One of the most important problems in the petroleum industry is the well-known petrol station replenishment problem with time windows, which calls for the determination of optimal routes by using a fleet of tank trucks to serve a set of petrol stations over a given planning horizon. In this paper, we introduce a model and solve a specific problem that originates from a real-life application arising in the fuel distribution where specific attention is paid to tank trucks with compartments and customers with different types of products and time windows. Literally, we call the resulting problem the multi-compartment vehicle routing problem with time windows (MCVRPTW). To solve the MCVRPTW, we begin by describing the problem, providing its mathematical formulation and discussing the sense of its constraints. As the problem is NP-hard, we propose an efficient tabu search algorithm for its solution. We introduce the Kolmogorov–Smirnov statistic into the framework of the tabu search to manage the neighbourhood size. We evaluate the performance of the algorithm on a set of vehicle routing problems with time windows instances as well as other realistic instances. Our results are compared to CPLEX, to the heuristics reported in the literature and also to those extracted from the company plans.

The aircraft landing problem (ALP) consists of scheduling the landing of aircrafts onto the available runways in an airport by assigning to each aircraft a landing time and a specific runway while respecting different operational constraints. This is a complex task for the air traffic controller, especially when the flow of aircrafts entering the radar range is continuous and the number of aircrafts is unknown a priori. In this paper, we study the dynamic version of the ALP when new aircrafts appear over time, which means that the landing of the previous aircrafts should be rescheduled. To solve this problem, we propose a memetic algorithm combining an ant colony algorithm and a local heuristic

This paper examines the impact of reducing ship turnaround time on the performance of container terminals, with a focus on leveraging artificial intelligence (AI) to enhance operational efficiency. It presents a novel framework combining machine learning algorithms with discrete-event simulation to predict ship turnaround times using historical data. The proposed approach is empirically validated with data from the Algiers Port Container Terminal, achieving an exceptionally high predictive precision of 0.9991. Simulating terminal operations with both real and predicted data offers valuable insights into improving performance. The results demonstrate that minimizing empty trips and reducing the waiting times for handling equipment significantly enhance turnaround time. Additionally, optimizing terminal operations reduces carbon emissions, aligning with sustainable development objectives in port logistics. This study proposes a novel integration of machine learning and simulation, demonstrating its effectiveness in optimizing ship turnaround times and reducing carbon emissions. By integrating machine learning and discrete-event simulation, this research offers new perspectives on port logistics, contributing to the advancement of sustainable and efficient terminal operations.

Handling rapidly evolving technology and almost daily changes in demand and customer satisfaction, while maintaining competitiveness in a highly competitive environment, requires good coordination and planning of both production and logistics activities on the shop floor, namely: machines and tools. The goal is to optimize costs and reduce delivery lead times in order to provide the customer just in time; we focus on the job shop scheduling problem (JSSP), which is one of the most complex problems encountered in real shop floor. In this paper, we study a generalized (JSSP) including transportation times and a set of additional constraints on the number of transporter vehicles and their multiple transfer capabilities and also on the limited capacity of input/output of machines. The objective is to minimize in one hand tardiness and earliness penalties on delays and advances compared to the lead-time delivery of finished jobs and on the other hand the number of empty moves of transporter vehicles.