Finance taught in schools generally starts with the efficient market hypothesis, which holds the assumptions of rational investors and markets where all information available is reflected. In recent years however, a lot of critique has been given to efficient markets and its assumptions of rationality. The greatest reason to this is because of crashes and irregularities in the market. The field of behavioural finance has been in existence for many years but is not as established as the efficient market hypothesis. It says that investors may act irrational and are mostly trying to explain the reasons why. People’s behaviour is being closely studied in order to see patterns of behaviour and this has resulted in different heuristics and biases. Heuristics are instances that come to mind when making a decision and differ a lot depending on what kind of decision you are making. Since there are many different heuristics, this thesis only focused on one: the affect heuristic. The method was constructed in a specific way in order to show if the students showed affect in their answers. Also, a check for home bias was made. This thesis presents the behaviour of two different groups of students, finance students from Sweden and MBE-students from Germany. It was proved that both of the groups were acting irrational in their investment decisions. The reason to their irrationality is both because the method was constructed in a way to strategically mislead them but also because of the data collection. There were also some differences noticed depending on age groups, former studies in finance and work experience in finance. The affect heuristic was clearly shown in the answers by both groups of students. A home bias was also noticed in the answers. It was proven that 10,3 percent of the Swedish students invested in Swedish companies in both their first and second choice, even though the three best companies were German. None of the German Students decided to invest in a Swedish company in both the first and second choice.

This thesis deals with the no-depot minmax Multiple Traveling Salesmen Problem (MTSP), which can be formulated as follows. Given a set of n cities and k salesmen,find k disjoint tours (one for each salesmen) such that each city belongs to exactly one tour and the length of the longest of k tours is minimized. The no-depot assumption means that the salesmen do not start from and return to one fixed depot. The no-depot model can be applied in designing patrolling routes, as well as in business situations, especially where salesmen work from home or the company has no central office. This model can be also applied to the job scheduling problem with n jobs and k identical machines. Despite its potential applicability to a number of important situations, the research literature on the no-depot minmax k-TSP has been limited, with no reports on computational experiments. The previously published results included the proof of NP-hardness of the problem of interest, which motivates using heuristics for its solution. This thesis proposes several construction heuristic algorithms, including greedy algorithms, cluster first and route second algorithms, and route first and cluster second algorithms. As a local search method for a single tour, 2-opt search and Lin-Kernighan were used, and for a local search method between multiple tours, relocation and exchange (edge heuristics) were used. Furthermore, to prevent the drawback of trapping in the local minima, the simulated annealing method is used. Extensive computational experiments were carried out using TSPLIB instances. Among construction algorithms, route first and cluster second algorithms including removing two edges method performed best. In terms of running time, clustering first and routing second algorithms took shorter time on large-scale instances. The simulated annealing could produce better solutions than the descent method, but did not always perform well in terms of average solution. To evaluate the performance of the proposed heuristic methods, their solutions were compared with the optimal solutions obtained using a mixed-integer programming formulation of the problem. For small-scale problems, heuristic solutions were equal to the optimal solution output by CPLEX.

This dissertation presents an algorithm to solve optimization problems with "black-box" objective functions, i.e., functions that can only be evaluated by running a computer program. Such optimization problems often arise in engineering applications, for example, the design of sensor placement. Due to the complexity in engineering systems, the objective functions usually have multiple local optima and depend on a huge number of decision variables. These difficulties make many existing methods less effective. The proposed algorithm is called adaptive evolutionary Monte Carlo (AEMC), and it combines sampling-based and metamodel-based search methods. AEMC incorporates strengths from both methods and compensates limitations of each individual method. Specifically, the AEMC algorithm combines a tree-based predictive model with an evolutionary Monte Carlo sampling procedure for the purpose of heuristic optimization. AEMC is able to escape local optima due to the random sampling component, and it improves the quality of solutions quickly by using information learned from the tree-based model. AEMC is also an adaptive Markov chain Monte Carlo (MCMC) algorithm, and is in fact the rst adaptive MCMC algorithm that simulates multiple Markov chains in parallel. The ergodicity property of the AEMC algorithm is studied. It is proven that the distribution of samples obtained by AEMC converges asymptotically to the "target" distribution determined by the objective function. This means that AEMC has a larger probability of collecting samples from regions containing the global optimum than from other regions, which implies that AEMC will reach the global optimum given enough run time. The AEMC algorithm falls into the category of heuristic optimization algorithms, and is applicable to the problems that can be solved by other heuristic methods, such as genetic algorithm. Advantages of AEMC are demonstrated by applying it to a sensor placement problem in a manufacturing process, as well as to a suite of standard test functions. It is shown that AEMC is able to enhance optimization effectiveness and efficiency as compared to a few alternative strategies, including genetic algorithm, Markov chain Monte Carlo algorithms, and meta-model based methods. The effectiveness of AEMC for sampling purposes is also shown by applying it to a mixture Gaussian distribution.

Wireless sensor network has applications in environmental surveillance, healthcare, and military operations. Because the energy of sensor nodes is limited and nodes are unable to supply energy in real time, the purpose of many researches is to prolong lifetime of sensor network. Lifetime is times that the sink can collect data from all sensor nodes. When a user proposes a query, then the sink gathers data from all sensor nodes. The problem defined in the previous research is given a sensor network and residual energy of each node, and the energy consumption of transmitting a unit message between two nodes. Then this problem is to find a directed tree that maximize minimum residual energy. In this thesis, we define a new problem that given a sensor network and residual energy of each node, and the energy consumption of transmitting a unit message between two nodes. Then our problem is to find a path of each node, which maximize minimum residual energy. We prove this problem is NP-complete. We propose a heuristic algorithm and a similar heuristic algorithm for this problem.

Thesis (M.S.)--West Virginia University, 2001. / Title from document title page. Document formatted into pages; contains x, 133 p. : ill. Includes abstract. Includes bibliographical references (p. 88-94).

Thesis (M.S.)--Air Force Institute of Technology, 2002. / Title from title screen (viewed Oct. 28, 2003). Vita. "AFIT/GOR/ENS/02-01." Includes bibliographical references (leaves 84-86). Also issued in paper format.

Thesis (Ph. D.)--University of Kentucky, 2001. / Title from document title page. Document formatted into pages; contains viii, 89 p. : ill. Includes abstract. Includes bibliographical references (p. 81-86).

Thesis (Ph. D.)--University of Arizona, 2005. / Includes bibliographical references (leaves 98-104). Also available online.

Title from title page of PDF (University of Missouri--St. Louis, viewed Febuary 22, 2010). Includes bibliographical references (p. 37-39).

published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy