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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

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Details

Details

003
Publications

2022

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

Authors
Fernandes, JMRC; Homayouni, SM; Fontes, DBMM;

Publication
SUSTAINABILITY

Abstract
Energy efficiency has become a major concern for manufacturing companies not only due to environmental concerns and stringent regulations, but also due to large and incremental energy costs. Energy-efficient scheduling can be effective at improving energy efficiency and thus reducing energy consumption and associated costs, as well as pollutant emissions. This work reviews recent literature on energy-efficient scheduling in job shop manufacturing systems, with a particular focus on metaheuristics. We review 172 papers published between 2013 and 2022, by analyzing the shop floor type, the energy efficiency strategy, the objective function(s), the newly added problem feature(s), and the solution approach(es). We also report on the existing data sets and make them available to the research community. The paper is concluded by pointing out potential directions for future research, namely developing integrated scheduling approaches for interconnected problems, fast metaheuristic methods to respond to dynamic scheduling problems, and hybrid metaheuristic and big data methods for cyber-physical production systems.

2022

Energy-Efficient Scheduling of Intraterminal Container Transport

Authors
Homayouni, SM; Fontes, DBMM;

Publication
Springer Optimization and Its Applications

Abstract
Maritime transportation has been, historically, a major factor in economic development and prosperity since it enables trade and contacts between nations. The amount of trade through maritime transport has increased drastically; for example, about 90% of the European Union’s external trade and one-third of its internal trade depend on maritime transport. Major ports, typically, incorporate multiple terminals serving containerships, railways, and other forms of hinterland transportation and require interterminal and intraterminal container transport. Many factors influence the productivity and efficiency of ports and hence their economic viability. Moreover, environmental concerns have been leading to stern regulation that requires ports to reduce, for example, greenhouse gas emissions. Therefore, port authorities need to balance economic and ecological objectives in order to ensure sustainable growth and to remain competitive. Once a containership moors at a container terminal, several quay cranes are assigned to the ship to load/unload the containers to/from the ship. Loading activities require the containers to have been previously made available at the quayside, while unloading ones require the containers to be removed from the quayside. The containers are transported between the quayside and the storage yard by a set of vehicles. This chapter addresses the intraterminal container transport scheduling problem by simultaneously scheduling the loading/unloading activities of quay cranes and the transport (between the quayside and the storage yard) activities of vehicles. In addition, the problem includes vehicles with adjustable travelling speed, a characteristic never considered in this context. For this problem, we propose bi-objective mixed-integer linear programming (MILP) models aiming at minimizing the makespan and the total energy consumption simultaneously. Computational experiments are conducted on benchmark instances that we also propose. The computational results show the effectiveness of the MILP models as well as the impact of considering vehicles with adjustable speed, which can reduce the makespan by up to 16.2% and the total energy consumption by up to 2.5%. Finally, we also show that handling unloading and loading activities simultaneously rather than sequentially (the usual practice rule) can improve the makespan by up to 34.5% and the total energy consumption by up to 18.3%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2022

A Hybrid Particle Swarm Optimization and Simulated Annealing Algorithm for the Job Shop Scheduling Problem with Transport Resources

Authors
Fontes, DB; Homayouni, SM; Gonçalves, JF;

Publication
European Journal of Operational Research

Abstract

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, APMS 2021, PT 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.