Amin Abbasi-Pooya
Graduate Reseach and Teaching Assistant at The University of Kansas School of Business
Graduate Reseach and Teaching Assistant at The University of Kansas School of Business
I am interested in the analysis, modeling, and optimization of complex systems through the use of operations research methods. In this regard, I particularly worked on problems with vehicle routing nature, namely, Helicopter Routing Problem and consolidation of milk-run logistics. In one project, the problem was to route a helicopter that transports crew members to and from offshore gas platforms, where weight and passenger capacity, and flight time constraints must be taken into account. In another project, the problem was to design an optimal logistics network for a branch of the largest Iranian automaker. The problem was to route various types of vehicles to pick up and deliver pallets of products in a network consisting of supplier and assembly plant nodes. I also collaborated in a project aimed at the joint determination of the optimal asset procurement method and its maintenance schedule. The objectives to be optimized are total cost, reliability, and availability. I also worked in the project for optimization of spare parts management at the National Iranian Copper Industries Co. (NICICO). My responsibilities include analyzing and optimizing spare parts inventory level, developing a software tool for forecasting spare parts demand, and analyzing and optimizing the process of spare parts management system.
The University of Kansas School of Business
https://www.linkedin.com/company/the-ku-school-of-business
Lawrence, Kansas Area
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Graduate Reseach and Teaching Assistant
Sazeh Gostar Saipa (S.G.S)
https://www.linkedin.com/company/sazeh-gostar-saipa-s-g-s-
Tehran Province, Iran
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R&D Expert
Sazeh Gostar Saipa (S.G.S)
https://www.linkedin.com/company/sazeh-gostar-saipa-s-g-s-
Tehran Province, Iran
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Transportation Expert
i-REAM
https://www.linkedin.com/company/i-ream
Focusing on Optimization of Spare Parts Management System: 
- Optimizing the spare parts' procurement process
- Forecasting spare parts' consumption
Tehran Province, Iran
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Industrial Engineering Expert
Ph.D. 
Supply Chain Management/ Decision Sciences
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The University of Kansas School of Business
https://www.linkedin.com/school/the-ku-school-of-business/
Master's degree
Industrial Engineering
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Tarbiat Modares University
https://www.linkedin.com/school/tarbiat-modares-university/
Bachelor's degree
Industrial Engineering
Yazd University
Crew must be transported to offshore platforms to regulate the production of oil and gas. Helicopter is the preferred means of transportation, which incurs a high cost on oil and gas companies. So, flight schedule should be planned in a way that minimizes the cost (or equivalently the makespan) while considering all conditions related to helicopter’s maximum flight time, passenger and weight capacity, and arrival time of crew members to airport. Therefore, a mathematical formulation is proposed in this paper that has the objective of minimizing the finish time of the final tour of the helicopter (i.e., makespan) while taking the above-mentioned conditions into account. The model is solved for problems that have at most 11 rigs, which results in optimal solution in a reasonable amount of time. For large-size problems for which the computational effort for finding exact solution is not possible in an efficient way, a metaheuristic, namely League Championship Algorithm (LCA), is proposed. Computational experiments demonstrate that LCA can find good solutions efficiently, so it may be employed for large-size helicopter routing problems in an efficient manner.
A Rig-Based Formulation and a League Championship Algorithm for Helicopter Routing in Offshore Transportation
Proceedings of the 2nd International Conference on Data Engineering and Communication Technology pp 23-38
https://link.springer.com/chapter/10.1007/978-981-13-1610-4_3
The multi-period portfolio optimization models were introduced to overcome the weaknesses of the single-period models via considering a dynamic optimization system. However, due to the nonlinear nature of the problem and rapid growth of the size complexity with increasing the number of periods and scenarios, this study is devoted to developing a novel league championship algorithm (LCA) to maximize the portfolio’s mean-variance function subject to different constraints. A Vector Auto Regression model is also developed to estimate the return on risky assets in different time periods and to simulate different scenarios of the rate of return accordingly. Besides, we proved a valid upper bound of the objective function based on the idea of using surrogate relaxation of constraints. Our computational results based on sample data collected from S&P 500 and 10-year T. Bond indices indicate that the quality of portfolios, in terms of the mean-variance measure, obtained by LCA is 10 to 20 percent better than those of the commercial software. This sounds promising that our method can be a suitable tool for solving a variety of portfolio optimization problems.
