Multi-objective biased randomised iterated greedy for robust permutation flow shop scheduling problem under disturbances

Abstract

Nowadays, scheduling problems under different disruptions are a key to become competitive in the global market of this century. Permutation flow shop scheduling problems are very important as they consider one of the important types of scheduling problems. In this paper, we consider a challenging scheduling problem of a permutation flow shop in the presence of different types of real-time events such as new job arrival and machine breakdown. A multi-objective optimisation model that takes into account multiple performance measures in order to minimise the effect of different real-time events is used in this paper. To solve this problem, we apply the proposed multi-objective model and adapt a predictive-reactive based Biased Randomised Iterated Greedy approach for the problem, which is hybridised a Biased Randomisation process and the Iterated Greedy algorithm. Furthermore, the proposed approach is compared against the predictive-reactive based Particle Swarm Optimisation method for the same problem. Additionally, to show the efficiency of the proposed model, we compare this model by testing the predictive-reactive based Biased Randomised Iterated Greedy approach to two other models: the biobjective model that consider only two objectives and the classical single-objective model of minimising the makespan. Further statistical analysis is performed in this study by using an Analysis of Variance measure. The extensive experiments and statistical analysis demonstrate that the proposed multi-objective model is better than the other models in reducing the relative percentage deviation. Additionally, despite their simplicity, the Biased Randomised Iterated Greedy algorithm is shown to be State-of-the-art method that outperform the Particle Swarm Optimisation algorithm.

Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning

“…Safe time of each job in BF. where ST j is determined by Equation (15). ST j reflects how much time will be left before the due date d j when job j is finished, if job j begins to be processed at present time t c .…”

“…To solve the NP-hard problem, many hybrid meta-heuristics have been proposed [ 2 , 3 ]. For the PFSP, its objective is to find a good job sequence to minimize makespan [ 4 , 5 , 6 ], flow time [ 7 , 8 , 9 , 10 ], tardiness [ 11 , 12 , 13 , 14 ], multiple objective [ 15 , 16 , 17 , 18 , 19 , 20 ], etc.…”

A Simheuristic Algorithm for Solving the Stochastic Omnichannel Vehicle Routing Problem with Pick-up and Delivery

“…In this random selection process, the higher-ranked candidates receive a higher probability of being selected. Biased-randomized algorithms have been employed for solving different COPs in transportation [40][41][42], scheduling [43,44], and facility location problems [39,45].…”

Comparison of lot streaming division methodologies for multi-objective hybrid flowshop scheduling problem by considering limited waiting time

“…In the existing academic literature, the flowshop scheduling problem (FSP) has been widely studied over the last two decades [1,10,46]. It is well known that HFSP is one of the main problems in the FSP scheduling domain due to the significance of its applicability.…”