the joint economic lot sizing problem review and extensions with the growing focus on supply chain management firms realize that inventories across the entire supply chain can be more efficiently managed through greater cooperation and better coordination this paper presents a comprehensive and up to date review of the joint economic lot sizing problem jelp and also provides some extensions of this important problem in particular a detailed mathematical description of and a unified framework for the main jelp models are given additionally a comparative empirical study of the main policies proposed for jelp is conducted the focus of this study is on assessing the deviation of these policies from the optimal solution studying the performance of different models provides additional insights that will help in justifying their use in more complex supply chain models that involve more stages or other practical considerations of interest

fuzzy multiobjective linear model for supplier selection in a supply chain abstract within new strategies for purchasing and manufacturing suppliers play a key role in achieving corporate competition hence selecting the right suppliers is a vital component of these strategies in practice vagueness and imprecision of the goals constraints and parameters in this problem make the decision making complicated in spite of the importance of the problem the literature on this subject is relatively scarce in this paper a fuzzy multiobjective linear model is developed to overcome the vagueness of the information for the first time in a fuzzy supplier selection problem an asymmetric fuzzy decision making technique is applied to enable the decision maker to assign different weights to various criteria the model is explained by an illustrative example

technology adoption under seed access constraints and the economic impacts of improved pigeonpea varieties in tanzania dry land legumes well adapted to drought prone areas have largely been neglected in the past despite the good opportunities they offer for income growth and food and nutritional security for the poor this study evaluated the adoption and impact of two farmer and market preferred and disease resistant pigeonpea varieties that were developed and promoted in semi arid tanzania the new varieties were resistant to fusarium wilt a fungal disease devastating the crop however farmers wanting to adopt new varieties did not adopt due to seed access constraints and under developed seed delivery systems adoption of new varieties is therefore analyzed using an augmented double hurdle model that allows estimating variety adoption conditional on seed access thresholds accounting for the additional information on sample separation the study identifies the crucial role of seed access local supply extension education participatory decision making capital and household assets in determining adoption the social economic benefits of the technology and policies for improved seed access were further analyzed using the extended economic surplus method dream model even under restrictive assumptions overall discounted benefits were found to be quite attractive indicating the need for additional efforts to scale up the success story analysis of changes in research benefits from relaxing the seed access constraint showed that net gains would increase by up to 30 if farmer access to improved seeds can be assured smallholder farmers are the major beneficiaries along with consumers and rural net buyers who gain from productivity induced lower market prices

integrated fuzzy multi criteria decision making method and multi objective programming approach for supplier selection and order allocation in a green supply chain abstract an organization s environmental performance is affected by its suppliers environmental performance and selecting green suppliers is a strategic decision in order to be more competitive in today s global market the supplier selection problem involves several quantitative and qualitative criteria in the supplier selection process if suppliers have limited capacity or other constraints it is necessary to determine the best supplier and order quantity of each supplier in this paper we present an integrated approach of fuzzy multi attribute utility theory and multi objective programming for rating and selecting the best green suppliers according to economic and environmental criteria and then allocating the optimum order quantities among them at first the fuzzy analytic hierarchy process and fuzzy technique for order preference by similarity to ideal solution is applied in order to analyze the importance of multiple criteria by incorporating experts opinion and to determine the best green suppliers next multi objective linear programming is used to consider and to formulate various constraints such as quality control capacity and other objectives the objective of the mathematical model is simultaneously to maximize the total value of purchasing and to minimize the total cost of purchasing to handle the subjectivity of decision makers preferences fuzzy logic has been applied the efficiency and application of the proposed approach has been illustrated with a case study in an automobile manufacturing company the obtained results help firms establish a systematic approach for tackling green supplier selection and order allocation problems in a realistic situation finally managerial implications conclusions and directions for additional research are introduced

testing and valuing dynamic correlations for asset allocation we evaluate alternative models of variances and correlations with an economic loss function we construct portfolios to minimize predicted variance subject to a required return it is shown that the realized volatility is smallest for the correctly specified covariance matrix for any vector of expected returns a test of relative performance of two covariance matrices is based on work of diebold and mariano the method is applied to stocks and bonds and then to highly correlated assets on average dynamically correct correlations are worth around 60 basis points in annualized terms but on some days they may be worth hundreds

