the influence of design dimensions on stated choices in an environmental context discrete choice experiments are increasingly used in the context of environmental valuation however there is still little known about the influence of the complexity of the choice task on model outcomes in this paper we investigate task complexity in terms of the design dimensionality of the choice experiment by systematically varying the number of choice sets alternatives attributes and levels as well as the level range we largely follow a design of designs approach originally introduced in transportation first we analyse the influence of the design dimensionality on participants dropout behaviour finding that the probability to drop out of the survey is influenced by socio demographic characteristics and increases with the number of choice sets attributes as well as with designs having five alternatives second we investigate the impact of the design dimensions on stated choices by estimating a multinomial logit model and heteroskedastic logit models results show that the error term variance is influenced by socio demographic characteristics as well as by all design dimensions moreover we find that accounting for the impact of the design dimension on the error variance does not significantly change willingness to pay estimates

uncovering context induced status quo effects in choice experiments abstract in this study five design dimensions are varied systematically investigating context induced status quo effects in choice experiments additionally two structural complexity measures entropy and the number of attribute level changes are used to capture status quo effects from the similarity between alternatives and the number of trade offs a crucial finding is that the frequency of status quo choices is negatively associated with the number of alternatives indicating preference matching effects by contrast the probability of choosing the status quo increases with a higher number of choice tasks a wider level range and the similarity between alternatives status quo choices are further affected by the current environmental situation perceived by respondents we also find that marginal and non marginal welfare estimates are significantly affected by the choice design one key finding is that the most used choice task format in environmental economics i e two hypothetical and a status quo alternative is likely to increase the propensity to choose the status quo option

parametric analysis of setup cost in the economic lot sizing model without speculative motives in this paper we consider the important special case of the economic lot sizing problem in which there are no speculative motives to hold inventory we analyze the effects of varying all setup costs by the same amount this is equivalent to studying the set of optimal production periods when the number of such periods changes we show that this optimal set changes in a very structured way this fact is interesting in itself and can be used to develop faster algorithms for such problems as the computation of the stability region and the determination of all efficient solutions of a lot sizing problem furthermore we generalize two related convexity results which have appeared in the literature

skewness and the relation between risk and return the relationship between risk and return has been one of the most important and extensively investigated issues in the financial economics literature the theoretical results predict a positive relation between the two nevertheless the empirical findings so far have been contradictory evidence presented in this paper shows that these contradictions are the result of negative skewness in the distribution of portfolio excess return and the fact that the estimation of intertemporal asset pricing models are based on symmetric log likelihood specifications data as supplemental material are available at http dx doi org 10 1287 mnsc 2015 2201 this paper was accepted by jerome detemple finance

strategic response to pollution taxes in supply chain networks dynamic spatial and organizational dimensions abstract this paper presents a model of the strategic behavior of firms operating in a spatial supply chain network the manufacturing and retailing firms engage in an oligopolistic noncooperative game by sharing customer demand such that a firm s decisions impact the product prices which in turn result in changes in all other firms decisions each firm s payoff is to maximize its own profit and we show that in response to such changes in prices and to exogenous environmental taxes the manufacturing firms may strategically alter a variety of choices such as make buy decisions with respect to intermediate inputs spatial distribution of production product shipment patterns and inventory management environmental tax payment vs recycling decisions and timing of all such choices to sustainably manage the profit and the environmental regulations an important implication is that effects of a tax depends on the oligopolistic game structure with respect to methods we show that this dynamic game can be represented as a set of differential variational inequalities dvis that motivate a computationally efficient nonlinear complementarity ncp approach that enables the full exploitation of above mentioned salient features we also provide a numerical example that confirms the utility of our proposed framework and shows substantial strategic reaction can be expected to a tax on pollution stocks

measuring the difference in mean willingness to pay when dichotomous choice contingent valuation responses are not independent dichotomous choice contingent valuation surveys frequently elicit multiple values in a single questionnaire if individual responses are correlated across scenarios the standard approach of estimating willingness to pay wtp functions independently for each scenario may result in biased estimates of the significance of the difference in mean wtp values this paper applies an alternative bivariate probit approach that explicitly accounts for correlation across errors in the estimation of wtp and mean wtp using data from three separate dichotomous choice contingent valuation studies this correlation is demonstrated to have an effect on the significance level of mean wtp difference tests

