Logistique et Supply Chain

La logistique urbaine regroupe les flux de marchandises, d’informations et les acteurs qui assurent les activités logistiques en ville. Indispensable au fonctionnement urbain, elle génère également des nuisances comme la pollution et la congestion. Des programmes accompagnent les collectivités dans la mise en place de solutions durables. La réussite repose sur la connaissance des besoins, la prise en compte de tous les acteurs et la concertation. Des initiatives collaboratives permettent ainsi d’améliorer durablement la logistique urbaine.

The optimal tool routing for cutting machines, also known as cutting path optimisation is an important problem in production research. This problem is relevant in various manufacturing environments such as aeronautic, automotive, garment and semiconductor industries. In this paper, we introduce a general solution framework for the discrete Cutting Path Problem which includes: (i) the universal approach to reduce numerous settings of this problem to the appropriate auxiliary instances of the well-known Precedence Constrained Generalized Traveling Salesman Problem; (ii) the proposition of efficient solution methods for finding (sub-) optimal solutions. We carry out extensive computational experiments in order to evaluate performance of the proposed framework and the obtained results demonstrate its efficiency for real-life industrial instances.

Forecasting is a key concept in management.
It consists of anticipating future events using available information.
Forecasting is mainly based on past and present data.
Its purpose is to support decision-making and planning.
Managers use forecasts to allocate resources and reduce uncertainty.
There are short-term and long-term forecasts.
Forecasting methods can be qualitative or quantitative.
Forecasts are estimates, not exact predictions.
They must be updated regularly.

Black-box models make decisions that are difficult for humans to understand or explain. We only see their inputs and outputs, not the reasoning behind them. For example, an algorithm that screens job applicants might reject qualified candidates without clear reasons. This lack of transparency can weaken trust and accountability. Hidden biases may be learned from past data and quietly amplified, leading to discrimination that often goes unnoticed until it causes harm. Since employment decisions are highly regulated and must be fair and auditable, black-box systems complicate compliance and investigations. Therefore, transparency and human oversight are crucial to mitigate these risks.