models are developed to determine the optimal adjustment decisions regarding production reallocation among plants under different fluctuating demands the judgments to adjust or to do nothing are investigated by comparing the results if the adjustment is made or not made results show that making adjustments benefits the manufacturers by reducing total production cost and avoiding revenue loss which outweighs the extra costs especially for high value added products results also suggest that the manufacturer should ignore a short period abnormal state since the benefits to respond to it might not compensate the high allocation costs the results of this study provide a reference for the manufacturer in their decision making process of network planning with demand fluctuations when they have to cope with benefits and costs during abnormal states

achieving the millennium development goals in sub saharan africa a macroeconomic monitoring framework this paper presents a macroeconomic approach to monitoring progress toward achieving the millennium development goals mdgs in sub saharan africa at the heart of our framework is a macro model which captures key linkages between foreign aid public investment disaggregated into education infrastructure and health the supply side and poverty the model is then linked through cross country regressions to indicators of malnutrition infant mortality life expectancy and access to safe water a composite mdg indicator is also calculated the functioning of our framework is illustrated by simulating the impact of an increase in foreign aid to niger at the mdg horizon of 2015 under alternative assumptions about the degree of efficiency of public investment our approach can serve as the building block for strategy papers for human development spahd a more encompassing concept than the current poverty reduction strategy papers

coordinating inventory control and pricing strategies under batch ordering in this paper we investigate joint pricing and inventory control problems in a finite horizon single product periodic review setting with certain uncertain supply capacities the demands in different periods are random variables whose distributions depend on the posted price exhibiting the additive form the order quantity in each period is required to be of integral multiples of a given specific batch size denoted by q inventory replenishment incurs a linear ordering cost referred to as the cost rate function the sum of holding and backorder costs can either be convex or quasi convex the objective is to determine a joint ordering and pricing decision that can maximize the total expected profit over the planning horizon we first consider the case in which the cost rate function is convex and show that the modifiedr q list price policy is optimal for the system with certain and limited capacities a special case of which is the r q list price policy when capacities become unlimited as supply capacities become random the optimal policy follows a new structure wherein the optimal order up to level and posted price must be coordinated to make the optimal safety stock level follow the r q policy we further consider the case of a quasi convex cost rate function which may arise when a service level constraint is used as a surrogate for the shortage cost we demonstrate that the r q list price policy is optimal for the system without supply capacity constraints in addition extensions to several other models are discussed the enabling technique is based on the notion of q jump convexity and its variants

production control in the face of storable raw material random supply and an outside market we study a periodic review production inventory control problem where both the supply of raw material and demand for the finished product are exogenous and random the raw material can be stored for future use can be purchased from or sold to an outside market we study both the lost sales and backlogging cases under both strict convex and linear raw material purchasing selling costs convexity of the purchasing selling cost implies that the more the firm purchases from or sells to the outside market the more expensive or less valuable the raw material becomes for all cases we find the partial characterizations for the optimal policies which all turn out to be very intuitively appealing in particular for each of the linear cost cases the optimal policy degenerates into a combination of two base stock policies one for the raw material inventory and the other for the finished product inventory

supply and demand uncertainty reduction efforts and cost comparison abstract in industries like health care consumer goods and agriculture shortages are widely observed and the consequences can be costly one of the main drivers of such shortages is the uncertain nature of supply and demand to reduce uncertainties sufficient information about supply and demand can be obtained by gathering relevant data e g auditing suppliers and conducting market research in this paper we conduct an analysis to examine the impacts of supply uncertainty demand uncertainty and uncertainty reduction efforts on production quantity and total cost we show that in the absence of uncertainty reduction efforts when the financial consequences of shortages are large or when the unit benefit is large supply uncertainty is more costly than demand uncertainty in addition exerting supply uncertainty reduction effort always causes the firm to produce fewer units than exerting demand uncertainty reduction effort although supply uncertainty reduction effort delivers a larger degree of improvement to total cost and hence is more efficient reduced supply uncertainty still leads to a higher system cost than does reduced demand uncertainty

optimal policies for inventory systems with finite capacity and partially observed markov modulated demand and supply processes we analyze a single item periodic review inventory system with random yield and finite capacity operating in a random environment the primary objective is to extend the model of gallego and hu 2004 to the more general case when the environment is only partially observable although our analysis is specific to inventory systems it can also be applied to production systems by replacing the fixed capacity supplier with a fixed capacity producer using sufficient statistics we consider single period multiple period and infinite period problems to show that a state dependent modified inflated base stock policy is optimal moreover we show that the multiple period cost converges to the infinite period cost as the length of the planning horizon increases