choice certainty and consistency in repeated choice experiments the main objective of this study is to examine how repeated choice affects preference learning in stated preference experiments we test different hypotheses related to preference learning by analyzing response patterns and asking respondents in a choice experiment to report their experienced certainty when going through the choice tasks in a split sample test we show that follow up choice certainty questions are procedural invariant the self reported certainty results indicate that learning occurs but econometric testing procedures do not identify any significant impact of learning effects on parameter estimates or variance across choice tasks additional tests of choice consistency suggest that preferences in the choice experiment are stable and coherent

unraveling the choice format effect a context dependent random utility model a few recent studies report that preferences elicited from a two alternative choice format are different from those elicited from a three alternative choice format when both choice set designs contain a status quo option we hypothesize that the choice format effect might disappear when we estimate a random utility model rum that allows for context dependent preferences based on the estimated context dependent rum we find that the choice format effect is likely the result of two offsetting effects a task complexity effect that increases the probability of choosing the status quo option and a preference matching effect which decreases the probability

complexity effects in choice experiment based models abstract many firms rely on choice experiment based models to evaluate future marketing actions under various market conditions this research investigates choice complexity i e number of alternatives number of attributes and utility similarity between the most attractive alternatives and individual differences in decision time as key factors that affect the predictive performance of models based on choice experiments both within and between complexity conditions the results show that complexity and individual decision time not only affect the error in consumer choice models but also consumers decision strategy and systematic utilities the authors introduce a complexity adjusted mixed logit cam logit model to capture the various influences of complexity in choice experiment based models they illustrate the consequences of complexity on choice behavior with market share predictions of the cam logit model for different complexity conditions

setup cost stability region for the dynamic lot sizing problem with backlogging abstract this paper develops a procedure to find the setup cost stability regions for the dynamic lot sizing problem with backlogging the setup cost stability region for a solution is defined as the set of all setup cost values for which the given solution is optimal such results can be useful in studying the robustness of a given solution with respect to errors in cost estimates

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

adapting the newsvendor model for infinite horizon inventory systems abstract we address inventory decisions in the context of the reorder point order quantity policy in infinite horizon stochastic lead time demand inventory systems in which the parameters may be non stationary we develop a heuristic policy based on the simple economic order quantity eoq model and the one period newsvendor model using illustrative numerical examples we demonstrate that our simple and easy to understand heuristic policy performs nearly as well as the optimal policy derived from complicated mathematical procedures our heuristic policy is simple easy to implement and flexible in that it can be easily adapted to situations when the parameters of the inventory system may change analytical modeling under non stationary parameters would be extremely complex and the derivation of closed form optimal policies is mathematically intractable in most cases in such cases practitioners will find our heuristic policy an attractive option

simulation based sequencing and lot size optimisation for a production and inventory system with multiple items abstract we investigate the following decision problem a manufacturing unit has to meet a random demand for n items at the same time only one item can be manufactured manufacturing times are random whereas set up times are known positive constants but different for different items finished production is stored in a warehouse with finite capacity the availability of raw material is always guaranteed demand that cannot be satisfied by items in the warehouse will be backordered in a queue with given capacity demand that meets a full backorder queue is lost we assume cost for manufacturing items for production switches for holding items in the warehouse for waiting and lost demand for sold items a given reward is earned the problem now is to define such a manufacturing policy i e a sequencing rule and a lot size rule which maximises the expected profit per time unit since that problem is too complex for an analytical solution we restrict our search for an optimal policy to simple structured policies which can be described by a few parameters to find optimal parameter values we use simulation optimisation where a simulator for the system is combined with a genetic algorithm as an optimiser the paper is finished with some numerical examples to show the applicability of the proposed approach

the single product lot sizing problem with constant parameters and backlogging exact results a new solution and all parameter stability regions we consider the single product lot sizing problem over a finite planning horizon demand at each period is constant and excess demand is completely backlogged holding and backlogging costs are proportional to the amount of inventory stocked or backlogged while ordering cost is fixed independent of the quantity ordered the optimal policy targets to minimize the total relevant costs over the planning horizon the key results of this paper are 1 an explicit formula for the optimal total cost as a function of the model parameters and the number of cycles of the policy 2 a new polynomial time algorithm which determines the overall optimal policy and 3 stability regions for any solution considering simultaneous variations on all cost and demand parameters the proposed algorithm is easy to implement and therefore is suitable for practical use

