Last-mile delivery problems where trucks and drones collaborate to deliver goods to final customers are considered. We focus on settings where a fleet with several homogeneous trucks work in parallel to collaborative drones, able to combine with each other to optimize speed and power consumption for deliveries. A heuristic for the min-max vehicle routing problem is coupled with constraint programming models, leading to an effective method able to provide several state-of-the-art solutions for the instances commonly adopted in the literature.
Julia is a programming language suitable for data analysis and scientific computing that combines simplicity of productivity languages with characteristics of performance-oriented languages. In this paper, we are interested in studying the use of Julia to implement Multi-Objective MetaHeuristics. Concretely, we use the Java-based jMetal framework as a reference support and investigate how Julia could be used to design and develop the component-based architecture for multi-objective evolutionary algorithms that jMetal provides. By using the NSGA-II algorithm as an example, we analyze the advantages and shortcomings of using Julia in this context, including aspects related to reusing jMetal code and a performance comparison.
This work proposes a Large Neighborhood Search Metaheuristic for solving a mixed-model assembly line balancing problem with walking workers and dynamic task assignment. The considered problem is a multi-stage stochastic program with integer recourse. These problems are very hard to solve because the number of binary variables increases exponentially with the number of production cycles. We study different decomposition approaches, and our results suggest that re-optimizing for a sub-tree outperforms other decompositions, such as model-based or station decomposition.
With the escalating demand for meal delivery services, this study delves into the Meal Delivery Routing Problem (MDRP) within the context of last-mile logistics. Focusing on the critical aspects of courier allocation and order fulfillment, we introduce a novel approach utilizing a GRASP metaheuristic. The algorithm optimizes the assignment of couriers to orders, considering dynamic factors such as courier availability, order demands, and geographical locations. Real-world instances from a Colombian delivery app form the basis of our computational analysis. Calibration of GRASP parameters reveals a delicate trade-off between solution quality and computational time. Comparative results with a simulation-optimization based study underscore GRASP’s competitive performance, demonstrating strengths in fulfilling orders and routing efficiency across diverse instances. This research enhances operational efficiency in the burgeoning food delivery industry, shedding light on practical algorithms for last-mile logistics optimization.
We study a general class of single-machine scheduling problems with setup time/cost and no idle time in the schedule. It includes a variety of other scheduling and routing problems as special cases. A polynomial size mixed-integer linear programming (MILP) formulation is presented. A tabu search (TS) algorithm, built upon a novel composite-move neighborhood structure, is developed for solving the addressed problem effectively and efficiently.