An Effective League Championship Algorithm for the Stochastic Multi-Period Portfolio Optimization Problem
Scientia Iranica
http://scientiairanica.sharif.edu/article_20995.html
Most jacket installations located in offshore regions require daily transportation of crew with helicopter, which imposes huge operational costs on oil and gas companies. In order to save on crew exchange costs, the daily flight schedule must be planned in a way that all constraints related to origin-destination itinerary, sortieing, flight length, capacity and crew member’s arrival time to the onshore base be met and the finish time of the final sortie be minimized. For this helicopter routing and crew exchange problem, two different mathematical models are developed which differ in the way the nodes and arcs are defined, and accordingly, the way the decision variables are defined. These models were solved to optimality for test problems up to 11 jackets and 28 crew members, and for the real case of South Pars Gas Field, the world's largest gas field, up to 11 jackets and 46 passengers. Considering the future extensions of the South Pars Gas Field and for problems of larger sizes, given the nature of the problem which is a grouping problem, a new algorithm, namely the hybrid Grouping Evolution Strategy (HGES), is proposed which works based on the composition of the flight sorties rather than working with each nodes in isolation. Several heuristics and a local search algorithm are incorporated in the body of GES to enhance its performance. Extensive computational experiments demonstrate that HGES can produce good solutions in an acceptable amount of time which is fit for operational use, as compared with particle swarm optimization (PSO) and pure Grouping Evolution Strategy algorithm. A GUI has also been designed to eliminate the complexity and inefficiency of the current method which is on “by-eye” basis.
New Mathematical Models and a Hybrid Grouping Evolution Strategy Algorithm for Optimal Helicopter Routing and Crew Pickup and Delivery
Computers & Industrial Engineering
https://www.sciencedirect.com/science/article/abs/pii/S0360835217303492
As a novel evolutionary technique, grouping evolutionary strategy (GES) has proved efficient and effective on grouping problems in which the task is to partition a set of items into disjoint groups. This paper investigates the first application of GES to tackle the parallel-machines scheduling problem as a well-known grouping problem in which machines can be treated as groups and jobs can be regarded as the items and the task is to partition a set of jobs into disjoint groups (and process all jobs in a same group by the same machines) to minimize makespan (C_max) criterion. The main features of GES algorithm that make it different from the typical evolutionary approaches proposed for the parallel-machines scheduling problem, lie in exploiting a suitable chromosomal representation and a well-designed mutation operator that works with the set of jobs assigned to each machine instead of jobs isolatedly, and uses a two-phase procedure to generate the new schedules more effectively. In addition, we hybridized GES with an efficient local search heuristic and proved that it has an important descent property. To verify the performance of our proposed algorithm, comparisons are made using available methodologies in the literature. Computational results signify that the proposed approach is fast and competitive in providing high quality results.
A simple yet effective grouping evolutionary strategy (GES) algorithm for scheduling parallel machines
Neural Computing and Applications
https://link.springer.com/article/10.1007/s00521-016-2789-3
The project is one of the three major projects aiming at optimization of the spare parts supply chain in the National Iranian Copper Industries Co. (NICICO). Launched in 2016, the project involves 11 phases including:
•	Spare parts management system assessment and excellence roadmap development, 
•	Optimization of more than 30000 Stock Keeping Units (SKUs),
•	Layout and warehousing improvement,
•	Promotion of management information systems, 
•	Business process reengineering of spare parts management,
•	Dashboard development for supply chain Key Performance Indicators (KPIs),
•	Training Personnel in spare parts, supply chain and inventory management, 
•	Design and development of optimization software tools for better management of spare part including:
o	The spare parts demand forecasting tool with more than 12 approaches for different situations.
