An analytic approach to distribution logistics strategic management

Logistics management is increasingly being recognised by many companies to be of critical concern. The logistics function includes directly or indirectly many of the new areas for achieving or maintaining competitive advantage that companies have been forced to develop due to increasing competitive pressures. The key to achieving a competitive advantage is to manage the logistics function strategically which involves determining the most cost effective method of providing the necessary customer service levels from the many combinations of operating procedures in the areas of transportation, warehousing, order processing and information systems, production, and inventory management. In this thesis, a comprehensive distribution logistics strategic management process is formed by integrating the periodic strategic planning process with a continuous strategic issues management process. Strategic planning is used for defining the basic objectives for a company and assuring co operation and synergy between the different functions of a company while strategic issues management is used on a continuous basis in order to deal with environmental and internal turbulence. The strategic planning subprocess consists of the following main phases: (1) situational analyses, (2) defining the vision and strategic goals for the logistics function, (3) determining objectives and strategies, (4) drawing up tactical action plans, and (5) evaluating the implementation of the plans and making the needed adjustments. The aim of the strategic issues management subprocess is to continuously scan the environment and the organisation for early identification of the issues having a significant impact on the logistics function using the following steps: (1) the identification of trends, (2) assessing the impact and urgency of the identified trends, (3) assigning priorities to the issues, and (4) planning responses to the, issues. The Analytic Hierarchy Process (AHP) is a systematic procedure for structuring any problem. AHP is based on the following three principles: decomposition, comparative judgements, and synthesis of priorities. AHP starts by decomposing a complex, multicriteria problem into a hierarchy where each level consists of a few manageable elements which are then decomposed into another set of elements. The second step is to use a measurement methodology to establish priorities among the elements within each level of the hierarchy. The third step in using AHP is to synthesise the priorities of the elements to establish the overall priorities for the decision alternatives. In this thesis, decision support systems are developed for different areas of distribution logistics strategic management by applying the Analytic Hierarchy Process. The areas covered are: (1) logistics strategic issues management, (2) planning of logistic structure, (3) warehouse site selection, (4) inventory forecasting, (5) defining logistic action and development plans, (6) choosing a distribution logistics strategy, (7) analysing and selecting transport service providers, (8) defining the logistic vision and strategic goals, (9) benchmarking logistic performance, and (10) logistic service management. The thesis demonstrates the potential of AHP as a systematic and analytic approach to distribution logistics strategic management.

Innovation process management of complex consumer products and services in new innovation paradigm – case location based business

Innovation is the word of this decade. According to innovation definitions, without positive sales impact and meaningful market share the company’s product or service has not been an innovation. Research problem of this master thesis is to find out what is the innovation process of complex new consumer products and services in new innovation paradigm. The objective is to get answers to two research questions: 1) What are the critical success factors what company should do when it is implementing the paradigm change in mass markets consumer business with complex products and services? 2) What is the process or framework one firm could follow? The research problem is looked from one company’s innovation creation process, networking and organization change management challenges point of views. Special focus is to look the research problem from an existing company perspective which is entering new business area. Innovation process management framework of complex new consumer products and services in new innovation paradigm has been created with support of several existing innovation theories. The new process framework includes the critical innovation process elements companies should take into consideration in their daily activities when they are in their new business innovation implementing process. Case company location based business implementation activities are studied via the new innovation process framework. This case study showed how important it is to manage the process, look how the target market and the competition in it is developing during company’s own innovation process, make decisions at right time and from beginning plan and implement the organization change management as one activity in the innovation process. In the end this master thesis showed that all companies need to create their own innovation process master plan with milestones and activities. One plan does not fit all, but all companies can start their planning from the new innovation process what was introduced in this master thesis.

CFD simulation of complex phenomena containing suspensions and flow throughporous media

