This Thesis Work will concentrate on a very interesting problem, the Vehicle Routing Problem (VRP). In this problem, customers or cities have to be visited and packages have to be transported to each of them, starting from a basis point on the map. The goal is to solve the transportation problem, to be able to deliver the packages-on time for the customers,-enough package for each Customer,-using the available resources- and – of course - to be so effective as it is possible.Although this problem seems to be very easy to solve with a small number of cities or customers, it is not. In this problem the algorithm have to face with several constraints, for example opening hours, package delivery times, truck capacities, etc. This makes this problem a so called Multi Constraint Optimization Problem (MCOP). What’s more, this problem is intractable with current amount of computational power which is available for most of us. As the number of customers grow, the calculations to be done grows exponential fast, because all constraints have to be solved for each customers and it should not be forgotten that the goal is to find a solution, what is best enough, before the time for the calculation is up. This problem is introduced in the first chapter: form its basics, the Traveling Salesman Problem, using some theoretical and mathematical background it is shown, why is it so hard to optimize this problem, and although it is so hard, and there is no best algorithm known for huge number of customers, why is it a worth to deal with it. Just think about a huge transportation company with ten thousands of trucks, millions of customers: how much money could be saved if we would know the optimal path for all our packages.Although there is no best algorithm is known for this kind of optimization problems, we are trying to give an acceptable solution for it in the second and third chapter, where two algorithms are described: the Genetic Algorithm and the Simulated Annealing. Both of them are based on obtaining the processes of nature and material science. These algorithms will hardly ever be able to find the best solution for the problem, but they are able to give a very good solution in special cases within acceptable calculation time.In these chapters (2nd and 3rd) the Genetic Algorithm and Simulated Annealing is described in details, from their basis in the “real world” through their terminology and finally the basic implementation of them. The work will put a stress on the limits of these algorithms, their advantages and disadvantages, and also the comparison of them to each other.Finally, after all of these theories are shown, a simulation will be executed on an artificial environment of the VRP, with both Simulated Annealing and Genetic Algorithm. They will both solve the same problem in the same environment and are going to be compared to each other. The environment and the implementation are also described here, so as the test results obtained.Finally the possible improvements of these algorithms are discussed, and the work will try to answer the “big” question, “Which algorithm is better?”, if this question even exists.

In this study, the design of a three-level distribution system is considered in which a single supplier ships a number of items to the retailers via a set of distribution centers (DC) and stochastic demand is observed at the retailers. The problem is to specify the number and location of the DCs, and the assignment of the retailers to the DCs in such a way that total facility, transportation, safety stock, and joint ordering and average inventory costs are minimized, and customer service requirements are satisfied. Single source constraints are imposed on the assignment of the retailers to the DCs. The integrated location/inventory model incorporates the inventory management decisions into the strategic location/allocation decisions by considering the benefits of risk pooling and the savings that result in the joint replenishment of a group of items. We develop two heuristic methods to solve the non-linear integer-programming model in an integrated way: (1) Improvement type heuristic, (2) Constructive type heuristic. The heuristic algorithms are tested on a number of problem instances with 81 demand points (retailers) and 4 different types of items. Both of the heuristics are able to generate solutions in very reasonable times. The results are compared to the results of the p-median problem and found that the total cost and the number of DCs can be lowered using our integrated model instead of the p-median problem. Finally, sensitivity analysis is performed with respect to the changes in inventory, transportation, and ordering cost parameters, and variability of the demand.

