Optimal Operation and Scheduling of Parallel Machines in Jobshop Environments

Abstract Scheduling of machines together with the optimization of the energy consumption (EC) play a crucial role in every manufacturing system. The current paper aims to present a multi-objective optimization approach for planning and scheduling of a manufacturing system in a job-shop environment. First, an inclusive review and comparison of the latest advancements in this field was performed. Then, a manufacturing system in a job-shop environment, which consists of six designed machines with zero buffer capacity, and 53 jobs, was simulated. Four heuristics for the purpose of sequencing are adopted during the analysis process. A comparison of the optimum results suggested the most efficient values for the EC, make-span, and tardiness. The EC variations and processing time of the machines were studied under sequencing rules to let the designers exploring more efficient solutions, and planning strategies towards enhancing the performance and reliability of studied machines. Next step of this study was to estimate the optimum completion time of each process in the considered manufacturing system and with consideration of the proposed scheduling methods.

[1]  Fei Zhang,et al.  A resource scheduling algorithm of cloud computing based on energy efficient optimization methods , 2012, 2012 International Green Computing Conference (IGCC).

[2]  Jordi Torres,et al.  Towards energy-aware scheduling in data centers using machine learning , 2010, e-Energy.

[3]  Ningjian Huang,et al.  Optimal vehicle batching and sequencing to reduce energy consumption and atmospheric emissions in automotive paint shops , 2011 .

[4]  S. M. Johnson,et al.  Optimal two- and three-stage production schedules with setup times included , 1954 .

[5]  Janet M. Twomey,et al.  Operational methods for minimization of energy consumption of manufacturing equipment , 2007 .

[6]  Lin Li,et al.  A multi-level optimization approach for energy-efficient flexible flow shop scheduling , 2016 .

[7]  John W. Sutherland,et al.  A New Shop Scheduling Approach in Support of Sustainable Manufacturing , 2011 .

[8]  Euiseong Seo,et al.  Energy-credit scheduler: An energy-aware virtual machine scheduler for cloud systems , 2014, Future Gener. Comput. Syst..

[9]  Jinwoo Park,et al.  Optimization of production scheduling with time-dependent and machine-dependent electricity cost for industrial energy efficiency , 2013 .

[10]  John W. Sutherland,et al.  Flow shop scheduling with peak power consumption constraints , 2013, Ann. Oper. Res..

[11]  Dehua Xu,et al.  Mixed Integer Programming Formulations for Two-Machine Flow Shop Scheduling with an Availability Constraint , 2017, Arabian Journal for Science and Engineering.

[12]  Amir Tavakoli Effective Progress Scheduling and Control for Construction Projects , 1990 .

[13]  Stephan Biller,et al.  Production system design to achieve energy savings in an automotive paint shop , 2011 .

[14]  Murat Köksalan,et al.  A Simulated Annealing Approach to Bicriteria Scheduling Problems on a Single Machine , 2000, J. Heuristics.

[15]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .

[16]  Jinliang Ding,et al.  Two-objective stochastic flow-shop scheduling with deteriorating and learning effect in Industry 4.0-based manufacturing system , 2017, Appl. Soft Comput..

[17]  Liang Gao,et al.  A Novel Teaching-Learning-Based Optimization Algorithm for Energy-Efficient Scheduling in Hybrid Flow Shop , 2018, IEEE Transactions on Engineering Management.

[18]  Wail Menesi,et al.  Multimode Resource-Constrained Scheduling and Leveling for Practical-Size Projects , 2015 .