Cellular manufacturing, which is also referred to as group technology among researchers, has primarily been used as a means to increase productivity, efficiency and flexibility. Under group technology, similar jobs, which have similar shape, material, and processing operations are assigned to the same group. Moreover, dissimilar machines are assigned to the same cell to meet the processing requirements of jobs in a group or multiple groups. Group scheduling problems have been studied extensively in the past as implementation of group technology became more prevalent in industry. However, most of the work that has been done has focused on single-criterion optimization. A bi-criteria group scheduling problem in a flow shop with sequence-dependent setup time is investigated in this research. Cellular manufacturing and flow shop are two popular scenarios in industry. To mimic real industry practice, dynamic job releases and dynamic machine availabilities are assumed. The goal is to minimize the weighted sum of total weighted completion time and total weighted tardiness, which satisfy the producer and customer goals separately. Normalized weights are assigned to both criteria to describe the trade-off between the two goals. Two different initial solution finding mechanisms are proposed, and a tabu-search based two-level search algorithm is developed to find near optimal solutions for the problem. An example problem is used to demonstrate the applicability of the search algorithm. A mathematical model is developed and implemented to evaluate the quality of the solutions obtained from the heuristics in small problem instances. Further, to uncover the difference in performance of initial solution finding mechanisms and heuristics, a detailed experimental design is performed. The results show that different heuristics have different performance in solving problems generated with different parameters. / Graduation date: 2012

Bland den äldre befolkningen är det relativt vanligt med någon form av sväljsvårigheter, dysfagi. Tidigare studier har visat att mellan 30 och 70 procent av boende inom äldreomsorgen har sväljbesvär. I dagsläget har de flesta äldreboenden i Östergötland inte tillgång till logoped. I föreliggande studie användes metoden Single Subject Experimental Design med upplägget ABB. Flera måltidsobservationer utfördes på de två deltagarna. I dessa observationer studerades tecken på s såsom hostningar, rosslingar, upprepade sväljningar och trögutlöst sväljreflex. Pulsoximeter användes för att öka säkerheten i mätningarna. I interventionsfaserna hölls två föreläsningar om dysfagi för personalen på det berörda äldreboendet. Syftet var att undersöka huruvida dessa föreläsningar förbättrade deltagarnas sväljförmåga. Dysfagi fanns hos båda deltagarna, dock med olika slags symtom. Personalen på boendet konsistensanpassade viss mat som primärt sväljfrämjande åtgärd redan innan studiens början. När detta utfördes hade det en positiv effekt hos båda deltagarna i form av färre tecken på dysfagi vid måltidsobservationerna. Vid föreläsningarna var deltagarantalet lågt, vilket kan ha bidragit till att ingen markant förändring hos deltagarna kunde observeras. Det kan konstateras att ett interventionsupplägg i form av två föreläsningar inte är tillräckligt för att göra skillnad när det gäller dysfagi. Tydligt är dock att behovet av kunskap om dysfagi på äldreboenden är stort.

Feelings and impressions of a product are important for the decision of purchasing it or not. Designing attractive products therefore requires knowledge about the feelings and impressions the products evoke on the customer and the user. Integrating such affective values in product design requires the introduction of suitable methods into companies’ product design processes, methods which can capture and convert subjective and even unconscious feelings about a product into concrete design parameters. This is sometimes referred to as ‘Affective Engineering’. One methodology in this context is Kansei Engineering, which has been developed in Japan in order to design feelings into products. The aim of this thesis was twofold: Firstly, to improve understanding of the nature of products making emotional impact on the users and customers. Secondly, to identify and improve methods capable of grasping those affective values and translating them into concrete product design solutions. This thesis presents three empirical studies and two methodological papers, relating to warehouse trucks and laminate flooring. The first study was made on user impact of warehouse trucks in three different European countries. A second study dealt with affective values of rocker-switches in work vehicles, such as warehouse trucks. A third study on this truck type compared the old manoeuvring panel evaluated in the previous studies with a newly introduced manoeuvring panel in order to validate the impact of the design improvements made after the first study. Further, a conceptual model on Kansei Engineering methodology was proposed in a methods paper based on the experience from the studies performed in order to provide a structure for performing Kansei Engineering studies. The fifth paper had the purpose of validating and improving the proposed model using laminate flooring as research object. More structured ways of identifying design parameters and relevant product properties was given high priority in the improvement work of the methodology. A model for spanning the Space of Product Properties was presented and applied. This thesis also deals with other improvement areas in the methodology and proposed new developments, including the use of scales, experimental design and validation methods. In conclusion, Kansei Engineering is a concept and a methodology in strong development, a framework in which tools and methods are continuously developed, added and integrated.

