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  • 1.
    Han, Mengjie
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Heuristic optimization of the p-median problem and population re-distribution2013Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis contributes to the heuristic optimization of the p-median problem and Swedish population redistribution.  

    The p-median model is the most representative model in the location analysis. When facilities are located to a population geographically distributed in Q demand points, the p-median model systematically considers all the demand points such that each demand point will have an effect on the decision of the location. However, a series of questions arise. How do we measure the distances? Does the number of facilities to be located have a strong impact on the result? What scale of the network is suitable? How good is our solution? We have scrutinized a lot of issues like those. The reason why we are interested in those questions is that there are a lot of uncertainties in the solutions. We cannot guarantee our solution is good enough for making decisions. The technique of heuristic optimization is formulated in the thesis.  

    Swedish population redistribution is examined by a spatio-temporal covariance model. A descriptive analysis is not always enough to describe the moving effects from the neighbouring population. A correlation or a covariance analysis is more explicit to show the tendencies. Similarly, the optimization technique of the parameter estimation is required and is executed in the frame of statistical modeling. 

  • 2.
    Jomaa, Diala
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A data driven approach for automating vehicle activated signs2016Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Vehicle activated signs (VAS) display a warning message when drivers exceed a particular threshold. VAS are often installed on local roads to display a warning message depending on the speed of the approaching vehicles. VAS are usually powered by electricity; however, battery and solar powered VAS are also commonplace. This thesis investigated devel-opment of an automatic trigger speed of vehicle activated signs in order to influence driver behaviour, the effect of which has been measured in terms of reduced mean speed and low standard deviation. A comprehen-sive understanding of the effectiveness of the trigger speed of the VAS on driver behaviour was established by systematically collecting data. Specif-ically, data on time of day, speed, length and direction of the vehicle have been collected for the purpose, using Doppler radar installed at the road. A data driven calibration method for the radar used in the experiment has also been developed and evaluated.

    Results indicate that trigger speed of the VAS had variable effect on driv-ers’ speed at different sites and at different times of the day. It is evident that the optimal trigger speed should be set near the 85th percentile speed, to be able to lower the standard deviation. In the case of battery and solar powered VAS, trigger speeds between the 50th and 85th per-centile offered the best compromise between safety and power consump-tion. Results also indicate that different classes of vehicles report differ-ences in mean speed and standard deviation; on a highway, the mean speed of cars differs slightly from the mean speed of trucks, whereas a significant difference was observed between the classes of vehicles on lo-cal roads. A differential trigger speed was therefore investigated for the sake of completion. A data driven approach using Random forest was found to be appropriate in predicting trigger speeds respective to types of vehicles and traffic conditions. The fact that the predicted trigger speed was found to be consistently around the 85th percentile speed justifies the choice of the automatic model.

  • 3.
    Laryea, Rueben
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A data-driven decision support system for coherency of experts’ judgment in complex classification problems: The case of food security as a UN sustainable development goal2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Everyday humans need to make individual or collective decisions. Often the decisions aim at achieving multiple goals (thus involving multiple criteria) and rely on the decision maker(s)’ intuition, internal data, as well as external sources of data. Faced with a complex decision problem of this kind, it is a great challenge to decisionmakers to be logically coherent over time with regard to their preferences. To aid in achieving coherency, operation researchers and decision analysts have developed formal methods to support decision makers. One such method is the UTADIS method that serves as the workhorse for this thesis. I received the request from UN officials who had to manage the sustainable development goals while addressing the issue of food security. They wished for a decision support system (DSS) that could aid in their classification of countries to mitigate the risk of failing on food security. The virtue of the DSS should be that their expert judgment was complemented by formal methods for better risk classification. The UTADIS method was fitting for the purpose, but it lacked implementability. In particular, it required an iterative approach engaging the experts multiple times, while not readily lending itself to making use of external data, making it inefficient as a DSS. The fundamental contribution of this thesis is that I have solved these shortcomings of the UTADIS method, such that it now readily can be used in a functionally efficient way for the desired purpose of the UN. In solving these problems, it is also more broadly implementable as a DSS, as I have validated the artifact to a DSS, by use of several demonstrations and exposed it to sensitivity analysis.

