Respiratory failure frequently complicates the care of elderly patients with or without chronic lung disease. Recent advances in techniques and applications of noninvasive ventilation provide an exceedingly useful means of managing respiratory compromise, and the clinical utilization of noninvasive mechanical ventilation has transformed the prognosis of acute and chronic respiratory failure in this age group. The majority of elderly patients can recover from an acute respiratory failure episode if adequate support is provided, although some may require long-term ventilatory assistance. Such assistance may be provided in the home setting if an adequate support system is available. As the size of the elderly population grows, an increased number of elderly patients with multifactorial respiratory failure will undoubtedly require episodic or sustained ventilatory assistance, and noninvasive ventilation can be provided for various forms of acute and chronic respiratory failure, including advanced chronic obstructive pulmonary disease, other parenchymal lung disease, and chest wall deformities. Health care organizations must incorporate long-term care facilities with ventilatory support capabilities into their health management strategies.

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The Vehicle Routing Problem with Time Windows is a complete NP combinatorial problem in which product deliveries to customers must be made under certain time constraints. This problem can be solved from a single objective approach, well studied in the state of the art, in which the objective of the total travel distance or the size of the fleet (number of vehicles) is generally minimized. However, recent studies have used a multiobjective approach (Multiobjective Vehicle Routing Problem with Time Windows, MOVRPTW) that solves the problem from a viewpoint closer to reality. This work presents a new multiobjective memetic algorithm based on the GRASP (Greedy Randomized Adaptive Search Procedures) algorithm called MOMGRASP for the minimization of three objectives in MOVRPTW (total travel time, waiting time of customers to be attended, and balance of total travel time between routes). The results of the experimentation carried out with 56 problems proposed by Solomon and 45 problems proposed by Castro-Gutiérrez show that the proposed algorithm finds better solutions in these three objectives and competitive solutions than those reported by Zhou (compared to LSMOVRPTW algorithm and optimizing 5 objectives: number of vehicles, total travel distance, travel time of the longest route, total waiting time due to early arrivals, and total delay time due to late arrivals) and by Baños (versus the MMOEASA algorithm in two scenarios; case 1: total travel distance and balance of distance and case 2: total travel distance and balance of workload).

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