This dissertation comprises two essays. The unifying theme is the evaluation of non-pecuniary (information or norm based messages) conservation programs. These types of policies are widely applied in developing and developed countries to promote conservation, however, their empirical evidence and their effectiveness are not well documented. Each chapter examines some methodological facets of the heterogeneity of non-pecuniary conservation programs and the reliability of non-experimental methods (program evaluation and econometric techniques) to evaluate treatment effects in the context of non-pecuniary conservation programs.

Mobile information and communication technologies (ICT) promise to significantly transform enterprises, their business processes and services, improve employee productivity, effectiveness, and efficiency, and create new competitive advantages and business agility. Despite the plethora of potential benefits, however, widespread enterprise adoption of mobile ICT has not been as extensive as initially anticipated. Drawing on the extant information systems, technology management, and organizational innovation literature, this dissertation investigates the salient drivers and inhibitors of emerging ICT adoption, in general, and mobile ICT in particular, and develops an integrative ICT adoption decision framework. From this synthesis we identify four broad elements that influence an enterprise s decision to adopt mobile ICT: (1) business value, (2) costs and economics, (3) strategic alignment, and (4) enterprise readiness. The latter decision element has received only little theoretical and practical attention. In order to fill this gap, this dissertation explored the concept of enterprise readiness in further detail and identified eight key dimensions and their associated assessment indicators. Using a two-stage expert study and experimental design approach, we empirically validated these dimensions and determined their relative importance. Results indicated that leadership readiness followed by technology, data and information, and resource readiness, contributed the most to enterprise readiness for mobile ICT. The results are implemented into a web-based readiness diagnostic tool (RDT) that enables decision makers to assess an enterprise s readiness for mobile ICT. The benefits of the RDT are multifold: first, it navigates the decision maker through the complex readiness assessment space; second, it identifies potential organizational deficiencies and provides a means to assess potential sources of risks associated with the adoption and implementation of mobile ICT; and third, it enables decision makers to benchmark their level of readiness against other organizations. The dissertation concludes by highlighting both theoretical and practical implications for emerging and mobile ICT adoption management and suggesting directions for future research.

Estimation of location and scale parameters from a random sample of size n is of paramount importance in Statistics. An estimator is called fully efficient if it attains the Cramer-Rao minimum variance bound besides being unbiased. The method that yields such estimators, at any rate for large n, is the method of modified maximum likelihood estimation. Apparently, such estimators cannot be made more efficient by using sample based classical methods. That makes room for Bayesian method of estimation which engages prior distributions and likelihood functions. A formal combination of the prior knowledge and the sample information is called posterior distribution. The posterior distribution is maximized with respect to the unknown parameter(s). That gives HPD (highest probability density) estimator(s). Locating the maximum of the posterior distribution is, however, enormously difficult (computationally and analytically) in most situations. To alleviate these difficulties, we use modified likelihood function in the posterior distribution instead of the likelihood function. We derived the HPD estimators of location and scale parameters of distributions in the family of Generalized Logistic. We have extended the work to experimental design, one way ANOVA. We have obtained the HPD estimators of the block effects and the scale parameter (in the distribution of errors) / they have beautiful algebraic forms. We have shown that they are highly efficient. We have given real life examples to illustrate the usefulness of our results. Thus, the enormous computational and analytical difficulties with the traditional Bayesian method of estimation are circumvented at any rate in the context of experimental design.

Celebrity as endorser is one of the important facilitators in advertising activities. Most of the extant master theses on advertising endorsement have focused on the present celebrity people. Seldom did they study intensely the communicative effects of celebrity-past as endorser in advertising. The current research investigates celebrity-past¡¦s effects as endorser on communication in print advertisement, using an quasi- 3x3x2 factorial design of experimental method to test the various empirical hypotheses. Measurement of the dependent variables¡Xcommunicative effects¡Xincluded: purchase intention, attitude toward product, and product concept. In addition to the main effect of celebrity-past as endorser, product category and advertising slogan are also involved in as two moderator variables, so that the interaction effect can be detected as well. Three past celebrity persons included in the current experiment are Terisa Teng, Audrey Hepbum, and Xi Shi, separately and solely acting as endorser in the print advertisement pages for skin-cream, watch, and chicken-essence, in that sequence. Participants of the experiment were 56 female undergraduate and master students at National Sun Yat-Sen University, Kaohsiung, Taiwan. In the research results, many of the hypotheses were supported significantly by the empirical data, such as interaction effect on purchase intention from celebrity-past and product category, interaction effect on attitude toward product from celebrity-past and advertising slogan, and the main effect of celebrity-past on product concept.

