Practical optimization problems are often too complex to be formulated exactly. Knowing multiple good alternatives can help decision-makers easily switch solutions when needed, such as when faced ...with unforeseen constraints. A multimodal optimization task aims to find multiple global optima as well as high-quality local optima of an optimization problem. Evolutionary algorithms with niching techniques are commonly used for such problems, where a rough estimate of the optima number is required to determine the population size. In this paper, a partition-based random search method is proposed, in which the entire feasible domain is partitioned into smaller and smaller subregions iteratively. Promising regions are partitioned faster than unpromising regions, thus, promising areas will be exploited earlier than unpromising areas. All promising areas are exploited in parallel, which allows multiple good solutions to be found in a single run. The proposed method does not require prior knowledge about the optima number and it is not sensitive to the distance parameter. By cooperating with local search to refine the obtained solutions, the proposed method demonstrates good performance in many benchmark functions with multiple global optima. In addition, in problems with numerous local optima, high-quality local optima are captured earlier than low-quality local optima.
Throughput is an important parameter to evaluate production system performance. It is typically constrained by one or more resources referred to as 'throughput bottlenecks'. To start improvement ...actions, the first step is to identify throughput bottlenecks. Consequently, several bottleneck detection methods were developed in the literature. But this literature remains largely unstructured, which makes it difficult for practitioners to select an appropriate method. To generate clarity and to consolidate the field, a systematic literature review was conducted. The review identified 14 different bottleneck detection methods that are classified according to the information used: queue states, process states, or combined queue and process states. It further identified three different modes used to operationalize the different bottleneck detection methods: gemba walk, discrete event simulation, and data science. This study further presents important research issues, identifies contingency factors for method application, and discusses important guidelines for the choice of operationalization mode in practice.
•Method to automatically discover manufacturing systems structure from data.•Automatic generation of digital twins with an appropriate level of detail.•Characteristics of production systems ...automatically retrieved from data.•Model tuning method that generates models able to estimate system performances.•1-min model development allows for online application.
The latest developments in industry involved the deployment of digital twins for both long and short term decision making, such as supply chain management, production planning and control. Modern production environments are frequently subject to disruptions and consequent modifications. As a result, the development of digital twins of manufacturing systems cannot rely solely on manual operations. Recent contributions proposed approaches to exploit data for the automated generation of the models. However, the resulting representations can be excessively accurate and may also describe activities that are not significant for estimating the system performance. Generating models with an appropriate level of detail can avoid useless efforts and long computation times, while allowing for easier understanding and re-usability. This paper proposes a method to automatically discover manufacturing systems and generate adequate digital twins. The relevant characteristics of a production system are automatically retrieved from data logs. The proposed method has been applied on two test cases and a real manufacturing line. The experimental results prove its effectiveness in generating digital models that can correctly estimate the system performance.
The implementation of control strategies that reduce energy consumption during the machine idle periods is becoming a challenging goal to achieve energy efficiency in production systems. A general ...framework for switching the machine off/on has been recently proposed in literature for single machines. This paper studies the performance of a production line when a general policy is applied at machine level. The considered performance measures are the total energy consumed per part and the system throughput. Numerical results are based on discrete event simulation, and a comparison with the most common practices in manufacturing is also reported.
Industry 4.0 determined the emergence of technologies that enable data-driven production planning and control approaches. A digital model can be used to make decisions based on the current state of a ...manufacturing system, and its efficacy strictly depends on the capability to correctly represent the physical counterpart at any time. Automated model generation techniques such as process mining can significantly accelerate the development of digital twins for manufacturing systems. However, complex production environments are characterized by the convergence of different material and information flows. The corresponding data logs present multiple part identifiers, resulting in the wrong finding of the system structure with traditional process mining techniques. This paper describes the problem of discovering manufacturing systems with complex material flows, such as assembly lines. An algorithm is proposed for the proper digital model generation, aided by the new concept of object-centric process mining. The proposed approach has been applied successfully to two test cases and a real manufacturing system. The results show the applicability of the proposed technique to realistic settings.
•Automatic discovery of complex manufacturing systems structures from data.•Automatic generation of digital models of assembly manufacturing systems.•The assembly blocking conditions are retrieved from the shop-floor data.•Object-centric process mining allows for using multiple product type identifiers.•Discovery of the assembly locations and representation in a graph model.
•Develop a new decoding method for job permutation representation.•Use instances with different size and processing time correlation patterns.•The developed decoding method outperforms conventional ...decoding methods.•The proposed genetic algorithm provides state-of-art results.
