The main purpose of this paper was to present the Forestry Routing Optimization Model (FRoM) as a version of the classical Vehicle Routing Problem (VRP). This work approaches for wood logistic problems consisting of simple displacement and multiple displacements of trucks toward the stands. The FRoM encompasses both steps into one single integer mixed linear programming model, considering cranes and trucks schedule, fleet reduction, reduction of overtime, reduction of half-load transportation, and approaching the minimum distance traveled along a fixed planning horizon. Some technique constraints were implemented to provide accurate model function. An executed real problem data was used to compare the outcomes. The objective was to carry and transport 21,881.82 tons of lumber from 10 stands using a total of 48 trucks and 5 cranes in a planning horizon of 6 days, which each day has 20 hours of effective work. The FRoM has performed a fleet reduction of 72.92%, eliminating overtime. It has reduced the half-load trips to the order of 3.17% of all routes. The crane's analysis allowed catching points of inefficiency due to operational idleness. The FRoM provided savings of 49.12% at all logistic costs. FRoM has shown to be a good option as a route optimizer for forestry logistics.
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http://dx.doi.org/10.1590/0001-3765202020200263 | DOI Listing |
Sensors (Basel)
December 2023
College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China.
Many emerging Internet of Things (IoT) applications deployed on cloud platforms have strict latency requirements or deadline constraints, and thus meeting the deadlines is crucial to ensure the quality of service for users and the revenue for service providers in these delay-stringent IoT applications. Efficient flow scheduling in data center networks (DCNs) plays a major role in reducing the execution time of jobs and has garnered significant attention in recent years. However, only few studies have attempted to combine job-level flow scheduling and routing to guarantee meeting the deadlines of multi-stage jobs.
View Article and Find Full Text PDFSensors (Basel)
September 2023
Key Laboratory of Smart Agriculture and Forestry, College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Knowledge distillation (KD) is a well-established technique for compressing neural networks and has gained increasing attention in object detection tasks. However, typical object detection distillation methods use fixed-level semantic features for distillation, which might not be best for all training stages and samples. In this paper, a multilayer semantic feature adaptive distillation (MSFAD) method is proposed that uses a routing network composed of a teacher and a student detector, along with an agent network for decision making.
View Article and Find Full Text PDFTop (Berl)
March 2022
Insight Centre for Data Analytics, School of Computer Science and IT, University College Cork, Cork, Ireland.
Sustainable forest management is concerned with the management of forests according to the principles of sustainable development. As a contribution to the field, this paper combines the Vehicle Routing Problem (VRP) (in which the vehicles are harvesters) with the Multiple Stock Size Cutting Stock Problem under uncertainty (in which the stock is logs). We present an Integer Linear Program that dynamically combines the cutting of the uncertain stock with vehicle routing, and uses it to address real-life problems.
View Article and Find Full Text PDFSensors (Basel)
January 2023
College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China.
With the construction and development of modern and smart cities, people's lives are becoming more intelligent and diversified. Surveillance systems increasingly play an active role in target tracking, vehicle identification, traffic management, etc. In the 6G network environment, facing the massive and large-scale data information in the monitoring system, it is difficult for the ordinary processing platform to meet this computing demand.
View Article and Find Full Text PDFTransp Res E Logist Transp Rev
February 2021
School of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, China.
Covid-19, the global pandemic, has taught us the importance of contactless delivery service and robotic automation. Using self-driving delivery robots can provide flexibility for on-time deliveries and help better protect both driver and customers by minimizing contact. To this end, this paper introduces a new vehicle routing problem with time windows and delivery robots (VRPTWDR).
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