Automatic identification of constraints and improvement actions in production systems using multi-objective optimization and post-optimality analysis

Pehrsson, Leif; Ng, Amos H. C.; Bernedixen, Jacob

doi:10.1016/j.jmsy.2016.02.001

Cited by 25 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is believed that the set of non-dominated solutions obtained in this study would enhance the decisionmaking process in industrial settings. In fact, the proposed reduction in the number of changes has been considered in prior studies (Pehrsson, Ng, and Bernedixen 2016;Ng, Siegmund, and Deb 2018) aimed at resolving problems concerning efficiency improvement production lines. However, consideration of the same to improve the machining process has not yet been reported.…”

Section: Problem Descriptionmentioning

confidence: 99%

Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm

Amouzgar

Nourmohammadi

2021

International Journal of Production Research

Self Cite

View full text Add to dashboard Cite

Machining process efficiencies can be improved by minimising the non-machining time, thereby resulting in short operation cycles. In automatic-machining centres, this is realised via optimum cutting tool allocation on turret-magazine indices-the "tool-indexing problem". Extant literature simplifies TIP as a single-objective optimisation problem by considering minimisation of only the tool-indexing time. In contrast, this study aims to address the multi-objective optimisation toolindexing problem (MOOTIP) by identifying changes that must be made to current industrial settings as an additional objective. Furthermore, tool duplicates and lifespan have been considered. In addition, a novel mathematical model is proposed for solving MOOTIP. Given the complexity of the problem, the authors suggest the use of a modified strength Pareto evolutionary algorithm combined with a customised environment-selection mechanism. The proposed approach attained a uniform distribution of solutions to realise the above objectives. Additionally, a customised solution representation was developed along with corresponding genetic operators to ensure the feasibility of solutions obtained. Results obtained in this study demonstrate the realization of not only a significant (70%) reduction in non-machining time but also a set of tradeoff solutions for decision makers to manage their tools more efficiently compared to current practices.

show abstract

Section: Problem Descriptionmentioning

confidence: 99%

Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm

Amouzgar

Nourmohammadi

2021

International Journal of Production Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…Machines in the production system can thus form throughput bottlenecks [16]. Improvements such as cycle time reductions and dynamic buffering are prioritizing the bottleneck machines for activities to improve the throughput [7], [15], [20].…”

Section: A Motivationmentioning

confidence: 99%

Adaptive Stream-based Shifting Bottleneck Detection in IoT-based Computing Architectures

Najdataei

Subramaniyan

Gulisano

et al. 2019

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

View full text Add to dashboard Cite

Cloud computing is revolutionizing the backbone of data analysis applications, including industrial ones. One of its main pillars is the separation of the logic with which data is accessed (e.g., to study the efficiency of a manufacturing system) from the actual hardware (e.g., server) that maintains and analyses the data. Large distributed cyber-physical systems enabled by, among other technologies, the Internet of Things (IoT), made nonetheless clear that "what to do" with the data and "where to do it" are not disjoint problems; i.e., cloud computing on its own is not enough. Fog and edge computing have emerged as complementary options, to distribute the analysis, helping with challenges by means of close-to-the-source data analysis. We show for a key problem for industrial processes, that of shifting bottleneck detection, how to take advantage of such multi-tier computing architectures, to perform continuous and configurable analysis of data from Manufacturing Execution Systems. We propose a processing framework, STRATUM, and an algorithm, AMBLE, for continuous, data stream processing. STRATUM seamlessly distributes and parallelizes the processing across the tiers and AMBLE guarantees consistent analysis in spite of timing fluctuations, which are commonly introduced due to e.g. the communication system; it also achieves efficiency through appropriate data structures for in-memory processing. The experimental study on a real-world dataset, taken from a production line over two years and including 8.5 million entries, shows the benefits of the proposed solution in enabling configurable and efficient analysis.

show abstract

“…Production systems improvement problems can be formulated readily as MOPs, as shown earlier in Pehrsson et al (2011, 2013) and more recently in Pehrsson et al (2016). The basic idea proposed was based on an observation that many decision-making situations in production system improvement projects can be effectively formulated into MOPs.…”

Section: Production Systems Improvement As Multi-objective Problemsmentioning

confidence: 99%

Reference point based evolutionary multi-objective optimization with dynamic resampling for production systems improvement

Siegmund

Deb

2018

JSIT

Self Cite

View full text Add to dashboard Cite

Purpose Stochastic simulation is a popular tool among practitioners and researchers alike for quantitative analysis of systems. Recent advancement in research on formulating production systems improvement problems into multi-objective optimizations has provided the possibility to predict the optimal trade-offs between improvement costs and system performance, before making the final decision for implementation. However, the fact that stochastic simulations rely on running a large number of replications to cope with the randomness and obtain some accurate statistical estimates of the system outputs, has posed a serious issue for using this kind of multi-objective optimization in practice, especially with complex models. Therefore, the purpose of this study is to investigate the performance enhancements of a reference point based evolutionary multi-objective optimization algorithm in practical production systems improvement problems, when combined with various dynamic re-sampling mechanisms. Design/methodology/approach Many algorithms consider the preferences of decision makers to converge to optimal trade-off solutions faster. There also exist advanced dynamic resampling procedures to avoid wasting a multitude of simulation replications to non-optimal solutions. However, very few attempts have been made to study the advantages of combining these two approaches to further enhance the performance of computationally expensive optimizations for complex production systems. Therefore, this paper proposes some combinations of preference-based guided search with dynamic resampling mechanisms into an evolutionary multi-objective optimization algorithm to lower both the computational cost in re-sampling and the total number of simulation evaluations. Findings This paper shows the performance enhancements of the reference-point based algorithm, R-NSGA-II, when augmented with three different dynamic resampling mechanisms with increasing degrees of statistical sophistication, namely, time-based, distance-rank and optimal computing buffer allocation, when applied to two real-world production system improvement studies. The results have shown that the more stochasticity that the simulation models exert, the more the statistically advanced dynamic resampling mechanisms could significantly enhance the performance of the optimization process. Originality/value Contributions of this paper include combining decision makers’ preferences and dynamic resampling procedures; performance evaluations on two real-world production system improvement studies and illustrating statistically advanced dynamic resampling mechanism is needed for noisy models.

show abstract

Automatic identification of constraints and improvement actions in production systems using multi-objective optimization and post-optimality analysis

Cited by 25 publications

References 15 publications

Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm

Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm

Adaptive Stream-based Shifting Bottleneck Detection in IoT-based Computing Architectures

Reference point based evolutionary multi-objective optimization with dynamic resampling for production systems improvement

Contact Info

Product

Resources

About