Elsevier

Applied Soft Computing

Volume 68, July 2018, Pages 847-855
Applied Soft Computing

Two-objective stochastic flow-shop scheduling with deteriorating and learning effect in Industry 4.0-based manufacturing system

https://doi.org/10.1016/j.asoc.2017.12.009Get rights and content

Highlights

  • Propose a novel flow-shop scheduling model with consideration of multiple objectives, uncertainty, deteriorating and learning effect.

  • Develop a novel multiobjective discrete fireworks optimization algorithm to solve the model above.

  • Validate the performance of the proposed model and algorithm.

Abstract

Industry 4.0 is widely accepted in manufacturing industry since it guides a novel and promising production paradigm. A new characteristic in Industry 4.0-based manufacturing systems is that the applications of advanced intelligent machines which have communication, self-optimization and self-training behaviors. Based on this new change, this study investigates a flow-shop scheduling problem under the consideration of multiple objectives, time-dependent processing time and uncertainty. A mixed integer programming model is formulated for this problem, and a fireworks algorithm is developed where some special strategies are designed, e.g., explosion sparks procedure and selection solution procedure. Simulation experiments on a set of test problems are carried out, and the experimental results demonstrate that the model and proposed algorithm can achieve a satisfactory performance by comparing with three state-of-the-art multi-objective optimization algorithms.

Introduction

Recently, the competition among manufacturing enterprises has become more and more fierce, many countries and organizations have proposed some novel production paradigms to integrate the manufacturing process [1], [2]. Industry 4.0 is one of the most popular concepts in advance manufacturing fields, and it has been regarded as a future direction of manufacturing industry [3], [4]. Industry 4.0 is the fourth industrial revolution which applies the principles of cyber-physical systems, internet and future-oriented technologies, and smart systems with human-machine interaction paradigms. The Internet of Things and Services enables to network the entire factory to form a smart factory [5], [6], [7]. It significantly influences the production environment in the execution of operations. In contrast to conventional manufacturing systems, the introduction of information and communication systems into industrial network leads to a steep rise in the degree of automation, and advance intelligent machines can collect real-time information for dynamic self-optimization, self-training and self-maintaining behaviors, which can synchronize themselves and influence upon production processes.

Although the framework of Industry 4.0 embeds with the latest technologies and intelligent algorithms, a smart factory allows itself to be built on the foundations of classical manufacturing systems [8], [9]. Therefore, some key issues and techniques also play an important role in an Industry 4.0-based manufacturing system [10], [11], [12], [13]. Scheduling has been accepted as a useful tool by manufacturing systems to realize a higher utilization of resource, and many optimization models and methods have been proposed and applied in the past few years [14], [15]. Nevertheless, scheduling in Industry 4.0-based manufacturing systems will become more complex and difficult since advanced intelligent machines are widespreadly applied. With more advanced analytics, the advent of cyber-physical systems and cloud computing framework, Industry 4.0-based manufacturing systems will be able to achieve huge amounts of data that helps advanced intelligent machines to be self-optimization, self-training and self-maintaining [16]. As the market environment becomes increasingly competitive, a decision-maker has to move toward frequent production change in order to provide the customers with more production variety [17]. In this situation, on the one hand, the advanced intelligent machines need to execute the self-optimization and self-training to make themselves more proficient for the new products, which makes the processing time for a product shorter with the time continues; on the other hand, they also wear away as the time continues, and the processing time for a product becomes longer if it is processed later. Thus, time-dependent processing time usually occurs. It is noticeable that the self-optimization and self-training of intelligent machines is difficult to know in advance since it depends on many factors, e.g., product structure and training algorithm. Moreover, customers’ involvement should be established to integrate their requirements with production process in making decisions [18]. Therefore, scheduling problem in Industry 4.0-based manufacturing systems is challenged by these characteristics.

In this study, we consider these characteristic in an Industry 4.0-based manufacturing system, and propose a flow-shop scheduling problem with multiple objective functions, time-dependent processing time and uncertainty. In order to deal with it, this study designs a multi-objective discrete fireworks algorithm that incorporates some novel techniques to make it more powerful.

The reminder of this paper is organized as follows. Section 2 devotes to literature review. Section 3 presents the problem definition in which the formulation and notations are described, and a mixed integer programming model is presented. Section 4 proposes the solution method and Section 5 carries out simulation experiments and shows the analysis on the experimental results. The final section gives the conclusion of this paper and outlooks the future work.

