A Review for Dynamic Scheduling in Manufacturing

A Review for Dynamic Scheduling in Manufacturing

Osama Mohammed Elmardi Suleiman Khayal

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Khalid Muhamadin Mohamed Ahmed Bukkur

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M.I. Shukri

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Osama Mohammed Elmardi

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Nile Valley University

A Review for Dynamic Scheduling in Manufacturing

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Abstract

This paper discusses review of literature of dynamic scheduling in manufacturing. First, the problem is defined. The scheduling problems are classified based on the nature of the shop configuration into five classes, i.e., single machine, parallel machines, flow shop, job shop, and open shop. A variety of approaches have been developed to solve the problem of dynamic scheduling. Dynamic scheduling could be classified into four categories, completely reactive scheduling, predictive-reactive scheduling, robust predictive reactive scheduling, and robust proactive scheduling. It is better to combine together different techniques such as operational research and artificial intelligence to overcome dynamic scheduling problems so as to endow the scheduling system with the required flexibility and robustness, and to suggest various orientations for further work is this area of research.

References

154 Cites in Article

Reference Format

A Madureira,I Pereira,P Pereira,A Abraham (2014). Negotiation mechanism for self-organized scheduling system with collective intelligence.
A Santos,M Varela,G Putnik,A Madureira (2014). Alternative approaches analysis for scheduling in an Extended Manufacturing Environment.
C Abdallah Elkhyari,Jussien Narendra (2003). Constraint Programming for Dynamic Scheduling Problems.
Adil Baykasoğlu,F (2017). Solving Comprehensive Dynamic Job Shop Scheduling Problem by using a Grasp-Based Approach.
Ahmad Shahrizal Muhamad,S (2011). An Artificial Immune System for Solving Production Scheduling Problems: A Review.
Aidin Delgoshaei,A,Mohd Khairol Anuar Ariffin,Chandima Gomes (2016). A Multi-Period Scheduling of Dynamic Cellular Manufacturing Systems in the Presence of Cost Uncertainty.
Ali Vatankhah Barenji,R,Danial Roudi,Majid Hashemipour (2016). A Dynamic Multi-Agent-Based Scheduling Approach for Smes.
G Alper Hamzadayi (2016). Event Driven Strategy Based Complete Rescheduling Approaches for Dynamic M Identical Parallel Machines Scheduling Problem with a Common Server.
S Alper Seker,Reha Botsali (2013). A Neuro-Fuzzy Model for A New Hybrid Integrated Process Planning and Scheduling System.
T Amer Fahmya,Hesham Bassionic (2014). what is Dynamic Scheduling?.
Amina Lamghari,Roussos Dimitrakopoulos,Jacques Ferland (2014). A hybrid method based on linear programming and variable neighborhood descent for scheduling production in open-pit mines.
Andrea Cataldo,Andrea Perizzato,Riccardo Scattolini (2015). Production scheduling of parallel machines with model predictive control.
Andrea Cataldo,A,Riccardo Scattolini (2015). Production Scheduling of Parallel Machines with Model Predictive Control.
Andrea Rossi,Andrea Pandolfi,Michele Lanzetta (2013). Dynamic set-up rules for hybrid flow shop scheduling with parallel batching machines.
Angus Kenny,Xiaodong Li,Andreas Ernst,Dhananjay Thiruvady (2017). Towards solving large-scale precedence constrained production scheduling problems in mining.
J Anja Feldmann,Shang-Hua,Teng (1994). Dynamic Scheduling on Parallel Machines.
M Arezoo Atighehchian (2013). An Environment-Driven, Function-Based Approach to Dynamic Single-Machine Scheduling.