  • 4.
    Li, Yujiao
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Who benefits when IKEA enters local markets in Sweden?: An empirical assessment using difference-in-difference analysis, synthetic control methods, and Twitter sentiment analysis2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Policy makers often spend considerable amounts of money to attract IKEA to their region despite not having any empirical measurements on its expected contribution to the local economy. As such, an empirical study of the economic and social impact of new IKEA stores can aid political decision making, and contribute to the literature regarding how big-box retail entry affects the regions where they enter.

    This dissertation aims to estimate: the impact of IKEA entry on incumbent retailers productivity, and investigate if the impact is heterogenus depending on local maket size, type of retail industry, distance to surrounding retailers, and firm size; IKEA entry effects on the average labor productivity in durable goods retailing in the entry regions; and, finally, public opinions regarding  IKEA entry.

    For IKEA entry effects on incumbent retailers, Paper I~III separately examine four factors of potential heterogeneity. Paper I finds that market size matters: smaller rural regions have bigger IKEA effects. Paper II considers two factors: firm industry and distance, and confirms that IKEA entry effects dissipate over distance. The positive impact of IKEA entry on incumbent retailers is limited to those selling complementary goods to IKEA. No positive effects were found for the urban entry in Gothenburg in the two first papers, which is somewhat surprising. Paper III found that a positive effect exist also in Gothenburg, but it is limited to relatively small incumbent retailers with a capital stock below 1 500 000 SEK. Policy making tends to consider IKEA overall effects on entry municipalities besides IKEA spillover effects on firms. Paper V shows that rural regions are affected by IKEA entry, while larger urban markets are not.

    For the social effects of IKEA, Paper VI uses Twitter text mining to study public opinions regarding IKEA entry into local markets. The new IKEA stores under study caught significant public attention at the time of entry, with mostly positive attitudes toward the new stores. The favorite topics for discussion at the time of the different IKEA entries were heterogeneous depending on location.

    Methodologically, Paper I uses traditional Difference-in-Difference (DID) to have an initial understanding of IKEA entry spillover effects in four regions; Paper II extends to Spatial DID to catch the spatial interaction between firms; Paper III uses Panel Smooth Transition Regression to identify heterogenous effects due to firms size. Paper IV investigates a new treatment effects estimation aproach, Synthetic Control Method (SCM), to explore when the SCM is powerful, and how to improve its performance; Paper V then uses SCM to estimate IKEA effects at municipality level. In addition, to make SCM developed readily available for other researchers, the author of this thesis also published one web-application to implement a synthetic control method power test, and another to implement parametric & non-parametric estimation and inference.  

    These findings confirm that IKEA has a positive effect on the regions where they enter. Nevertheless, governments that are to decide if to allow a big-box retail entry into their local community should be aware that the impact of such entry will depend on the size of the existing retail market, the type of existing retail industry, and the size of existing retailers in the entry region.

  • 5.
    Macuchova, Zuzana
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Essays on firm dynamics in the Swedish wholesale trade sector2015Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis consists of a summary and five self-contained papers addressing dynamics of firms in the Swedish wholesale trade sector.

    Paper [1] focuses upon determinants of new firm formation in the Swedish wholesale trade sector, using two definitions of firms’ relevant markets, markets defined as administrative areas, and markets based on a cost minimizing behavior of retailers. The paper shows that new entering firms tend to avoid regions with already high concentration of other firms in the same branch of wholesaling, while right-of-the-center local government and quality of the infrastructure have positive impacts upon entry of new firms. The signs of the estimated coefficients remain the same regardless which definition of relevant market is used, while the size of the coefficients is generally higher once relevant markets delineated on the cost-minimizing assumption of retailers are used.

    Paper [2] analyses determinant of firm relocation, distinguishing between the role of the factors in in-migration municipalities and out-migration municipalities. The results of the analysis indicate that firm-specific factors, such as profits, age and size of the firm are negatively related to the firm’s decision to relocate. Furthermore, firms seems to be avoiding municipalities with already high concentration of firms operating in the same industrial branch of wholesaling and also to be more reluctant to leave municipalities governed by right-of-the- center parties. Lastly, firms seem to avoid moving to municipalities characterized with high population density.

