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

2021 ◽  
pp. 955-982
Author(s):  
Humyun Fuad Rahman ◽  
Mukund Nilakantan Janardhanan ◽  
Peter Axel Nielsen
Keyword(s):  
Real Time ◽  
Industrial Revolution ◽  
Flow Shop ◽  
Production Capacity ◽  
Flow Shop Scheduling ◽  
Fourth Generation ◽  
Production Environment ◽  
Practical Applications ◽  
Make To Order ◽  
Realistic Problem

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.

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

2019 ◽  
pp. 210-243
Author(s):  
Humyun Fuad Rahman ◽  
Mukund Nilakantan Janardhanan ◽  
Peter Axel Nielsen
Keyword(s):  
Real Time ◽  
Industrial Revolution ◽  
Flow Shop ◽  
Production Capacity ◽  
Flow Shop Scheduling ◽  
Fourth Generation ◽  
Production Environment ◽  
Practical Applications ◽  
Make To Order ◽  
Realistic Problem

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.

scholarly journals Solving a flow shop scheduling problem with missing operations in an Industry 4.0 production environment

2021 ◽  
pp. 33-44 ◽  
Author(s):  
Daniel Alejandro Rossit ◽  
Adrián Toncovich ◽  
Diego Gabriel Rossit ◽  
Sergio Nesmachnow
Keyword(s):  
Industry 4.0 ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Mathematical Formulation ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Production Environment ◽  
Modern Approach ◽  
The Real ◽  
Shop Scheduling

Industry 4.0 is a modern approach that aims at enhancing the connectivity between the different stages of the production process and the requirements of consumers. This paper addresses a relevant problem for both Industry 4.0 and flow shop literature: the missing operations flow shop scheduling problem. In general, in order to reduce the computational effort required to solve flow shop scheduling problems only permutation schedules (PFS) are considered, i.e., the same job sequence is used for all the machines involved. However, considering only PFS is not a constraint that is based on the real-world conditions of the industrial environments, and it is only a simplification strategy used frequently in the literature. Moreover, non-permutation (NPFS) orderings may be used for most of the real flow shop systems, i.e., different job schedules can be used for different machines in the production line, since NPFS solutions usually outperform the PFS ones. In this work, a novel mathematical formulation to minimize total tardiness and a resolution method, which considers both PFS and (the more computationally expensive) NPFS solutions, are presented to solve the flow shop scheduling problem with missing operations. The solution approach has two stages. First, a Genetic Algorithm, which only considers PFS solutions, is applied to solve the scheduling problem. The resulting solution is then improved in the second stage by means of a Simulated Annealing algorithm that expands the search space by considering NPFS solutions. The experimental tests were performed on a set of instances considering varying proportions of missing operations, as it is usual in the Industry 4.0 production environment. The results show that NPFS solutions clearly outperform PFS solutions for this problem.

An Effective Immune Based Approach for the No-Wait Flow Shop Scheduling Problems with Multiple Machines

2010 ◽  
Vol 97-101 ◽  
pp. 2432-2435 ◽  
Author(s):  
Yi Chih Hsieh ◽  
Y.C. Lee ◽  
Peng Sheng You ◽  
Ta Cheng Chen
Keyword(s):  
Flow Shop ◽  
Immune Algorithm ◽  
Flow Shop Scheduling ◽  
Benchmark Problems ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Practical Applications ◽  
Shop Scheduling ◽  
No Wait ◽  
Better Than

For scheduling problems, no-wait constraint is an important requirement for many industries. As known, the no-wait scheduling problem is NP-hard and has several practical applications. This paper applies an immune algorithm to solve the multiple-machine no-wait flow shop scheduling problem with minimizing the makespan. Twenty-three benchmark problems on the OR-Library are solved by the immune algorithm. Limited numerical results show that the immune algorithm performs better than the other typical approaches in the literature for most of instances.

scholarly journals Field-synchronized Digital Twin framework for production scheduling with uncertainty