estimation risk in portfolio selection the mean variance model versus the mean absolute deviation model konno and yamazaki konno h k yamazaki 1992 mean absolute deviation portfolio optimization model and its applications to tokyo stock market management sci 39 519 531 propose the mean absolute deviation mad model as an alternative to the mean variance mv model they claim it retains all the positive features of the mv model saves the investor computing time and does not require the covariance matrix this paper shows that ignoring the covariance matrix results in greater estimation risk that outweighs the benefits in both models estimation error is more severe in small samples small observations relative to the number of assets and for investors with high risk tolerance the mv model s lower estimation risk is most striking in small samples and for investors with a low risk tolerance

periodic review production models with variable capacity random yield and uncertain demand we investigate a production planning problem in a periodic review environment with variable production capacity random yields and uncertain demand the implications of random yields and variable capacity for lot sizing previously have been explored separately but not jointly many production environments are likely to be subject to both types of uncertainties to minimize the total discounted expected costs production holding and shortage costs we formulate the problem as a stochastic dynamic program for the finite horizon problem we prove that the objective function is quasi convex and that the structure of the optimal policy is characterized by a single critical point for the initial stock level at each period that is if the initial stock is greater than this critical point the optimal planned production is zero otherwise it is greater than zero expressions for solving the critical point and the optimal planned production are obtained we further show that the solution for the finite horizon problem converges to that of the infinite horizon problem

dynamic replenishment production and pricing decisions in the face of supply disruption and random price sensitive demand abstract we study a joint decision problem for replenishment production pricing strategies in the face of both supply and demand uncertainties the supply of the raw material suffers from a potential supply disruption while the demand for the finished goods is price sensitive and random we assume that the raw material is storable and unfilled demands are fully backordered we characterize the optimal policies for raw material replenishment finished goods production and pricing we find that the optimal replenishment policy is an order up to type for a given finished goods stock level while it is a threshold type for a given raw material stock level for either a given finished goods stock level or a given raw material stock level the optimal production policy is a threshold type and the optimal pricing policy is a modified list price type respectively

continuous review inventory control when capacity is variable abstract many complex production inventory systems are characterized by uncertain capacities due to unexpected breakdowns unplanned repairs etc we analyze the effects of variable capacity on optimal lot sizing in continuous review environments two models are considered the basic eoq model and the order quantity reorder point model with backlogging for both cases we obtain the optimality conditions for generally distributed variable capacity we then show that the optimal order quantities for both models and the optimal reorder point for the second one are greater than those without variable capacity when the variable capacity distribution is exponential we develop effective procedures for finding the optimal solutions

integrating dynamic pricing and replenishment decisions under supply capacity uncertainty this paper examines an integrated decision making process regarding pricing for uncertain demand and sourcing from uncertain supply which are often studied separately in the literature our analysis of the integrated system suggests that the base stock list price policy fails to achieve optimality even under deterministic demand instead the optimal policy is characterized by two critical values a reorder point and a target safety stock under this policy a positive order is issued if and only if the inventory level is below the reorder point when this happens the optimal order and price are coordinated to achieve a constant target safety stock which aims at hedging the demand uncertainty we further investigate the profit improvement obtained from deploying dynamic pricing as opposed to static pricing our results indicate that either supply limit or supply uncertainty may induce a significant benefit from dynamic pricing and the compound effect of supply limit and uncertainty can be much more pronounced than the individual effects whether or not the supply capacity is limited has a major implication on the value of dynamic pricing under unlimited supply dynamic pricing is more valuable when procurement cost is high or when demand is more sensitive to price with limited supply however the capacity restriction tends to be relaxed reducing the value of dynamic pricing

inventory management with random supply and imperfect information a hidden markov model in most of the papers on inventory models operating in a random environment the state of the environment in each period is assumed to be fully observed with perfect information however this assumption is not realistic in most real life situations and we provide a remedy in this paper by assuming that the environment is only partially observed with imperfect information we accomplish this by analyzing two formulations of single item models with periodic review and random supply in a random environment in the first one supply is random due to random capacity of production and random availability of transportation we show that state dependent base stock policy is optimal if the capacity and all costs are observed while demand and availability are unobserved in the second model we consider a model with random availability only with fixed ordering cost we show that state dependent s s policy is optimal if the availability process is observable