    Paper [3] addresses determinants of firm growth, adopting OLS and a quantile regression technique. The results of this paper indicate that very little of the firm growth can be explained by the firm-, industry- and region-specific factors, controlled for in the estimated models. Instead, the firm growth seems to be driven by internal characteristics of firms, factors difficult to capture in conventional statistics. This result supports Penrose’s (1959) suggestion that internal resources such as firm culture, brand loyalty, entrepreneurial skills, and so on, are important determinants of firm growth rates.

    Paper [4] formulates a forecasting model for firm entry into local markets and tests this model using data from the Swedish wholesale industry. The empirical analysis is based on directly estimating the profit function of wholesale firms and identification of low- and high-return local markets. The results indicate that 19 of 30 estimated models have more net entry in high-return municipalities, but the estimated parameters is only statistically significant at conventional level in one of our estimated models, and then with unexpected negative sign.

    Paper [5] studies effects of firm relocation on firm profits of relocating firms, employing a difference-in-difference propensity score matching. Using propensity score matching, the pre-relocalization differences between relocating and non-relocating firms are balanced, while the difference-in-difference estimator controls for all time-invariant unobserved heterogeneity among firms. The results suggest that firms that relocate increase their profits significantly, in comparison to what the profits would be had the firms not relocated. This effect is estimated to vary between 3 to 11 percentage points, depending on the length of the analyzed period. 

  • 6.
    Meng, Xiangli
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Optimization heuristic solutions, how good can they be?: With empirical applications in location problems2015Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Combinatorial optimization problems, are one of the most important types of problems in operational research. Heuristic and metaheuristics algorithms are widely applied to find a good solution. However, a common problem is that these algorithms do not guarantee that the solution will coincide with the optimum and, hence, many solutions to real world OR-problems are afflicted with an uncertainty about the quality of the solution. The main aim of this thesis is to investigate the usability of statistical bounds to evaluate the quality of heuristic solutions applied to large combinatorial problems. The contributions of this thesis are both methodological and empirical. From a methodological point of view, the usefulness of statistical bounds on p-median problems is thoroughly investigated. The statistical bounds have good performance in providing informative quality assessment under appropriate parameter settings. Also, they outperform the commonly used Lagrangian bounds. It is demonstrated that the statistical bounds are shown to be comparable with the deterministic bounds in quadratic assignment problems. As to empirical research, environment pollution has become a worldwide problem, and transportation can cause a great amount of pollution. A new method for calculating and comparing the CO2-emissions of online and brick-and-mortar retailing is proposed. It leads to the conclusion that online retailing has significantly lesser CO2-emissions. Another problem is that the Swedish regional division is under revision and the border effect to public service accessibility is concerned of both residents and politicians. After analysis, it is shown that borders hinder the optimal location of public services and consequently the highest achievable economic and social utility may not be attained.

  • 7.
    Svenson, Kristin
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A Microdata Analysis Approach to Transport Infrastructure Maintenance2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Maintenance of transport infrastructure assets is widely advocated as the key in minimizing current and future costs of the transportation network. While effective maintenance decisions are often a result of engineering skills and practical knowledge, efficient decisions must also account for the net result over an asset's life-cycle. One essential aspect in the long term perspective of transport infrastructure maintenance is to proactively estimate maintenance needs. In dealing with immediate maintenance actions, support tools that can prioritize potential maintenance candidates are important to obtain an efficient maintenance strategy.

    This dissertation consists of five individual research papers presenting a microdata analysis approach to transport infrastructure maintenance. Microdata analysis is a multidisciplinary field in which large quantities of data is collected, analyzed, and interpreted to improve decision-making. Increased access to transport infrastructure data enables a deeper understanding of causal effects and a possibility to make predictions of future outcomes. The microdata analysis approach covers the complete process from data collection to actual decisions and is therefore well suited for the task of improving efficiency in transport infrastructure maintenance.

