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Yes / This paper proposes the application of a genetic algorithm based methodology for the scheduling of storage tanks. The proposed approach is an integration of GA and heuristic rule-based techniques, which decomposes the complex mixed integer optimisation problem into integer and real number subproblems. The GA string considers the integer problem, and the heuristic approach solves the real number problems within the GA framework. The algorithm is demonstrated for a test problem related to a water treatment facility at a port, and has been found to give a significantly better schedule than those generated using a heuristic-based approach.

Yes / Communication is fundamental to the transfer of information between individuals, agencies and organizations, and therefore, it is crucial to planning and decision-making particularly in cases of uncertainty and risk. This paper brings forth some critical aspects of communication that need to be acknowledged and considered while managing risks. Most of the previous studies and theories on natural hazards and disaster management have limited perspective on communication, and hence, its implication is limited to awareness, warnings and emergency response to some selected events. This paper exposes the role of communication as a moderator of not just risk interpretation and action but also various factors responsible for shaping overall response, such as individual decision-making under uncertainty, heuristics, past experiences, learning, trust, complexity, scale and the social context. It suggests that communication is a process that influences decision-making in multiple ways, and therefore, it plays a critical role in shaping local responses to various risks. It opens up the scope for using communication beyond its current use as a tool to manage emergency situations. An in-depth understanding of ongoing communication and its implications can help to plan risk management more effectively over time rather than as a short-term response.

Novice instructional designers have limited experience working with ill-structured problems, and often do not possess the mental models to effectively analyze, manage, and communicate the overall design process of new instructional design projects (Wedman and Tessmer, 1993; Rowland, 1992; Perez and Emery, 1995; Liu, Gibby, Quiros, and Demps, 2002). In their 2016 article of expert instructional design principles applied by experienced designers in practice, York and Ertmer proposed the following questions for future research, "(a) Can we teach principles to novice instructional designers? (b) What methods should we use to provide this information?" (York and Ertmer, 2016, p. 189). This research further explored these questions and offers a new model of expert instructional design heuristics incorporating design thinking methods. The purpose of this study was to identify design thinking methods that aligned with heuristics of expert instructional design practitioners, and to design and develop a new model of heuristics and design thinking methods, which could assist novice instructional designers as they enter the instructional design field. The literature outlines challenges reported among novice instructional designers throughout the instructional design process, which includes their ability to solve ill-structured problems; conduct thorough analyses; collaborate in teams; negotiate priorities; generate a variety of ideas for solutions; overcome resource, budget and time constraints; communicate and manage projects with stakeholders; and prototype, iterate and pilot new design solutions (Rowland, 1992; Hoard, Stefaniak, Baaki, and Draper, 2019; Roytek, 2010; Liu, Gibby, Quiros, and Demps, 2002; Chang and Kuwata, 2020; Tracey and Boling, 2014; Perez and Emery, 1995; Williams van Rooij, 1993). The model offers novice instructional designers specific methods and combinations of methods to use for every stage of the instructional design process. As instructional designers implement design thinking methods within the context of their daily situations, they should become more comfortable and begin to adapt the methods to meet their individual needs for each stage of their process. / Doctor of Philosophy / Instructional design is a system of procedures for developing education and training curricula in a consistent and reliable fashion (Branch and Merrill, 2011; Branch and Kopcha, 2014). It embodies an iterative process for outlining outcomes, selecting teaching and learning strategies, choosing support technologies, identifying media, and measuring performance (Branch and Kopcha, 2014). Instructional designers use models of instructional design and instructional development to communicate tasks and procedures of the instructional design process (Andrews and Goodson, 1980). Over the years, numerous models of instructional design have been developed and adapted to meet the varying needs of instructional designers and developers. There is a consensus that most instructional processes consist of five core elements or stages: analysis, design, development, implementation, and evaluation, which are commonly referred to as ADDIE (Seels and Glasgow, 1990; Branch and Kopcha, 2014). While often considered generic, the ADDIE framework contains a useful set of common criteria, which most designers state as important or necessary as a part of any instructional design process (Pittenger, Janke, and Bumgardner, 2009; York and Ertmer, 2011; 2016). Novice instructional designers have limited experience working with ill-structured problems, and often do not possess the mental models (prior experience) to effectively analyze, manage, and communicate the overall design process of new instructional design projects (Wedman and Tessmer, 1993; Rowland, 1992; Perez and Emery, 1995; Liu, Gibby, Quiros, and Demps, 2002). In their 2016 article of expert instructional design principles applied by experienced designers in practice, York and Ertmer proposed the following questions for future research, "(a) Can we teach principles to novice instructional designers? (b) What methods should we use to provide this information?" (York and Ertmer, 2016, p. 189). This research further explored these questions and offers a new model of expert instructional design heuristics incorporating design thinking methods. For this study, heuristics were defined as generalized stages of an instructional designer's process and design thinking was defined as a human-centered design process for solving complex problems. The purpose of this study was to identify design thinking methods that aligned with heuristics of expert instructional design practitioners, and to design and develop a new model of heuristics and design thinking methods, which could assist novice instructional designers as they enter the instructional design field. The literature outlines challenges reported among novice instructional designers throughout the instructional design process, which includes their ability to solve ill-structured problems; conduct thorough analyses; collaborate in teams; negotiate priorities; generate a variety of ideas for solutions; overcome resource, budget and time constraints; communicate and manage projects with stakeholders; and prototype, iterate and pilot new design solutions (Rowland, 1992; Hoard, Stefaniak, Baaki, and Draper, 2019; Roytek, 2010; Liu, Gibby, Quiros, and Demps, 2002; Chang and Kuwata, 2020; Tracey and Boling, 2014; Perez and Emery, 1995; Williams van Rooij, 1993). The model offers novice instructional designers specific methods and combinations of methods to use for every stage of the instructional design process. As instructional designers implement design thinking methods within the context of their daily situations, they should become more comfortable and begin to adapt the methods to meet their individual needs for each stage of their process.

