The Tolerance Scheduling Problem in a Single Machine Case

This chapter introduces the Tolerance Scheduling problem, which involves the decision-making issues in rescheduling processes. The solutions to this problem can be incorporated in the design of Decision Support Systems (DSS) in Industry 4.0 environments. We present here the mathematical foundations for the solutions of the Tolerance Scheduling problem as well as the technical requirements of their embodiment in Industry 4.0’s DSS. We illustrate these ideas in a case study with a single machine, in which we analyze the performance of the model at different tolerances.

[1]  Alexandre Dolgui,et al.  Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak , 2020, Int. J. Prod. Res..

[2]  Francisco Almada-Lobo,et al.  The Industry 4.0 revolution and the future of Manufacturing Execution Systems (MES) , 2016 .

[3]  Rubén Ruiz,et al.  The hybrid flow shop scheduling problem , 2010, Eur. J. Oper. Res..

[4]  Dmitry Ivanov,et al.  Coronavirus (COVID-19/SARS-CoV-2) and supply chain resilience: a research note , 2020 .

[5]  Jay Lee,et al.  A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems , 2015 .

[6]  Mariano Frutos,et al.  Production planning and scheduling in Cyber-Physical Production Systems: a review , 2019, Int. J. Comput. Integr. Manuf..

[7]  Mostafa Zandieh,et al.  An immune algorithm for scheduling a hybrid flow shop with sequence-dependent setup times and machines with random breakdowns , 2009 .

[8]  Peter Brucker,et al.  Inverse scheduling: two-machine flow-shop problem , 2011, J. Sched..

[9]  Andrew Kusiak,et al.  From data to big data in production research: the past and future trends , 2019, Int. J. Prod. Res..

[10]  Mariano Frutos,et al.  Industry 4.0: Smart Scheduling , 2018, Int. J. Prod. Res..

[11]  Jose M. Framiñan,et al.  Manufacturing Scheduling Systems - An Integrated View on Models, Methods and Tools , 2014 .

[12]  Alexandre Dolgui,et al.  A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0 , 2016 .

[13]  Jean-Paul Arnaout Rescheduling of parallel machines with stochastic processing and setup times , 2014 .

[14]  Daniel Alejandro Rossit,et al.  Scheduling research contributions to Smart manufacturing , 2017 .

[15]  Xiaohang Yue,et al.  On the Robust and Stable Flowshop Scheduling Under Stochastic and Dynamic Disruptions , 2017, IEEE Transactions on Engineering Management.

[16]  Rubén Ruiz,et al.  Flow shop rescheduling under different types of disruption , 2013 .

[17]  Alexandre Dolgui,et al.  A taxonomy of line balancing problems and their solutionapproaches , 2013 .

[18]  Suresh P. Sethi,et al.  A survey on control theory applications to operational systems, supply chain management, and Industry 4.0 , 2018, Annu. Rev. Control..

[19]  Clemens Heuberger,et al.  Inverse Combinatorial Optimization: A Survey on Problems, Methods, and Results , 2004, J. Comb. Optim..

[20]  Mariano Frutos,et al.  An Industry 4.0 approach to assembly line resequencing , 2019, The International Journal of Advanced Manufacturing Technology.

[21]  Tarek Y. ElMekkawy,et al.  Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm , 2011 .

[22]  Boris V. Sokolov,et al.  Applicability of optimal control theory to adaptive supply chain planning and scheduling , 2012, Annu. Rev. Control..

[23]  Peter Brucker,et al.  Inverse scheduling with maximum lateness objective , 2009, J. Sched..

[24]  Jaejin Jang,et al.  Production rescheduling for machine breakdown at a job shop , 2012 .

[25]  László Monostori,et al.  ScienceDirect Variety Management in Manufacturing . Proceedings of the 47 th CIRP Conference on Manufacturing Systems Cyber-physical production systems : Roots , expectations and R & D challenges , 2014 .

[26]  Edward A. Lee Cyber Physical Systems: Design Challenges , 2008, 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC).

[27]  Alexandre Dolgui,et al.  ON APPLICABILITY OF OPTIMAL CONTROL THEORY TO ADAPTIVE SUPPLY CHAIN PLANNING AND SCHEDULING , 2011 .

[28]  Mariano Frutos,et al.  Designing a scheduling logic controller for industry 4.0 environments , 2019 .

[29]  Alexandre Dolgui,et al.  Scheduling in production, supply chain and Industry 4.0 systems by optimal control: fundamentals, state-of-the-art and applications , 2019, Int. J. Prod. Res..

[30]  Quan-Ke Pan,et al.  A comprehensive review and evaluation of permutation flowshop heuristics to minimize flowtime , 2013, Comput. Oper. Res..

[31]  Ravindra K. Ahuja,et al.  Inverse Optimization , 2001, Oper. Res..

[32]  Maurizio Faccio,et al.  Assembly system design in the Industry 4.0 era: a general framework , 2017 .

[33]  Jacek Blazewicz,et al.  Scheduling in Computer and Manufacturing Systems , 1990 .

[34]  Christos Koulamas Inverse scheduling with controllable job parameters , 2005 .

[35]  Jeffrey W. Herrmann,et al.  Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods , 2003, J. Sched..

[36]  Liang Gao,et al.  An Improved Artificial Bee Colony algorithm for real-world hybrid flowshop rescheduling in Steelmaking-refining-Continuous Casting process , 2018, Comput. Ind. Eng..

[37]  Sanja Petrovic,et al.  SURVEY OF DYNAMIC SCHEDULING IN MANUFACTURING SYSTEMS , 2006 .

[38]  Donya Rahmani,et al.  Robust and stable flow shop scheduling with unexpected arrivals of new jobs and uncertain processing times , 2014 .

[39]  Mariano Frutos,et al.  The Non-Permutation Flow-Shop scheduling problem: A literature review , 2017, Omega.

[40]  A.H.G. Rinnooy Kan,et al.  Single‐machine scheduling subject to stochastic breakdowns , 1990 .

[41]  Mariano Frutos,et al.  A data-driven scheduling approach to smart manufacturing , 2019, J. Ind. Inf. Integr..

[42]  Mostafa Zandieh,et al.  A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms , 2016, Comput. Oper. Res..

[43]  Soundar R. T. Kumara,et al.  Cyber-physical systems in manufacturing , 2016 .

[44]  Jian Zhang,et al.  Review of job shop scheduling research and its new perspectives under Industry 4.0 , 2017, Journal of Intelligent Manufacturing.

[45]  Boris V. Sokolov,et al.  Optimal Control Algorithms and Their Analysis for Short-Term Scheduling in Manufacturing Systems , 2018, Algorithms.

[46]  Alexandre Dolgui,et al.  The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics , 2018, Int. J. Prod. Res..

[47]  Jay Lee,et al.  Cyber physical systems for predictive production systems , 2017, Production Engineering.