2,121 research outputs found
Recommended from our members
Resource constrained routing and scheduling: Review and research prospects
In the service industry, it is crucial to efficiently allocate scarce resources to perform tasks and meet particular service requirements. What considerably complicates matters is when these resources, for example skilled technicians, nurses, and home carers have to visit different customer locations. This paper provides a comprehensive survey on resource constrained routing and scheduling that unveils the problem characteristics with respect to resource qualifications, service requirements and problem objectives. It also identifies the most effective exact and heuristic algorithms for this class of problems. The paper closes with several research prospects
Improving the Process of Preventive Maintenance for Critical Telecommunications Stations in Qatar
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
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.
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 Dellâs 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
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
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,
techniciansâ costs and incentives for staff. Unlike existing reports, the current
investigation attempts to solve the maintenance task balancing problem. It combines
preventive maintenance techniciansâ 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, techniciansâ 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
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
- âŠ