2020 ◽  
Author(s):  
Elisa Negri ◽  
Vibhor Pandhare ◽  
Laura Cattaneo ◽  
Jaskaran Singh ◽  
Marco Macchi ◽  
...  
Keyword(s):  
Real Time ◽  
Production Scheduling ◽  
Flow Shop ◽  
Health Management ◽  
Discrete Event ◽  
Operating Conditions ◽  
Sensor Data ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Digital Twin

Abstract Research on scheduling problems is an evergreen challenge for industrial engineers. The growth of digital technologies opens the possibility to collect and analyze great amount of field data in real-time, representing a precious opportunity for an improved scheduling activity. Thus, scheduling under uncertain scenarios may benefit from the possibility to grasp the current operating conditions of the industrial equipment in real-time and take them into account when elaborating the best production schedules. To this end, the article proposes a proof-of-concept of a simheuristics framework for robust scheduling applied to a Flow Shop Scheduling Problem. The framework is composed of genetic algorithms for schedule optimization and discrete event simulation and is synchronized with the field through a Digital Twin (DT) that employs an Equipment Prognostics and Health Management (EPHM) module. The contribution of the EPHM module inside the DT-based framework is the real time computation of the failure probability of the equipment, with data-driven statistical models that take sensor data from the field as input. The viability of the framework is demonstrated in a flow shop application in a laboratory environment.

A genetic algorithm for permutation flowshop scheduling under practical make-to-order production system

2016 ◽  
Vol 31 (1) ◽  
pp. 87-103 ◽  
Author(s):  
Humyun Fuad Rahman ◽  
Ruhul Sarker ◽  
Daryl Essam
Keyword(s):  
Real Time ◽  
Production System ◽  
Production Scheduling ◽  
Real Life ◽  
Real Data ◽  
Production Environment ◽  
Permutation Flowshop ◽  
Make To Order ◽  
Wide Range ◽  
Range Of Variation

AbstractThe aim of this work is to bridge the gap between the theory and actual practice of production scheduling by studying a problem from a real-life production environment. This paper considers a practical Sanitaryware production system as a number of make-to-order permutation flowshop problems. Due to the wide range of variation in its products, real-time arrival of customer orders, dynamic batch adjustments, and time for machine setup, Sanitaryware production system is complex and also time sensitive. In practice, many such companies run with suboptimal solutions. To tackle this problem, in this paper, a memetic algorithm based real-time approach has been proposed. Numerical experiments based on real data are also been presented in this paper.

scholarly journals Are We Ready for the Challenge of Banks 4.0? Designing a Roadmap for Banking Systems in Industry 4.0

2020 ◽  
Vol 8 (2) ◽  
pp. 32 ◽  
Author(s):  
Amir Mehdiabadi ◽  
Mariyeh Tabatabeinasab ◽  
Cristi Spulbar ◽  
Amir Karbassi Yazdi ◽  
Ramona Birau
Keyword(s):  
Industry 4.0 ◽  
Industrial Revolution ◽  
Distinct Pattern ◽  
Fourth Generation ◽  
Fully Integrated ◽  
Practical Applications ◽  
Industrial Companies ◽  
Key Indicators ◽  
Global Banks ◽  
Technology Trends

The purpose of the present paper is to provide an advanced overview of the practical applications of Banking 4.0 in Industry 4.0. This paper examines the technology trends in the Fourth Industrial Revolution and identifies the key indicators behind the creation of a strategic map for the fourth-generation banks and their readiness to enter Industry 4.0. This paper examines a systematic review of fully integrated Banking 4.0 and the application of the technologies of Industry 4.0 and illustrates a distinct pattern of integration of Banking 4.0 and Industry 4.0. One of the prominent features of this article is the performance of successful global banks in applying these technologies. The results showed that Banking 4.0 in Industry 4.0 is an integrative value creation system consisting of six design principles and 14 technology trends. The roadmap designed for banks to enter Industry 4.0 and how they work with industrial companies will be a key and important guide.

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