o	Fast moving spare part optimization tool
o	Slow moving spare part optimization tool
o	Spare part classification tool
o	Bootstrapping tool for spare parts
Optimization of Spare Part Management System at NICICO
Education
Elsevier
https://www.linkedin.com/company/elsevier
Contributed eleven extensive reviews to the Journal of Computers and Industrial Engineering
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Reviewer of Computers and Industrial Engineering
https://ir.linkedin.com/in/abbas-monzavi-7b5a4a44
Iran
abbas monzavi
Sales expert
https://ir.linkedin.com/in/abbas-monzavi-74186377
Iran
abbas monzavi
--
https://ir.linkedin.com/in/mohammad-amin-abbasi
Tehran, Iran
mohammad amin abbasi
Market Analyst (Oil & Energy - Petrochemicals)
https://www.linkedin.com/in/ashleighalecusan
Lawrence, KS
Ashleigh Alecusan Bakke
PhD Candidate at The University of Kansas
https://www.linkedin.com/in/yanru-yang-cpa-3aa41341
Greater Boston
Yanru Yang, CPA
PhD student in Accounting
https://www.linkedin.com/in/ethan-lafountain-3a9056191
Lawrence, KS
Ethan LaFountain
Student at The University of Kansas School of Business
https://www.linkedin.com/in/haley-m-mitchell
Lawrence, KS
Haley Mitchell
Marketing and Information Systems Graduate from The University of Kansas
https://www.linkedin.com/in/jessicabfoley3
Wilmington, NC
Jessica Foley
Developing scalable and automated growth within the customer life cycle journey
https://www.linkedin.com/in/jessica-samuelson-mba-phr-04389b84
Kansas City Metropolitan Area
Jessica Samuelson, MBA, PHR
Lead Clinical Talent Recruiter
https://www.linkedin.com/in/caty-arnold-536a361a2
Lawrence, KS
Caty Arnold
Student at University of Kansas
https://www.linkedin.com/in/zahra-faeli
United States
Zahra Faeli, Ph.D., P.E.
Research Assistant in Geotechnical Eng. at North Carolina State University
https://www.linkedin.com/in/a-montazeri-9069
Ithaca, NY
Amir Montazeri
PhD Candidate at Cornell University
https://www.linkedin.com/in/carson-dinkel-904632152
Mission, KS
Carson Dinkel
New Business Account Executive at KC Current
https://www.linkedin.com/in/molly-fimian-7b0ba616b
Dallas-Fort Worth Metroplex
Molly Fimian
Teaching Assistant for Fundamentals of Accounting at The University of Kansas School of Business
https://www.linkedin.com/in/kathleenj-nelson
Lawrence, KS
Kathleen Nelson
Finance and Spanish | School of Business and School of Languages Literatures & Cultures
https://www.linkedin.com/in/kristinatirol-carmody
Lawrence, KS
Kristina Tirol-Carmody
KU Self Graduate Fellow | PhD candidate in Organizational Behavior and Human Resources at KU School of Business | GRA
https://ir.linkedin.com/in/sajjad-najmaie-33819261
Iran
Sajjad Najmaie
Structure designing at TMVN
https://www.linkedin.com/in/parikshitsarda
Toledo, OH
Parikshit Sarda
Chemical Engineering | Polymer Synthesis, Packaging & Recycling | Process Design, Simulation &  Modelling | Continuous Improvement
Iranian Industrial Engineers
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Manufacturing, Energy & Industrial Management and Technology | Startups | Blockchain (BIG)
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Tarbiat Modares University (TMU) Alumni
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Institute of Industrial and Systems Engineers (IISE)
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APQC -  Process Classification Framework® Community
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Career Hub - Startups, Financing, Blockchain & Jobs
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IEOM Society
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Tarbiat Modares University of Iran
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