There is an increasing reliance on computers to solve complex engineering problems. This is because computers, in addition to supporting the development and implementation of adequate and clear models, can especially minimize the financial support required. The ability of computers to perform complex calculations at high speed has enabled the creation of highly complex systems to model real-world phenomena. The complexity of the fluid dynamics problem makes it difficult or impossible to solve equations of an object in a flow exactly. Approximate solutions can be obtained by construction and measurement of prototypes placed in a flow, or by use of a numerical simulation. Since usage of prototypes can be prohibitively time-consuming and expensive, many have turned to simulations to provide insight during the engineering process. In this case the simulation setup and parameters can be altered much more easily than one could with a real-world experiment. The objective of this research work is to develop numerical models for different suspensions (fiber suspensions, blood flow through microvessels and branching geometries, and magnetic fluids), and also fluid flow through porous media. The models will have merit as a scientific tool and will also have practical application in industries. Most of the numerical simulations were done by the commercial software, Fluent, and user defined functions were added to apply a multiscale method and magnetic field. The results from simulation of fiber suspension can elucidate the physics behind the break up of a fiber floc, opening the possibility for developing a meaningful numerical model of the fiber flow. The simulation of blood movement from an arteriole through a venule via a capillary showed that the model based on VOF can successfully predict the deformation and flow of RBCs in an arteriole. Furthermore, the result corresponds to the experimental observation illustrates that the RBC is deformed during the movement. The concluding remarks presented, provide a correct methodology and a mathematical and numerical framework for the simulation of blood flows in branching. Analysis of ferrofluids simulations indicate that the magnetic Soret effect can be even higher than the conventional one and its strength depends on the strength of magnetic field, confirmed experimentally by Völker and Odenbach. It was also shown that when a magnetic field is perpendicular to the temperature gradient, there will be additional increase in the heat transfer compared to the cases where the magnetic field is parallel to the temperature gradient. In addition, the statistical evaluation (Taguchi technique) on magnetic fluids showed that the temperature and initial concentration of the magnetic phase exert the maximum and minimum contribution to the thermodiffusion, respectively. In the simulation of flow through porous media, dimensionless pressure drop was studied at different Reynolds numbers, based on pore permeability and interstitial fluid velocity. The obtained results agreed well with the correlation of Macdonald et al. (1979) for the range of actual flow Reynolds studied. Furthermore, calculated results for the dispersion coefficients in the cylinder geometry were found to be in agreement with those of Seymour and Callaghan.

Algorithmic Solutions for Combinatorial Problems in Resource Management of Manufacturing Environments

This thesis studies the use of heuristic algorithms in a number of combinatorial problems that occur in various resource constrained environments. Such problems occur, for example, in manufacturing, where a restricted number of resources (tools, machines, feeder slots) are needed to perform some operations. Many of these problems turn out to be computationally intractable, and heuristic algorithms are used to provide efficient, yet sub-optimal solutions. The main goal of the present study is to build upon existing methods to create new heuristics that provide improved solutions for some of these problems. All of these problems occur in practice, and one of the motivations of our study was the request for improvements from industrial sources. We approach three different resource constrained problems. The first is the tool switching and loading problem, and occurs especially in the assembly of printed circuit boards. This problem has to be solved when an efficient, yet small primary storage is used to access resources (tools) from a less efficient (but unlimited) secondary storage area. We study various forms of the problem and provide improved heuristics for its solution. Second, the nozzle assignment problem is concerned with selecting a suitable set of vacuum nozzles for the arms of a robotic assembly machine. It turns out that this is a specialized formulation of the MINMAX resource allocation formulation of the apportionment problem and it can be solved efficiently and optimally. We construct an exact algorithm specialized for the nozzle selection and provide a proof of its optimality. Third, the problem of feeder assignment and component tape construction occurs when electronic components are inserted and certain component types cause tape movement delays that can significantly impact the efficiency of printed circuit board assembly. Here, careful selection of component slots in the feeder improves the tape movement speed. We provide a formal proof that this problem is of the same complexity as the turnpike problem (a well studied geometric optimization problem), and provide a heuristic algorithm for this problem.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Nested Rollout Policy Adaptation for optimizing vehicle selection in complex VRPs

The goal of Vehicle Routing Problems (VRP) and their variations is to transport a set of orders with the minimum number of vehicles at least cost. Most approaches are designed to solve specific problem variations independently, whereas in real world applications, different constraints are handled concurrently. This research extends solutions obtained for the traveling salesman problem with time windows to a much wider class of route planning problems in logistics. The work describes a novel approach that:  supports a heterogeneous fleet of vehicles  dynamically reduces the number of vehicles  respects individual capacity restrictions  satisfies pickup and delivery constraints  takes Hamiltonian paths (rather than cycles) The proposed approach uses Monte-Carlo Tree Search and in particular Nested Rollout Policy Adaptation. For the evaluation of the work, real data from the industry was obtained and tested and the results are reported.