N Atif Shahzad (2016). Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation.
J Balicki (2007). Tabu Programming For Multiobjective Optimization Problems.
S Banu Çaliş (2013). A Research Survey: Review of Ai Solution Strategies of Job Shop Scheduling Problem.
R Barták (1999). Dynamic Constraint Models for Planning And Scheduling Problems.
Bing-Hai Zhou,X Richard,Fung (2013). Dynamic Scheduling Of Photolithography Process Based on Kohonen Neural Network.
S Binodini Tripathy,Kumari Sasmita,Padhy (2015). Dynamic Task Scheduling using A Directed Neural Network.
P Brucker (2007). Scheduling Algorithms.
P Byung Jun Joo (2015). A Production Scheduling Problem with Uncertain Sequence-Dependent Set-Up Times and Random Yield.
C Sung,J Rhee (1987). A dynamic production scheduling model with lost-sales or backlogging.
C Wong,F Chan,S Chung (2013). A joint production scheduling approach considering multiple resources and preventive maintenance tasks.
L Chao Lu,Xinyu Li,Quanke Pan,Qi Wang ; A (2017). Energy-Efficient Permutation flow Shop Scheduling Problem using a Hybrid Multi-Objective Backtracking Search Algorithm.
L Chao Lu,Xinyu Li,Shengqiang Xiao (2017). A Hybrid Multi-Objective Grey Wolf Optimizer for Dynamic Scheduling in a Real-World Welding Industry.
Christoph Pickardt,T,Jurgen Branke,Jens Heger,Bernd Scholz-Reiter (2013). Evolutionary Generation of Dispatching Rule Sets For Complex Dynamic Scheduling Problems.
Chuanli Zhao,Ji Fang,T Cheng,Min Ji (2017). A note on the time complexity of machine scheduling with DeJong’s learning effect.
Chuanyu Zhao,Jie Fu,Qiang Xu (2013). Real‐time dynamic hoist scheduling for multistage material handling process under uncertainties.
Cyrille Pach,Thierry Berger,Thérèse Bonte,Damien Trentesaux (2014). ORCA-FMS: a dynamic architecture for the optimized and reactive control of flexible manufacturing scheduling.
Daria Terekhov,T Tran,D Down,J Beck (2010). Integrating Queueing Theory and Scheduling for Dynamic Scheduling Problems.
Daria Terekhov,Douglas Down,J Beck (2014). Queueing-theoretic approaches for dynamic scheduling: A survey.
Djamila Ouelhadj,Sanja Petrovic (2008). A survey of dynamic scheduling in manufacturing systems.
X Dongjuan (2010). A Dynamic Scheduling Model Oriented to Flexible Production.
Dongni Li,Yan Wang,Guangxue Xiao,Jiafu Tang (2013). Dynamic parts scheduling in multiple job shop cells considering intercell moves and flexible routes.
Edna Barbosa Da Silva,M Marilda,F´atima Souza,Da Silva,Fabio,Henrique Pereira (2014). Simulation Study of Dispatching Rules In Stochastic Job Shop Dynamic Scheduling.
Eliana González-Neira,Jairo Montoya-Torres,David Barrera (2017). Flow-shop scheduling problem under uncertainties: Review and trends.
Florian Hecker,M,Thomas Becker,Bernd Hitzmann (2014). Application of A Modified Ga, Aco and a Random Search Procedure to Solve the Production Scheduling of A Case Study Bakery.
Gabriela Maschietto,Yassine Ouazene,Martín Ravetti,Maurício De Souza,Farouk Yalaoui (2016). Crane scheduling problem with non-interference constraints in a steel coil distribution centre.
S Gomes (2014). Selection Constructive Based Hyper-Heuristic for Dynamic Scheduling.
J Heger,Branke,Jurgen,Hildebrandt,Torsten,Bernd Scholz-Reiter (2016). Dynamic Adjustment of Dispatching Rule Parameters in flow Shops With Sequence Dependent Setup Times.
J Herrmann (2006). HETA 94-0374-2534, University of Maryland, College Park, Maryland..
I Pereira,A Madureira (2013). Self-Optimization module for Scheduling using Case-based Reasoning.
Ihsan Sabuncuoglu,Burckaan Gurgun (1996). A neural network model for scheduling problems.
