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.

Purpose In real manufacturing systems, schedules are often disrupted with uncertainty factors such as random machine breakdown, random process time, random job arrivals or job cancellations. This paper aims to investigate robust scheduling for a two-stage assembly flow shop scheduling with random machine breakdowns and considers two objectives makespan and robustness simultaneously. Design/methodology/approach Owing to its structural and algorithmic complexity, the authors proposed imperialist competitive algorithm (ICA), genetic algorithm (GA) and hybridized with simulation techniques for handling these complexities. For better efficiency of the proposed algorithms, the authors used artificial neural network (ANN) to predict the parameters of the proposed algorithms in uncertain condition. Also Taguchi method is applied for analyzing the effect of the parameters of the problem on each other and quality of solutions. Findings Finally, experimental study and analysis of variance (ANOVA) is done to investigate the effect of different proposed measures on the performance of the obtained results. ANOVA's results indicate the job and weight of makespan factors have a significant impact on the robustness of the proposed meta-heuristics algorithms. Also, it is obvious that the most effective parameter on the robustness for GA and ICA is job. Originality/value Robustness is calculated by the expected value of the relative difference between the deterministic and actual makespan.