Digital twin data-driven proactive job-shop scheduling strategy towards asymmetric manufacturing execution decision

Abstract

The information asymmetry phenomenon widely exists in production management decisions due to the latency of manufacturing data transmissions. Also, stochastic events on the physical production site will result in information asymmetry, which may lead to inconsistency between current execution and previous resource allocation plans. It is meaningful and important for developing an information model based on the Internet of Manufacturing Things to timely and actively adjust the scheduling strategy to meet the symmetry requirements of the production execution process. Based on the digital twin data collected from the workshop, a proactive job-shop scheduling strategy was discussed in this paper. Firstly, the mechanism for the influence of delayed local operations on makespan was deduced. Then, a framework for implementing the proactive job-shop scheduling strategy was proposed. Coordination point was used to determine the adjustment interval of local operations; right-shift rule with delay time constraints was used to adjust the unprocessed operation sequences on machines. Finally, the examples including 6*6 (6 jobs, 6 machines) and 20*40 (20 jobs, 40 machines) were presented to verify the effectiveness and scalability of the proposed method. It can be predicted that the proactive scheduling strategy provides the online decisions for the efficient and smooth execution of the digital twin-driven workshop production.

Occupational hazards and economic indicators in the scheduling of a make-to-order system

“…Independently of the fact that in the literature reviewed [9,10,13,28,[41][42][43]45,46,52] and its theoretical support, research developments in manufacturing processes concerning the production of "non-standardized" goods using techniques based on metaheuristic methods have been widely extended since the late 1990s. It is noted that the variables associated with certain occupational hazards (high sound level, poor lighting and factors affecting the behavior of the proportion of "non-conforming" production -exposure to vibrations, weight handled and ambient temperature-) and, also, with certain economic indicators (processing time and direct personnel costs), have not been assessed simultaneously, nor have they been analyzed in the required depth: vibrations, weight handled and ambient temperature).…”

“…Regarding the observed variables, it is necessary to establish the minimum processing time in the first step. Consequently, the parameter estimation in question, since there are "JT" jobs and "PC" processing centers, implies the analysis of JT!PC solutions [10][11][12]. Therefore, the difficulty arises to explore with the necessary timeliness JT!PC feasible points .…”

Digital twin for product versus project lifecycles’ development in manufacturing and construction industries

“…During this phase, the digital twin has been investigated for flexible scheduling and production, process optimisation and production improving, equipment maintenance and fault diagnosis, energy management, human factors engineering optimisation, as well as real-time data management and tracing. In terms of digital twin implementation to assist production scheduling, intelligent scheduling of workshops enabled by digital twin could solve the problem of inconsistent resource allocation to physical production sites caused by information imbalance, and real-time active workshop scheduling was achieved through data provided by digital twin (Zhang et al, 2022). Nie et al (2021) enhanced the allocation of resources and resistance to disruption in the production process through the integration of physical and virtual shopfloor with digital twin service systems, enabling a more efficient allocation of resources.…”

Towards Digital Twin-Driven Performance Evaluation Methodology of FMS

“…Therefore, the FMS simulation model implemented with the aid of it can be used both to plan the processes implemented virtually in it and to correct previously planned and then physically implemented in FMS. Indeed, this approach to FMSs design and control is increasingly common and finds its applications in systems supporting preventive maintenance scheduling and proactive job-shop as well as dynamic scheduling in manufacturing (Coito et al, 2022;David, Lobov & Lanz, 2018;Hatono et al, 1989;Neto et al, 2021;Nielsen, Michna & Do, 2014;Nielsen, Sung & Nielsen, 2019;Patalas-Maliszewska & Kłos, 2019;Zhang, Bai & Yang, 2022;Stączek et al, 2021;Świć & Gola, 2013).…”