A quantitative approach to the organizational design problem

The span of control is the most discussed single concept in classical and modern management theory. In specifying conditions for organizational effectiveness, the span of control has generally been regarded as a critical factor. Existing research work has focused mainly on qualitative methods to analyze this concept, for example heuristic rules based on experiences and/or intuition. This research takes a quantitative approach to this problem and formulates it as a binary integer model, which is used as a tool to study the organizational design issue. This model considers a range of requirements affecting management and supervision of a given set of jobs in a company. These decision variables include allocation of jobs to workers, considering complexity and compatibility of each job with respect to workers, and the requirement of management for planning, execution, training, and control activities in a hierarchical organization. The objective of the model is minimal operations cost, which is the sum of supervision costs at each level of the hierarchy, and the costs of workers assigned to jobs. The model is intended for application in the make-to-order industries as a design tool. It could also be applied to make-to-stock companies as an evaluation tool, to assess the optimality of their current organizational structure. Extensive experiments were conducted to validate the model, to study its behavior, and to evaluate the impact of changing parameters with practical problems. This research proposes a meta-heuristic approach to solving large-size problems, based on the concept of greedy algorithms and the Meta-RaPS algorithm. The proposed heuristic was evaluated with two measures of performance: solution quality and computational speed. The quality is assessed by comparing the obtained objective function value to the one achieved by the optimal solution. The computational efficiency is assessed by comparing the computer time used by the proposed heuristic to the time taken by a commercial software system. Test results show the proposed heuristic procedure generates good solutions in a time-efficient manner.

The order selection and lot sizing problem in the make-to-order environment

This research is motivated by the need for considering lot sizing while accepting customer orders in a make-to-order (MTO) environment, in which each customer order must be delivered by its due date. Job shop is the typical operation model used in an MTO operation, where the production planner must make three concurrent decisions; they are order selection, lot size, and job schedule. These decisions are usually treated separately in the literature and are mostly led to heuristic solutions. The first phase of the study is focused on a formal definition of the problem. Mathematical programming techniques are applied to modeling this problem in terms of its objective, decision variables, and constraints. A commercial solver, CPLEX is applied to solve the resulting mixed-integer linear programming model with small instances to validate the mathematical formulation. The computational result shows it is not practical for solving problems of industrial size, using a commercial solver. The second phase of this study is focused on development of an effective solution approach to this problem of large scale. The proposed solution approach is an iterative process involving three sequential decision steps of order selection, lot sizing, and lot scheduling. A range of simple sequencing rules are identified for each of the three subproblems. Using computer simulation as the tool, an experiment is designed to evaluate their performance against a set of system parameters. For order selection, the proposed weighted most profit rule performs the best. The shifting bottleneck and the earliest operation finish time both are the best scheduling rules. For lot sizing, the proposed minimum cost increase heuristic, based on the Dixon-Silver method performs the best, when the demand-to-capacity ratio at the bottleneck machine is high. The proposed minimum cost heuristic, based on the Wagner-Whitin algorithm is the best lot-sizing heuristic for shops of a low demand-to-capacity ratio. The proposed heuristic is applied to an industrial case to further evaluate its performance. The result shows it can improve an average of total profit by 16.62%. This research contributes to the production planning research community with a complete mathematical definition of the problem and an effective solution approach to solving the problem of industry scale.

Branch and price solution approach for order acceptance and capacity planning in make -to -order operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. ^ For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver.^ The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. ^ The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.^

Branch and Price Solution Approach for Order Acceptance and Capacity Planning in Make-to-Order Operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver. The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.

Ecological Science and Transformation to the Sustainable City

There is growing urgency to enhance the sustainability of existing and emerging cities. The science of ecology, especially as it interacts with disciplines in the social sciences and urban design, has contributions to make to the sustainable transformation of urban systems. Not all possible urban transformations may lead toward sustainability. Ecological science helps identify components of resilience that can favor transformations that are more sustainable. To summarize the dynamics and choices involved in sustainable transformations, a “metacity” framework is presented, embracing ecological processes in cities as complementary to those involving society, power, and economy.