an integrated inventory model for a single vendor and multiple buyers with ordering cost reduction abstract forming strategic alliances and utilizing modern information technologies have been the two most important ways for firms to gain such competitive advantages as lower logistics costs and secure customers loyalty in this paper we consider an integrated inventory system where a single vendor purchases and processes raw materials in order to deliver finished items to multiple buyers the vendor and all buyers are willing to invest in reducing the ordering cost e g establishing an electronic data interchange based inventory control system in order to decrease their joint total cost the amount of investment determines the planned ordering cost and hence affects their replenishment decisions one major managerial implication from this ordering cost reduction is that the efforts to streamline and speedup transactions via the application of information technologies may result in a higher degree of coordination and automation among allied trading parties an analytical model is developed to derive the optimal investment amount and replenishment decisions for both vendor and buyers the exponential ordering cost function is then applied to our general model and a numerical analysis is performed to provide interesting insights of the model numerical results show that the vendor and all the buyers can benefit directly from substantial cost savings by this ordering cost reduction investment

a multivariate garch model of international transmissions of stock returns and volatility the case of the united states and canada this study examines the short run dynamics of returns and volatility for stocks traded on the new york and toronto stock exchanges the main finding is that inferences about the magnitude and persistence of return innovations that originate in either market and that transmit to the other market depend importantly on how the cross market dynamics in volatility are modeled moreover much weaker cross market dynamics in returns and volatility prevail during later subperiods and especially for canadian stocks with shares dually listed in new york implications for international asset pricing hedging strategies and regulatory policy are discussed

modeling synergies in multi criteria supplier selection and order allocation an application to commodity trading abstract we propose a novel meta approach to support collaborative multi objective supplier selection and order allocation ssoa decisions by integrating multi criteria decision analysis and linear programming lp the proposed model accounts for suppliers performance synergy effects within a hierarchical decision structure it incorporates both heterogeneous objective data and subjective judgments of the decision makers dms representing various groups with different voting powers vps we maximize the total value of purchasing tvp by optimizing order quantity assignment to suppliers and taking into consideration their synergies encountered in different time horizons we apply the proposed model to a contractor selection and order quantity assignment problem in an agricultural commodity trading act company we maximize the strategic effectiveness of both the customers and the suppliers minimize risks increase the degree of cooperation between trading partners on all levels of supply chain integration enhance transparent knowledge sharing and aggregation and support collaborative decision making

multi objective models for lot sizing with supplier selection abstract in this paper two multi objective mixed integer non linear models are developed for multi period lot sizing problems involving multiple products and multiple suppliers each model is constructed on the basis of three objective functions cost quality and service level and a set of constraints the total costs consist of purchasing ordering holding and backordering and transportation costs ordering cost is seen as an ordering frequency dependent function whereas total quality and service level are seen as time dependent functions the first model represents this problem in situations where shortage is not allowed while in the second model all the demand during the stock out period is backordered considering the complexity of these models on the one hand and the ability of genetic algorithms to obtain a set of pareto optimal solutions we apply a genetic algorithm in an innovative approach to solve the models comparison results indicate that in a backordering situation buyers are better able to optimize their objectives compared to situations where there is no shortage if we take ordering frequency into account the total costs are reduced significantly

multi product lot sizing with a transportation capacity reservation contract abstract although transportation costs can form more than 50 of the total logistics costs of a product they are often neglected in lot sizing research and modelling nonetheless the body of research that does take transportation costs into account is gradually growing the bulk of this type of research however assumes that freight rates decrease as shipping weights and or volumes increase inspired by the situation with a large european manufacturing company we study a multi product lot sizing model where in any period any portion of a reserved transportation capacity can be used in exchange for a guaranteed price if the capacity is insufficient then the shipper needs to contract additional transportation capacity on the spot market where the prevailing price is higher accordingly the freight rates are piece wise linearly increasing in our model we prove that the problem of determining transportation lot sizes so as to meet warehouse demand with no backlogging allowed and to minimize total costs that is the sum of inventory carrying ordering and transportation costs is np hard in the strong sense furthermore we present a lagrangean relaxation algorithm to compute lower and upper bounds of which comprehensive computational experiments show the compelling performance in terms of quality and speed