J Behnamian,S Fatemi Ghomi (2014). A survey of multi-factory scheduling.
P Jiewu Leng (2017). Dynamic Scheduling in Rfid-Driven Discrete Manufacturing System by using Multi-Layer Network Metrics As Heuristic Information.
Y-T Joseph,J Leung"sanjoy Baruah (2004). Handbook of Scheduling.
Jun Zhao,Wei Wang,Kan Sun,Ying Liu (2014). A Bayesian Networks Structure Learning and Reasoning-Based Byproduct Gas Scheduling in Steel Industry.
S Jurgen Branke,Christoph Pickardt,Mengjie Zhang (2016). Automated Design of Production Scheduling Heuristics: A Review.
S Jurgenbranke,Christoph Pickardt,Mengjie Zhang (2016). Automated Design of Production Scheduling Heuristics: A Review.
A Kaban,Z Othman,D Rohmah (2012). Comparison of dispatching rules in job-shop scheduling problem using simulation: a case study.
K Kalinowski Krzysztof,Grabowik Cezary (2013). Predictive -Reactive Strategy for Real time Scheduling of Manufacturing Systems.
P Kaminsky (2006). Models and Algorithms for Integratedmulti-Stage Production/ Distribution Systems: Third Party Logistics.
Rakesh Kumar,Bipin Singh,Amit Kumar,Ashwini Kumar,Ajay Kumar,Parveen Kumar (2014). Integrating selective flocculation techniques for enhanced efficiency in manufacturing processes: A novel approach through artificial neural network modeling.
Lei Wang,Jingcao Cai,Ming Li,Zhihu Liu (2017). Flexible Job Shop Scheduling Problem Using an Improved Ant Colony Optimization.
Y Li,Y Chen (2010). A Genetic Algorithm for Job-Shop Scheduling.
W Li Yuqing,Minqiang Xu (2014). Rescheduling of Observing Spacecraft using Fuzzy Neural Network and Ant Colony Algorithm.
Liping Zhang,Xinyu Li,Liang Gao,Guohui Zhang (2013). Dynamic rescheduling in FMS that is simultaneously considering energy consumption and schedule efficiency.
Lixin Tang,W,J I Y I N Li,U (2005). A Neural Network Model And Algorithm for the Hybrid Flow Shop Scheduling Problem in a Dynamic Environment.
M Adibi,M Zandieh,M Amiri (2010). Multi-objective scheduling of dynamic job shop using variable neighborhood search.
Mehdi Abedi,Hany Seidgar,Hamed Fazlollahtabar (2017). Hybrid scheduling and maintenance problem using artificial neural network based meta-heuristics.
Fatma Omara,Mona Arafa (2010). Genetic algorithms for task scheduling problem.
D Ouelhadj,P (2009). Survey of Dynamic Scheduling In Manufacturing Systems.
Paolo Priore,David De La Fuente,Rau´l Pino,Javier Puente (2001). Dynamic scheduling of flexible manufacturing systems using neural networks and inductive learning.
Paolo Priore,Raúl Pino,José Parreño,Javier Puente,Borja Ponte (2015). Real-Time Scheduling of Flexible Manufacturing Systems Using Support Vector Machines and Case-Based Reasoning.
M Rakesh Kumar,Haryana (2016). Proceedings of the 26th World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2022.
Sudip Sahana,Aruna Jain,Prabhat Mahanti (2014). Ant Colony Optimization for Train Scheduling: An Analysis.
Sana Alyaseri,K,-M (2013). Multi Objective Bee Colony Optimization Framework for Grid Job Scheduling.
Toru Eguchi,Fuminori Oba,Toshiki Hirai (1999). A neural network approach to dynamic job shop scheduling.
Tarun Kanti,Jana,B,Soumen Paul,Bijan Sarkar,Jyotirmoy Saha (2013). Dynamic Schedule Execution in an Agent Based Holonic Manufacturing System.
Tarun Kanti,Jana,B,Soumen Paul,Bijan Sarkar,Jyotirmoy Saha (2013). Dynamic Schedule Execution in an Agent based Holonic Manufacturing System.
F Tubilla (2011). Dynamic Scheduling of Manufacturing Systems with Setups And Random Disruptions.
J Yiping Wen,Zhigang Chen,Buqing Cao (2014). Dynamic Scheduling Optimization for Instance Aspect Handling In Workflows.
