An Artificial Neural Network Based Algorithm For Real Time Dispatching Decisions

Chakravorty, Shiladitya; Nagarur, Nagendra N.

doi:10.1109/asmc49169.2020.9185213

Cited by 10 publications

(2 citation statements)

References 13 publications

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“…The last kilometer dispatching services [43] Management Business modeling of urban logistics [44] Simulation-optimization Different dispatching modes integration [45] Simulation-optimization Future automated parcel locker implementations [46] The aggregation The last-mile delivery [47] Genetic Algorithm and Extended Particle Swarm Optimization Secondary logistics dispatching path optimization [48] Receding horizon task assignment (RHTA) heuristic Distribution of unmanned aerial vehicles [14] dual objective optimization and Tabu search algorithm Low-carbon logistics dispatching [49] Hybrid agent-based Exchanging information and making advanced decisions among all stakeholders in smart logistics delivery [36] Dimension reduction Dynamic route optimization [50] Operational research and mathematical Quality and cost of the last mile delivery service [51] Improved clone process Vehicle route problem [52] Improved particle swarm optimization Route optimization for vehicle driving [53] The hybrid route planning method Route optimization for a delivery process [54] Reverse labeling Dijkstra algorithm Path Planning [55] Hybrid dispatching Predictive vehicle dispatching [56] Artificial Neural Network (ANN) Dispatching decision [57] Hybrid prediction Pickup and delivery [58] not expose the main steps to reach sustainable dispatching. Therefore, Frehe [44] analyzed the characteristics of group logistics in the dispatching process and revealed the steps to implement sustainable dispatching.…”

Section: B Analysis Of Smart Logistics Delivery Methodsmentioning

confidence: 99%

“…Zhu et al [55] constructed a novel reverse Dijkstra to solve path planning issues. Chakravorty et al [57] proposed an ANN-based approach to overcome the challenges that influence real-time dispatching decisions. Also, other research teams adopted more neural networkbased methods for dispatching and delivery problems [56,58].…”

Section: B Analysis Of Smart Logistics Delivery Methodsmentioning

confidence: 99%

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An Advanced System to Enhance and Optimize Delivery Operations in a Smart Logistics Environment

et al. 2022

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Optimization of order dispatch operations and delivery time prediction is a major concern in supply chains, mainly for e-commerce, which requires the implementation of advanced solutions to reduce delivery time, minimize costs and maximize customer satisfaction. In practice, they fail to warrant scalable and sustainable solutions as the numbers of orders become larger. For that, proper prediction and optimization for delivery operations are required for optimal logistics management. This paper presents an advanced logistics service, which warrants dynamic coordination among all the actors in the smart logistics environment. The proposed advanced shipping system consists of two main parts: the delivery prediction model to compute the expected arrival time, and a hybrid optimization model to tackle path issues. We demonstrate that the advanced system consistently outperforms conventional standard dispatching methods, which means that the proposed approach effectively contributes to optimizing the distribution chain and reducing costs.

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Section: B Analysis Of Smart Logistics Delivery Methodsmentioning

confidence: 99%

Section: B Analysis Of Smart Logistics Delivery Methodsmentioning

confidence: 99%

An Advanced System to Enhance and Optimize Delivery Operations in a Smart Logistics Environment

et al. 2022

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Managing product-inherent constraints with artificial intelligence: production control for time constraints in semiconductor manufacturing

May,

Oberst,

Lanza

2024

J Intell Manuf

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Continuous product individualization and customization led to the advent of lot size one in production and ultimately to product-inherent uniqueness. As complexities in individualization and processes grow, production systems need to adapt to unique, product-inherent constraints by advancing production control beyond predictive, rigid schedules. While complex processes, production systems and production constraints are not a novelty per se, modern production control approaches fall short of simultaneously regarding the flexibility of complex job shops and product unique constraints imposed on production control. To close this gap, this paper develops a novel, data driven, artificial intelligence based production control approach for complex job shops. For this purpose, product-inherent constraints are resolved by restricting the solution space of the production control according to a prediction based decision model. The approach validation is performed in a real semiconductor fab as a job shop that includes transitional time constraints as product-inherent constraints. Not violating these time constraints is essential to avoid scrap and similarly increase quality-based yield. To that end, transition times are forecasted and the adherence to these product-inherent constraints is evaluated based on one-sided prediction intervals and point estimators. The inclusion of product-inherent constraints leads to significant adherence improvements in the production system as indicated in the real-world semiconductor manufacturing case study and, hence, contributes a novel, data driven approach for production control. As a conclusion, the ability to avoid a large majority of violations of time constraints shows the approaches effectiveness and the future requirement to more accurately integrate such product-inherent constraints into production control.

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Using neural network to establish manufacture production performance forecasting in IoT environment

Liu

2022

J Supercomput

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An Artificial Neural Network Based Algorithm For Real Time Dispatching Decisions

Cited by 10 publications

References 13 publications

An Advanced System to Enhance and Optimize Delivery Operations in a Smart Logistics Environment

An Advanced System to Enhance and Optimize Delivery Operations in a Smart Logistics Environment

Managing product-inherent constraints with artificial intelligence: production control for time constraints in semiconductor manufacturing

Using neural network to establish manufacture production performance forecasting in IoT environment

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