continuous time models realized volatilities and testable distributional implications for daily stock returns we provide an empirical framework for assessing the distributional properties of daily speculative returns within the context of the continuous time jump diffusion models traditionally used in asset pricing finance our approach builds directly on recently developed realized variation measures and non parametric jump detection statistics constructed from high frequency intra day data a sequence of simple to implement moment based tests involving various transformations of the daily returns speak directly to the importance of different distributional features and may serve as useful diagnostic tools in the specification of empirically more realistic continuous time asset pricing models on applying the tests to the 30 individual stocks in the dow jones industrial average index we find that it is important to allow for both time varying diffusive volatility jumps and leverage effects to satisfactorily describe the daily stock price dynamics copyright 2009 john wiley sons ltd

realized garch a joint model for returns and realized measures of volatility summary we introduce a new framework realized garch for the joint modeling of returns and realized measures of volatility a key feature is a measurement equation that relates the realized measure to the conditional variance of returns the measurement equation facilitates a simple modeling of the dependence between returns and future volatility realized garch models with a linear or log linear specification have many attractive features they are parsimonious simple to estimate and imply an arma structure for the conditional variance and the realized measure an empirical application with dow jones industrial average stocks and an exchange traded index fund shows that a simple realized garch structure leads to substantial improvements in the empirical fit over standard garch models that only use daily returns copyright 2011 john wiley sons ltd

the total cost of logistics in supplier selection under conditions of multiple sourcing multiple criteria and capacity constraint abstract little attention is given in the literature to decisions on the appropriate selection of suppliers and on assigning order quantities to these suppliers in the case of multiple sourcing with multiple criteria and with suppliers capacity constraints only a few mathematical programming models to analyse such decisions have been published to date and these have tended to consider only net price as the cost of purchasing although the costs of transportation ordering and storage may be significantly important to the decision in this paper a mixed integer non linear programming model is presented to solve the multiple sourcing problem which takes into account the total cost of logistics including net price storage transportation and ordering costs buyer limitations on budget quality service etc can also be considered in the model an algorithm is proposed to solve the model and the model is illustrated using a numerical example

modelling and forecasting multivariate realized volatility summary this paper proposes a methodology for dynamic modelling and forecasting of realized covariance matrices based on fractionally integrated processes the approach allows for flexible dependence patterns and automatically guarantees positive definiteness of the forecast we provide an empirical application of the model which shows that it outperforms other approaches in the extant literature both in terms of statistical precision as well as in terms of providing a superior mean variance trade off in a classical investment decision setting copyright 2010 john wiley sons ltd

backorder penalty cost coefficient b what could it be abstract the classical economic order quantity eoq model with planned penalized backorders pb relies on postulating a value for the backorder penalty cost coefficient b which is supposed to reflect the intangible adverse effect of the future loss of customer goodwill following a stockout recognizing that the effect of the future loss of customer goodwill should be not a direct penalty cost but a change in future demand schwartz 1966 a new approach to stockout penalties management science 12 12 b538 b544 modified the classical eoq pb model by eliminating the backorder penalty cost term from the objective function and assuming that the long run demand rate is a decreasing strictly convex function of the customer s disappointment factor defined as the complement of the demand fill rate following a stockout which in turn is an increasing strictly convex function of the demand fill rate he called the new model a perturbed demand pd model schwartz provided convincing justification for his pd model and presented several variations of it in a follow up paper but he did not solve any of these models in this paper we solve schwartz s original pd model and its variations and we discuss the implications of their solutions thus filling a gap in the literature left by schwartz moreover having been convinced that schwartz s approach is more valid than the classical approach for representing the effect of the loss of customer goodwill following a stockout but also recognizing that the classical approach is far more popular than the pd approach because of its simplicity and because of tradition we use the solution of the pd model to infer the value of b in the classical model thus providing one possible answer to the question what could b be a noteworthy implication of the solution of schwartz s original pd model is that the optimal fill rate is always 0 or 1 rendering the inferred value of b in the classical model 0 or respectively suspecting that the property of the pd function which is most likely responsible for producing this bang bang type of result is strict convexity we show that for the case where the pd function is proportional to an integer power say n of the fill rate the optimal fill rate is always 0 or 1 if and only if n 1 in which case the pd function is strictly convex in the fill rate