Yuxin Zhai,Konstantin Biel,Fu Zhao,John Sutherland (2017). Dynamic scheduling of a flow shop with on-site wind generation for energy cost reduction under real time electricity pricing.
Zaki Ahmad Khan,Jamshed Siddiqui,Mahfooz Alam (2017). Dynamic Scheduling Algorithm for Variants of Hypercube Interconnection Networks.
Zhicheng Cai,Xiaoping Li,Rubén Ruiz,Qianmu Li (2017). A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds.
A Madureira,I,P Pereira,A Abraham (2014). Negotiation Mechanism for Self-Organized Scheduling System with Collective Intelligence.
A Santos,M Varela,G Putnik,A Madureira (2014). Alternative approaches analysis for scheduling in an Extended Manufacturing Environment.
C Abdallah Elkhyari,Jussien Narendra (2003). Constraint Programming for Dynamic Scheduling Problems.
Adil Baykasoğlu,Fatma Karaslan (2017). Solving comprehensive dynamic job shop scheduling problem by using a GRASP-based approach.
Ahmad Shahrizal Muhamad,S (2011). An Artificial Immune System for Solving Production Scheduling Problems: A Review.
Aidin Delgoshaei,Ahad Ali,Mohd Ariffin,Chandima Gomes (2016). A multi-period scheduling of dynamic cellular manufacturing systems in the presence of cost uncertainty.
Ali Vatankhah Barenji,R,Danial Roudi,Majid Hashemipour (2016). A Dynamic Multi-Agent-Based Scheduling Approach for Smes.
G Alper Hamzadayi (2016). Event Driven Strategy Based Complete Rescheduling Approaches for Dynamic M Identical Parallel Machines Scheduling Problem with a Common Server.
S Alper Seker,Reha Botsali (2013). A Neuro-Fuzzy Model for A New Hybrid Integrated Process Planning and Scheduling System.
T Amer Fahmya,Hesham Bassionic (2014). what is Dynamic Scheduling?.
Amina Lamghari,Roussos Dimitrakopoulos,Jacques Ferland (2014). A hybrid method based on linear programming and variable neighborhood descent for scheduling production in open-pit mines.
Andrea Cataldo,A,Riccardo Scattolini (2015). Production Scheduling of Parallel Machines with Model Predictive Control.
Andrea Cataldo,Andrea Perizzato,Riccardo Scattolini (2015). Production scheduling of parallel machines with model predictive control.
Andrea Rossi,Andrea Pandolfi,Michele Lanzetta (2013). Dynamic set-up rules for hybrid flow shop scheduling with parallel batching machines.
Angus Kenny,X,Andreas Ernst,Dhananjay Thiruvady (2017). Towards Solving Large-Scale Precedence Constrained Production Scheduling Problems in Mining.
J Anja Feldmann,Shang-Hua,Teng (1994). Dynamic Scheduling on Parallel Machines.
M Arezoo Atighehchian (2013). An Environment-Driven, Function-Based Approach to Dynamic Single-Machine Scheduling.
N Atif Shahzad (2016). Learning Dispatching Rules for Scheduling: a Synergistic View Comprising Decision Trees, Tabu Search and Simulation.
J Balicki (2007). Tabu Programming for Multiobjective Optimization Problems.
S Banu Çaliş (2013). A Research Survey: Review of Ai Solution Strategies of Job Shop Scheduling Problem.
Roman Barták (1999). Dynamic Constraint Models for Planning and Scheduling Problems.
Bing-Hai Zhou,X Richard,Fung (2013). Dynamic Scheduling of Photolithography Process Based on Kohonen Neural Network.
S Binodini Tripathy,Kumari Sasmita,Padhy (2015). Dynamic Task Scheduling using a Directed Neural Network.
P Brucker (2007). Scheduling Algorithms.
P Byung Jun Joo (2015). A Production Scheduling Problem with Uncertain Sequence-Dependent Set-Up Times and Random Yield.
C Sung,J Rhee (1987). A dynamic production scheduling model with lost-sales or backlogging.
C Wong,F Chan,S Chung (2013). A joint production scheduling approach considering multiple resources and preventive maintenance tasks.
