Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.

Integrating operations research into green logistics:A review

Logistical activities have a significant global environmental impact,necessitating the adoption of green logistics practices to mitigate environmental effects.The COVID-19 pandemic has further emphasized the urgency to address the environmental crisis.Operations research provides a means to balance environmental concerns and costs,thereby enhancing the management of logistical activities.This paper presents a comprehensive review of studies integrating operations research into green logistics.A systematic search was conducted in the Web of Science Core Collection database,covering papers published until June 3,2023.Six keywords(green logistics OR sustainable logistics OR cleaner logistics OR green transportation OR sustainable transportation OR cleaner transportation)were used to identify relevant papers.The reviewed studies were categorized into five main research directions:Green waste logistics,the impact of costs on green logistics,the green routing problem,green transport network design,and emerging challenges in green logistics.The review concludes by outlining suggestions for further research that combines green logistics and operations research,with particular emphasis on investigating the long-term effects of the pandemic on this field.

Emerging approaches applied to maritime transport research:Past and future

Maritime transport is the backbone of international trade and globalization.Maritime transport research can be roughly divided into two categories,namely the shipping side and the port side.Most of the classic approaches adopted to address practical problems in these research topics are based on long-term observations and expert knowledge,while few of them are based on historical data accumulated from practice.In recent years,emerging approaches,which we refer to as machine learning and deep learning techniques in this essay,have been receiving a wider attention to solve practical problems.As a relatively conservative industry,there are some initial trials of applying the emerging approaches to solve practical problems in the maritime sector.The objective of this essay is to review the application of emerging approaches to maritime transport research.The main research topics in maritime transport and classic methods developed to solve them are first presented.The introduction of emerging approaches and their suitability to be applied in maritime transport research is then discussed.Related existing studies are then reviewed according to problem settings,main data sources,and emerging approaches adopted.Challenges and solutions in the process are also discussed from the perspectives of data,model,users,and targets.Finally,promising future research directions are identified.This essay is the first to give a comprehensive review of existing studies on developing machine learning and deep learning models together with popular data sources used to address practical problems in maritime transport.