Genetic algorithm to production planning and scheduling problems for manufacturing systems

Fundamental and extended multi-objective (MO) models are designed to address earliness/tardiness production scheduling planning (ETPSP) problems with multi-process capacity balance, multi-product production and lot-size consideration. A canonical genetic algorithm (GA) approach and a prospective multi-objective GA (MOGA) approach are proposed as solutions for different practical problems. Simulation results as well as comparisons with other techniques demonstrate the effectiveness of the MOGA approach, which is a noted improvement to any of the existing techniques, and also in practice provides a new trend of integrating manufacturing resource planning (MRPII) with just-in-time (JIT) in the production planning procedure.     

A speciation-based bilevel niching method for multimodal truss design problems

Truss design is a well-known structural optimization problem that has important practical applications in various fields. Truss design problems are typically multimodal by nature, meaning that it offers multiple optimal solutions concerning the topology (combinatorial optimization problem) and/or sizes (continuous optimization problem) of the members, but they are evaluated to have similar or equally good objective function values. From a practical standpoint, it is desirable to find as many alternative designs as possible, rather than finding a single design, as often practiced. Several techniques based on classical and metaheuristic optimization methods have been developed for simultaneous optimization of topology and size of a truss. However, all these methods unable to find multiple topologies and their corresponding size solutions in a single run. A few metaheuristics incorporating niching techniques have been developed for finding multiple topologies for the truss design problem, but these studies ignored the fact that for each known topology, multiple design solutions in terms of size can be found. To address this issue, this paper proposes a bi-level truss formulation and subsequently a speciation-based bilevel niching method (BiL-NM) using such a formulation. The BiL-NM consists of a modified SPSO niching method which is robust to find multiple topologies and a canonical PSO for their corresponding size solutions. Extensive empirical studies are carried out to analyze the accuracy, robustness, and efficiency of the BiL-NM. The results confirm that the proposed BiL-NM is superior in all these three aspects over the state-of-the-art methods on several low to high-dimensional truss design problems.

     

AN APPROACH FOR IMPROVED INTERPRETATION OF MULTIVARIATE ANALYSIS *

This paper proposes a method, canonical rotation analysis, which facilitates the substantive interpretation of results in multivariate analysis. Canonical rotation analysis is developed as a model which integrates multivariate least squares approaches and the varimax rotation criterion. The generalized applicability of the model to canonical correlation, multiple discriminant analysis, and multivariate analysis of variance is developed. The advantages and limitations of canonical rotation analysis are discussed and illustrated in the context of an industrial marketing research problem.     

Foreign Competition for the U.S. banking world

A study of 66 New York branches, agencies and subsidiaries of foreign banks was undertaken to evaluate marketing planning strategies. Opinions and perceptions towards planning and promotional options of key officials of foreign banks were examined. Using canonical statistical techniques the analysis differentiated between the various types of strategies by organizational form as well as by regional affiliation. The survey concluded that while promotional philosophies may be somewhat similar, the planning process differs significantly between branches and agencies.     

Combining Neural Networks and Statistical Predictions to Solve the Classification Problem in Discriminant Analysis*

A number of recent studies have compared the performance of neural networks (NNs) to a variety of statistical techniques for the classification problem in discriminant analysis. The empirical results of these comparative studies indicate that while NNs often outperform the more traditional statistical approaches to classification, this is not always the case. Thus, decision makers interested in solving classification problems are left in a quandary as to what tool to use on a particular data set. We present a new approach to solving classification problems by combining the predictions of a well-known statistical tool with those of an NN to create composite predictions that are more accurate than either of the individual techniques used in isolation.     

Games and Mechanism Design in Machine Scheduling—An Introduction

We survey different models, techniques, and some recent results to tackle machine scheduling problems within a distributed setting. In traditional optimization, a central authority is asked to solve a (computationally hard) optimization problem. In contrast, in distributed settings there are several agents, possibly equipped with private information that is not publicly known, and these agents must interact to derive a solution to the problem. Usually the agents have their individual preferences, which induces them to behave strategically to manipulate the resulting solution. Nevertheless, one is often interested in the global performance of such systems. The analysis of such distributed settings requires techniques from classical optimization, game theory, and economic theory. The paper therefore briefly introduces the most important of the underlying concepts and gives a selection of typical research questions and recent results, focusing on applications to machine scheduling problems. This includes the study of the so‐called price of anarchy for settings where the agents do not possess private information, as well as the design and analysis of (truthful) mechanisms in settings where the agents do possess private information.     