quadratic arch models we introduce a new model for time varying conditional variances as the most general quadratic version possible within the arch class hence it encompasses all the existing restricted quadratic variance functions its properties are very similar to those of garch models but avoids some of their criticisms in univariate applications to daily u s and monthly u k stock market returns qarch adequately represents volatility and risk premia qarch is easy to incorporate in multivariate models to capture dynamic asymmetries that garch rules out such asymmetries are found in an empirical application of a conditional factor model to 26 u k sectorial stock returns

designing discrete choice experiments do optimal designs come at a price in discrete choice experiments design decisions are crucial for determining data quality and costs while high statistical efficiency designs are desirable they may come at a price if they increase the cognitive burden for respondents we address this problem by designing 44 experiments that systematically vary numbers of attributes and attribute level differences our results for two product categories suggest that respondents systematically are less consistent in answering choice questions as statistical efficiency increases this relationship holds regardless of the number of attributes and is statistically significant even if one accommodates preference heterogeneity implications for practice and future research are discussed

endogenous bank risk and efficiency we develop a framework to incorporate bank risk as measured from the variance of profits or returns within a model of frontier efficiency our framework follows the premise that risk is endogenously related to efficiency we estimate our model using panel data for u s banks and bayesian techniques we show that excluding risk from the efficiency model significantly biases the efficiency estimates and the ranking of banks according to their competitive advantage we also demonstrate that there is a negative risk efficiency nexus with causality running both ways while our estimates of risk are fully consistent with the developments in the banking industry over the period 1976 2014

forecast horizons in the discounted dynamic lot size model we derive a sharp upper bound on the minimal forecast horizon in the discounted dynamic lot size model with constant initial demand this bound is given by mm 1 where m is the eoq s worth i e the number of periods for which the total demand equals economic order quantity our results do not require the solution of the infinite horizon problem to be unique nor do they require the infinite horizon problem to be well defined we also prove some sensitivity results with respect to the discount factor and the setup cost

ending inventory valuation in multiperiod production scheduling when making lot sizing decisions managers often use a model horizon t that is much smaller than any reasonable estimate of the firm s future horizon this is done because forecast accuracy deteriorates rapidly for longer horizons while computational burden increases however what is optimal over the short horizon may be suboptimal over the long run resulting in errors known as end effects a common end effect in lot sizing models is to set end of horizon inventory to zero this policy can result in excessive setup costs or stock outs in the long run we present a method to mitigate end effects in lot sizing by including a valuation term vit for end of horizon inventory it in the objective function of the short horizon model we develop this concept within the classical eoq modeling framework and then apply it to the dynamic lot sizing problem dlsp if demand in each period of the dlsp equals the long run average demand rate then our procedure induces an optimal ordering policy over the short horizon that coincides with the long run optimal ordering policy we test our procedure empirically against the wagner whitin algorithm and the silver meal heuristic under several demand patterns within a rolling horizon framework with few exceptions our approach significantly outperforms the other approaches tested for modest to long model horizons we discuss applicability to more general lot sizing problems

production inventory games a new class of totally balanced combinatorial optimization games in this paper we introduce a new class of cooperative games that arise from production inventory problems several agents have to cover their demand over a finite time horizon and shortages are allowed each agent has its own unit production inventory holding and backlogging cost cooperation among agents is given by sharing production processes and warehouse facilities agents in a coalition produce with the cheapest production cost and store with the cheapest inventory cost we prove that the resulting cooperative game is totally balanced and the owen set reduces to a singleton the owen point based on this type of allocation we find a population monotonic allocation scheme for this class of games finally we point out the relationship of the owen point with other well known allocation rules such as the nucleolus and the shapley value

analytical evaluation of heuristics performance for the single level lot sizing problem for products with constant demand abstract in this paper we examine the performance of several commonly used heuristics for the single level lot sizing problem for products with stationary demand and cost parameters a particular combination of demand and cost combination introduced by blackburn and millen called the lot sizing index lsi is shown to be an excellent predictor of heuristics performance for any finite horizon problem the importance of the lsi as a performance predictor is established through an analysis of the heuristics objective function and the optimal cost function

operations research in inventory management a review and critique the objectives of inventory management including the relevant related costs are examined in this paper a brief review of standard problems that have been effectively solved is presented however we point out that a serious gap exists between theory and practice in many organizations suggestions are made for bridging this gap finally a list is provided of a number of research problems whose implementable solution would have a major beneficial impact on the practice of inventory management