on the forecasting accuracy of multivariate garch models this paper addresses the question of the selection of multivariate garch models in terms of variance matrix forecasting accuracy with a particular focus on relatively large scale problems we consider 10 assets from nyse and nasdaq and compare 125 model based one step ahead conditional variance forecasts over a period of 10 years using the model confidence set mcs and the superior predicitive ability spa tests model per formances are evaluated using four statistical loss functions which account for different types and degrees of asymmetry with respect to over under predictions when consid ering the full sample mcs results are strongly driven by short periods of high market instability during which multivariate garch models appear to be inaccurate over rel atively unstable periods i e dot com bubble the set of superior models is composed of more sophisticated specifications such as orthogonal and dynamic conditional correlation dcc both with leverage effect in the conditional variances however unlike the dcc models our results show that the orthogonal specifications tend to underestimate the conditional variance over calm periods a simple assumption like constant conditional correlation and symmetry in the conditional variances cannot be rejected finally during the 2007 2008 financial crisis accounting for non stationarity in the conditional variance process generates superior forecasts the spa test suggests that independently from the period the best models do not provide significantly better forecasts than the dcc model of engle 2002 with leverage in the conditional variances of the returns

dynamic version of the economic lot size model this article originally appeared in management science october 1958 volume 5 number 1 pp 89 96 published by the institute of management sciences a forward algorithm for a solution to the following dynamic version of the economic lot size model is given allowing the possibility of demands for a single item inventory holding charges and setup costs to vary over n periods we desire a minimum total cost inventory management scheme which satisfies known demand in every period disjoint planning horizons are shown to be possible which eliminate the necessity of having data for the full n periods

production and shipment lot sizing in a vendor buyer supply chain with transportation cost abstract previous research on the joint vendor buyer problem focused on the production shipment schedule in terms of the number and size of batches transferred between the two parties it is a fact that transportation cost is a major part of the total operational cost however in most joint vendor buyer models the transportation cost is only considered implicitly as a part of fixed setup or ordering cost and thus is assumed to be independent of the size of the shipment as such the effect of the transportation cost is not adequately reflected in final planning decisions there is a need for models involving transportation cost explicitly for better decision making in this study we analyze the vendor buyer lot sizing problem under equal size shipment policy we introduce the complete solution of the problem in an explicit and extended manner that has not existed in the literature we incorporate transportation cost explicitly into the model and develop optimal solution procedures for solving the integrated models all unit discount transportation cost structures with and without over declaration have been considered numerical examples are presented for illustrative purpose

a master production scheduling procedure for stochastic demand and rolling planning horizons abstract the problem of interest is a one product uncapacitated master production schedule mps in which decisions are made under rolling planning horizons demand is stochastic and time varying and effectiveness is measured by inventory holding production setup and backorder costs typically in both the research literature and the business practice the stochastic nature of the problem is modeled in an ad hoc fashion the stochastic mps problem is usually solved by adding safety stock to production quantities obtained from a deterministic lot sizing algorithm here the stochastic nature of the problem is explicitly considered as an optimal algorithm for solving the static probabilistic dynamic lot sizing problem is adapted to rolling planning horizons the resulting algorithm is found to dominate traditional approaches over a wide variety of experimental factors reducing total costs by an average of 16 over traditional methods

a multi objective supplier selection model under stochastic demand conditions abstract supplier selection is a typical multi criteria decision problem attracting great attention recently cost quality delivery and flexibility are generally involved in the supplier selection decision making in this paper a measurement of supplier flexibility is extended to consider demand quantity and timing uncertainties comprehensively a multi objective supplier selection model is developed under stochastic demand conditions stochastic supplier selection is determined with simultaneous consideration of the total cost the quality rejection rate the late delivery rate and the flexibility rate involving constraints of demand satisfaction and capacity using a problem specific genetic algorithm computational results are presented