This paper presents a genetic algorithm to solve the hybrid flow shop scheduling problem to minimize the total tardiness. Practical assumptions as unrelated machines and machine eligibility are considered. The proposed algorithm incorporates a new decoding method developed for total tardiness objective, which is able to obtain tight schedule meanwhile guarantee the influence of the chromosome on the schedule. The proposed algorithm has been calibrated with a full factorial design of experiment, and compared to several calibrated state-of-art algorithms on 450 instances with different size and correlation patterns of operation processing time. The results validate the effectiveness of the proposed algorithm.
Data-driven bottleneck identification has received an increasing interest during the recent years. This approach locates the throughput bottleneck of manufacturing systems based on indicators derived ...from measured machine performance metrics. However, the variability in manufacturing systems may affect the quality of bottleneck indicators, leading to possible inaccurate detection results. This paper presents a statistical framework (SF) to decrease the data-driven detection inaccuracy caused by system variability. Using several statistical tools as building blocks, the proposed SF is able to analyse the logical conditions under which a machine is detected as the bottleneck, and rejects the proposal of bottleneck when no sufficient statistical evidence is collected. A full factorial design experiment is used to study the parameter effects of the SF, and to calibrate the SF. The proposed SF was numerically verified to be effective in decreasing the wrong bottleneck detection rate in serial production lines.
Manufacturing accounts for a significant portion of energy expenditure. Thus, energy efficient and environmentally friendly (EEEF) manufacturing practices are of significant importance. ...Sustainability plays a key role to manufacturing, becoming a major factor for manufacturers to be competitive in the global market. It would not only be important to the energy and environmental sectors, but also be substantially beneficial to society and economy. Sustainable manufacturing covers a broad spectrum of manufacturing, including both implementation of advanced manufacturing technology and developing energy-efficient manufacturing systems, as well as extension of product life cycle. Automation, as a vital factor to the success of sustainable manufacturing, plays a critical role. In recent years, it has attracted substantial effort from researchers in both academia and industry to provide efficient scientific and engineering solutions for sustainable manufacturing. This edited book of Sustainable Production Automation presents the recent development of innovative algorithms, models, heuristics, and techniques for production control and operation management in the area of sustainable manufacturing systems. In addition to overviews of recent development in sustainable manufacturing technology and practices, optimization and control methodologies for energy efficient manufacturing are the focuses in this volume. Remanufacturing system modeling and analysis, which are key elements for product life cycle, are also studied. We expect this volume can stimulate more original, significant, visionary, and in-depth research in sustainable production automation, to improve process, efficiency, productivity, quality, and reliability in manufacturing.
•The control of machine state when machine is idle can reduce energy consumption.•The model includes time-dependent startup duration to resume the service.•The selection of which machine component to ...control is included in the model.•Production constraints on machine are handled by the optimization problem.•Multiple sleeping states improve the efficiency of a controlled machine.
Energy efficient control strategies have been of recent interest in literature as promising measures to reduce the energy consumed by production equipment during interruptions of part flow. A general time-based control policy is analyzed. Machine idle times are assumed stochastic and the expected value of the energy consumed per produced part is reduced while assuring a certain target of expected production rate. The startup time required to resume the service depends on the time period the machine stays in a low power consumption state. Since the startup is often related to one or more machine components, the optimal policy might be to partially switch the machine. Therefore, a policy acting at component level is also analyzed where the duration of the startup depends on the set of machine components that are switched off. The policy is compared to state-of-the-art policies and discussed for a set of simulated numerical cases representing several production environments.
Throughput is one of the key performance indicators for manufacturing systems, and its improvement remains an interesting topic in both industrial and academic fields. One way to achieve improvement ...is to reduce the downtime of unreliable machines. Along this direction, it is natural to pose questions about the optimal allocation of improvement effort to a set of machines and failure modes. This article develops mixed-integer linear programming models to improve system throughput by reducing downtime in the case of multi-stage serial lines. The models take samples of processing time, uptime and downtime as input, generated from random distributions or collected from real system. To improve computational efficiency while guaranteeing the exact optimality of the solution, algorithms based on Benders Decomposition and discrete-event relationships of serial lines are proposed. Numerical cases show that the solution approach can significantly improve efficiency. The proposed modeling and algorithm is applied to throughput improvement of various systems, including a long line and a multi-failure system, and also to the downtime bottleneck detection problem. Comparison with state-of-the-art approaches shows the effectiveness of the approach.
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