ABSTRACT
Emergencies, such as pandemics (e.g., COVID-19), warrant urgent production and distribution of goods under disrupted supply chain conditions. An innovative logistics solution to meet the urgent demand during emergencies could be the factory-in-a-box manufacturing concept. The factory-in-a-box manufacturing concept deploys vehicles to transport containers that are used to install production modules (i.e., factories). The vehicles travel to customer locations and perform on-site production. Factory-in-a-box supply chain optimization is associated with a wide array of decisions. This study focuses on selection of vehicles for factory-in-a-box manufacturing and decisions regarding the optimal routes within the supply chain consisting of a depot, suppliers, manufacturers, and customers. Moreover, in order to contrast the options of factory-in-a-box manufacturing with those of conventional manufacturing, the location of the final production is determined for each customer (i.e., factory-in-a-box manufacturing with production at the customer location or conventional manufacturing with production at the manufacturer locations). A novel multi-objective optimization model is presented for the vehicle routing problem with a factory-in-a-box that aims to minimize the total cost associated with traversing the edges of the network and the total cost associated with visiting the nodes of the network. A customized multi-objective hybrid metaheuristic solution algorithm that directly considers problem-specific properties is designed as a solution approach. A case study is performed for a vaccination project involving factory-in-a-box manufacturing along with conventional manufacturing. The case study reveals that the developed solution method outperforms the ε-constraint method, which is a classical exact optimization method for multi-objective optimization problems, and several well-known metaheuristics. © 2022 Elsevier Ltd
ABSTRACT
Drones are receiving popularity with time due to their advanced mobility. Although they were initially deployed for military purposes, they now have a wide array of applications in various public and private sectors. Further deployment of drones can promote the global economic recovery from the COVID-19 pandemic. Even though drones offer a number of advantages, they have limited flying time and weight carrying capacity. Effective drone schedules may assist with overcoming such limitations. Drone scheduling is associated with optimization of drone flight paths and may include other features, such as determination of arrival time at each node, utilization of drones, battery capacity considerations, and battery recharging considerations. A number of studies on drone scheduling have been published over the past years. However, there is a lack of a systematic literature survey that provides a holistic overview of the drone scheduling problem, existing tendencies, main research limitations, and future research needs. Therefore, this study conducts an extensive survey of the scientific literature that assessed drone scheduling. The collected studies are grouped into different categories, including general drone scheduling, drone scheduling for delivery of goods, drone scheduling for monitoring, and drone scheduling with recharge considerations. A detailed review of the collected studies is presented for each of the categories. Representative mathematical models are provided for each category of studies, accompanied by a summary of findings, existing gaps in the state-of-the-art, and future research needs. The outcomes of this research are expected to assist the relevant stakeholders with an effective drone schedule design. IEEE
ABSTRACT
The maritime transportation flows and container demand have been increasing over time, although the COVID-19 pandemic may slow down this trend for some time. One of the common strategies adopted by shipping lines to efficiently serve the existing customers is the deployment of large ships. The current practice in the liner shipping industry is to deploy a combination of ships of different types with different carrying capacities (i.e., heterogeneous fleet), especially at the routes with a significant demand. However, heterogeneous fleets of ships have been investigated by a very few studies addressing the tactical liner shipping decisions (i.e., determination of service frequency, ship fleet deployment, optimization of ship sailing speed, and design of ship schedules). Moreover, limited research efforts have been carried out to simultaneously capture all the major tactical liner shipping decisions using a single solution methodology. Therefore, this study proposes an integrated optimization model that addresses all the major tactical liner shipping decisions and allows the deployment of a heterogeneous ship fleet at each route, considering emissions generated throughout liner shipping operations. The model's objective maximizes the total turnaround profit generated from liner shipping operations. A decomposition-based heuristic algorithm is presented in this study to solve the model proposed and efficiently tackle large-size problem instances. Numerical experiments, carried out for a number of real-world liner shipping routes, demonstrate the effectiveness of the proposed methodology. A set of managerial insights, obtained from the proposed methodology, are also provided. © 2021 Elsevier Ltd