Dr Mojtaba Heydar

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

  • Operations Research
  • Supply Chain
  • Logistics
  • Mixed Integer Programming
  • Scheduling
  • Emergency Logistics
  • 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
Post Doctoral Fellow in Supply Chain Optimisation University of Newcastle
School of Electrical Engineering and Computing
Australia
Casual Academic University of Newcastle
School of Mathematical and Physical Sciences
Australia

Teaching

Code Course Role Duration
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
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Publications

For publications that are currently unpublished or in-press, details are shown in italics.


Chapter (1 outputs)

Year Citation Altmetrics Link
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]
DOI 10.1016/B978-0-08-100596-5.21069-X
Co-authors Richard Middleton, Regina Berretta

Journal article (12 outputs)

Year Citation Altmetrics Link
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]
DOI 10.1287/trsc.2015.0652
Citations Scopus - 3Web of Science - 2
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.

DOI 10.1007/s12351-016-0284-3
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]
DOI 10.1002/atr.1403
Citations Scopus - 3Web of Science - 2
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]
DOI 10.4018/IJISSCM.2016100102
2014 Ebrahimnejad S, Mousavi SM, Tavakkoli-Moghaddam R, Heydar M, 'Risk ranking in mega projects by fuzzy compromise approach: A comparative analysis', Journal of Intelligent and Fuzzy Systems, 26 949-959 (2014) [C1]
DOI 10.3233/IFS-130785
Citations Scopus - 9Web of Science - 5
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]
DOI 10.1016/j.cie.2013.06.003
Citations Scopus - 15Web of Science - 15
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]
DOI 10.1007/s13369-012-0361-8
Citations Scopus - 35Web of Science - 24
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.

DOI 10.5267/j.ijiec.2011.12.001
Citations Scopus - 6
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.

Citations Scopus - 35
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.

Citations Scopus - 3
2009 Taghavifard MT, Heydar M, Mousavi SS, 'A genetic algorithm for scheduling flexible manufacturing cells', Journal of Applied Sciences, 9 97-104 (2009)

In this study, scheduling of Flexible Manufacturing Cells (EMC) is taken into consideration. This type of production system combines the merit of job shop and flow shop production... [more]

In this study, scheduling of Flexible Manufacturing Cells (EMC) is taken into consideration. This type of production system combines the merit of job shop and flow shop production systems. FMS Scheduling belongs to the class of problems that are known as NP-hard. This study presents a genetic algorithm-based technique to schedule machines and Automated Guided Vehicle (AGV), simultaneously. To generate schedules from a given chromosome, four Priority Dispatching Rules (PDR) are considered. Maximum completion time or makespan is defined as the objective function. The algorithm was coded and many randomly generated problems were solved. The obtained results were compared with optimum values obtained from the most comprehensive mathematical formulation in the literature. The experimental results show that the proposed method performs well in terms of efficiency and quality of solutions. For further study, the researchers will consider this problem in multi-objective environment. © 2009 Asian Network for Scientific Information.

DOI 10.3923/jas.2009.97.104
Citations Scopus - 5
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.

DOI 10.3923/jas.2009.521.527
Citations Scopus - 10
Show 9 more journal articles

Conference (7 outputs)

Year Citation Altmetrics Link
2017 Esmaeilbeigi R, Eshragh A, Garcia-Flores R, Heydar M, 'Whey Reverse Logistics Network Design: A Stochastic Hierarchical Facility Location Model', 22nd International Congress on Modelling and Simulation (MODSIM2017), Hobart, Tasmania (2017) [E1]
Co-authors Ali Eshragh
2017 Ghazi Nezami F, Heydar M, Berretta R, 'Optimizing Production Schedule with Energy Consumption and Demand Charges in Parallel Machine Setting', 8th Multidisciplinary International Conference on Scheduling : Theory and Applications (MISTA 2017), Kuala Lumpur, Malaysia (2017) [E1]
Co-authors Regina Berretta
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]
Co-authors Regina Berretta
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)
DOI 10.1109/ICCIE.2009.5223824
Citations Scopus - 10Web of Science - 7
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)
DOI 10.1109/IEEM.2008.4737895
Citations Scopus - 1Web of Science - 1
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)
DOI 10.1109/IEEM.2008.4738051
Citations Scopus - 3Web of Science - 1
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)
DOI 10.1109/ICMIT.2008.4654521
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Research Supervision

Number of supervisions

Completed0
Current3

Total current UON EFTSL

PhD1.3

Current Supervision

Commenced Level of Study Research Title Program Supervisor Type
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
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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
Iran, Islamic Republic of 12
United States 8
Australia 5
Canada 1
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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

Contact Details

Email mojtaba.heydar@newcastle.edu.au

Office

Building ES Building
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