Smart Make-to-Order Production in a Flow Shop Environment for Industry 4.0

The permutation flow shop scheduling problem is one of the popular problems in operations research due to its complexity and also its practical applications in industries. With the fourth generation industrial revolution, decisional aspects in make to order flow shop environment needs to be decentralized and autonomous. One of the aspects is to consider a real-time or dynamic production environment where customers place orders into the system dynamically and the decision maker has to decide whether the order can be accepted considering the available production capacity and how to schedule the jobs of an accepted order. To answer these research questions, in this chapter, the authors introduce a new decision-making, real-time strategy intended to yield flexible and efficient flow shop production schedules with and without setup conditions, Numerical experiments based on realistic problem scenarios show the superiority of the proposed real-time approach over traditional right shifting approaches.

[1]  Mohamed Abdel-Basset,et al.  A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem , 2018, Future Gener. Comput. Syst..

[2]  Ruhul A. Sarker,et al.  Multiple-order permutation flow shop scheduling under process interruptions , 2018 .

[3]  Khamdi Mubarok,et al.  Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives , 2018, Frontiers of Mechanical Engineering.

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

[5]  Ruhul A. Sarker,et al.  A genetic algorithm for permutation flow shop scheduling under make to stock production system , 2015, Comput. Ind. Eng..

[6]  Ruhul A. Sarker,et al.  Production , Manufacturing and Logistics A real-time order acceptance and scheduling approach for permutation flow shop problems , 2015 .

[7]  Swagatam Das,et al.  A Discrete Inter-Species Cuckoo Search for flowshop scheduling problems , 2015, Comput. Oper. Res..

[8]  Daryl Essam,et al.  Permutation Flow Shop Scheduling with dynamic job order arrival , 2013, 2013 IEEE Conference on Cybernetics and Intelligent Systems (CIS).

[9]  Ruhul A. Sarker,et al.  A memetic algorithm for Permutation Flow Shop Problems , 2013, 2013 IEEE Congress on Evolutionary Computation.

[10]  Ammar Oulamara,et al.  Permutation flow shops with exact time lags to minimise maximum lateness , 2009 .

[11]  Christos D. Tarantilis,et al.  Minimizing makespan in permutation flow shop scheduling problems using a hybrid metaheuristic algorithm , 2009, Comput. Oper. Res..

[12]  Rui Alberto F. S. Alves,et al.  A methodology for planning and controlling workload in a job-shop: a four-way decision-making problem , 2009 .

[13]  Ruhul A. Sarker,et al.  Memetic algorithms for solving job-shop scheduling problems , 2009, Memetic Comput..

[14]  Jing Liu,et al.  A survey of scheduling problems with setup times or costs , 2008, Eur. J. Oper. Res..

[15]  P. Rogers,et al.  Judicious order acceptance and order release in make-to-order manufacturing systems , 2007 .

[16]  Mehmet Fatih Tasgetiren,et al.  A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem , 2007, Eur. J. Oper. Res..

[17]  Rubén Ruiz,et al.  TWO NEW ROBUST GENETIC ALGORITHMS FOR THE FLOWSHOP SCHEDULING PROBLEM , 2006 .

[18]  Fawaz S. Al-Anzi,et al.  A branch-and-bound algorithm for three-machine flowshop scheduling problem to minimize total completion time with separate setup times , 2006, Eur. J. Oper. Res..

[19]  Fawaz S. Al-Anzi,et al.  Using a Hybrid Evolutionary Algorithm to Minimize Variance in Response Time for Multimedia Object Requests , 2005, J. Math. Model. Algorithms.

[20]  Guoqing Wang,et al.  Complexity results for flow-shop problems with a single server , 2005, Eur. J. Oper. Res..

[21]  Rubén Ruiz,et al.  A comprehensive review and evaluation of permutation flowshop heuristics to minimize flowtime , 2013, Comput. Oper. Res..

[22]  Mieczysław Wodecki,et al.  A very fast tabu search algorithm for the permutation flow shop problem with makespan criterion , 2004, Comput. Oper. Res..

[23]  Chandrasekharan Rajendran,et al.  Ant-colony algorithms for permutation flowshop scheduling to minimize makespan/total flowtime of jobs , 2004, Eur. J. Oper. Res..

[24]  Paul Rogers,et al.  Using Simulation to Make Order Acceptance/Rejection Decisions , 2004, Simul..

[25]  Celia A. Glass,et al.  Scheduling for Parallel Dedicated Machines with a Single Server , 2000 .

[26]  Ali Allahverdi,et al.  Minimizing mean flowtime in a two-machine flowshop with sequence-independent setup times , 2000, Comput. Oper. Res..

[27]  Fuh-Der Chou,et al.  Heuristic for scheduling in a two-machine bicriteria dynamic flowshop with setup and processing times separated , 2000 .

[28]  Chelliah Sriskandarajah,et al.  One-operator-two-machine flowshop scheduling with setup and dismounting times , 1999, Comput. Oper. Res..

[29]  Konstantin Kogan,et al.  A polynomial algorithm for scheduling small-scale manufacturing cells served by multiple robots , 1998, Comput. Oper. Res..

[30]  Fengyuan Ren,et al.  Scheduling " , , 1997 .

[31]  Konstantin Kogan,et al.  Flowshop Scheduling of Robotic Cells with Job-dependent Transportation and Set-up Effects , 1995 .

[32]  Éric D. Taillard,et al.  Benchmarks for basic scheduling problems , 1993 .

[33]  I. Osman,et al.  Simulated annealing for permutation flow-shop scheduling , 1989 .

[34]  Willem Selen,et al.  A Mixed-Integer Goal-Programming Formulation of the Standard Flow-Shop Scheduling Problem , 1986 .

[35]  Inyong Ham,et al.  A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem , 1983 .

[36]  Ravi Sethi,et al.  The Complexity of Flowshop and Jobshop Scheduling , 1976, Math. Oper. Res..

[37]  E. Ignall,et al.  Application of the Branch and Bound Technique to Some Flow-Shop Scheduling Problems , 1965 .

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