Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems

Genetic algorithm (GA) approach is developed for solving the P-model of chance constrained data envelopment analysis (CCDEA) problems, which include the concept of “Satisficing”. Problems here include cases in which inputs and outputs are stochastic, as well as cases in which only the outputs are stochastic. The basic solution technique for the above has so far been deriving “deterministic equivalents”, which is difficult for all stochastic parameters as there are no compact methods available. In the proposed approach, the stochastic objective function and chance constraints are directly used within the genetic process. The feasibility of chance constraints are checked by stochastic simulation techniques. A case of Indian banking sector has been presented to illustrate the above approach.     

Using partially observed Markov processes to select optimal termination time of TV shows

This paper presents a method for optimal control of a running television show. The problem is formulated as a partially observed Markov decision process (POMDP). A show can be in a “good” state, i.e., it should be continued, or it can be in a “bad” state and therefore it should be changed. The ratings of a show are modeled as a stochastic process that depends on the show's state. An optimal rule for a continue/change decision, which maximizes the expected present value of profits from selling advertising time, is expressed in terms of the prior probability of the show being in the good state. The optimal rule depends on the size of the investment in changing a show, the difference in revenues between a “good” and a “bad” show and the number of time periods remaining until the end of the planning horizon. The application of the method is illustrated with simulated ratings as well as real data.     

中国农业绿色全要素生产率时空演变

基于"资源-能源-经济-环境"构建农业绿色全要素生产率理论分析框架,设计测算指标,改进EBM模型并结合ML指数从静态和动态视角测算并解析农业绿色全要素生产率增长源泉,采用核密度函数和空间马尔科夫链从时空视域分析中国农业绿色全要素生产率的动态演变规律。结论：一是农业绿色全要素生产率更真实、合理反映农业效率。二是2006-2016年间中国农业绿色全要素生产率呈微幅波动上升趋势,增长的动力源泉在于农业绿色技术进步。三是农业绿色全要素生产率区域差异明显,长期内区域差异不会缩小,区域差异的根源在于农业绿色技术效率。四是中国农业绿色全要素生产率具有空间集聚特性,空间滞后类型对区域转移的稳定程度具有显著影响;"以邻为善"与"以邻为壑"并存;与农业绿色全要素生产率水平较高区域相邻,会降低向低水平转移的概率,但跨界式增长难以实现。政策含义是制定区域差异化的AGTFP增长策略,实现中国农业区域协调、绿色增长。     

Bayesian Forecasting via Deterministic Model

Rational decision making requires that the total uncertainty about a variate of interest (a predictand) be quantified in terms of a probability distribution, conditional on all available information and knowledge. Suppose the state-of-knowledge is embodied in a deterministic model, which is imperfect and outputs only an estimate of the predictand. Fundamentals are presented of two Bayesian methods for producing a probabilistic forecast via any deterministic model. The Bayesian Processor of Forecast (BPF) quantifies the total uncertainty in terms of a posterior distribution, conditional on model output. The Bayesian Forecasting System (BFS) decomposes the total uncertainty into input uncertainty and model uncertainty, which are characterized independently and then integrated into a predictive distribution. The BFS is compared with Monte Carlo simulation and ensemble forecasting technique, none of which can alone produce a probabilistic forecast that quantifies the total uncertainty, but each can serve as a component of the BFS.     

MODELING LEARNING CURVE AND LEARNING COMPLEMENTARITY FOR RESOURCE ALLOCATION AND PRODUCTION SCHEDULING*

Research on learning effects in mathematical programming models for optimum resource allocation has called attention to the difficulty in solving such models in their original nonlinear form. In this paper, systematically varying sizes of linear segments are designed to approximate productivity changes along the learning curve, and a single separable linear programming model is developed. With production complementarity and learning transmission between products, a more realistic resource allocation and production scheduling problem emerges. Two cases of learning transmissions are considered, and the model design process, which defines a decision problem that can be solved by a simplex algorithm, is demonstrated.     

A process-oriented perspective of IS success: Examining the impact of IS on operational cost

Effective measurement of information system (IS) success continues to be a top concern for both practitioners and researchers. Labeled the “productivity paradox”, previous attempts to quantify the benefits of IS have often resulted in inconclusive or inconsistent results. The present study contributes to IS success research through the development and empirical testing of a process-oriented model of IS success. Using an operational perspective and combining primary and secondary measures, this study examines the influence of lower-level intangible IS and information technology (IT) benefits on higher-level financial measures. Additionally, we introduce IS plan quality as an antecedent to the model's input variables. Primary survey data from CIOs of primarily Fortune 2000 size firms are combined with secondary data, total operational cost, from Compustat to facilitate the examination of the IS benefits. This study also addresses shortcomings cited in other studies that might have contributed to the productivity paradox by including control variables in the model, defining a research perspective—the operational success perspective—and clearly articulating the level of analysis as the organizational level. The results supported a process-oriented view of the benefits from IS and showed how the effects of IS along a path can lead to better organizational performance, in this case, lower overall costs. These results are discussed and future research implications are presented.     

