5,555 research outputs found

    A bibliographic review of production line design and balancing under uncertainty

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    This bibliography reviews the solution methods developed for the design and balancing problems of production lines such as assembly and disassembly lines. The line design problem aims in determining the number of workstations along with the corresponding assignment of tasks to each workstation, while the line balancing problem seeks an assignment of tasks, to the existing workstations of the line, which ensures that the workloads are as equal as possible among the workstations. These two optimisation problems can be also integrated and treated as a multi-objective optimisation problem. This review considers both deterministic and stochastic formulations for disassembly lines and is limited to assembly line design and balancing under uncertainty. This bibliography covers more than 90 publications since 1976 for assembly and 1999 for disassembly

    Simulation Optimization Studies of Routing and Process Flow Problems

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    Computer aided simulation is emerging as a powerful tool for numerical analysis and in conducting performance evaluations of complex systems that depend on a multitude of variables. The primary objective in such simulation studies is to gauge the performance of the system under a various constraints and operating conditions. The effects of changing the operating parameter space can thus be analyzed without having to implement costly changes. Simulations are also carried out for the baseline scenarios to verity and validate the basic underlying system model. In this thesis research, two practical problems were studied through numerical modeling, and optimized solutions obtained for both. Optimizing the pick-up and delivery routes using a commercial software tool was the first task. Optimization of a production assembly line using a discrete event simulation tool was the second project that was carried out. The primary objective for the first task was to explore various routing scenarios and determine delivery routes that would minimize the total network mileage, while maintaining the pick-up time slots requested by the clients. A related task was to evaluate the possible advantages of centralizing all routing activity from a single site, instead of the two-hub scenario currently in effect. A total of eight different scenarios were studied as part of this effort. The second task involved optimization of the throughput of a fuel injector plant by placing buffers within the assembly lines for increased productivity

    Multi-level evolutionary algorithms resource allocation utilizing model-based systems engineering

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    This research presents an innovative approach to solve the resource allocation problems using Multi-level Evolutionary Algorithms. Evolutionary Algorithms are used to solve resource allocation problems in different domains and their results are then incorporated into a higher level system solution using another Evolutionary Algorithm to solve base camp planning problems currently faced by the U.S. Department of Defense. Two models are introduced to solve two domain specific models: a logistics model and a power model. The logistic model evaluates routes for logistics vehicles on a daily basis with a goal of reducing fuel usage by delivery trucks. The evaluation includes distance traveled and other constraints such as available resource levels and priority of refilling. The Power model incorporates an open source electrical distribution simulator to evaluate the placement of structures and generators on a map to reduce fuel usage. These models are used as the fitness function for two separate Evolutionary Algorithms to find solutions that reduce fuel consumption within the individual domains. A multi-level Evolutionary Algorithm is then presented, where the two Evolutionary Algorithms share information with a higher level Evolutionary Algorithm that combines the results to account for problem complexity from the interfacing of these systems. The results of using these methods on 5 different base camp sizes show that the techniques provide a considerable reduction of fuel consumption. While the Evolutionary Algorithms show significant improvement over the current methods, the multi-level Evolutionary Algorithm shows better performance than using individual Evolutionary Algorithms, with the results showing a 19.25 % decrease in fuel consumption using the multi-level Evolutionary Algorithm --Abstract, page iii

    Sea Container Terminals

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    Due to a rapid growth in world trade and a huge increase in containerized goods, sea container terminals play a vital role in globe-spanning supply chains. Container terminals should be able to handle large ships, with large call sizes within the shortest time possible, and at competitive rates. In response, terminal operators, shipping liners, and port authorities are investing in new technologies to improve container handling infrastructure and operational efficiency. Container terminals face challenging research problems which have received much attention from the academic community. The focus of this paper is to highlight the recent developments in the container terminals, which can be categorized into three areas: (1) innovative container terminal technologies, (2) new OR directions and models for existing research areas, and (3) emerging areas in container terminal research. By choosing this focus, we complement existing reviews on container terminal operations

    Planning and Scheduling Optimization

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    Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

    Service scheduling and vehicle routing problem to minimise the risk of missing appointments

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    This research studies a workforce scheduling and vehicle routing problem where technicians drive a vehicle to customer locations to perform service tasks. The service times and travel times are subject to stochastic events. There is an agreed time window for starting each service task. The risk of missing the time window for a task is defined as the probability that the technician assigned to the task arrives at the customer site later than the time window. The problem is to generate a schedule that minimises the maximum of risks and the sum of risks of all the tasks considering the effect of skill levels and task priorities. A new approach is taken to build schedules that minimise the risks of missing appointments as well as the risks of technicians not being able to complete their daily tours on time.We first analyse the probability distribution of the arrival time to any customer location considering the distributions of activities prior to this arrival. Based on the analysis, an efficient estimation method for calculating the risks is proposed, which is highly accurate and this is verified by comparing the results of the estimation method with a numerical integral method.We then develop three new workforce scheduling and vehicle routing models that minimise the risks with different considerations such as an identical standard deviation of the duration for all uncertain tasks in the linear risk minimisation model, and task priorities in the priority task risk minimisation model. A simulated annealing algorithm is implemented for solving the models at the start of the day and for re-optimisation during the day. Computational experiments are carried out to compare the results of the risk minimisation models with those of the traditional travel cost model. The performance is measured using risks and robustness. Simulation is used to compare the numbers of missed appointments and test the effect of re-optimisation.The results of the experiments demonstrate that the new models significantly reduce the risks and generate schedules with more contingency time allowances. Simulation results also show that re-optimisation reduces the number of missed appointments significantly. The risk calculation methods and risk minimisation algorithm are applied to a real-world problem in the telecommunication sector.</div
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