Composite dispatching rule design for dynamic scheduling with customer‐oriented production priority control

Abstract

In practice, the criteria considered for production scheduling objectives are generally derived from some desired production performance factors of a manufacturing system. On the other hand, customer-oriented production priority management focuses on the analysis of operational production management policies that cannot be readily measured in a quantitative manner. This paper presents an approach for multiobjective composite dispatching rule design, which simultaneously considers scheduling criteria and the customer-oriented production priority management policies. The correctness of elementary dispatching rule selection is a critical aspect of composite dispatching rule design. Data envelopment analysis is adopted to select a set of corresponding elementary rules that appropriately convey the scheduling criteria. The selected elementary rules are composed with weighted aggregation to optimize the scheduling criteria of interest. Analytic hierarchy process is used for overall job prioritization by combining the different schedules derived from the composite dispatching rule and the customer-oriented production priority rules. The implementation of the presented approach on single-machine, multiobjective, dynamic scheduling is demonstrated by a case study. The production performance of composite dispatching rules that would be subsequently achieved by the presented approach is compared with some conventional dispatching rules, and the results are found to be satisfactory.

A Knowledge-Enriched Computational Model to Support Lifecycle Activities of Computational Models in Smart Manufacturing

“…Thus, a lot of research effort has been made on the selection of dispatching rules with respect to three or four production objectives (Geiger et al, 2006;El-Bouri and Shah, 2006;Azadeh et al, 2012;Mouelhi-Chibani and Pierreval, 2010;Shiue, 2009;Baykasoglu et al, 2010;Scholz-Reiter et al, 2010;Heger et al, 2015;Chen et al, 2012;El-Bouri and Amin, 2015;Lin et al, 2008;Azadeh et al, 2015;Liu and Dong, 1996;Kızıl et al, 2006;Zhong et al, 2014;Shafiq et al, 2010;Joseph and Sridharan, 2011;Kashfi and Javadi, 2015).…”

Multiagent and Bargaining-Game-Based Real-Time Scheduling for Internet of Things-Enabled Flexible Job Shop

“…(2) How to design a new multi-agent-based real-time task allocation strategy to implement real-time scheduling based on real-time data in an IoT-based manufacturing environment. In the existing multi-agent-based dynamic scheduling methods, a dynamic scheduling approach focuses on dynamic dispatching rules [36] and event-driven rescheduling policies [37]. Between them, event-driven rescheduling policies are used by most of the methods.…”