One of the most innovative solutions treated in the literature in order to reduce the environmental impact of urban parcel delivery logistics is the use of drones for delivery on the last kilometer. Consequently, nowadays, the primary challenge is essentially related to the drones’ fleet sizing according to its means of support for the urban delivery of parcels. In this paper, we will discuss the issue of dimensioning from a forecast of deliveries of an urban perimeter, the size of the fleet, the stock of battery to dispose of and the strategy of battery charging. We will present an analytical model expressing the proposed problem of the optimal drones’ delivery mission taking into account the issues of autonomy and energy consumption related to the drone’s technical specification. According to the developed analytical model, two optimization policies will be proposed. The first policy consists of planning missions under reducing distance. The second policy tries to make a compromise between the distance and the number of drones. A case study will be presented in order to compare the two policies based on the overall cost of a plan. The main objective of the study is to create a decision-making tool for the design of a drone fleet in the case of forecast deliveries over a time horizon under operational constraints.

In this paper, we develop a preventive maintenance (PM) strategy for a solar photovoltaic system composed of solar panels functioning as a series system. The photovoltaic system is considered in a failed state whenever its efficiency drops below a predefined threshold or any electrical wiring element is damaged. In such a situation of failure, a minimal repair is performed. The proposed PM strategy suggests systematically replacing n panels with their respective wiring system every time units T over a finite operating time span H. The panels to be preventively replaced are selected by the maintenance agent after an on-site overall assessment of all panels, making sure every time not to replace panels previously replaced during a given replacement cycle of all panels of the system. An analytical model is proposed in order to simultaneously determine the optimal PM period, T, and the optimal number of solar panels, n, to be replaced at each PM. This is done by modeling and minimizing the expected total maintenance cost over the finite operating time horizon H. A numerical example is presented to illustrate the use of the proposed modelling approach and to discuss the obtained results. The latter provide the optimal solutions (T*, n*) for different combinations of input parameters. They also show the economic relevance of the proposed PM strategy through estimation of the economic gain when comparing the situations with and without preventive maintenance.

The selection of the suitable production parameters for dry machining remains a challenging task which impacts the productivity, the system degradation and the quality of the output product. This study proposes an integrated production-maintenance policy allowing the simultaneous consideration of the production parameters, mainly the cutting speed, the production time and cost, the preventive maintenance period and the quality levels and the selling prices of the output product. A special feature of this strategy, defined on a finite time span, is to allow a change of the cutting speed at a certain instant to be determined in an optimal way concurrently with the preventive maintenance period, such as to maximise the total expected profit per time unit. A mathematical model is developed to express the average profit per time unit as a function of the decision variables. A solving procedure is also proposed. A numerical example and a sensitivity analysis are presented in order to show the impact of some major input parameters on the optimal policy.

This paper deals the combination between production and maintenance plan for a manufacturing system satisfying a random demand. A jointly optimization is made in order to establish an optimal production planning and scheduling maintenance strategy showing the machine degradation. The key of this study is to consider the influence of the production rate on the degradation degree. We prove simultaneously, with a constrained stochastic production-maintenance planning problem under hypotheses of inventory and failure rate variables, an optimal production plan and maintenance scheduling which minimizes the average total holding, production and maintenance costs. A numerical example is studied in order to apply the developed approach.