Hamed Fazlollahtabar

Assistant Professor of Industrial Engineering


  • Postdoctoral 2015-2017

    Industrial Engineering

    Sharif University of Technology, Tehran, Iran

  • Ph.D. 2011-2015

    Industrial Engineering

    Iran University of Science and Technology (IUST), Tehran, Iran

  • M.Sc. 2008-2010

    Industrial Engineering

    Mazandaran University of Science and Technology, Babol, Iran

Selected Publications

Fazlollahtabar, H. Lagrangian relaxation method for optimizing delay of multiple autonomous guided vehicles (2018) Transportation Letters, 10 (6), pp. 354-360.

DOI: 10.1080/19427867.2017.1386871

In this paper, a scheduling and routing based on AGVs processing and waiting times and the existing paths in a jobshop manufacturing system is proposed. A mathematical formulation is developed to schedule and route multiple AGVs handling jobs to shops so that the total delay of AGVs including the earliness and tardiness is minimized. A Lagrangian relaxation method is developed and a sub-gradient algorithm is composed to update the iterations in the searching process of method. The results show that the method is efficient in larger sizes problems while exact method cannot obtain the solutions in reasonable time. Two different problems are solved using two algorithms of Lagrangian relaxation and linear relaxation. Statistical comparisons showed much better performance of Lagrangian relaxation approach in a negligible run time. For larger sizes that exact method cannot be obtained even in long run times, Lagrangian relaxation approach is useful and efficient. © 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group.

AUTHOR KEYWORDS: Autonomous guided vehicle (AGV); delay optimization; Lagrangian relaxation; path planning
INDEX KEYWORDS: Lagrange multipliers; Manufacture; Motion planning, Autonomous guided vehicles; Delay optimization; Earliness and tardiness; LaGrangian relaxation; Lagrangian relaxation method; Mathematical formulation; Statistical comparisons; Sub-gradient algorithm, Automatic guided vehicles
PUBLISHER: Taylor and Francis Ltd.

Arabshahi, H., Fazlollahtabar, H. Classifying Innovative Activities Using Decision Tree and Gini Index (2018) International Journal of Innovation and Technology Management, 15 (3), art. no. 1850025, .

DOI: 10.1142/S0219877018500256

This paper proposes a framework for classification of innovative activities in production systems based on corresponding calculated risk intensity using decision tree method. A three-step process is developed. The basis of the framework is the innovative activities collected from the literature. These activities are collected from the related literature and are classified by decision tree based on Gini Index. The configured tree is then used to extract and compose rules applying rule mining technique. The resulting rules can be useful sources of information for managers, investors and predictors of innovation domain to take the appropriate approaches for innovation risk management and innovation investment. © 2018 World Scientific Publishing Company.

AUTHOR KEYWORDS: classification; decision tree; Gini index; innovation risk; Innovative activities
PUBLISHER: World Scientific Publishing Co. Pte Ltd

Gholizadeh, H., Javadian, N., Fazlollahtabar, H. Fuzzy regression integrated with genetic-tabu algorithm for prediction and optimization of a turning process (2018) International Journal of Advanced Manufacturing Technology, 96 (5-8), pp. 2781-2790.

DOI: 10.1007/s00170-018-1655-0

Prediction of surface roughness is a key element for an automated machining center. In this regard, it is important to optimize the machining process. In this paper, fuzzy linear regression approach is employed to predict the surface roughness for a turning process in an uncertain condition. The important process parameters such as cutting speed, cutting depth, speed, and tool tip radius are considered as inputs to determine their significance for prediction. To handle uncertainty, fuzzy theory is employed. Thus, fuzzy liner regression is modeled. To optimize the estimated values of prediction errors, a genetic algorithm (GA) is developed. In addition, tabu search is used to facilitate GA for better performance. A numerical example is worked out to show the effectiveness of the proposed method. © 2018, Springer-Verlag London Ltd., part of Springer Nature.