Section snippets

Literature review

Flow-shop scheduling is one of the most well-known scheduling problems in manufacturing systems [19], [20]. It also has widespread applications in Industry 4.0-based manufacturing systems [21]. The time-dependent processing in flow-shop can be classified into two categories, i.e., deteriorating and learning effect [22], [23], [24]. The deteriorating effect indicates that the delay in starting to process a job will increase its processing time, while the learning effect means that the actual

Problem description

A two-objective stochastic flow-shop deteriorating and learning scheduling problem in this study can be described as follows. There are n jobs that need to be processed on m machines following the same route, their normal processing time on machines are random variables that obey a known Normal distribution, and the actual processing time of jobs depends on their starting time and processed positions in a schedule. Considering the cost-desired and service-oriented factors are both important

The proposed algorithm

Recently, a new efficient population-based meta-heuristic algorithm, called as fireworks algorithm (FWA), was initially proposed by Tan et al. when addressing continuous optimization problems [35], [36]. Through simulating the fireworks explosion at night, FWA had been applied successfully into many real-world optimization problems, such as variable-rate fertilization problem [37], network optimization problem [38] and portfolio optimization problem [39]. The framework of the basic FWA can be

Experimental results and analysis

In order to examine the performance of MO-DFWA in solving the investigated problem, simulation experiments are made on a set of test problems, and the state-of-the-art algorithms, i.e., NSGAII [40], MOEA/D [41] and BMSA [42], are chosen as the compared algorithms. NSGAII and MOEA/D are two classical and popular MOEAs that are also utilized to tackle multi-objective flow-shop scheduling problems in the existing studies [46]. BMSA is a multi-objective simulated annealing algorithm that is

Conclusion and future work

This study addresses a two-objective stochastic flow-shop deteriorating and learning scheduling problem under the consideration of the widespread application of advanced intelligent machines in Industry 4.0-based manufacturing systems. An integer programming model with minimizing the makespan and the total tardiness is built for the investigated problem. In order to cope with it efficiently, a multi-objective discrete fireworks algorithm is proposed, and experimental results show that it is a

References (49)

  • J.B. Wang et al.

    Minimizing makespan in three-machine flow shops with deteriorating jobs

    Comput. Operat. Res.

    (2013)
  • W.C. Lee et al.

    Total tardiness minimization in permutation flowshop with deterioration consideration

    Appl. Math. Modell.

    (2014)
  • M. Cheng et al.

    Bicriteria hierarchical optimization of two-machine flow shop scheduling problem with time-dependent deteriorating jobs

    Eur. J. Oper. Res.

    (2014)
  • Y.R. Shiau et al.

    Two-agent two-machine flowshop scheduling with learning effects to minimize the total completion time

    Comput. Ind. Eng.

    (2015)
  • Y. Liu et al.

    Two-machine no-wait flowshop scheduling with learning effect and convex resource-dependent processing times

    Comput. Ind. Eng.

    (2014)
  • R.L. Graham et al.

    Optimization and approximation in deterministic sequencing and scheduling: a survey

    Annals Discrete Math.

    (1979)
  • M. Nawaz et al.

    A heuristic algorithm for the m-machine: n-job flow-shop sequencing problem

    Omega

    (1983)
  • Y.J. Zheng et al.

    Multiobjective fireworks optimization for variable-rate fertilization in oil crop production

    Appl. Soft Comput.

    (2013)
  • S.W. Lin et al.

    Minimizing makespan and total flowtime in permutation flowshops by a bi-objective multi-start simulated-annealing algorithm

    Comput. Operat. Res.

    (2013)
  • S.C. Horng et al.

    Evolutionary algorithm for stochastic job shop scheduling with random processing time

    Expert Syst. Appl.

    (2012)
  • H. Mokhtari et al.

    Scheduling optimization of a stochastic flexible job-shop system with time-varying machine failure rate

    Comput. Operat. Res.

    (2015)
  • A. Khan et al.

    A survey of current challenges in manufacturing industry and preparation for Industry 4.0

    Proceedings of the First International Scientific Conference Intelligent Information Technologies for Industry

    (2016)
  • A.C. Valdeza et al.

    Reducing complexity with simplicity-usability methods for industry 4.0

    Proceedings 19th Triennial Congress of the IEA

    (2015)
  • A. Gilchrist

    Introduction to the Industrial Internet[M]//Industry 4.0

    (2016)
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