Browsing by Author "Marianov, V."
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- ItemA single vehicle routing problem with fixed delivery and optional collections(TAYLOR & FRANCIS INC, 2009) Gutierrez Jarpa, G.; Marianov, V.; Obreque, C.The Single-Vehicle Routing Problem with Fixed Delivery and Optional Collections considers a set of delivery customers receiving goods from a depot and a set of collection customers sending goods to the same depot. All delivery customers must be visited by the vehicle, while a collection customer is visited only if the capacity of the vehicle is large enough to fit the collected load and the visit reduces collection costs that would be otherwise incurred. The goal is to minimize the transportation and collection costs. A model is proposed and solved utilizing a branch-and-cut method. Efficient new cuts are proposed. Computational experience is offered on two sets of test problems. It is proved possible to solve instances that previous methods were unable to solve. The method was tested on larger instances.
- ItemLocation of single-server immobile facilities subject to a loss constraint(PALGRAVE MACMILLAN LTD, 2010) Boffey, B.; Galvao, R. D.; Marianov, V.Waiting may be unacceptable, even a short time, at a facility providing a service involving medical or other emergencies. Hence, it is appropriate to locate such facilities so that the rate at which users are lost is limited. Each facility will here be modelled as an M/E(r)/m/N queueing system subject to a loss restriction constraint and the single-server case (m = 1) will be treated in detail. Introduction of the Erlang distribution for service times allows a better fit of the model to actual values of both mean and variance than do currently available models that use an exponential distribution. Location of facilities will be such that the average travel time to a facility is minimized. It is shown how a deterministic constraint, equivalent to the loss constraint, can be generated resulting in an integer linear program, and values of a parameter (rho c) which facilitates this linearization are tabulated for various values of r, N and service level demanded. Numerical experiments are performed including an application loosely related to the location of neonatal clinics in the Municipality of Rio de Janeiro. Finally, there is a discussion of how further improved modelling of the service time distribution might be effected.
- ItemOptimal location of multi-server congestible facilities operating as M/E-r/m/N queues(TAYLOR & FRANCIS LTD, 2009) Marianov, V.; Boffey, T. B.; Galvao, R. D.Most models for location of immobile congested facilities assume exponentially distributed service time at the facilities. Although the resulting formulations are tractable, they do not adequately represent service time distributions with small variances, as often occur in practice. In a recent paper, the authors utilized an order r Erlang distribution for the service time, applied to the simple case of single-server facilities. We generalize this approach to multiple-server facilities, which need a different mathematical treatment. The constraint on service availability is cast as a linear constraint on the proportion of time the servers are busy, and its right-hand side parameter is provided for different situations. Extensive analysis is offered on the influence of the parameters of the service time and the capacity of the facilities on the performance of the system. Numerical results are given for a data set relating to the municipality of Rio de Janeiro.
- ItemWorkload assignment with training, hiring and firing(TAYLOR & FRANCIS LTD, 2008) Eiselt, H. A.; Marianov, V.This article discusses a workload allocation model in which tasks are matched to employees on the basis of a multi-dimensional skill measure. The main idea is to match positions and tasks to available and potential positions so as to minimize the differences in individuals' abilities and skill requirements. In addition to allocating existing personnel to positions, it is also possible to fire employees and hire new employees. The objectives of the mixed-integer optimization model include different types of costs and the proximity of an individual's capabilities to a task's ability requirements. A number of policies are formulated that allow different combinations of retraining of employees, as well as hiring and firing. These policies are applied to a real-life example that is solved by means of the constraint method. A variety of sensitivity analyses demonstrate the usefulness of the approach as a decision aid.