L Chao Lu,Xinyu Li,Quanke Pan,Qi Wang ; A (2017). Energy-Efficient Permutation flow Shop Scheduling Problem using A Hybrid Multi-Objective Backtracking Search Algorithm.
L Chao Lu,Xinyu Li,Shengqiang Xiao (2017). A Hybrid Multi-Objective Grey Wolf Optimizer for Dynamic Scheduling in a Real-World Welding Industry.
Christoph Pickardt,Torsten Hildebrandt,Jürgen Branke,Jens Heger,Bernd Scholz-Reiter (2013). Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems.
Chuanli Zhao,Ji Fang,T Cheng,Min Ji (2017). A note on the time complexity of machine scheduling with DeJong’s learning effect.
Chuanyu Zhao,Jie Fu,Qiang Xu (2013). Real‐time dynamic hoist scheduling for multistage material handling process under uncertainties.
Cyrille Pach,Thierry Berger,Thérèse Bonte,Damien Trentesaux (2014). ORCA-FMS: a dynamic architecture for the optimized and reactive control of flexible manufacturing scheduling.
Daria Terekhov,T Tran,D Down,J Beck (2010). Integrating Queueing Theory and Scheduling for Dynamic Scheduling Problems.
Daria Terekhov,Douglas Down,J Beck (2014). Queueing-theoretic approaches for dynamic scheduling: A survey.
Djamila Ouelhadj,S (2008). A Survey of Dynamic Scheduling In Manufacturing Systems.
X Dongjuan (2010). A Dynamic Scheduling Model Oriented to Flexible Production.
Dongni Li,Y,Guangxue Xiao,Jiafu Tang (2013). Dynamic Parts Scheduling in Multiple Job Shop Cells Considering Intercell Moves and Flexible Routes.
Edna Barbosa Da Silva,M Marilda,F´atima Souza,Da Silva,Fabio,Henrique Pereira (2014). Simulation Study of Dispatching Rules in Stochastic Job Shop Dynamic Scheduling.
Eliana María González-Neira,A,-T,David Barrera (2017). flow-Shop Scheduling Problem under Uncertainties: Review and Trends.
Florian Hecker,Marc Stanke,Thomas Becker,Bernd Hitzmann (2014). Application of a modified GA, ACO and a random search procedure to solve the production scheduling of a case study bakery.
Gabriela Maschietto,Yassine Ouazene,Martín Ravetti,Maurício De Souza,Farouk Yalaoui (2016). Crane scheduling problem with non-interference constraints in a steel coil distribution centre.
S Gomes (2014). Selection Constructive Based Hyper-Heuristic for Dynamic Scheduling.
Jens Heger,Jürgen Branke,Torsten Hildebrandt,Bernd Scholz-Reiter (2016). Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times.
J Herrmann (2006). Self-Optimization Module for Scheduling using Case-Based Reasoning.
Ihsan Sabuncuoglu,B (1996). A Neural Network Model for Scheduling Problems.
J Behnamian,S (2014). A Survey of Multi-Factory Scheduling.
P Jiewu Leng (2017). Dynamic Scheduling in Rfid-Driven Discrete Manufacturing System by using Multi-Layer Network Metrics As Heuristic Information.
Y-T Joseph,J Leung"sanjoy Baruah (2004). Handbook of Scheduling.
Jun Zhao,Wei Wang,Kan Sun,Ying Liu (2014). A Bayesian Networks Structure Learning and Reasoning-Based Byproduct Gas Scheduling in Steel Industry.
S Jurgen Branke,Christoph Pickardt,Mengjie Zhang (2016). Automated Design of Production Scheduling Heuristics: A Review.
S Jurgenbranke,Christoph Pickardt,Mengjie Zhang (2016). Automated Design of Production Scheduling Heuristics: A Review.
A Kaban,Z Othman,D Rohmah (2012). Comparison of dispatching rules in job-shop scheduling problem using simulation: a case study.
K Kalinowski Krzysztof,Grabowik Cezary (2013). Predictive -Reactive Strategy for Real Time Scheduling of Manufacturing Systems.
P Kaminsky (2006). Models and Algorithms for Integratedmulti -Stage Production/Distribution Systems: Third Party Logistics.
Rakesh Kumar,Bipin Singh,Amit Kumar,Ashwini Kumar,Ajay Kumar,Parveen Kumar (2014). Integrating selective flocculation techniques for enhanced efficiency in manufacturing processes: A novel approach through artificial neural network modeling.