A follow-up outcome evaluation of independent living services for foster care youth

This study aimed to evaluate the effectiveness of the Independent Living (IL) program targeting foster youths to prepare them for effective adulthood. The study employed a pre-post, two-group comparative research design. The IL group was composed of 49 young adults who participated in the IL or SIL (subsidized IL) program prior to their emancipation from foster care (mean age, 20.6 years). The comparison was made up of 18 young adults who experienced foster care but never participated in the IL or SIL programs (mean age, 20.2 years). Data were collected via a mailed survey that included the Daniel Memorial Independent Living Assessment (DMILA) and an additional questionnaire developed by the researcher. The study also examined: (1) why youth in foster care do not participate in IL programs, (2) how participating youth evaluate IL services and what recommendations they make to improve services, and (3) the internal consistency of the DMILA. Results suggest that the DMILA assessment has mediocre reliability. IL program participation is associated with better educational, employment, income, housing, early parenting-prevention, transportation, anger control, criminal-prevention, and self-evaluation outcomes. However, IL participation is not associated with better social support, perceived parenting competence, substance abuse-prevention, sexual risk-prevention outcomes, increased knowledge in money management skills, job seeking and job maintenance skills, interpersonal skills, or lower depression. Results also suggest that the outreach activities of the IL program may be flawed. IL participants reported the IL program was doing best in educational preparation, criminal involvement prevention, and money management preparation and least well in parenting preparation, housing preparation, employment preparation, and substance abuse prevention. To improve services, youths recommended primarily that IL counselors develop closer relationships with youths, that IL training better address organizational skills, and that monthly subsidy be raised and SIL eligibility requirements softened. The study's political context and limitations are also discussed and implications are derived regarding prevention, intervention, outreach, mentorship, empowerment, cross-systems collaboration, and future research. ^

Sales Promotion in Hotels:A British Perspective

In their article - Sales Promotion In Hotels: A British Perspective - by Francis Buttle, Lecturer, Department of Hotel, Restaurant, and Travel Administration, University of Massachusetts and Ini Akpabio, Property Manager, Trusthouse Forte, Britain, Buttle and Akpabio initially state: “Sales promotion in hotels is in its infancy. Other industries, particularly consumer goods manufacturing, have long recognized the contribution that sales promotion can make to the cost-effective achievement of marketing objectives. Sales promotion activities in hotels have remained largely uncharted. The authors define, identify and classify these hotel sales promotion activities to understand their function and form, and to highlight any scope for improvement.” The authors begin their discussion by attempting to define what the phrase sales promotion [SP] actually means. “The Institute of Sales Promotion regards sales promotions as “adding value, usually of a temporary nature, to a product or service in order to persuade the end user to purchase that particular brand as opposed to a competitive brand,” the authors offer. Williams, however, describes sales promotions more broadly as “short term tactical marketing tools which are used to achieve specific marketing objectives during a defined time period,” Buttle and Akpabio present with attribution. “The most significant difference between these two viewpoints is that Williams does not limit his definition to activities which are targeted at the consumer,” is their educated view. A lot of the discussion is centered on the differences in the collective marketing-promotional mix. “…it is not always easy to definitively categorize promotional activity,” Buttle and Akpabio say. “For example, in personal selling, a sales promotion such as a special bonus offer may be used to close the sale; an advertisement may be sales promotional in character in that it offers discounts.” Are promotion and marketing distinguishable as two separate entities? “…not only may there be conceptual confusion between components of the promotional mix, but there is sometimes a blurring of the boundaries between the elements of the marketing mix,” the authors suggest. “There are several reasons why SP is particularly suitable for use in hotels: seasonality, increasing competitiveness, asset characteristics, cost characteristics, increased use of channel intermediaries, new product launches, and deal proneness.” Buttle and Akpabio offer their insight on each of these segments. The authors also want you to know that SP customer applications are not the only game in town, SP trade applications are just as essential. Bonuses, enhanced commission rates, and vouchers are but a few examples of trade SP. The research for the article was compiled from several sources including, mail surveys, telephone surveys, personal interviews, trade magazines and newspapers; essentially in the U.K.