Made available in DSpace on 2016-06-02T19:50:15Z (GMT). No. of bitstreams: 1 4224.pdf: 4088163 bytes, checksum: f36f82cf58386b4174743eccaa446df4 (MD5) Previous issue date: 2011-02-12 / The number of successful applications that use optimization models has followed the evolution of the computers, as much in hardware, with more powerful machines, as in software, with more intelligent algorithms. Due to importance of the modeling as a decision support tool, much effort has been made to mathematically describe systems of interest and devise techniques for solving such models. This work presents a detailed description of the operations involved in production planning and control of the electrofused grain industry and proposes the use of exact and heuristic methods to support decisions in such activities, particularly in production scheduling. Several visits were made to companies in this sector and a case study was carried out one of these companies in order to formulate alternatives to increase productivity and improve customer service. Optimizing the production scheduling of electrofused grains is not a simple task mainly because of the scale of the equipment setup times, the diversity of the products, and the narrow orders due dates. Based on the case study, mixed linear programming models that combine known models of process selection and single-stage lot sizing were developed, and a constructive heuristic, local search variants, and a GRASP algorithm were proposed to solve one of the models. Computational results with a real instance and randomly generated instance sets show that the exact methods as well as the heuristics can produce as good or better production scheduling than the ones currently employed by the studied company / O número de aplicações bem sucedidas que utilizam modelos de otimização têm acompanhado a evolução dos computadores, tanto em hardware, com máquinas mais poderosas, como em software, com algoritmos mais inteligentes. Devido à importância da modelagem como ferramenta de apoio à tomada de decisão, muitos trabalhos que exploram formas de representação de problemas e técnicas de solução de modelos vêm sendo desenvolvidos. Este trabalho apresenta uma descrição detalhada das operações envolvidas no planejamento e controle da produção na indústria de grãos eletrofundidos e propõe o uso de modelos e métodos exatos e heurísticos para apoio à tomada de decisões nesta atividade, em particular, na programação da produção. Várias visitas foram realizadas a empresas do setor, e em uma dessas empresas foi empreendido um estudo de caso com o objetivo de formular alternativas para aumento da produtividade e a melhoria do nível de serviço aos clientes. Otimizar a programação da produção de grãos eletrofundidos não é uma tarefa simples, principalmente devido à grandeza dos tempos de preparação dos equipamentos, à diversidade de produtos e às limitações dos prazos de entrega da carteira de pedidos. Com base no estudo de caso, modelos de programação linear inteira mista que combinam modelos clássicos de seleção de processos e dimensionamento de lotes monoestágio foram desenvolvidos, e uma heurística construtiva, duas variantes de busca local, e um algoritmo GRASP foram propostos para resolver um dos modelos. Resultados computacionais com uma instância real e conjuntos de instâncias geradas aleatoriamente indicam que tanto os métodos exatos como heurísticos propostos são capazes de gerar programações da produção tão boas ou melhores do que as atualmente empregadas pela empresa estudada

The Vehicle Routing Problem has been around for more than 50 years and has been of major interest to the operations research community. The VRP pose a complex problem with major benefits for the industry. In every supply chain transportation occurs between customers and suppliers. In this thesis, we analyze the use of a multiple pheromone trial in using Ant Systems to solve the VRP. The goal is to find a reasonable solution for data environments of derivatives of the basic VRP. An adaptive object model approach is followed to allow for additional constraints and customizable cost functions. A parallel method is used to improve speed and traversing the solution space. The Ant System is applied to the local search operations as well as the data objects. The Tabu Search method is used in the local search part of the solution. The study succeeds in allowing for all of the key performance indicators, i.e. efficiency, effectiveness, alignment, agility and integration for an IT system, where the traditional research on a VRP algorithm only focuses on the first two. / Thesis (PhD)--University of Pretoria, 2010. / Industrial and Systems Engineering / unrestricted

This study investigates the Usability of the podcast service User Interface (UI) of the iOS app Aftonbladet. The method used originates from a heuristic evaluation technique where semi- structured interviews were conducted with users of the podcast service to explore the usability and to find usability problems. The conducted interviews determined that there existed usability problems with the UI and thus, usability heuristics have been violated in the UI.   Nielsen’s Ten Usability Heuristics for User Interface Design and Nielsen’s Severity Ratings for Usability Problems are applied to the interview answers for evaluating the usability of the podcast service UI.   The heuristics that are interpreted as being of critical importance are:   Heuristic 3. User Control and Freedom  Heuristic 4. Consistency and Standards  Heuristic 5. Error Prevention  Heuristic 6. Recognition rather than Recall  Heuristic 7. Flexibility and Efficiency of Use  Heuristic 8. Aesthetic and Minimalist Design  Heuristic 10. Help and Documentation    Three out of the ten heuristics were not of critical importance:    Heuristic 1. Visibility of System Status  Heuristic 2. Match Between System and Real World  Heuristic 9. Help Users Recognize, Recover and Diagnose from Errors