the impact of offering two versus three alternatives in choice modelling experiments researchers designing choice modelling experiments have some latitude over the number of choice alternatives that can be offered in each choice set there is some evidence that design dimensions including the number of alternatives available in each choice set can influence model outcomes a key issue is whether referendum formats with binary options are preferable to choice sets with multiple alternatives a choice modelling experiment was performed where questionnaires delivered to two split samples differed only according to whether two or three alternatives were offered to respondents in each choice set the results indicate that more robust models could be constructed from the three alternative split compared to the two alternative split one reason for the difference is that respondents tended to display serial non participation in the two alternative format choosing an alternative consistently without regard for changes in the attributes for practitioners of the cm technique the results suggest that it may be preferable to offer more than two alternatives in a choice set that includes a status quo option

exploring scale effects of best worst rank ordered choice data to estimate benefits of tourism in alpine grazing commons in many environmental valuation applications standard sample sizes for choice modelling surveys are impractical to achieve one can improve data quality using more in depth surveys administered to fewer respondents we report on a study using high quality rank ordered data elicited with the best worst approach the resulting exploded logit choice model estimated on 64 responses per person was used to study the willingness to pay for external benefits by visitors for policies which maintain the cultural heritage of alpine grazing commons we find evidence supporting this approach and reasonable estimates of mean wtp which appear theoretically valid and policy informative

learning and fatigue effects revisited investigating the effects of accounting for unobservable preference and scale heterogeneity using multiple choice tasks per respondent in discrete choice experiment studies increases the amount of available information however respondents learning and fatigue may lead to changes in observed utility function preference taste parameters as well as the variance in its error term scale they need to be controlled to avoid potential bias a sizable body of empirical research offers mixed evidence in terms of whether these ordering effects are observed we point to a significant component in explaining these differences we show how accounting for unobservable preference and scale heterogeneity can influence the magnitude of observed ordering effects

stock returns and volatility most asset pricing models postulate a positive relationship between a stock portfolio s expected returns and risk which is often modeled by the variance of the asset price this paper uses garch in mean models to examine the relationship between mean returns on a stock portfolio and its conditional variance or standard deviation after estimating a variety of models from daily and monthly portfolio return data we conclude that any relationship between mean returns and own variance or standard deviation is weak the results suggest that investors consider some other risk measure to be more important than the variance of portfolio returns

dynamic lot sizing with batch ordering and truckload discounts this paper studies two important variants of the dynamic economic lot sizing problem that are applicable to a wide range of real world situations in the first model production in each time period is restricted to a multiple of a constant batch size where backlogging is allowed and all cost parameters are time varying several properties of the optimal solution are discussed based on these properties an efficient dynamic programming algorithm is developed the efficiency of the dynamic program is further improved through the use of monge matrices using the results developed for the first model an o n3log n algorithm is developed to solve the second model which has a general form of product acquisition cost structure including a fixed charge for each acquisition a variable unit production cost and a freight cost with a truckload discount this algorithm can also be used to solve a more general problem with concave cost functions

production allocation with dual provisioning abstract consider a manufacturer that has existing in house capacity to produce certain products however due to recent growth in demand the existing capacity is insufficient to meet the total requirements over a finite planning horizon as a short term solution the manufacturer has decided to order part of the total requirements from an external supplier this paper presents a model that focuses on the issue of allocating a production quantity among the in house facility and an external supplier we construct a product allocation problem that determines an optimal allocation so that the total relevant cost in house production cost ordering cost etc is minimized in addition we analyze the complexity of the problem and develop two different lagrangian heuristics for generating near optimal allocations

designing choice sets for stated preference methods the effects of complexity on choice consistency abstract we vary the complexity of choice sets used in stated preference methods to evaluate the impact on choice consistency we define five measures of complexity that capture either the amount of information or the correlational structure of information in a choice set using these measures as randomly assigned treatments in a large experiment we analyze the consistency of respondents choices using a heteroskedastic multinomial logit model we find that i all of our measures of choice set complexity affect choice consistency ii changes in the correlational structure of information have the largest impact on choice consistency and iii choice complexity significantly distorts welfare estimates our results confirm economists initial worry that moving from contingent valuation to conjoint methods will increase complexity induced choice inconsistency however our analysis also shows that these negative impacts may be mitigated if precautions are taken at the design and estimation stages of stated preference methods