Nowadays, empirical researches on corporate post-performance of mergers and acquisitions (M&A) focusing on cost-down synergy and financial accounting phase have not been consistently identified. The lack knowledge of revenue-based marketing value and consumer insights of M&A indicates that other impacts may exist to influence corporate post-acquisition performance. Recent studies have pointed that acquiring company fails to take over all the customers of acquired company after the merge. The results probably provide an important issue about why most M&A activities failed. The study assesses that consumer perceptions of corporate association of acquiring/acquired company have diverse impacts on consumer attitudes and purchase intentions based on consumer-goods classification. Therefore, a 2x2 experimental design manipulated corporation associations and congruity was used for three types of products (i.e. laptops, casual shoes, and raw milk). In the study, one important finding is that diverse intensity of effects, in the relationship of consumer¡¦s attitude and consumer¡¦s repurchase intention after the merge, has showed up with empirical validation among three products. Based on the consumer-goods classification (i.e. specialty goods, shopping goods, and convenient goods), the results indicate that acquiring company fails to take over all the customers of acquired company after the merge, especially in convenient goods. In short, corporate selling different products in different industries should seriously consider about their objectives and focus, during and after the merge.

This thesis consists of two parts. The first part reviews a Variable Search, a variable selection procedure for mean modeling. The second part deals with variance modeling for robust parameter design in computer experiments. In the first chapter of my thesis, Variable Search (VS) technique developed by Shainin (1988) is reviewed. VS has received quite a bit of attention from experimenters in industry. It uses the experimenters' knowledge about the process, in terms of good and bad settings and their importance. In this technique, a few experiments are conducted first at the best and worst settings of the variables to ascertain that they are indeed different from each other. Experiments are then conducted sequentially in two stages, namely swapping and capping, to determine the significance of variables, one at a time. Finally after all the significant variables have been identified, the model is fit and the best settings are determined. The VS technique has not been analyzed thoroughly. In this report, we analyze each stage of the method mathematically. Each stage is formulated as a hypothesis test, and its performance expressed in terms of the model parameters. The performance of the VS technique is expressed as a function of the performances in each stage. Based on this, it is possible to compare its performance with the traditional techniques. The second and third chapters of my thesis deal with variance modeling for robust parameter design in computer experiments. Computer experiments based on engineering models might be used to explore process behavior if physical experiments (e.g. fabrication of nanoparticles) are costly or time consuming. Robust parameter design (RPD) is a key technique to improve process repeatability. Absence of replicates in computer experiments (e.g. Space Filling Design (SFD)) is a challenge in locating RPD solution. Recently, there have been studies (e.g. Bates et al. (2005), Chen et al. (2006), Dellino et al. (2010 and 2011), Giovagnoli and Romano (2008)) of RPD issues on computer experiments. Transmitted variance model (TVM) proposed by Shoemaker and Tsui. (1993) for physical experiments can be applied in computer simulations. The approaches stated above rely heavily on the estimated mean model because they obtain expressions for variance directly from mean models or by using them for generating replicates. Variance modeling based on some kind of replicates relies on the estimated mean model to a lesser extent. To the best of our knowledge, there is no rigorous research on variance modeling needed for RPD in computer experiments. We develop procedures for identifying variance models. First, we explore procedures to decide groups of pseudo replicates for variance modeling. A formal variance change-point procedure is developed to rigorously determine the replicate groups. Next, variance model is identified and estimated through a three-step variable selection procedure. Properties of the proposed method are investigated under various conditions through analytical and empirical studies. In particular, impact of correlated response on the performance is discussed.

The aim of this study is to quantify the extent to which political correctness, understood as an implicit social convention of restraint on public expression, operates within a community. Due to a scarcity of prior experimental research in the area, a new method was developed for the purpose of the study. Using random selection, the treatment consists of exposing groups averaging 10 individuals to a survey on diversity and immigration matters. The effect is measured as the discrepancy in attitudes that is revealed between anonymous and non-anonymous responses to the treatment survey. Control groups are similarly exposed to a survey on traditional left-right matters. The discrepancies are then compared. The results are controlled for possible influence by factors including group size, sex ratio, and length of study. The general effect of the treatment is not statistically significant, possibly as a result of small sample size. Further analysis shows a significant positive correlation between the proportion of women and the extent of attitude discrepancy. On one interpretation, this means that women in the population are on average more responsive to political correctness than men. Due to the population’s skewed characteristics, the results are not generalised. Future researchers in the area are advised to draw their sample from a more representative population, to investigate additional subject matters and to collect more sophisticated data, in particular on the level of the individual.