Evaluation is the key to effective interface design. It becomes even more important when the interfaces are for cutting edge technology, in application areas that are new and with little prior design knowledge. Knowing how to evaluate new interfaces can decrease development effort and increase the returns on resources spent on formative evaluation. The problem is that there are few, if any, readily available evaluation tools for these new interfaces. This work focuses on the creation and testing of a new set of heuristics that are tailored to the large screen information exhibit (LSIE) system class. This new set is created through a structured process that relies upon critical parameters associated with the notification systems design space. By inspecting example systems, performing claims analysis, categorizing claims, extracting design knowledge, and finally synthesizing heuristics; we have created a usable set of heuristics that is better equipped for supporting formative evaluation. Contributions of this work include: a structured heuristic creation process based on critical parameters, a new set of heuristics tailored to the LSIE system class, reusable design knowledge in the form of claims and high level design issues, and a new usability evaluation method comparison test. These contributions result from the creation of the heuristics and two studies that illustrate the usability and utility of the new heuristics. / Ph. D.

There are many real world assignment, scheduling and planning tasks which can be classified as combinatorial optimisation problems (COPs). These are usually formulated as a mathematical problem of minimising or maximising some cost function subject to a number of constraints. Usually, such problems are NP hard, and thus, whilst it is possible to find exact solutions to specific problems, in general only approximate solutions can be found. There are many algorithms that have been proposed for finding approximate solutions to COPs, ranging from special purpose heuristics to general search meta-heuristics such as simulated annealing and tabu search. General meta-heuristic algorithms like simulated annealing have been applied to a wide range of problems. In most cases, the designer must choose an appropriate data structure and a set of local operators that define a search neighbourhood. The variability in representation techniques, and suitable neighbourhood transition operators, has meant that it is usually necessary to develop new code for each problem. Toolkits like the one developed by Ingber's Adaptive Simulated Annealing (Ingber 1993, 1996) have been applied to assist rapid prototyping of simulated annealing codes, however, these still require the development of new programs for each type of problem. There have been very few attempts to develop a general meta-heuristic solver, with the notable exception being Connolly's General Purpose Simulated Annealing (Connolly 1992). In this research, a general meta-heuristic based system is presented that is suitable for a wide range of COPs. The main goal of this work is to build an environment in which it is possible to specify a range of COPs using an algebraic formulation, and to produce a tailored solver automatically. This removes the need for the development of specific software, allowing very rapid prototyping. Similar techniques have been available for linear programming based solvers for some years in the form of the GAMS (General Algebraic Modelling System) (Brooke, Kendrick, Meeraus and Raman 1997) and AMPL (Fourer, Gay and Kernighan 1993) interfaces. The new system is based on a novel linked list data structure rather than the more conventional vector notation due to the natural mapping between COPS and lists. In addition, the modelling system is found to be very suitable for processing by meta-heuristic search algorithms as it allows the direct application of common local search operators. A general solver is built that is based on the linked list modelling system. This system is capable of using meta-heuristic search engines such as greedy search, tabu search and simulated annealing. A number of implementation issues such as generating initial solutions, choosing and invoking appropriate local search transition operators and producing suitable incremental cost expressions, are considered. As such, the system can been seen as a good test-bench for model prototypers and those who wish to test various meta-heuristic implementations in a standard way. However, it is not meant as a replacement or substitute for efficient special purpose search algorithms. The solver shows good performance on a wide range of problems, frequently reaching the optimal and best-known solutions. Where this is not the case, solutions within a few percent deviation are produced. Performance is dependent on the chosen transition operators and the frequency with which each is applied. To a lesser extent, the performance of this implementation is influenced by runtime parameters of the meta-heuristic search engine.