MODIFIED STEPPING-STONE METHOD AS A TEACHING AID FOR CAPACITATED TRANSPORTATION PROBLEMS

This paper extends the standard stepping-stone method to the case of capacitated transportation problems. The purpose is to offer an alternative to network-oriented or simplex-based methods (such as the out-of-kilter and upper-bounding techniques) often used in handling the capacitated transportation problem but generally beyond the scope of introductory decision sciences courses. The proposed procedure is based on the simple stepping-stone method and requires only a few modifications in the cost/assignment matrix and some alterations in the pivoting rules. As such, it is much easier to understand and apply than the above-mentioned solution algorithms. It therefore can serve as an effective tool for teaching capacitated transportation problems. Two numerical examples illustrate applications of the proposed method, procedures for sensitivity analysis, and comparisons with linear programming.     

A Multivariate Methodology for Simultaneously Capturing and Clustering Judgment Policies

This paper presents a new linear model methodology for clustering judges with homogeneous decision policies and differentiating dimensions which distinguish judgment policies. This linear policy capturing model based on canonical correlation analysis is compared to the standard model based on regression analysis and hierarchical agglomerative clustering. Potential advantages of the new methodology include simultaneous instead of sequential consideration of information in the dependent and independent variable sets, decreased interpretational difficulty in the presence of multicollinearity and/or suppressor/moderator variables, and a more clearly defined solution structure allowing assessment of a judge's relationship to all of the derived, ideal policy types. An application to capturing policies of information systems recruiters responsible for hiring entry-level personnel is used to compare and contrast the two techniques.     

Incorporating quality into data envelopment analysis of nursing home performance: A case study

When using data envelopment analysis (DEA) as a benchmarking technique for nursing homes, it is essential to include measures of the quality of care. We survey applications where quality has been incorporated into DEA models and consider the concerns that arise when the results show that quality measures have been effectively ignored. Three modeling techniques are identified that address these concerns. Each of these techniques requires some input from management as to the proper emphasis to be placed on the quality aspect of performance. We report the results of a case study in which we apply these techniques to a DEA model of nursing home performance. We examine in depth not only the resulting efficiency scores, but also the benchmark sets and the weights given to the input and output measures. We find that two of the techniques are effective in insuring that DEA results discriminate between high and low quality performance.     

The value of information concept applied to data systems

Cost-benefit analysis has a considerable literature in which information systems have been patently ignored. This reflects the considerable difficulties of applying the theory to information systems, and the state-of-the art remains relatively as Koopmans described it some 19 years ago (1957). A bar to further development would appear to be the lack of an applicable value-of-information concept. This paper seeks to clarify the issues and provide a robust theoretical and data analysis framework that will cover most situations. The approach here is to separate explicitly the dimensions of cost from those of information benefit, and examine the implications. The Null Information Benefit condition emerges as a special theoretical case, but potentially a most important one in applications. This case together with the Pareto optimum defines a large class of such problems that can be handled by the decision criteria and data analysis techniques tabulated and discussed here. The selection of input data techniques defines the limits of later project justification and may be crucial to the political viability of the projects throughout its life. Finally, the general management vs information systems management relationships are discussed in terms of this situation.     

对偶性在线性规划预处理中的应用分析

在当今大数据背景下,从实际应用中抽象出来的线性规划问题的规模越来越大,复杂性越来越高,因此数据预处理技术在线性规划问题求解中的重要性日渐突显。对偶性不仅有助于原始问题的算法（如对偶单纯形法）求解,而且是进行算法求解前的预处理步的重要组成部分。针对后者,本文基于有上下界的线性规划模型,详细分析总结了将对偶性应用于预处理中的两种方法：优先列和比例列的处理,并利用无效约束的概念证明了弱优先列的性质,最后应用C语言将预处理方法进行编程实现,以国际通用题库中变量个数大于1500的标准线性规划问题为实例进行测试。实例测试结果表明：（1）对于一般线性规划问题而言,对偶性在预处理中的应用能够有效减小问题规模,一方面体现在直接减少问题的变量数和非零元数,另一方面通过影响其他预处理方法间接减少问题的约束个数;（2）从减小问题规模的角度,对大部分问题而言比例列的预处理效果优于优先列。     

动态投入占用产出分析模型应用中的三个特殊处理问题

动态投入占用产出分析模型应用于经济分析实践有不少复杂问题需要相机处理,本文对其中的投资时滞、生产能力利用程度和库存与固定资本的生产性质差异等三个问理提出了相应的处理技术,建立了实用I—O—O模型．     

