64,011 research outputs found
Multi-objective model for optimizing railway infrastructure asset renewal
Trabalho inspirado num problema real da empresa Infraestruturas de Portugal, EP.A multi-objective model for managing railway infrastructure asset renewal is presented. The model aims to optimize three objectives, while respecting operational constraints: levelling investment throughout multiple years, minimizing total cost and minimizing work start postponements. Its output is an optimized intervention schedule. The model is based on a case study from a Portuguese infrastructure management company, which specified the objectives and constraints, and reflects management practice on railway infrastructure. The results show that investment levelling greatly influences the other objectives and that total cost fluctuations may range from insignificant to important, depending on the condition of the infrastructure. The results structure is argued to be general and suggests a practical methodology for analysing trade-offs and selecting a solution for implementation.info:eu-repo/semantics/publishedVersio
Finish Them!: Pricing Algorithms for Human Computation
Given a batch of human computation tasks, a commonly ignored aspect is how
the price (i.e., the reward paid to human workers) of these tasks must be set
or varied in order to meet latency or cost constraints. Often, the price is set
up-front and not modified, leading to either a much higher monetary cost than
needed (if the price is set too high), or to a much larger latency than
expected (if the price is set too low). Leveraging a pricing model from prior
work, we develop algorithms to optimally set and then vary price over time in
order to meet a (a) user-specified deadline while minimizing total monetary
cost (b) user-specified monetary budget constraint while minimizing total
elapsed time. We leverage techniques from decision theory (specifically, Markov
Decision Processes) for both these problems, and demonstrate that our
techniques lead to upto 30\% reduction in cost over schemes proposed in prior
work. Furthermore, we develop techniques to speed-up the computation, enabling
users to leverage the price setting algorithms on-the-fly
Budget Allocation for Permanent and Contingent Capacity under Stochastic Demand.
We develop a model of budget allocation for permanent and contingent workforce under stochastic demand. The level of permanent capacity is determined at the beginning of the horizon and is kept constant throughout, whereas the number of temporary workers to be hired must be decided in each period. Compared to existing budgeting models, this paper explicitly considers a budget constraint. Under the assumption of a restricted budget, the objective is to minimize capacity shortages. When over-expenditures are allowed, both budget deviations and shortage costs are to be minimized. The capacity shortage cost function is assumed to be either linear or quadratic with the amount of shortage, which corresponds to different market structures or different types of services. We thus examine four variants of the problem that we model and solve either approximately or to optimality when possible. A comprehensive experimental design is designed to analyze the behavior of our models when several levels of demand variability and parameter values are considered. The parameters consist of the initial budget level, the unit cost of temporary workers and the budget deviation penalty/reward rates. Varying these parameters produce several trade-offs between permanent and temporary workforce levels, and between capacity shortages and budget deviations. Numerical results also show that the quadratic cost function leads to smooth and moderate capacity shortages over the time periods, whereas all shortages are either avoided or accepted when the cost function is linear.Stochastic; Capacity planning; Contingent workers; Budget allocation; Non-linear stochastic dynamic programming; Optimization;
An efficient method for multiobjective optimal control and optimal control subject to integral constraints
We introduce a new and efficient numerical method for multicriterion optimal
control and single criterion optimal control under integral constraints. The
approach is based on extending the state space to include information on a
"budget" remaining to satisfy each constraint; the augmented
Hamilton-Jacobi-Bellman PDE is then solved numerically. The efficiency of our
approach hinges on the causality in that PDE, i.e., the monotonicity of
characteristic curves in one of the newly added dimensions. A semi-Lagrangian
"marching" method is used to approximate the discontinuous viscosity solution
efficiently. We compare this to a recently introduced "weighted sum" based
algorithm for the same problem. We illustrate our method using examples from
flight path planning and robotic navigation in the presence of friendly and
adversarial observers.Comment: The final version accepted by J. Comp. Math. : 41 pages, 14 figures.
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