Managing risk in production scheduling under uncertain disruption

Abstract

The job scheduling problem (JSP) is considered as one of the most complex combinatorial optimization problems. JSP is not an independent task, but is rather a part of a company business case. In this paper, we have studied JSPs under sudden machine breakdown scenarios that introduce a risk of not completing the jobs on time. We have first solved JSPs using an improved memetic algorithm and extended the algorithm to deal with the disruption situations, and then developed a simulation model to analyze the risk of using a job order and delivery scenario. This paper deals with job scheduling under ideal conditions and rescheduling under machine breakdown, and provides a risk analysis for a production business case. The extended algorithm provides better understanding and results than existing algorithms, the rescheduling shows a good way of recovering from disruptions, and the risk analysis shows an effective way of maximizing return under such situations.

A Recovery Model for Production Scheduling: Combination of Disruption Management and Internet of Things

“…Paul et al [12] developed the recovery model and dynamic solution approach to deal with disruptions for production-inventory system. Sarker et al [13] focused on production scheduling under uncertain disruption and adopted an improved memetic algorithm to solve the problem.…”

Fuzzy approach for production planning by using a three-dimensional printing-based ubiquitous manufacturing system

“…Artificial intelligence (AI) has been widely applied in the production planning and scheduling of conventional manufacturing systems (e.g., Sarker et al ., 2016; Rahman et al ., 2017; Bao et al ., 2018). This study represents a novel application of AI because it is the first attempt to plan a ubiquitous mass-production system based on 3D printing using AI techniques.…”