Dr Mojtaba Heydar
Post Doctoral Fellow in Supply Chain Optimisation
School of Electrical Engineering and Computing
Career Summary
Biography
Dr. Mojtaba Heydar is a postdoctoral research fellow in food supply chain optimisation at the University of Newcastle. He joined UON in June 2015 after spending one year as a research scholar at the Supply Chain Management Institute in the Lubar School of Business, where he was an adjunct lecturer, at the University of Wisconsin-Milwaukee, USA. He received his Ph.D. in Industrial Engineering from UWM in 2014.
Research Expertise
His research projects are related to the application of Operations Research in the areas such as food supply chain optimisation, high-speed train timetabling, transportation, logistics, scheduling, and supply chain. He published his work in Transportation Science (a highly recognised and prestigious journal published by INFORMS). Mojtaba is a member of INFORMS (the Institute for Operations Research and Management Science) and The OR Society.
Teaching Expertise
Mojtaba has taught courses in industrial engineering, operations research, and supply chain. His primary areas of teaching are related to industrial engineering, operations research, operation management, and supply chain management.
Qualifications
- Doctor of Philosophy, University of Wisconsin-Milwaukee - USA
Keywords
- Emergency Logistics
- Logistics
- Mixed Integer Programming
- Operations Research
- Scheduling
- Supply Chain
- Transportation
Languages
- English (Fluent)
Fields of Research
Code | Description | Percentage |
---|---|---|
150702 | Rail Transportation and Freight Services | 10 |
010206 | Operations Research | 60 |
150309 | Logistics and Supply Chain Management | 30 |
Professional Experience
UON Appointment
Title | Organisation / Department |
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Casual Academic | University of Newcastle School of Mathematical and Physical Sciences Australia |
Post Doctoral Fellow in Supply Chain Optimisation | University of Newcastle School of Electrical Engineering and Computing Australia |
Teaching
Code | Course | Role | Duration |
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IND ENG-360 |
Engineering Economic Analysis University of Wisconsin-Milwaukee |
Lecturer | 2/01/2013 - 15/05/2013 |
BUSADM 478 |
Supply Chain Analytics University of Wisconsin-Milwaukee |
Lecturer | 3/09/2014 - 11/05/2015 |
Publications
For publications that are currently unpublished or in-press, details are shown in italics.
Chapter (1 outputs)
Year | Citation | Altmetrics | Link | |||||
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2016 |
Paam P, Berretta R, Heydar M, Middleton RH, García-Flores R, Juliano P, 'Planning Models to Optimize the Agri-Fresh Food Supply Chain for Loss Minimization: A Review', Reference Module in Food Science, Elsevier, Netherlands 1-16 (2016) [B1]
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Journal article (11 outputs)
Year | Citation | Altmetrics | Link | |||||
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2016 |
Petering MEH, Heydar M, Bergmann DR, 'Mixed-Integer Programming for Railway Capacity Analysis and Cyclic, Combined Train Timetabling and Platforming', TRANSPORTATION SCIENCE, 50 892-909 (2016) [C1]
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2016 |
Nezami FG, Heydar M, 'Energy-aware Economic Production Quantity model with variable energy pricing', Operational Research, 1-18 (2016) © 2016 Springer-Verlag Berlin Heidelberg In this paper, an energy-aware Economic Production Quantity (EPQ) model is presented to determine optimum production run length and batch ... [more] © 2016 Springer-Verlag Berlin Heidelberg In this paper, an energy-aware Economic Production Quantity (EPQ) model is presented to determine optimum production run length and batch size with respect to variable energy cost. Here, variable unit production cost includes energy consumption charge which is a function of production time and time-of-use, and alternates between two prices during peak and off-peak hours. This paper addresses the above integration in order to minimize the overall cost of the system. In the first phase of this study, a new scenario-based framework is proposed to find the optimal value of production time. In the second phase, a general mixed integer nonlinear programming (MINLP) model is developed for the given framework. The energy cost defined by the framework and mathematical model depends on the number of peak periods during the production period and is calculated using floor functions. The MINLP is solved numerically and analytically, and a closed form solution is obtained for the production run length. The model is analyzed for different scenarios and the results are discussed.
