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About

About

Mahdi Homayouni is a Researcher at the Laboratory of Artificial Intelligence and Decision Support ( LIAAD), INESC TEC, Porto, Portugal, since Feb 2017. He has been an Assistant Professor at the Department of Industrial Engineering, Islamic Azad University, Lenjan Branch, Isfahan, Iran, since Feb 2011. 

Mahdi gained his PhD in Industrial and Systems Engineering, from Universiti Putra Malaysia in May 2012. He gained his MSc and BEng degrees in 2008 from Universiti Putra Malaysia, and 2003 from Najafabad Branch- Islamic Azad University- Iran, respectively. He was formerly the head of the Industrial Engineering Department at IAULn, and the director of the young researchers club, IAULn for 2013 to 2016. 

Https://scholar.google.com/citations?user=D0QT05YAAAAJ&hl=en

Https://www.researchgate.net/profile/Seyed_Mahdi_Homayouni?ev=hdr_xprf

Http://www.linkedin.com/profile/preview?locale=en_US&trk=prof-0-sb-preview-primary-button

Http://orcid.org/0000-0001-6833-9316

Interest
Topics
Details

Details

  • Name

    Mahdi Homayouni
  • Cluster

    Computer Science
  • Role

    Assistant Researcher
  • Since

    01st February 2017
003
Publications

2021

A MILP Model for Energy-Efficient Job Shop Scheduling Problem and Transport Resources

Authors
Homayouni, SM; Fontes, DBMM;

Publication
Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems - IFIP WG 5.7 International Conference, APMS 2021, Nantes, France, September 5-9, 2021, Proceedings, Part I

Abstract
This work addresses the energy-efficient job shop scheduling problem and transport resources with speed scalable machines and vehicles which is a recent extension of the classical job shop problem. In the environment under consideration, the speed with which machines process production operations and the speed with which vehicles transport jobs are also to be decided. Therefore, the scheduler can control both the completion times and the total energy consumption. We propose a mixed-integer linear programming model that can be efficiently solved to optimality for small-sized problem instances. © 2021, IFIP International Federation for Information Processing.

2020

Optimization of Sustainable Single-Machine Scheduling Problem : Short Research Paper, CSCI-ISCI

Authors
Homayouni S.M.; Fontes D.B.M.M.;

Publication
Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

Abstract
This work considers sustainable scheduling of manufacturing operations and preventive maintenance activities in a single-machine environment where the machine works continuously in three eight-hour shifts per day. The jobs can be produced at different processing speeds, which reduces energy consumption and/or processing times. In a tri-objective mixed integer linear programming model, sustainability is attained through minimizing total weighted earliness/ tardiness - economic pillar, total energy consumption - environmental pillar, and number of undesired activities - social pillar. Moreover, a multi-objective genetic algorithm finds near optimal solutions in a timely manner. Numerical results will be presented at the conference.

2019

Joint scheduling of production and transport with alternative job routing in flexible manufacturing systems

Authors
Homayouni, SM; Fontes, DBMM;

Publication

Abstract

2019

A BRKGA for the integrated scheduling problem in FMSs

Authors
Homayouni, SM; Fontes, DBMM; Fontes, FACC;

Publication
Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '19

Abstract

2019

Joint production and transportation scheduling in flexible manufacturing systems

Authors
Fontes, DBMM; Homayouni, SM;

Publication
Journal of Global Optimization

Abstract
This work proposes an integrated formulation for the joint production and transportation scheduling problem in flexible manufacturing environments. In this type of systems, parts (jobs) need to be moved around as the production operations required involve different machines. The transportation of the parts is typically done by a limited number of Automatic Guided Vehicles (AGVs). Therefore, machine scheduling and AGV scheduling are two interrelated problems that need to be addressed simultaneously. The joint production and transportation scheduling problem is formulated as a novel mixed integer linear programming model. The modeling approach proposed makes use of two sets of chained decisions, one for the machine and another for the AGVs, which are inter-connected through the completion time constraints both for machine operations and transportation tasks. The computational experiments on benchmark problem instances using a commercial software (Gurobi) show the efficiency of the modeling approach in finding optimal solutions. © 2018 Springer Science+Business Media, LLC, part of Springer Nature