2,121 research outputs found

    Improving the Process of Preventive Maintenance for Critical Telecommunications Stations in Qatar

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    Critical public safety telecommunications networks in Qatar shall be secure, reliable, and fast response networks. These networks are serving the security teams and forces of Qatar. As a result, these networks shall be maintained on the highest standards in order to meet the basic requirements of providing an available and reliable Mission Critical Communications Networks (MCCN). Hence, the goal of this project is to improve the process of preventive maintenance by the Field Maintenance Teams (FMT) in the Ministry of Interior (MOI). Several limitations and challenges are facing these teams while planning and performing the Preventive Maintenance (PM) tasks. This project shall be used to increase the productivity of the FMT by improving the current practices of performing PM activities. A detailed literature review on the areas of lean thinking and scheduling maintenance tasks has been conducted. Then, it was decided to use the VSM (one of the lean thinking tools) to enhance and improve the current PM execution system. There were multiple non-value adding activities that can be planned for and executed before each day of preforming the PM tasks. These activities have been identified and then eliminated, and hence a future state was proposed in this project. This future state system will be implemented directly by the FMT management as it can save almost 40.3% of the total lead time of the system (192 minutes improvement from current to the future system)

    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

    A simulation based supply partner selection decision support tool for service provision in Dell.

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    Partner selection is an important aspect of all outsourcing processes. Traditional partner selection, typically involves steps to determine the criteria for outsourcing, followed by a qualification of potential suppliers and concluding with a final selection of partner(s). Reverse auctions (RAs) have widely been used for partner selection in recent times. However, RAs, although proven successful in initial price reduction strategies for product and service provision, can suffer from reduced effectiveness as the number of executions increases. This paper illustrates Dell’s experience of such diminishing returns for its outsourced after sales product repair service and presents the development, of a new partner selection methodology which incorporates a new process improvement stage to be executed in combination with the final selection phase. This new methodology is underpinned by the development of a computer based simulation supply partner selection decision support tool for service provision. The paper highlights the significant additional cost saving benefits achievable and improvement in service through the use of advanced simulation based decision supports

    Personaneinsatz- und Tourenplanung fĂŒr Mitarbeiter mit Mehrfachqualifikationen

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    In workforce routing and scheduling there are many applications in which differently skilled workers must perform jobs that occur at different locations, where each job requires a particular combination of skills. In many such applications, a group of workers must be sent out to provide all skills required by a job. Examples are found in maintenance operations, the construction sector, health care operations, or consultancies. In this thesis, we analyze the combined problem of composing worker groups (teams) and routing these teams under goals expressing service-, fairness-, and cost-objectives. We develop mathematical optimization models and heuristic solution methods for an integrated solution and a sequential solution of the teaming- and routing-subproblems . Computational experiments are conducted to identify the tradeoff of better solution quality and computational effort

    Preventive maintenance task balancing with spare parts optimisation via big-bang big-crunch algorithm

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    Work balancing increasingly plays an important role in both the production and maintenance functions. However, the literature on work balancing problems in transfer line manufacturing systems provides little information on the contributions of maintenance technicians and spare parts with a focus on penalty, technicians’ costs and incentives for staff. Unlike existing reports, the current investigation attempts to solve the maintenance task balancing problem. It combines preventive maintenance technicians’ assignments with product demand and spares utilisation in a transfer line manufacturing system. It uses an optimisation framework that measures the success of post-line balancing solution performance in a system from a holistic perspective. The novelty of the approach lies in the integration of technicians and spare parts theory and the introduction of penalty, technicians’ costs and incentive for staff. The proposed optimisation method was applied to a case study for detergent manufacturing system as a means of testing the effectiveness and robustness of the approach. The results show that the proposed model appears to be effective. Some simulations were also carried out to complement practical result

    Deep Learning Approach to Technician Routing and Scheduling Problem

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    This paper proposes a hybrid algorithm including the Adam algorithm and body change operator (BCO). Feasible solutions to technician routing and scheduling problems (TRSP) are investigated by performing deep learning based on the Adam algorithm and the hybridization of Adam-BCO. TRSP is a problem where all tasks are routed, and technicians are scheduled. In the deep learning method based on the Adam algorithm and Adam-BCO algorithm, the weights of the network are updated, and these weights are evaluated as Greedy approach, and routing and scheduling are performed. The performance of the Adam-BCO algorithm is experimentally compared with the Adam and BCO algorithm by solving the TRSP on the instances developed from the literature. The numerical results evidence that Adam-BCO offers faster and better solutions considering Adam and BCO algorithm. The average solution time increases from 0.14 minutes to 4.03 minutes, but in return, Gap decreases from 9.99% to 5.71%. The hybridization of both algorithms through deep learning provides an effective and feasible solution, as evidenced by the results
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