Internal audit departments strive to control risk within an organization. To do this they choose specific audit areas to include in an audit plan. In order to select areas, they usually focus on those areas with the highest risk. Even though high risk areas are considered, there are various other restrictions such as resource constraints (in terms of funds, manpower and hours) that must also be considered. In some cases, management might also have special requirements. Traditionally this area selection process is conducted using manual processes and requires significant decision maker experience. This makes it difficult to take all possibilities into consideration while also catering for all resource constraints and special management requirements. In this study, mathematical techniques used in capital budgeting problems are explored to solve the IT audit area selection problem. A DSS is developed which implements some of these mathematical techniques such as a linear programming model, greedy heuristic, improved greedy heuristic and evolutionary heuristic. The DSS also implements extensions to the standard capital budgeting model to make provision for special management requirements. The performance of the mathematical techniques in the DSS is tested by applying different decision rules to each of the techniques and comparing those results. The DSS, empirical experiments and results are also presented in this research study. Results have shown that in most cases a binary 0-1 model outperformed the other techniques. Internal audit management should therefore consider this model to assist with the construction of an IT internal audit plan. / MSc (Computer Science), North-West University, Potchefstroom Campus, 2015

Internal audit departments strive to control risk within an organization. To do this they choose specific audit areas to include in an audit plan. In order to select areas, they usually focus on those areas with the highest risk. Even though high risk areas are considered, there are various other restrictions such as resource constraints (in terms of funds, manpower and hours) that must also be considered. In some cases, management might also have special requirements. Traditionally this area selection process is conducted using manual processes and requires significant decision maker experience. This makes it difficult to take all possibilities into consideration while also catering for all resource constraints and special management requirements. In this study, mathematical techniques used in capital budgeting problems are explored to solve the IT audit area selection problem. A DSS is developed which implements some of these mathematical techniques such as a linear programming model, greedy heuristic, improved greedy heuristic and evolutionary heuristic. The DSS also implements extensions to the standard capital budgeting model to make provision for special management requirements. The performance of the mathematical techniques in the DSS is tested by applying different decision rules to each of the techniques and comparing those results. The DSS, empirical experiments and results are also presented in this research study. Results have shown that in most cases a binary 0-1 model outperformed the other techniques. Internal audit management should therefore consider this model to assist with the construction of an IT internal audit plan. / MSc (Computer Science), North-West University, Potchefstroom Campus, 2015

Hyper-heuristics is a new field of optimisation which has recently emerged and is receiving growing exposure in the research community and literature. Hyper-heuristics are optimisation methods which are designed with a high level of abstraction from any one specific problem or class of problems and therefore are more generally applicable than specialised meta-heuristic and heuristic methods. Instead of being designed to solve a specific real-world problem, hyper-heuristics are designed to solve the problem of heuristic generation and selection. As such, hyper-heuristics can be thought of as methods for optimising the operations of an optimisation process which finds good solutions to a problem as a by-product. This approach has been shown to be very effective and in some cases provides improvement in search performance as well as reducing the burden associated with tailoring meta-heuristics which is often required when solving new problems. In this thesis, the hypothesis that hyper-heuristics can be competitively applied to real-world multi-objective optimisation problems such as the water distribution design problem is tested. Although many single-objective hyper-heuristics have been proposed in the literature, only a few multi-objective methods have been proposed. This thesis explores two different novel multi-objective hyper-heuristics: one designed for generating new specialised heuristics; and one designed for solving the online selection of heuristics. Firstly, the behaviour of a set of heuristics is explored to create a base understanding of different heuristic behavioural traits in order to better understand the hyper-heuristic behaviours and dynamics later in the study. Both approaches are tested on a range of benchmark optimisation problems and finally applied to real-world instances of the water distribution network design problem where the selective hyper-heuristics is demonstrated as being very effective at solving this difficult problem. Furthermore, the thesis demonstrates how heuristic selection can be improved by incorporating a greater level of information about heuristic performance, namely the historical joint performance of different heuristics, and shows that exploiting this sequencing information in heuristic selection can produce highly competitive results.

