The increasing adoption of electric scooters in urban areas has prompted new challenges in energy management and infrastructure optimization. Efficient deployment of Battery Swapping Stations (BSS) is crucial to support this growing demand while maintaining sustainable and convenient services for users. This study addresses the problem of optimally locating BSS and allocating batteries to meet dynamic customer demands. The problem is a variant of the Facility Location Problem (FLP) but with additional complexities, such as battery availability constraints, station capacity limitations, and time-sensitive customer demand patterns. To tackle these challenges, we propose a Mixed-Integer Linear Programming (MILP) model to optimize station deployment and customer assignments, minimizing both station opening and assignment costs. Two experimental studies were conducted. The first analyzed the impact of station opening and customer distance costs on the number of open stations and the objective function value. The second study examined the influence of the number of batteries per station on operational efficiency. Results demonstrate valuable insights for companies, enabling more effective decision-making for station deployment and battery management.

In this article, we present a comprehensive strategy for managing production and preventive maintenance in a system that is prone to random failures. The system consists of multiple machines in series, each designed to meet customer demand, while having a distinct quality rate. Our primary contribution involves the development of an optimized production plan. This plan is capable of satisfying customer requirements within predefined timeframes, while simultaneously maintaining product quality, avoiding excessive stock levels and preventing demand shortages. The production plan is designed to efficiently reduce production costs, storage expenses and the costs associated with shortages. Secondly, we integrate the optimal production quantities into the failure rate equation to determine the economic number of preventive maintenance actions required to minimize overall maintenance costs. Numerical results highlight the profound impact of quality rate on machine degradation.

Rescheduling process consists in ordering in the time all production orders in response to disruptions for optimizing a criterion. When the disruption is due to orders arrival, the heavy weight orders fit into the schedule forcing the low weight ones to wait. Therefore, the low weight orders may not run. For dealing with this issue, in this paper, we propose to reschedule with time dependent variable weights. Indeed, the longer the task remains in the system, the more its weight increases. A single machine rescheduling problem is treated with orders arriving over time. In the predictive phase, a MILP model is implemented to solve a scheduling problem with the Total Weighted Waiting Time (TWWT) as an objective. In the reactive phase, when a new order arrives, a MILP is regenerated solving the new problem with the TWWT and the stability as an objective. Experimental results discuss the impact of increasing the jobs weight and its relationship with the system performance and the resolution time.

In the context of the fourth revolution, today's industry is developing new technologies and solutions for the production management. Moreover, customers' practices are also changing, they can modify or cancel an order at any time, thus impacting the already established planning. Therefore, rescheduling is necessary to revise the initial schedule in a cost-effective manner. In this work, a new performance measure is investigated, considering simultaneously the weighted waiting time as an efficiency criterion, and the total completion time deviation as a stability criterion. The objective function which considers several criteria, could be very significant and helpful for industrial actors. In addition, an innovative concept is introduced, consisting in increasing the jobs weight at each time they are rescheduled, to take fairly into account the low weight earliest jobs. This paper addresses a parallel machine rescheduling problem, disrupted by new jobs arrival. Based on predictive reactive strategy, a linear mathematical model is developed, as well as an iterative methodology to solve the described problem. Finally, numerical results are analyzed to show the effect produced by the combination of efficiency and stability criteria.