Discrete time-varying demand lot-sizing models with learning and forgetting effects

This paper deals with the problem of incorporating both learning and forgetting effects into discrete timevarying demand lot-sizing models to determine lot sizes. Forgetting is retrogression in learning which causes a loss of labour productivity due to breaks between intermittent production runs. Formulae are derived for calculating the production cost required to produce the first unit of each successive lot over a finite planning horizon. An optimal lotsizing model and three heuristic models are developed by extending the existing models without learning and forgetting considerations. Numerical examples and computational experience indicate that larger lot sizes are needed when the phenomenon of learning and forgetting exists. Several important conclusions are drawn from a comparison of the three heuristic solutions with the optimal solution, and suggestions for future research and for lot-size users to choose an appropriate lot-sizing technique are made.     

R&D project evaluation: An integrated DEA and balanced scorecard approach

We present and demonstrate a multi-criteria approach for evaluating R&D projects in different stages of their life cycle. Our approach integrates the balanced scorecard (BSC) and data envelopment analysis (DEA) and develops an extended DEA model. The input and output measures for the integrated DEA–BSC model are grouped in “cards” which are associated with a “BSC for R&D projects”. The BSC is embedded in the DEA model through a hierarchical structure of constraints that reflect the BSC balance considerations. We illustrate the proposed approach with a case study involving an industrial research laboratory that selects and executes dozens of R&D projects every year.     

Economic manufacturing quantities under conditions of learning and forgetting (EMQ/LaF)

Abstract

Abstract. It has been empirically observed that productivity improves as production continues due to system 'learning’, but that it deteriorates once the activity is stopped due to system 'forgetting’. Both learning and forgetting follow an exponential form with a 'doubling factor’ ranging between 0.75 and 0.98. We review and critique two previously proposed models, correct some minor errors in them, and expand one of them to accommodate a finite horizon. We also propose a new model that is more in harmony with the established learning function, for the determination of the optimal number and size of the lots in the finite and infinite horizon. The methodology used throughout is dynamic programming. We investigate the impact of all three models on the optimal lot sires and their costs, and establish the functional relations between the total cost and the various factors affecting them.     

Data envelopment analysis 1978–2010: A citation-based literature survey

This study surveys the data envelopment analysis (DEA) literature by applying a citation-based approach. The main goals are to find a set of papers playing the central role in DEA development and to discover the latest active DEA subareas. A directional network is constructed based on citation relationships among academic papers. After assigning an importance index to each link in the citation network, main DEA development paths emerge. We examine various types of main paths, including local main path, global main path, and multiple main paths. The analysis result suggests, as expected, that Charnes et al. (1978) [Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research 1978; 2(6): 429–444] is the most influential DEA paper. The five most active DEA subareas in recent years are identified; among them the “two-stage contextual factor evaluation framework” is relatively more active. Aside from the main path analysis, we summarize basic statistics on DEA journals and researchers. A growth curve analysis hints that the DEA literature’s size will eventually grow to at least double the size of the existing literature.     

A COMPARISON OF LEARNING CURVE ANALYSIS AND MOVING AVERAGE RATIO ANALYSIS FOR DETAILED OPERATIONAL PLANNING*

This study tests the use of learning curve analysis for production planning at the detailed component level under various conditions, represented by factors of product turnover rate, learning rate, variance levels, and planning horizon length. It also presents an alternative to learning curve analysis that considers aggregation of cost data across time. This alternative is periodic revision of standard cost data using moving average forecasts to reflect productivity trends. Results of this study indicate that in most circumstances a moving average analysis can provide better estimates of short-term, detailed component operations costs than either a learning curve analysis or a standard analysis.     

A unifying framework for duality and modeling in robust linear programs

In this paper, our major theme is a unifying framework for duality in robust linear programming. We show that there are two pair of dual programs allied with a robust linear program; one in which the primal is constructed to be “ultra-conservative” and one in which the primal is constructed to be “ultra-optimistic.” Furthermore, as one would expect, if the uncertainly in the primal is row-based, the corresponding uncertainty in the dual is column-based, and vice-versa. Several examples are provided that illustrate the properties of these primal and dual models.     

Acquisition of Telecommunications Bandwidth Under Economies of Scale in Size and Duration of Contracts*

Significant advances in information technology have brought about increased demand for bandwidth. Buyers of bandwidth often encounter bandwidth prices that are decreasing over time. Additionally, bandwidth prices at any point in time are decreasing in total bandwidth purchased and length of contracts. Therefore, buyers face complex decisions in terms of the number of contracts to buy, their bandwidth, and their lengths. In this article, we formulate models for the acquisition of bandwidth from a buyer's perspective. We begin with a model that allows varying contract durations under deterministic demand and without allowing shortages or overlapping contracts. We then formulate a simpler model, which restricts contract lengths over the planning horizon to be equal. We also solve the problem under probabilistic demand and allowing for shortages, which are satisfied by buying additional bandwidth at a premium. We perform numerical sensitivity analysis to compare the results of the models and illustrate the results with numerical examples. The numerical analyses illustrate that using relatively simple equal‐length contracts produces approximately the same results as the more complicated unequal‐length contract strategy.     