AUTHOR KEYWORDS: Fuzzy regression; Genetic algorithm (GA); Tabu search; Turning process
INDEX KEYWORDS: Forecasting; Genetic algorithms; Machining; Machining centers; Numerical methods; Optimization; Regression analysis; Surface roughness; Tabu search, Automated machining; Fuzzy linear regression; Fuzzy regressions; Machining Process; Prediction errors; Process parameters; Turning process; Uncertain condition, Turning
PUBLISHER: Springer London

Abbasian, P., Mahdavi-Amiri, N., Fazlollahtabar, H. Multiple utility constrained multi-objective programs using Bayesian theory (2018) Journal of Industrial Engineering International, 14 (1), pp. 111-118.

DOI: 10.1007/s40092-017-0211-0

A utility function is an important tool for representing a DM’s preference. We adjoin utility functions to multi-objective optimization problems. In current studies, usually one utility function is used for each objective function. Situations may arise for a goal to have multiple utility functions. Here, we consider a constrained multi-objective problem with each objective having multiple utility functions. We induce the probability of the utilities for each objective function using Bayesian theory. Illustrative examples considering dependence and independence of variables are worked through to demonstrate the usefulness of the proposed model. © 2017, The Author(s).

AUTHOR KEYWORDS: Bayesian theory; Multi-objective program; Utility function
PUBLISHER: SpringerOpen

Fazlollahtabar, H., Hassanli, S. Hybrid cost and time path planning for multiple autonomous guided vehicles (2018) Applied Intelligence, 48 (2), pp. 482-498.

DOI: 10.1007/s10489-017-0997-x

In this paper, simultaneous scheduling and routing problem for autonomous guided vehicles (AGVs) is investigated. At the beginning of the planning horizon list of orders is processed in the manufacturing system. The produced or semi-produced products are carried among stations using AGVs according to the process plan and the earliest delivery time rule. Thus, a network of stations and AGV paths is configured. The guide path is bi-direction and AGVs can only stop at the end of a node. Two kinds of collisions exist namely: AGVs move directly to a same node and AGVs are on a same path. Delay is defined as an order is carried after the earliest delivery time. Therefore, the problem is defined to consider some AGVs and material handling orders available and assign orders to AGVs so that collision free paths as cost attribute and minimal waiting time as time attribute, are obtained. Solving this problem leads to determine: the number of required AGVs for orders fulfillment assign orders to AGVs schedule delivery and material handling and route different AGVs. The problem is formulated as a network mathematical model and optimized using a modified network simplex algorithm. The proposed mathematical formulation is first adapted to a minimum cost flow (MCF) model and then optimized using a modified network simplex algorithm (NSA). Numerical illustrations verify and validate the proposed modelling and optimization. Also, comparative studies guarantee superiority of the proposed MCF-NSA solution approach. © 2017, Springer Science+Business Media New York.

AUTHOR KEYWORDS: Autonomous guided vehicles (AGVs); Network simplex algorithm (NSA); Path planning; Routing; Scheduling
INDEX KEYWORDS: Costs; Intelligent vehicle highway systems; Linear programming; Manufacture; Materials handling; Motion planning; Optimization; Problem solving; Scheduling; Vehicles, Autonomous guided vehicles; Collision-free paths; Comparative studies; Mathematical formulation; Network of stations; Routing; Simplex algorithm; Simultaneous scheduling, Automatic guided vehicles
PUBLISHER: Springer New York LLC

Fazlollahtabar, H. Supply chain management models: Forward, reverse, uncertain, and intelligent: Foundations with case studies (2018) Supply Chain Management Models: Forward, Reverse, Uncertain, and Intelligent: Foundations with Case Studies, pp. 1-378.

DOI: 10.1201/b22492

Supply Chain Management (SCM) is a wide field in which several specialties are included. In general, operations and production management players use SCM to organize the problems and analyze the solution approaches. Due to these points, a reference which can encompass a range of problems and their modelling approaches is required. This book will contain three general sections of forward, reverse, intelligent, and uncertain problems. While the book provides different problems in the three commonly used categories in SCM, it is very helpful for the readers to find out, or adapt their own application studies to the ones given in the book and employ the corresponding modeliing approach. © 2018 by Taylor & Francis Group, LLC.


Rahimi, M., Fazlollahtabar, H. Optimization of a closed loop green supply chain using particle swarm and genetic algorithms (2018) Jordan Journal of Mechanical and Industrial Engineering, 12 (2), pp. 77-91.