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2016 |
Heydar M, Yu J, Liu Y, Petering MEH, 'Strategic evacuation planning with pedestrian guidance and bus routing: a mixed integer programming model and heuristic solution', JOURNAL OF ADVANCED TRANSPORTATION, 50 1314-1335 (2016) [C1]
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2016 |
Alavi S, Azad N, Heydar M, Davoudpour H, 'Integrated Production, Inventory, and Location-Allocation Decisions in Designing Supply Chain Networks', INTERNATIONAL JOURNAL OF INFORMATION SYSTEMS AND SUPPLY CHAIN MANAGEMENT, 9 22-42 (2016) [C1]
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2013 |
Heydar M, Petering MEH, Bergmann DR, 'Mixed integer programming for minimizing the period of a cyclic railway timetable for a single track with two train types', COMPUTERS & INDUSTRIAL ENGINEERING, 66 171-185 (2013) [C1]
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2013 |
Mousavi SM, Tavakkoli-Moghaddam R, Heydar M, Ebrahimnejad S, 'Multi-Criteria Decision Making for Plant Location Selection: An Integrated Delphi-AHP-PROMETHEE Methodology', ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 38 1255-1268 (2013) [C1]
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2012 |
Ebrahimnejad S, Mousavi SM, Tavakkoli-Moghaddam R, Heydar M, 'Evaluating high risks in large-scale projects using an extended VIKOR method under a fuzzy environment', International Journal of Industrial Engineering Computations, 3 463-476 (2012) The complexity of large-scale projects has led to numerous risks in their life cycle. This paper presents a new risk evaluation approach in order to rank the high risks in large-s... [more] The complexity of large-scale projects has led to numerous risks in their life cycle. This paper presents a new risk evaluation approach in order to rank the high risks in large-scale projects and improve the performance of these projects. It is based on the fuzzy set theory that is an effective tool to handle uncertainty. It is also based on an extended VIKOR method that is one of the well-known multiple criteria decision-making (MCDM) methods. The proposed decision-making approach integrates knowledge and experience acquired from professional experts, since they perform the risk identification and also the subjective judgments of the performance rating for high risks in terms of conflicting criteria, including probability, impact, quickness of reaction toward risk, event measure quantity and event capability criteria. The most notable difference of the proposed VIKOR method with its traditional version is just the use of fuzzy decision-matrix data to calculate the ranking index without the need to ask the experts. Finally, the proposed approach is illustrated with a real-case study in an Iranian power plant project, and the associated results are compared with two well-known decision-making methods under a fuzzy environment. © 2012 Growing Science Ltd. All rights reserved.
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2011 |
Tavakkoli-Moghaddam R, Mousavi SM, Heydar M, 'An integrated AHP-VIKOR methodology for plant location selection', International Journal of Engineering, Transactions B: Applications, 24 127-137 (2011) Plant location selection has invariably a significant impact on the performance of many companies or manufacturing systems. In this paper, a new integrated methodology is structur... [more] Plant location selection has invariably a significant impact on the performance of many companies or manufacturing systems. In this paper, a new integrated methodology is structured to solve this selection problem. Two well-known decision making methods, namely analytic hierarchical process (AHP) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), are combined in order to make the best use of information available, either implicitly or explicitly. In addition, the Delphi method is utilized to select the most influential criteria by a few experts. The aim of using the AHP is to give the weights of the selected criteria. Finally, the VIKOR method is taken into account to rank potential alternatives. Finally, an application example demonstrates the suitability of the proposed methodology.
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2009 |
Mojaveri HRS, Mousavi SS, Heydar M, Aminian A, 'Validation and selection between machine learning technique and traditional methods to reduce bullwhip effects: A data mining approach', World Academy of Science, Engineering and Technology, 37 555-561 (2009) The aim of this paper is to present a methodology in three steps to forecast supply chain demand. In first step, various data mining techniques are applied in order to prepare dat... [more] The aim of this paper is to present a methodology in three steps to forecast supply chain demand. In first step, various data mining techniques are applied in order to prepare data for entering into forecasting models. In second step, the modeling step, an artificial neural network and support vector machine is presented after defining Mean Absolute Percentage Error index for measuring error. The structure of artificial neural network is selected based on previous researchers' results and in this article the accuracy of network is increased by using sensitivity analysis. The best forecast for classical forecasting methods (Moving Average, Exponential Smoothing, and Exponential Smoothing with Trend) is resulted based on prepared data and this forecast is compared with result of support vector machine and proposed artificial neural network. The results show that artificial neural network can forecast more precisely in comparison with other methods. Finally, forecasting methods' stability is analyzed by using raw data and even the effectiveness of clustering analysis is measured. © 2009 WASET.ORG.
