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Collaborative Planning and Event Monitoring Over Supply Chain Network

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Collaborative Planning and Event Monitoring Over Supply Chain Network

Ray, Sujoy (2017) Collaborative Planning and Event Monitoring Over Supply Chain Network. PhD thesis, Concordia University.

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Abstract

The shifting paradigm of supply chain management is manifesting increasing reliance on automated collaborative planning and event monitoring through information-bounded interaction across organizations. An end-to-end support for the course of actions is turning vital in faster incident response and proactive decision making. Many current platforms exhibit limitations to handle supply chain planning and monitoring in decentralized setting where participants may divide their responsibilities and share computational load of the solution generation. In this thesis, we investigate modeling and solution generation techniques for shared commodity delivery planning and event monitoring problems in a collaborative setting. In particular, we first elaborate a new model of Multi-Depot Vehicle Routing Problem (MDVRP) to jointly serve customer demands using multiple vehicles followed by a heuristic technique to search near-optimal solutions for such problem instances. Secondly, we propose two distributed mechanisms, namely: Passive Learning and Active Negotiation, to find near-optimal MDVRP solutions while executing the heuristic algorithm at the participant's side. Thirdly, we illustrate a collaboration mechanism to cost-effectively deploy execution monitors over supply chain network in order to collect in-field plan execution data. Finally, we describe a distributed approach to collaboratively monitor associations among recent events from an incoming stream of plan execution data. Experimental results over known datasets demonstrate the efficiency of the approaches to handle medium and large problem instances. The work has also produced considerable knowledge on the collaborative transportation planning and execution event monitoring.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (PhD)
Authors:Ray, Sujoy
Institution:Concordia University
Degree Name:Ph. D.
Program:Computer Science
Date:6 April 2017
Thesis Supervisor(s):Debbabi, Mourad
Keywords:Vehicle routing, Facility deployment, Incremental association rule mining, Heuristics, Meta-heuristics, Distributed system design
ID Code:982325
Deposited By: SUJOY RAY
Deposited On:31 May 2017 18:08
Last Modified:18 Jan 2018 17:54
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