Agent-based computing has attracted much attention as a promising technique for application domains that are distributed, complex and heterogeneous. Current research on multi-agent systems (MAS) has become mature enough to be applied as a technology for solving problems in an increasingly wide range of complex applications. The main formal architectures used to describe the relationships between agents in MAS are centralised and distributed architectures. In computational complexity theory, researchers have classified the problems into the followings categories: (i) P problems, (ii) NP problems, (iii) NP-complete problems, and (iv) NP-hard problems. A method for computing the solution to NP-hard problems, using the algorithms and computational power available nowadays in reasonable time frame remains undiscovered. And unfortunately, many practical problems belong to this very class. On the other hand, it is essential that these problems are solved, and the only possibility of doing this is to use approximation techniques. Heuristic solution techniques are an alternative. A heuristic is a strategy that is powerful in general, but not absolutely guaranteed to provide the best (i.e. optimal) solutions or even find a solution. This demands adopting some optimisation techniques such as Evolutionary Algorithms (EA). This research has been undertaken to investigate the feasibility of running computationally intensive algorithms on multi-agent architectures while preserving the ability of small agents to run on small devices, including mobile devices. To achieve this, the present work proposes a new Hybrid Multi-Agent Architecture (HMAA) that generates new heuristics for solving NP-hard problems. This architecture is hybrid because it is "semi-distributed/semi-centralised" architecture where variables and constraints are distributed among small agents exactly as in distributed architectures, but when the small agents become stuck, a centralised control becomes active where the variables are transferred to a super agent, that has a central view of the whole system, and possesses much more computational power and intensive algorithms to generate new heuristics for the small agents, which find optimal solution for the specified problem. This research comes up with the followings: (1) Hybrid Multi-Agent Architecture (HMAA) that generates new heuristic for solving many NP-hard problems. (2) Two frameworks of HMAA have been implemented; search and optimisation frameworks. (3) New SMA meeting scheduling heuristic. (4) New SMA repair strategy for the scheduling process. (5) Small Agent (SMA) that is responsible for meeting scheduling has been developed. (6) “Local Search Programming” (LSP), a new concept for evolutionary approaches, has been introduced. (7) Two types of super-agent (LGP_SUA and LSP_SUA) have been implemented in the HMAA, and two SUAs (local and global optima) have been implemented for each type. (8) A prototype for HMAA has been implemented: this prototype employs the proposed meeting scheduling heuristic with the repair strategy on SMAs, and the four extensive algorithms on SUAs. The results reveal that this architecture is applicable to many different application domains because of its simplicity and efficiency. Its performance was better than many existing meeting scheduling architectures. HMAA can be modified and altered to other types of evolutionary approaches.

Major League Baseball (MLB) is a 30-team North American professional baseball league and Minor League Baseball (MiLB) is the hierarchy of developmental professional baseball teams for MLB. Most MLB players first develop their skills in MiLB, and MLB teams employ scouts, experts who evaluate the strengths, weaknesses, and overall potential of these players. In this dissertation, we study the problem of designing a scouting network for a Major League Baseball (MLB) team. We introduce the problem to the operations research literature to help teams make strategic and operational level decisions when managing their scouting resources. The thesis consists of three chapters that aim to address decisions such as how the scouts should be assigned to the available MiLB teams, how the scouts should be routed around the country, how many scouts are needed to perform the major scouting tasks, are there any trade-off s between the scouting objectives, and if there are any, what are the outcomes and insights. In the first chapter, we study the problem of assigning and scheduling minor league scouts for Major League Baseball (MLB) teams. There are multiple objectives in this problem. We formulate the problem as an integer program, use decomposition and both column-generation-based and problem-specific heuristics to solve it, and evaluate policies on multiple objective dimensions based on 100 bootstrapped season schedules. Our approach can allow teams to improve operationally by finding better scout schedules, to understand quantitatively the strategic trade-offs inherent in scout assignment policies, and to select the assignment policy whose strategic and operational performance best meets their needs. In the second chapter, we study the problem under uncertainty. In reality we observe that there are always disruptions to the schedules: players are injured, scouts become unavailable, games are delayed due to bad weather, etc. We presented a minor league baseball season simulator that generates random disruptions to the scout's schedules and uses optimization based heuristic models to recover the disrupted schedules. We evaluated the strategic benefits of different policies for team-to-scout assignment using the simulator. Our results demonstrate that the deterministic approach is insufficient for evaluating the benefits and costs of each policy, and that a simulation approach is also much more effective at determining the value of adding an additional scout to the network. The real scouting network design instances we solved in the first two chapters have several detailed complexities that can make them hard to study, such as idle day constraints, varying season lengths, off days for teams in the schedule, days where some teams play and others do not, etc. In the third chapter, we analyzed a simplified version of the Single Scout Problem (SSP), stripping away much of the real-world complexities that complicate SSP instances. Even for this stylized, archetypal version of SSP, we find that even small instances can be computationally difficult. We showed by reduction from Minimum Cost Hamiltonian Path Problem that archetypal version of SSP is NP-complete, even without all of the additional complexity introduced by real scheduling and scouting operations.