Using extreme value theory to value stock market returns

Extreme stock price movements are of great concern to both investors and the entire economy. For investors, a single negative return, or a combination of several smaller returns, can possible wipe out so much capital that the firm or portfolio becomes illiquid or insolvent. If enough investors experience this loss, it could shock the entire economy. An example of such a case is the stock market crash of 1987. Furthermore, there has been a lot of recent interest regarding the increasing volatility of stock prices. ^ This study presents an analysis of extreme stock price movements. The data utilized was the daily returns for the Standard and Poor's 500 index from January 3, 1978 to May 31, 2001. Research questions were analyzed using the statistical models provided by extreme value theory. One of the difficulties in examining stock price data is that there is no consensus regarding the correct shape of the distribution function generating the data. An advantage with extreme value theory is that no detailed knowledge of this distribution function is required to apply the asymptotic theory. We focus on the tail of the distribution. ^ Extreme value theory allows us to estimate a tail index, which we use to derive bounds on the returns for very low probabilities on an excess. Such information is useful in evaluating the volatility of stock prices. There are three possible limit laws for the maximum: Gumbel (thick-tailed), Fréchet (thin-tailed) or Weibull (no tail). Results indicated that extreme returns during the time period studied follow a Fréchet distribution. Thus, this study finds that extreme value analysis is a valuable tool for examining stock price movements and can be more efficient than the usual variance in measuring risk. ^

A study of stock market fluctuations and their relation to business conditions

Most research on stock prices is based on the present value model or the more general consumption-based model. When applied to real economic data, both of them are found unable to account for both the stock price level and its volatility. Three essays here attempt to both build a more realistic model, and to check whether there is still room for bubbles in explaining fluctuations in stock prices. In the second chapter, several innovations are simultaneously incorporated into the traditional present value model in order to produce more accurate model-based fundamental prices. These innovations comprise replacing with broad dividends the more narrow traditional dividends that are more commonly used, a nonlinear artificial neural network (ANN) forecasting procedure for these broad dividends instead of the more common linear forecasting models for narrow traditional dividends, and a stochastic discount rate in place of the constant discount rate. Empirical results show that the model described above predicts fundamental prices better, compared with alternative models using linear forecasting process, narrow dividends, or a constant discount factor. Nonetheless, actual prices are still largely detached from fundamental prices. The bubblelike deviations are found to coincide with business cycles. The third chapter examines possible cointegration of stock prices with fundamentals and non-fundamentals. The output gap is introduced to form the nonfundamental part of stock prices. I use a trivariate Vector Autoregression (TVAR) model and a single equation model to run cointegration tests between these three variables. Neither of the cointegration tests shows strong evidence of explosive behavior in the DJIA and S&P 500 data. Then, I applied a sup augmented Dickey-Fuller test to check for the existence of periodically collapsing bubbles in stock prices. Such bubbles are found in S&P data during the late 1990s. Employing econometric tests from the third chapter, I continue in the fourth chapter to examine whether bubbles exist in stock prices of conventional economic sectors on the New York Stock Exchange. The ‘old economy’ as a whole is not found to have bubbles. But, periodically collapsing bubbles are found in Material and Telecommunication Services sectors, and the Real Estate industry group.

A Study of Stock Market Fluctuations and their Relations to Business Conditions

Most research on stock prices is based on the present value model or the more general consumption-based model. When applied to real economic data, both of them are found unable to account for both the stock price level and its volatility. Three essays here attempt to both build a more realistic model, and to check whether there is still room for bubbles in explaining fluctuations in stock prices. In the second chapter, several innovations are simultaneously incorporated into the traditional present value model in order to produce more accurate model-based fundamental prices. These innovations comprise replacing with broad dividends the more narrow traditional dividends that are more commonly used, a nonlinear artificial neural network (ANN) forecasting procedure for these broad dividends instead of the more common linear forecasting models for narrow traditional dividends, and a stochastic discount rate in place of the constant discount rate. Empirical results show that the model described above predicts fundamental prices better, compared with alternative models using linear forecasting process, narrow dividends, or a constant discount factor. Nonetheless, actual prices are still largely detached from fundamental prices. The bubble-like deviations are found to coincide with business cycles. The third chapter examines possible cointegration of stock prices with fundamentals and non-fundamentals. The output gap is introduced to form the non-fundamental part of stock prices. I use a trivariate Vector Autoregression (TVAR) model and a single equation model to run cointegration tests between these three variables. Neither of the cointegration tests shows strong evidence of explosive behavior in the DJIA and S&P 500 data. Then, I applied a sup augmented Dickey-Fuller test to check for the existence of periodically collapsing bubbles in stock prices. Such bubbles are found in S&P data during the late 1990s. Employing econometric tests from the third chapter, I continue in the fourth chapter to examine whether bubbles exist in stock prices of conventional economic sectors on the New York Stock Exchange. The ‘old economy’ as a whole is not found to have bubbles. But, periodically collapsing bubbles are found in Material and Telecommunication Services sectors, and the Real Estate industry group.