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