Nowadays, due to increase of environmental hazards and legislation in this context by governments and also restriction of manufacturing resources, researchers paid special attention to the design of closed-loop green supply chain network. To establish better coordination between the components of the supply chain and gain more profits in the network, special decisions are required during the product lifecycle. The network presented in this study consists of four layers in the forward chain including suppliers, manufacturers, distribution centers and customer markets, and it also includes three facilities containing collection, dismantler and disposal centers in reverse chain. A mixed integer linear programming model proposed to optimize closed-loop green supply chain by considering the level of quality for constituent components of manufacturing parts along with the pricing policy and product life cycles to maximize profits. Genetic algorithm and particle swarm optimization are used to find the optimal solutions. Having analyzed the results and due to the relative percentage deviation and solution time, it was found that genetic algorithm performs better compared with the particle swarm optimization. © 2018 Jordan Journal of Mechanical and Industrial Engineering.

AUTHOR KEYWORDS: Closed-loop supply chain (CLSC); Genetic algorithm (GA); Green supply chain (GrSC); Mathematical programming; Particle swarm optimization (PSO)
INDEX KEYWORDS: Genetic algorithms; Integer programming; Life cycle; Manufacture; Mathematical programming; Profitability; Supply chains, Closed-loop supply chains (CLSC); Distribution centers; Environmental hazards; Genetic algorithm and particle swarm optimizations; Green supply chain; Manufacturing resource; Mixed integer linear programming model; Relative percentage deviations, Particle swarm optimization (PSO)
PUBLISHER: Hashemite University

Dorabati, S.E., Hamadani, A.Z., Fazlollahtabar, H. Investigating the effects of operators on warranty cost under sales delay conditions (2018) Journal of Quality in Maintenance Engineering, 24 (2), pp. 244-259.

DOI: 10.1108/JQME-03-2017-0023

Purpose: Due to the fact that the non-standard products, being used by customers, may cause failures in products with sales delays, which naturally affect the warranty policy. Thus, it seems to be necessary to study these two concepts simultaneously. The paper aims to discuss these issues. Design/methodology/approach: In this paper, a model is developed for estimating the expected warranty costs under sales delay conditions when two operator costs (failing but not reported and non-failing but reported) are included. Findings: The proposed model is validated using a numerical example for a two types of intermittent and fatal failures occur under a non-renewing warranty policy. Originality/value: Sales delay is the time interval between the date of production and the date of sale. Most reported literature on warranty claims data analysis related to sales delay have mainly focussed on estimating the probability distribution of the sales delay. © 2018, Emerald Publishing Limited.

AUTHOR KEYWORDS: Fatal and intermittent failures; Non-renewing warranty policy; Sales delay; Warranty servicing
INDEX KEYWORDS: Cost benefit analysis; Costs; Probability distributions, Delay condition; Design/methodology/approach; Intermittent failure; Operator costs; Standard products; Warranty claims data; Warranty policy; Warranty servicing, Sales
PUBLISHER: Emerald Group Publishing Ltd.

Kazemi, Z., Fazlollahtabar, H. Integrated model of knowledge sharing and tax willingness to pay (2017) International Journal of Operations and Quantitative Management, 23 (4), pp. 295-315.

Nowadays, in all countries, governments are supposed to have various economic tasks to perform; they are expected to incur heavy expenses to administer the required activities. In order to finance these expenditures, governments have to employ various ways for gaining revenues. One of the government's solutions to finance their expenditures is from tax revenues, which in current economy is undoubtedly the most important way for financing the government investment. This paper deals with the impact of knowledge sharing on the tax willingness to pay of individuals. When it's said that an individual shares his own knowledge it means that he is using his own knowledge, insight and ideas about taxes and its goals and impacts in order to guide others. In fact, the tax culture would be developed as a result of knowledge sharing. So, it could be concluded that knowledge sharing would improve the tax willingness to pay and as a result tax revenues are increased.

AUTHOR KEYWORDS: Knowledge sharing; Risk; Tax; Willingness to pay
PUBLISHER: International Forum of Management Scholars