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2009 |
Shahrabi J, Mousavi SS, Heydar M, 'Supply chain demand forecasting: A comparison of machine learning techniques and traditional methods', Journal of Applied Sciences, 9 521-527 (2009) In this study, supply chain demand is forecasted with different methods and their results are compared. In this research traditional time series forecasting methods including movi... [more] In this study, supply chain demand is forecasted with different methods and their results are compared. In this research traditional time series forecasting methods including moving average, exponential smoothing, exponential smoothing with trend at the first stage and finally two machine learning techniques including Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), are used to forecast the long-term demand of supply chain. By using the data set of the component supplier of the biggest Iranian's car company this research is then implemented. The comparison reveals that the results producing by machine learning techniques are more accurate and much closer to the actual data in contrast with traditional forecasting methods. © 2009 Asian Network for Scientific Information.
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Show 8 more journal articles |
Conference (7 outputs)
Year | Citation | Altmetrics | Link | |||||
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2017 |
Eshragh A, Esmaeilbeigi R, Garcia-Flores R, Heydar M, 'Whey Reverse Logistics Network Design: A Stochastic Hierarchical Facility Location Model', To Appear in the Proceedings of the 22nd International Congress on Modelling and Simulation (MODSIM2017), Hobart (2017)
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2017 |
Ghazi Nezami F, Heydar M, Berretta R, 'Optimizing Production Schedule with Energy
Consumption and Demand Charges in Parallel Machine Setting', Kuala Lumpur, Malaysia (2017)
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2016 |
Paam P, Berretta R, Heydar M, 'Designing a Recycling Supply Chain Network for a Bottle Manufacturing Factory', 2016 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM), Bali, INDONESIA (2016) [E1]
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2009 |
Mousavi SS, Nezami FG, Heydar M, Aryanejad MB, 'A Hybrid Fuzzy Group Decision Making and Factor Analysis for Selecting Maintenance Strategy', CIE: 2009 INTERNATIONAL CONFERENCE ON COMPUTERS AND INDUSTRIAL ENGINEERING, VOLS 1-3, Troyes, FRANCE (2009)
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2008 |
Tavakkoli-Moghaddam R, Panahi H, Heydar M, 'Minimization of Weighted Tardiness and Makespan in an Open shop Environment by a Novel Hybrid Multi-objective Meta-heuristic Method', IEEM: 2008 INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT, VOLS 1-3, Singapore, SINGAPORE (2008)
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2008 |
Heydar M, Tavakkoli-Moghaddam R, Mousavi SM, Mojtahedi SMH, 'Fuzzy Multi Criteria Decision Making Method for Temporary Storage Design in Industrial Plants', IEEM: 2008 INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT, VOLS 1-3, Singapore, SINGAPORE (2008)
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2008 |
Mousavi SE, Heydar M, Mojtahedi SMH, Mousavi SM, 'A Fuzzy Multi Objective Decision Making Approach for Locating Undesirable Facilities and Hazardous Materials', 2008 IEEE INTERNATIONAL CONFERENCE ON MANAGEMENT OF INNOVATION AND TECHNOLOGY, VOLS 1-3, Bangkok, THAILAND (2008)
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Show 4 more conferences |
Research Supervision
Number of supervisions
Total current UON EFTSL
Current Supervision
Commenced | Level of Study | Research Title | Program | Supervisor Type |
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2016 | PhD | Food Supply Chain Optimisation | PhD (Computer Science), Faculty of Engineering and Built Environment, The University of Newcastle | Co-Supervisor |
2016 | PhD | Food Supply Chain Optimisation | PhD (Computer Science), Faculty of Engineering and Built Environment, The University of Newcastle | Co-Supervisor |
2015 | PhD | Hierarchical Location-Routing Problems | PhD (Mathematics), Faculty of Science, The University of Newcastle | Co-Supervisor |
Research Collaborations
The map is a representation of a researchers co-authorship with collaborators across the globe. The map displays the number of publications against a country, where there is at least one co-author based in that country. Data is sourced from the University of Newcastle research publication management system (NURO) and may not fully represent the authors complete body of work.
Country | Count of Publications | |
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Iran, Islamic Republic of | 11 | |
United States | 8 | |
Australia | 5 | |
Canada | 1 |
Dr Mojtaba Heydar
Positions
Post Doctoral Fellow in Supply Chain Optimisation
School of Electrical Engineering and Computing
Faculty of Engineering and Built Environment
Casual Academic
School of Mathematical and Physical Sciences
Faculty of Science