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2017-07-05 15:00:00 2017-07-05 16:00:00 America/New_York PhD Seminar - Mohammad Moshref Javadi "Optimization Modeling and Analysis of Customer-Centric Delivery Logistics" GRIS 302

July 5, 2017

PhD Seminar - Mohammad Moshref Javadi

Event Date: July 5, 2017
Hosted By: Dr. Seokcheon Lee
Time: 2:00 - 3:00 PM
Location: GRIS 302
Contact Name: Cheryl Barnhart
Contact Phone: Cheryl Barnhart
Contact Email: cbarnhar@purdue.edu
Open To: all
Priority: No
School or Program: Industrial Engineering
College Calendar: Show
“Optimization Modeling and Analysis of Customer-Centric Delivery Logistics”

ABSTRACT

Distribution of products and services is an essential part of production and service systems. Customer-centric delivery systems are a class of logistics systems that focuses on minimizing the delivery time by making decisions on questions such as vehicle routing, locations of distribution centers, and capacity planning. The minimum delivery time plays a crucial role in both disaster relief operations and commercial delivery systems. While disaster relief logistics seeks to minimize loss and damage, commercial delivery systems aim at enhancing customer satisfaction and profit maximization.

In this research, first a taxonomy of customer-centric routing problems is developed. Then, the first part of the research considers the customer-centric routing problems with large customer demands which result in split delivery among more than a vehicle. Assuming multiple commodities are distributed to customers, the goal is to find the optimal routes of the vehicles allowing the split delivery. This problem is mathematically formulated in two models and valid inequalities are provided to enhance the understandings of the problem and strengthen the computational performance of the mathematical models. The properties of the problem are used to develop an efficient algorithm which combines the concepts and operators of Simulated Annealing and Variable Neighborhood Search.

The second part of the research studies the customer-centric routing problems in combination with location problem due to their interrelated nature. In this research, the goal is to decide distribution center locations and vehicle routes simultaneously to minimize delivery time. The problem is formulated and two efficient metaheuristic algorithms are designed to deal with large-scale problems: Memetic Algorithm (MA) and Recursive Granular Algorithm (RGA)

In the third study, the goal is to study how we can benefit from the emerging "drone technology" in reducing delivery time. This research assumes a combined system of vehicles and drones in which a vehicle carries both supplies and drones and launches drones at some points in its route to deliver packages to recipients. The objective is to decide vehicle route, drone launching locations, and customers to serve by the vehicle and drones. The problem is then extended to the multi-trip case in which each drone can be relaunched to serve several customers at each stop. These problems are mathematically formulated and a bound analysis is conducted to investigate the maximum possible savings achievable by employing drones. The models are tested and analyzed in various hypothetical problems and in a case study developed in Richmond, Virginia.