Lei Wang,J,Ming Li,Zhihu Liu (2017). Flexible Job Shop Scheduling Problem using an Improved Ant Colony Optimization.
Yuqing Li,Rixin Wang,Minqiang Xu (2010). Rescheduling of observing spacecraft using fuzzy neural network and ant colony algorithm.
Liping Zhang,Xinyu Li,Liang Gao,Guohui Zhang (2013). Dynamic rescheduling in FMS that is simultaneously considering energy consumption and schedule efficiency.
Lixin Tang,Wenxin Liu,Jiyin Liu (2005). A neural network model and algorithm for the hybrid flow shop scheduling problem in a dynamic environment.
M Adibi,M Zandieh,M Amiri (2010). Multi-objective scheduling of dynamic job shop using variable neighborhood search.
Mehdi Abedi,Hany Seidgar,Hamed Fazlollahtabar (2010). Hybrid scheduling and maintenance problem using artificial neural network based meta-heuristics.
Djamila Ouelhadj,Sanja Petrovic (2009). A survey of dynamic scheduling in manufacturing systems.
Paolo Priore,David De La Fuente,Rau´l Pino,Javier Puente (2001). Dynamic scheduling of flexible manufacturing systems using neural networks and inductive learning.
Paolo Priore,Raúl Pino,José Parreño,Javier Puente,Borja Ponte (2015). Real-Time Scheduling of Flexible Manufacturing Systems Using Support Vector Machines and Case-Based Reasoning.
M Rakesh Kumar,Haryana (2016). Proceedings of the 26th World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2022.
Sudip Sahana,Aruna Jain,Prabhat Mahanti (2014). Ant Colony Optimization for Train Scheduling: An Analysis.
Sana Alyaseri,K,-M (1999). Multi Objective Bee Colony Optimization Framework for Grid Job Scheduling.
Tarun Kanti,Jana,B,Soumen Paul,Bijan Sarkar,Jyotirmoy Saha (2013). Dynamic Schedule Execution In an Agent Based Holonic Manufacturing System.
Tarun Kanti,Jana,B,Soumen Paul,Bijan Sarkar,Jyotirmoy Saha (2013). Dynamic Schedule Execution in an Agent Based Holonic Manufacturing System.
F Tubilla (2011). Dynamic Scheduling of Manufacturing Systems With Setups And Random Disruptions.
J Yiping Wen,Zhigang Chen,Buqing Cao (2014). Dynamic Scheduling Optimization for Instance Aspect Handling In Workflows.
Yuxin Zhai,Konstantin Biel,Fu Zhao,John Sutherland (2017). Dynamic scheduling of a flow shop with on-site wind generation for energy cost reduction under real time electricity pricing.
Zaki Ahmad Khan,Jamshed Siddiqui,Mahfooz Alam (2017). Dynamic Scheduling Algorithm for Variants of Hypercube Interconnection Networks.
Zhicheng Cai,Xiaoping Li,Rubén Ruiz,Qianmu Li (2017). A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds.

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No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

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How to Cite This Article

Osama Mohammed Elmardi Suleiman Khayal. 2018. \u201cA Review for Dynamic Scheduling in Manufacturing\u201d. Global Journal of Research in Engineering - J: General Engineering GJRE-J Volume 18 (GJRE Volume 18 Issue J5).

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Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

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GJRE-J Classification FOR Code: 091399

Submission ReceivedDecember 9, 2017
Peer Review Double Blind
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Accepted January 4, 2018
Published January 15, 2018

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Total Score: 73

Country: Sudan

Subject: Global Journal of Research in Engineering - J: General Engineering

Authors: Khalid Muhamadin Mohamed Ahmed Bukkur, M.I. Shukri, Osama Mohammed Elmardi (PhD/Dr. count: 0)

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