Browsing by Author "Tiwari, Manoj Kumar"

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A Particle Swarm Optimization Approach for Route Planning with Cross-Docking
(2015)
Chen, Mu-Chen
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Hsiao, Yu-Hsiang
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Reddy, Himadeep
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Tiwari, Manoj Kumar
;
運輸與物流管理系註：原交通所+運管所
In cross-docking operations, planners need to coordinate the inbound, docking and outbound logistics operations to ensure a smooth flow of goods across the supply chain. The operation management of cross docking is a crucial task with high complexity for the logistics systems. This paper attempts to address the Vehicle Routing Problems (VRPs) of distribution centers with multiple cross-docks for processing multiple products. In this paper, the mathematical model intends to minimize the total cost of operations subjected to a set of time and capacity constraints. Due to high complexity of model, a variant of Particle Swarm Optimization (PSO) with a Self-Learning approach is tailored to solve the VRP. Two test problems are generated and results are obtained.
An efficient ICA-DW-SVDD fault detection and diagnosis method for non-Gaussian processes
(2016)
Chen, Mu-Chen
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Hsu, Chun-Chin
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Malhotra, Bharat
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Tiwari, Manoj Kumar
;
運輸與物流管理系註：原交通所+運管所
Independent Component Analysis (ICA) has been extensively used for detecting faults in industrial processes. While applying ICA to process monitoring, the inability of identifying the important components affect the fault diagnosis ability. For further improving the competence of ICA, this paper proposes an approach integrating ICA, Durbin Watson (DW) criterion and Support Vector Data Description (SVDD) to monitor non-Gaussian process for detecting faults. In the proposed approach, namely ICA-DW-SVDD, ICA is a non-Gaussian information extractor from original variables, DW identifies dominating ICs, and SVDD plays the role of fault detector. This paper also discusses the retracing method to detect original variables causing disturbance in the process. One simulation case and the Tennessee Eastman Process are used to demonstrate the effectiveness of our proposed approach.
The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks
(2016-07)
Chen, Mu-Chen
;
Hsiao, Yu-Hsiang
;
Reddy, Reddivari Himadeep
;
Tiwari, Manoj Kumar
;
運輸與物流管理系註：原交通所+運管所
Vehicle Routing Problems (VRPs) in distribution centers with cross-docking operations are more complex than the traditional ones. This paper attempts to address the VRP of distribution centers with multiple cross-docks for processing multiple products. In this paper, the mathematical model intends to minimize the total cost of operations subjected to a set of constraints. Due to high complexity of model, it is solved by using a variant of Particle Swarm Optimization (PSO) with a Self-Learning strategy, namely SLPSO. To validate the effectiveness of SLPSO approach, benchmark problems in the literature and test problems are solved by SLPSO. (C) 2016 Elsevier Ltd. All rights reserved.