The postal sector is essential in enhancing services for businesses and individuals by establishing a reliable communication network that ensures the efficient collection, transfer, and global delivery of mail, funds, and parcels. Consequently, optimizing routing and packing systems for the collection and transportation of letters and parcels is a critical component of an effective delivery management system. Traditionally, postal distribution challenges are modeled as Capacitated Vehicle Routing Problems (CVRP). To account for parcel dimensions, this paper introduces the Capacitated Vehicle Routing Problems with Two-Dimensional Loading Constraints (2L-CVRP). The problem involves designing routes that originate and terminate at a central depot, using a fleet of identical vehicles to meet demands at multiple locations. Furthermore, items transported in each vehicle must comply with two-dimensional orthogonal packing constraints, with the primary objective of minimizing total transportation costs. Given the NP-hard complexity of this problem, the paper proposes a hybrid metaheuristic that integrates Variable Neighborhood Search (VNS) with a Genetic Algorithm (GA) to improve GA’s convergence toward high-quality solutions. This hybrid method capitalizes on GA’s exploratory strengths and VNS’s ability to effectively exploit the solution space. VNS is incorporated into GA’s mutation operator, broadening and diversifying the solution space, thereby enabling the hybrid algorithm to explore new regions within the search space more effectively. We design a Decision Support System (DSS) that uses a hybrid HGAVNS to fulfill customer needs and enhance the efficiency of vehicle routing. The proposed method is tested against both generated and existing approaches using benchmark instances. Empirical results on 210 benchmark instances demonstrate the competitiveness of the hybrid approach hybrid approach is highly competitive in terms of solution quality. Overall, the experiments highlight that the Hybrid GA-VNS provides an efficient solution for the 2L-CVRP, delivering results that are on par with state-of-the-art methods.

Efficient management of healthcare waste is crucial for public health and environmental sustainability. However, the dynamic nature of waste generation and transportation presents challenges in optimizing collection routes. This study proposes a novel architecture using big data analytics and a parallel Spark-Genetic Algorithm (Spark-GA) to address the dynamic healthcare waste collection and transportation vehicle routing problems (DHWVRP). The architecture integrates real-time data streams from various sources to dynamically update waste generation rates and transportation conditions. However, the real-time processing using Spark-GA can facilitate improved decision-making. The Spark-GA optimizes collection routes based on evolving data, considering factors such as waste volume, vehicle capacity, and time constraints. Results demonstrate significant improvements in route efficiency and resource utilization compared to traditional methods. This research contributes to enhancing the sustainability and effectiveness of healthcare waste management through advanced data analytics and optimization techniques. In order to evaluate the effectiveness of the suggested model, we execute our research using a real-time environment that is stimulated by injecting 10,000 messages every 0.1s and second. Results improve the performance of spark-GA processing and involve the correct function of spark through the data loading from kafka in the two cases. As a result, the performance of the suggested architecture is apparent.

The dynamic pollution routing problem poses a significant challenge in optimizing transportation routes to minimize environmental impact. This study presented a novel approach using big data analytics to address this issue effectively. Specifically, we employ a parallel Spark-Genetic Algorithm (Spark-GA) to efficiently manage a large amounts of data generated from diverse sources including traffic cameras, sensors, social media, and GPS and optimize routing decisions in real-time. Our method offers a scalable and efficient solution for dynamically adapting to changing pollution levels and traffic conditions to deal with the computational power of Spark and the optimization capabilities of genetic algorithms. We demonstrate the effectiveness and scalability of our approach in achieving improved route optimization and reduced environmental impact with experiments. Our findings highlight the potential of big data analytics in tackling complex transportation and environmental challenges, paving the way for more sustainable and efficient urban logistics systems. To evaluate the effectiveness of the suggested model, we execute our research using a dynamic environment that is stimulated by injecting 10,000 messages every 0.1s and second. Results improve the performance of spark-GA processing and involve the correct function of spark through the data loading from kafka in the two cases. As a result, the performance of the suggested architecture is apparent.

In recent years, the explosion of a large, complex and various amount of data in the transportation field required the implementation of advanced technologies. However, a numerous Intelligent Transportation System (ITS) technologies have been deployed. Data generated in ITS can be stored, managed, analysed and processed using Big Data analytics. The main contributions of this paper are first to design an architecture to deal with big data analytics in ITS and then to implement a parallel Genetic Algorithm(GA)-MapReduce (MR) as an optimization technique in order to minimize the travel distance. The purpose of this parallelism is to improve the population diversity using the parallel processing of the MapReduce and the exploration of the search space using GA in the case of Vehicle routing problem. To test the performance of the proposed model, our analysis is conducted from data collected from sensors located on the highways of Tunisia.

Recently, the explosion of large amounts of traffic data has guided data scientists to create models with big data for a better decision-making. Big Data applications process and analyze this huge amounts of data (collected from a variety of heterogeneous data sources) that cannot be processed with traditional technologies. In this paper, Big Data frameworks are used for solving an optimization problem known as Dynamic Vehicle Routing Problem (DVRP). Hence, due to the NP-Hardness of the problem and to deal with a large size of data, we develop a parallel Spark Genetic Algorithm named (S-GA). This parallelism aims to take the advantage of Spark’s in-memory computing ability (as a master-slave distribution computing) and GA’s iterations operations. Parallel operations were used for fitness evaluation and genetic operations. Based on the parallel S-GA a decision support system is developed for the DVRP in order to generate the best routes. The experiments show that our proposed architecture is improved due to its capacity when coping with Big Data optimization problems by interconnecting components and deploying on different nodes of a cluster.

In this paper, we present a generalisation of the Capacitated Vehicle Routing problem where customer demand is composed of two-dimensional weighted items, with a time windows, called 2L-CVRPTW. The objective consists in designing a set of trips, starting and terminating at a central depot, minimizing the total transportation cost with a homogenous fleet of vehicles based on a depot node. Items in each vehicle trip must satisfy the two-dimensional orthogonal packing constraint. In literature, several exact and approximate approaches was proposed to find the best and the most feasible solution to the problem. In this paper, we use an adaptive genetic algorithm (GA) to solve the 2L-CVRPTW using an ordered crossover operator. The effectiveness of our approach was demonstrated through experiments on benchmark instances. The results showed that our proposed approach is competitive in terms of the quality of the solutions found.