[pt] Esta tese define os problemas das p-medianas conectadas e o de localização de facilidades não-capacitadas conectadas. Possíveis aplicações incluem problemas de planejamento regional e o projeto de redes de telecomunicações ou de transporte. Para o primeiro problema, duas formulações de programação linear inteira são apresentadas e comparadas. Um destes modelos é adaptado para o segundo problema. Para o problema das p-medianas conectadas, algoritmos aproximados são desenvolvidos. Uma estratégia de busca local híbrida é proposta. Para acelerar as iterações do algoritmo de busca local, idéias como circularidade, melhoria iterativa e o descarte de vizinhos são incorporadas. Heurísticas GRASP e VNS são desenvolvidas incluindo a utilização de um filtro com o objetivo de diminuir os tempos de processamento e do procedimento de reconexão por caminhos com o objetivo de melhorar a qualidade das soluções encontradas. Diversos testes são realizados comparando-se esses algoritmos. Os resultados mostraram a necessidade de se executar um passo adicional de pós-otimização às heurísticas GRASP e VNS propostas. / [en] In this work, the connected p-median and the connected facility location problems are defined. Applications arise in regional planning, design of telecommunications and transportation networks. For the first problem, two integer linear programming formulations are proposed. Adaptations are made in one of these formulations and are used to model the second problem. Approximation algorithms to solve the connected p-median problem are developed. A hybrid local search strategy is proposed. In order to speed up the local search iterations, ideas as circularity, first- improving strategy and discard neighbors are incorporated. A GRASP algorithm and a VNS heuristic are also proposed. A filter is used to reduce the computational time required and a path-relinking is applied to improve the results found. Computational experiments to compare the algorithms are reported. To improve these results, it is applied a post-optimization step to the GRASP and VNS heuristics.

Supply chain optimization is one of the key components for the effective management of a company with a complex manufacturing process and distribution network. Companies with a global presence in particular are motivated to optimize their distribution plans in order to keep their operating costs low and competitive. Changing condition in the global market and volatile energy prices increase the need for an automatic decision and optimization tool. In recent years, many techniques and applications have been proposed to address the problem of supply chain optimization. However, such techniques are often too problemspecific or too knowledge-intensive to be implemented as in-expensive, and easy-to-use computer system. The effort required to implement an optimization system for a new instance of the problem appears to be quite significant. The development process necessitates the involvement of expert personnel and the level of automation is low. The aim of this project is to develop a set of strategies capable of increasing the level of automation when developing a new optimization system. An increased level of automation is achieved by focusing on three areas: multi-objective optimization, optimization algorithm usability, and optimization model design. A literature review highlighted the great level of interest for the problem of multiobjective optimization in the research community. However, the review emphasized a lack of standardization in the area and insufficient understanding of the relationship between multi-objective strategies and problems. Experts in the area of optimization and artificial intelligence are interested in improving the usability of the most recent optimization algorithms. They stated the concern that the large number of variants and parameters, which characterizes such algorithms, affect their potential applicability in real-world environments. Such characteristics are seen as the root cause for the low success of the most recent optimization algorithms in industrial applications. Crucial task for the development of an optimization system is the design of the optimization model. Such task is one of the most complex in the development process, however, it is still performed mostly manually. The importance and the complexity of the task strongly suggest the development of tools to aid the design of optimization models. In order to address such challenges, first the problem of multi-objective optimization is considered and the most widely adopted techniques to solve it are identified. Such techniques are analyzed and described in details to increase the level of standardization in the area. Empirical evidences are highlighted to suggest what type of relationship exists between strategies and problem instances. Regarding the optimization algorithm, a classification method is proposed to improve its usability and computational requirement by automatically tuning one of its key parameters, the termination condition. The algorithm understands the problem complexity and automatically assigns the best termination condition to minimize runtime. The runtime of the optimization system has been reduced by more than 60%. Arguably, the usability of the algorithm has been improved as well, as one of the key configuration tasks can now be completed automatically. Finally, a system is presented to aid the definition of the optimization model through regression analysis. The purpose of the method is to gather as much knowledge about the problem as possible so that the task of the optimization model definition requires a lower user involvement. The application of the proposed algorithm is estimated that could have saved almost 1000 man-weeks to complete the project. The developed strategies have been applied to the problem of Caterpillar’s global supply chain optimization. This thesis describes also the process of developing an optimization system for Caterpillar and highlights the challenges and research opportunities identified while undertaking this work. This thesis describes the optimization model designed for Caterpillar’s supply chain and the implementation details of the Ant Colony System, the algorithm selected to optimize the supply chain. The system is now used to design the distribution plans of more than 7,000 products. The system improved Caterpillar’s marginal profit on such products by a factor of 4.6% on average.

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International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, California / The Broadcast Scheduling Problem (BSP) is a well known NP-complete problem that arises in the study of wireless networks. In the BSP, a finite set of stations are to be scheduled in a time division multiple access (TDMA) frame. The objective is a collision free transmission schedule with the minimum number of TDMA slots and maximal slot utilization. Such a schedule will minimize the total system delay. We present variations of a Greedy Randomized Adaptive Search Procedure (GRASP) for the BSP. Path-relinking, a post-optimization strategy is applied. Also, a reactivity method is used to balance GRASP parameters. Numerical results of our research are reported and compared with other heuristics from the literature.