A control approach to scheduling flexibly configurable jobs with dynamic structural-logical constraints

Abstract We study the problem of scheduling in manufacturing environments which are dynamically reconfigurable for supporting highly flexible individual operation compositions of the jobs. We show that such production environments yield the simultaneous process design and operation sequencing with dynamically changing hybrid structural-logical constraints. We conceptualize a model to schedule jobs in such environments when the structural-logical constraints are changing dynamically and offer a design framework of algorithmic development to obtain a tractable solution analytically within the proven axiomatic of the optimal control and mathematical optimization. We further develop an algorithm to simultaneously determine the process design and operation sequencing. The algorithm is decomposition-based and leads to an approximate solution of the underlying optimization problem that is modeled by optimal control. We theoretically analyze the algorithmic complexity and apply this approach on an illustrative example. The findings suggest that our approach can be of value for modeling problems with a simultaneous process design and operation sequencing when the structural and logical constraints are dynamic and interconnected. Utilizing the outcomes of this research could also support the analysis of processing dynamics during the operations execution.

[1]  Frank Werner,et al.  Flexible job shop scheduling with lot streaming and sublot size optimisation , 2018, Int. J. Prod. Res..

[2]  Zhaohui Liu,et al.  Routing open shop and flow shop scheduling problems , 2011, Eur. J. Oper. Res..

[3]  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..

[4]  Leyuan Shi,et al.  Data mining-based dispatching system for solving the local pickup and delivery problem , 2013, Ann. Oper. Res..

[5]  Kien Ming Ng,et al.  A water-flow algorithm for flexible flow shop scheduling with intermediate buffers , 2011, J. Sched..

[6]  A. Kusiak Smart manufacturing , 2018, Int. J. Prod. Res..

[7]  Ray Y. Zhong,et al.  Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors , 2017, Int. J. Prod. Res..

[8]  Jose M. Framiñan,et al.  New hard benchmark for flowshop scheduling problems minimising makespan , 2015, Eur. J. Oper. Res..

[9]  Ashutosh Nayak,et al.  Resource sharing in cyber-physical systems: modelling framework and case studies , 2016 .

[10]  Lihui Wang,et al.  Scheduling in cloud manufacturing: state-of-the-art and research challenges , 2019, Int. J. Prod. Res..

[11]  Alexandre Dolgui,et al.  Scheduling of recovery actions in the supply chain with resilience analysis considerations , 2018, Int. J. Prod. Res..

[12]  M. Caramanis,et al.  Efficient Lagrangian relaxation algorithms for industry size job-shop scheduling problems , 1998 .

[13]  Suresh P. Sethi,et al.  Stability of Real-Time Lot-Scheduling and Machine Replacement Policies with Quality Levels , 2000, IEEE Trans. Autom. Control..

[14]  M. Veatch,et al.  Production control with backlog-dependent demand , 2009 .

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

[16]  Xiaolan Xie,et al.  Multiresource Shop Scheduling With Resource Flexibility and Blocking , 2011, IEEE Transactions on Automation Science and Engineering.

[17]  Satish T. S. Bukkapatnam,et al.  The internet of things for smart manufacturing: A review , 2019, IISE Trans..

[18]  James R. Wilson,et al.  A practical method for evaluating worker allocations in large-scale dual resource constrained job shops , 2014 .

[19]  Barış Tan,et al.  Simulation and optimization of continuous-flow production systems with a finite buffer by using mathematical programming , 2017 .

[20]  Alexandre Dolgui,et al.  Supply Chain Engineering , 2010 .

[21]  Sun Hur,et al.  Performance computation methods for composition of tasks with multiple patterns in cloud manufacturing , 2019, Int. J. Prod. Res..

[22]  Alexandre Dolgui,et al.  Multi-stage supply chain scheduling with non-preemptive continuous operations and execution control , 2014 .

[23]  Barış Tan,et al.  Mathematical programming representations of the dynamics of continuous-flow production systems , 2015 .

[24]  Alexandre Dolgui,et al.  Combinatorial design of a minimum cost transfer line with parallel operations at workstations , 2007 .

[25]  Malgorzata Sterna,et al.  Handbook on Scheduling , 2007 .

[26]  Konstantin Kogan,et al.  Scheduling: Control-Based Theory and Polynomial-Time Algorithms , 2000 .

[27]  Alexandre Dolgui,et al.  Schedule robustness analysis with the help of attainable sets in continuous flow problem under capacity disruptions , 2016 .

[28]  Andrew Kusiak,et al.  Data-driven smart manufacturing , 2018, Journal of Manufacturing Systems.

[29]  Bengt Lennartson,et al.  An event-driven manufacturing information system architecture for Industry 4.0 , 2017, Int. J. Prod. Res..

[30]  Riccardo Minciardi,et al.  Optimal Strategies for Multiclass Job Scheduling on a Single Machine With Controllable Processing Times , 2008, IEEE Transactions on Automatic Control.

[31]  Boris V. Sokolov,et al.  Robust dynamic schedule coordination control in the supply chain , 2016, Comput. Ind. Eng..

[32]  Joseph Geunes,et al.  Production Planning with Flexible Product Specifications: An Application to Specialty Steel Manufacturing , 2003, Oper. Res..

[33]  Kevin M. Passino,et al.  Path-clearing policies for flexible manufacturing systems , 1999, IEEE Trans. Autom. Control..

[34]  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..

[35]  Ling Shi,et al.  Optimal sensor scheduling for multiple linear dynamical systems , 2017, Autom..

[36]  Konstantin Kogan,et al.  Maximum principle-based methods for production scheduling with partially sequence-dependent setups , 1997 .

[37]  J. Barnes,et al.  Solving the job shop scheduling problem with tabu search , 1995 .

[38]  Yoram Koren,et al.  Value creation through design for scalability of reconfigurable manufacturing systems , 2017, Int. J. Prod. Res..

[39]  R. Sargent Optimal control , 2000 .

[40]  Christos Koulamas,et al.  Flexible flow shop scheduling with uniform parallel machines , 2006, Eur. J. Oper. Res..

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

[42]  Frank Werner,et al.  A comparison of scheduling algorithms for flexible flow shop problems with unrelated parallel machines, setup times, and dual criteria , 2009, Comput. Oper. Res..

[43]  Hui Xiong,et al.  Rebalancing Bike Sharing Systems: A Multi-source Data Smart Optimization , 2016, KDD.

[44]  Lionel Amodeo,et al.  A linear programming approach for identical parallel machine scheduling with job splitting and sequence-dependent setup times , 2006 .

[45]  Jung-Min Yang,et al.  Supervisory control for real-time scheduling of periodic and sporadic tasks with resource constraints , 2009, Autom..

[46]  Izabela Nielsen,et al.  A methodology for implementation of mobile robot in adaptive manufacturing environments , 2017, J. Intell. Manuf..

[47]  Dmitry Ivanov,et al.  Adaptive Supply Chain Management , 2009 .

[48]  Houmin Yan,et al.  Optimal production control in a discrete manufacturing system with unreliable machines and random demands , 2000, IEEE Trans. Autom. Control..

[49]  S. G. Ponnambalam,et al.  A Differential Evolution-Based Algorithm to Schedule Flexible Assembly Lines , 2013, IEEE Transactions on Automation Science and Engineering.

[50]  Olga Battaïa,et al.  Future trends in management and operation of assembly systems: from customized assembly systems to cyber-physical systems , 2018, Omega.

[51]  Satish T. S. Bukkapatnam,et al.  Joint production and maintenance operations in smart custom-manufacturing systems , 2019, IISE Trans..

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