项目关联分析与宏观知识挖掘

简要介绍了后评估为"特优"的国家自然科学基金项目《项目管理中项目关联分析与立项决策支持系统研究》的研究内容、研究思路和研究特点以及部分研究成果,包括文本处理与文本建模研究、关联分析的事例推理研究、宏观知识挖掘研究,知识生产线的研究与开发4个方面。     

Econometric analysis and managerial decision making

Econometric analysis is concerned with the quantitative relationships between economic variables and it can provide an important input into the decision making process of managers. Typically econometrics differs from other apsects of management science in that it considers problems primarily, though not exclusively, from a background of economics rather than of other disciplines and behaviour is usually dealt with at higher levels of data aggregation than the individual firm.This paper considers some applications of typical econometrics to the general area of managerial decision making, where primarily the techniques have a role to play in assisting the general process of data analysis. Initially discussion is pointed towards the use of the analysis for predictive purposes and the contrast with time series methods. Subsequently examples are presented where the objective is to obtain a better understanding of individual economic relationships that aim to be important inputs into the decision making process, for example cost and revenue analysis. Finally, an example is given of how these ideas contribute more generally to the activity of model building for the firm as a whole both for the purpose of forecasting and policy simulation.     

Questioning the reliability of market segmentation techniques

Market segmentation techniques and analyses have received considerable attention in the academic literature. This paper compares and contrasts the results from conducting cluster analysis and factor analysis on two data sets, one which is genuine and the other which has been randomly generated. This analysis helps test claims about the validity and effectiveness of such techniques and looks at the reliability of the output. The findings suggest that researchers need to be careful to carry out valid preliminary work before presenting their results. Without such preliminary work it may be difficult to justify the segments which emerge.     

Person characteristics and the perception of organization climate

An issue in research on organization climate is the extent to which the climate reflects the personality of the organization members. The purpose of this study is to explore the overall relationship of person characteristics to climate and to explore ways in which person factors relate to specific climate dimensions. Previous research has used distal measures of person characteristics such as demographic variables or biographical measures. The present study uses proximal measures of person characteristics, 16PF scales, and the Embedded Figures Test. The finding, which results from the canonical correlation analysis and the multivariate regression analysis, is that person-climate relationships are of sufficient size that they deserve the attention of corporate culture theorists. A significant relationship was found between cognitive style and the climate dimension of administrative efficiency, job challenge, management, concern with employee involvement, open-mindedness, egalitarianism, altruism. A significant relationship was found between the aggregated scales of the 16PF and the aggregated climate scales. The process by which climate perceptions are formed represents the organization members' attempt to structure social reality to define their place within it and to guide their search for meaning and gratification. Personality and cognitive style have been shown to be important determinants of the perceptions employees have of their work organization.     

TOWARD UNDERSTANDING THE CONTENTS OF THE “BLACK BOX” FOR PREDICTING COMPLEX DECISION-MAKING OUTCOMES

The purpose of this research is to show the usefulness of three relatively simple nonlinear classification techniques for policy-capturing research where linear models have typically been used. This study uses 480 cases to assess the decision-making process used by 24 experienced national bank examiners in classifying commercial loans as acceptable or questionable. The results from multiple discriminant analysis (a linear technique) are compared to those of chi-squared automatic interaction detector analysis (a search technique), log-linear analysis, and logit analysis. Results show that while the four techniques are equally accurate in predicting loan classification, chi-squared automatic interaction detector analysis (CHAID) and log-linear analysis enable the researcher to analyze the decision-making structure and examine the “human” variable within the decision-making process. Consequently, if the sole purpose of research is to predict the decision maker's decisions, then any one of the four techniques turns out to be equally useful. If, however, the purpose is to analyze the decision-making process as well as to predict decisions, then CHAID or log-linear techniques are more useful than linear model techniques.     

MANUFACTURING STRATEGY ON THE “S” CURVE

The concept and techniques of “manufacturing strategy” offer managers the opportunity to use their production function as a strategic weapon in competition, an apparently attractive objective. Yet after about 25 years, the use of manufacturing in corporate strategy (MCS) as a management practice is not widespread. In contrast, however, in academic literature it appears to be flourishing and rapidly growing in popularity. This paper seeks to answer this apparent paradox, beginning with the history of MCS as it was developed as a theory of design to enable a manufacturing system to be focused on a key competitive task. Common criticisms of MCS, such as “tradeoffs,” “focus” and “undynamic,” are examined and refuted as valid reasons for its only modest usage. Instead, three “new” problems in the MCS concept and its techniques are suggested as genuine needs for the completion of the theory and for its becoming more universally understood and used by industrial managers.