A real option theoretic fuzzy evaluation model for enterprise resource planning investment

The high failure rate of ERP implementation is due to a common pitfall that ERP projects are often enacted as merely investment into installation of IT infrastructure, rather than systematic planning of operation changes, business process re-engineering and a paradigm shift for the operation and management. To manage ERP investment in a changing environment for high payoff, this paper adopts a real option theoretic method. Fuzzy payoff valuation is introduced to deal with uncertainties in order to minimize the risk of failure. The proposed ERP evaluation model is geared towards small and medium enterprises. A case study is presented to validate the proposed fuzzy real options. The results indicate the potential of modeling ERP investment as “Expand”, “Contain” and “Abandon” options in different scenarios. The fuzzy real option model bestows a novel ex-ante cost analysis for justifying ERP investment in the implementation cycle.     

Mixed Hitting‐Time Models

We study mixed hitting‐time models that specify durations as the first time a Lévy process—a continuous‐time process with stationary and independent increments—crosses a heterogeneous threshold. Such models are of substantial interest because they can be deduced from optimal‐stopping models with heterogeneous agents that do not naturally produce a mixed proportional hazards structure. We show how strategies for analyzing the identifiability of the mixed proportional hazards model can be adapted to prove identifiability of a hitting‐time model with observed covariates and unobserved heterogeneity. We discuss inference from censored data and give examples of structural applications. We conclude by discussing the relative merits of both models as complementary frameworks for econometric duration analysis.     

James Gardner March: Founder of organization theory,decision theorist,and advocate of sensible foolishness

James G. March helped to create organization theory and a “behavioral theory of the firm”, and he contributed very important insights about decision making and organizational learning. His articles challenge prevalent assumptions and rely on casual observation rather than systematic empiricism. His influence demonstrates the power of conceptualization and creative logic, and examples that capture the imaginations of listeners and readers.     

Turnover‐induced Forgetting and its Impact on Productivity

In this paper we investigate organizational forgetting, the notion that a firm's knowledge does not persist over time, exploring knowledge depreciation caused by changes in human capital. We first conduct an in‐depth exploratory case study to identify organizational forgetting. The empirical evidence suggests that organizational forgetting occurs when abrupt personnel turnover takes place. The data reveal a forgetting pattern that is entirely episodic in nature. Thereupon we conduct a confirmatory piece, using multiple regression models, to measure the impact of organizational forgetting on overall productivity. We find that, as a result of repeated forgetting processes, productivity falls in spite of continued output accumulation because of changes in the characteristics of the resource where experience resides.     

Stock screening with use of multiple criteria decision making and optimization

A new method for the stock ranking based on the multiple criterion decision making and optimization is proposed. Two general criteria are used in the analysis. The first of them is based on the financial indices and may be treated as the criterion of firm's “health” or its financial performance. The second one is the two-criteria performance of firm based on the stock prices. It represents the firm's market success. The method rests on the selection of the stocks with a great correlation of the firm's financial performance and its market success. The local criteria are built in the form of the membership function of corresponding fuzzy subsets. Two different strategies for stock ranking and three most popular methods for local criteria aggregation are compared. As the example the values of financial rations and prices from database comprising the data of 162 firms from subsector of the biotechnology of US economy were used. It is shown that the proposed method makes it possible to select a small group of “good” stocks characterized by a great coincidence of firm's financial performance and its market success. The method rejects from the consideration all the “unsafe” firms, i.e., such ones that their market success is based rather on the public relations, rumors and other rather unreliable information. The method is addressed to those who prefer to select for a portfolio only the firms which demonstrate the closeness of their overall financial performance in the past year and success in the Stock Exchange in the following year.     

Adding life cycles to learning curves

The price-oriented experience curve has been extensively utilized as a model in corporate planning. However, managers exercise a greater degree of control over cost than they do over price. The use of the cost-oriented learning curve together with the product life cycle model in strategic planning is explored in this paper. The Ford Motor Company's experience with the Model-T, which was the first mass-produced automobile in the U.S., is used to reinforce the fact that the simultaneous use of the product life cycle and the learning curve would have provided Ford management with better planning information. With this information, Ford could have avoided the subsequent disastrous results which it experienced.     

Mixed Levels of Uncertainty in Complex Policy Models

The characterization and treatment of uncertainty poses special challenges when modeling indeterminate or complex coupled systems such as those involved in the interactions between human activity, climate and the ecosystem. Uncertainty about model structure may become as, or more important than, uncertainty about parameter values. When uncertainty grows so large that prediction or optimization no longer makes sense, it may still be possible to use the model as a behavioral test bed to examine the relative robustness of alternative observational and behavioral strategies. When models must be run into portions of their phase space that are not well understood, different submodels may become unreliable at different rates. A common example involves running a time stepped model far into the future. Several strategies can be used to deal with such situations. The probability of model failure can be reported as a function of time. Possible alternative surprises can be assigned probabilities, modeled separately, and combined. Finally, through the use of subjective judgments, one may be able to combine, and over time shift between models, moving from more detailed to progressively simpler order-of-magnitude models, and perhaps ultimately, on to simple bounding analysis.