lot sizing for products with finite demand horizon and periodic review inventory policy abstract this paper analyzes and develops an efficient algorithm for solving a simple generalization of the classical economic order quantity eoq model where the demand horizon is finite and a periodic review inventory policy is followed such problems could arise in production shops where the product under consideration is going to be discontinued and or replaced by some other product after a certain known number of periods such problems could also arise as subproblems in large prodution inventory problems were demand rate is constant in some periods and varying in others

note a note on the dynamic lot size model with uncertainty in demand and supply processes in this paper we give some extensions of a result of nevison and burstein nevison c h m burstein 1984 the dynamic lot size model with stochastic lead times management sci 30 100 109 who discuss a dynamic lot sizing model with stochastic lead times they consider a situation in which the lead time distribution is unaltered by the amount which is ordered but is otherwise arbitrary and characterize the optimal solution in terms of points at which there is zero inventory here we give a more precise characterization and show that these results can be extended to a situation in which the demands are also stochastic and in which there is uncertainty relating to quantity as well as to the timing of demands and order arrivals the method of proof that we use is different from that used by nevison and burstein

remanufacturing planning for the reverse wagner whitin models abstract in this paper the reverse wagner whitin s dynamic production planning and inventory control model and some of its extensions are studied in such reverse product recovery models used products arrive to be stored and to be remanufactured at minimum cost for the reverse model with given demand the zero inventory property of optimal solutions is proved the corresponding wagner whitin algorithm is presented and the stability of optimal solutions is discussed for the case of a large quantity of low cost used products furthermore the model of the alternate application of remanufacturing and manufacturing processes is analysed again for the case of a large quantity of low cost used products the zero inventory property is proved the wagner whitin algorithm is applied to determine the periods in which used products are remanufactured or new products are produced and the stability of silver meal solutions for this model is sketched

existence of forecast horizons in undiscounted discrete time lot size models we are concerned with a discrete time undiscounted dynamic lot size model in which demand and cost parameters are constant for an initial few periods as our main result we obtain an upper bound on the number of these periods which guarantees the optimality of the economic order quantity eoq as the size of the production lot to be produced in the first period the upper bound is given and the optimality holds for every problem with an horizon not less than the upper bound and for the infinite horizon problem the data beyond the upper bound are allowed to be specified arbitrarily in the context of forecast horizon theory we obtain conditions for a finite forecast horizon to exist in the undiscounted dynamic lot size model furthermore existence results for forecast horizons in an undiscounted optimization problem are obtained for the first time in this paper

managing new and remanufactured products we study a firm that makes new products in the first period and uses returned cores to offer remanufactured products along with new products in future periods we introduce the monopoly environment in two period and multiperiod scenarios to identify thresholds in remanufacturing operations next we focus our attention on the duopoly environment where an independent operator io may intercept cores of products made by the original equipment manufacturer oem to sell remanufactured products in future periods we characterize the production quantities associated with self selection and explore the effect of various parameters in the nash equilibrium among other results we find that if remanufacturing is very profitable the original equipment manufacturer may forgo some of the first period margin by lowering the price and selling additional units to increase the number of cores available for remanufacturing in future periods further as the threat of competition increases the oem is more likely to completely utilize all available cores offering the remanufactured products at a lower price

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

the reverse wagner whitin model with variable manufacturing and remanufacturing cost abstract in this paper the reverse wagner whitin model is extended to the case with additional variable manufacturing and remanufacturing cost this model can be regarded as a combination of the classical wagner whitin model and a pure reverse wagner whitin model with given returns of used products the combined model is more appropriate to practice if the alternate application of remanufacturing and manufacturing processes is analysed for the case of time constant cost and demand data we prove the optimality of a policy starting with remanufacturing before switching to manufacturing and give an estimation for the optimal switching point finally it is investigated how the disposal of excess inventory would change the solution

infinite horizon production planning in time varying systems with convex production and inventory costs we consider the planning of production over the infinite horizon in a system with time varying convex production and inventory holding costs this production lot size problem is frequently faced in industry where a forecast of future demand must be made and production is to be scheduled based on the forecast because forecasts of the future are costly and difficult to validate a firm would like to minimize the number of periods into the future it needs to forecast in order to make an optimal production decision today in this paper we first prove that under very general conditions finite horizon versions of the problem exist that lead to an optimal production level at any decision epoch in particular we show it suffices for the first period infinite horizon production decision to solve for a horizon that exceeds the longest time interval over which it can prove profitable to carry inventory we then develop a closed form expression for computing such a horizon and provide a simple finite algorithm to recursively compute an infinite horizon optimal production schedule