    Statistical modeling was the selected analysis method in this dissertation and provided solutions to the different problems presented in each of the five papers. In Paper I, a time-to-event model was used to estimate remaining road pavement lifetimes in Sweden. In Paper II, an extension of the model in Paper I assessed the impact of latent variables on road lifetimes; displaying the sections in a road network that are weaker due to e.g. subsoil conditions or undetected heavy traffic. The study in Paper III incorporated a probabilistic parametric distribution as a representation of road lifetimes into an equation for the marginal cost of road wear. Differentiated road wear marginal costs for heavy and light vehicles are an important information basis for decisions regarding vehicle miles traveled (VMT) taxation policies.

    In Paper IV, a distribution based clustering method was used to distinguish between road segments that are deteriorating and road segments that have a stationary road condition. Within railway networks, temporary speed restrictions are often imposed because of maintenance and must be addressed in order to keep punctuality. The study in Paper V evaluated the empirical effect on running time of speed restrictions on a Norwegian railway line using a generalized linear mixed model.

  • 8.
    Thomas, Ilias
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Automating levodopa dosing schedules for Parkinson’s disease2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Parkinson’s disease (PD) is the second most common neurodegenerative disease. Levodopa is mainly used to manage the motor symptoms of PD. However, disease progression and long-term use of levodopa cause reduced medication efficacy and side effects. When that happens, precise individualized dosing schedules are required.

    This doctoral thesis in the field of Micro-data analysis introduces an end-to-end solution for the automation of the pharmacological management of PD with levodopa, and offers some insight on levodopa pharmacodynamics. For that purpose, an algorithm that derives objective ratings for the patients’ motor function through wearable sensors is introduced, a method to construct individual patient profiles is developed, and two dosing algorithms for oral and intestinal administration of levodopa are presented. Data from five different sources were used to develop the methods and evaluate the performance of the proposed algorithms.

    The dose automation algorithms can work both with clinical and objective ratings (through wearable devices), and their application was evaluated against dosing adjustments from movement disorders experts. Both dosing algorithms showed promise and their dosing suggestions were similar to those of the clinicians.

    The objective ratings algorithm had good test-retest reliability and its application during a clinical study was successful. Furthermore, the method of fitting individual patient models was robust and worked well with the objective ratings algorithm. Finally, a study was carried out that showed that about half the patients on levodopa treatment show reduced response during the afternoon hours, pointing to the need for more precise modelling of levodopa pharmacodynamics.

  • 9.
    Zhao, Xiaoyun
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Government vs Market in Sustainable Residential Development?: Microdata analysis of car travel, CO2 emission and residence location2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Increasing car usage and travel demands between residential locations and destinations in order to fulfill the various needs of residents is a primary cause of CO2 emissions. To win the battle against climate change, a better understanding of the question relating to which urban residential form may most effectively mitigate the CO2 emissions is the key pathway.

    This dissertation is concerned with the above problem and it mainly considers three objectives in providing insights on answering the question. The first objective is to comprehensively and microscopically understand intra-urban car travel behavior. The second objective is to estimate the induced CO2 emissions from daily intra-urban car travel and to ex-ante evaluate residential plans. The third objective is to assess whether the governmental sustainable residential development objective is aligned with the objectives of the estate market actors. To explore the research questions related to the objectives, a microdata analysis process (data collection, data assessment and transformation, data storage, data analysis and decision-making) is applied and is found essential in gaining access to key variables in exploring the answer of a preferable urban form. The dissertation offers many new solutions to various technical aspects through a microdata analysis process.

    The primary contribution of this dissertation is that it outlines an operational model that comprehensively integrates the investors’ investment strategy, the residents’ choice behavior, and the governmental sustainability objective in the interest of making an ex-ante assessment of residential plans. This ex-ante assessment provides decision-support in sustainable residential development at foremost local level.

    The first finding from the implementation of the model on the case study is that the market actors’ objectives are, in general, aligned with the government’s sustainable residential development objective. The second finding indicates that re-shaping the urban form into a compact city is preferable in mitigating CO2 emissions, in spite of the fact that the case city is of a polycentric urban form. These findings provide support for those advocating the compact city as the ideal for sustainable residential development, and also provide foresight on settling the answer to the preferred re-shaping of urban forms in climate change.

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