A hybrid particle swarm optimisation for scheduling just-in-time single machine with preemption, machine idle time and unequal release times

Abstract

This paper addresses preemption in just-in-time (JIT) single-machine-scheduling problem with unequal release times and allowable unforced machine idle time as realistic assumptions occur in the manufacturing environments aiming to minimise the total weighted earliness and tardiness costs. Delay in production systems is a vital item to be focussed to counteract lost sale and back order. Thus, JIT concept is targeted including the elements required such as machine preemption, machine idle time and unequal release times. We proposed a new mathematical model and as the problem is proven to be NP-hard, three meta-heuristic approaches namely hybrid particle swarm optimisation (HPSO), genetic algorithm and imperialist competitive algorithm are employed to solve the problem in larger sizes. In HPSO, cloud theorybased simulated annealing is employed with a certain probability to avoid being trapped in a local optimum. Taguchi method is applied to calibrate the parameters of the proposed algorithms. A number of numerical examples are solved to demonstrate the effectiveness of the proposed approach. The performance of the proposed algorithms is evaluated in terms of relative percent deviation and computational time where the computational results clarify better performance of HPSO than other algorithms in quality of solutions and computational time.

Due date assignment and convex resource allocation scheduling with variable job processing times

“…In addition, many scheduling researchers considered various earliness and/or tardiness penalties sequencing and scheduling (Wang, Liu, and Wang 2013;Yin, Cheng, and Wu 2014;Liu, Wang, and Wang 2015;Seidgar et al 2015;Wu, Wan, and Wang 2015;Yang, Wan, and Yin 2015;Yin et al 2015). In this paper, we continue the work of , we study single-machine sequencing and scheduling problem with learning effect, deteriorating jobs and convex resource dependent processing times in the context of the due date assignment problem.…”

Relationship between Just in Time, Lean Manufacturing, and Performance Practices: a meta-analysis

“…The study by Pascarella et al (2019) points out that it is possible to predict with up to 82% of defective files, which would allow to minimise inspection expenses, in the face of the standard just in time technique. The research by Seidgar et al (2015) used the JIT concepts, such as machine preemption, machine downtime, and unequal release times, in proposing a new mathematical model that validates the percentage deviation related to computational time. They also clarify better performance than other algorithms in solution quality and computational time.…”