Abstract
An improved discrete migrating birds optimization (IDMBO) algorithm is presented in this work to solve the no-wait flow shop scheduling problem (NWFSSP) with makespan criterion. In the algorithm, all of the solutions in population are treated as birds aligned in a V formation named the leader and followers. To guarantee the quality and the diversity of initial population, the leader is provided by the standard deviation heuristic (SDH) algorithm, and the rest (the followers) are generated randomly. Given that IDMBO is a neighborhood-based search algorithm, the quality of algorithm depends heavily on the neighborhood structures, where the two variants of the hybrid multi-neighborhood strategy, which are multiply neighborhood structures embedded in variable neighborhood search (VNS) in different forms, are performed to generate the neighborhood solutions for leader and followers, respectively. Furthermore, the population reset mechanism is performed after a given number iterations without improving the current solution. The local search (LS) method can further ameliorate the quality of solutions. The computational study is conducted to analyze the efficiency of IDMBO algorithm on benchmarks designed by Reeve and Taillard. And the comparison results are shown that the presented algorithm is superior to several existing algorithms for NWFFSP.
Highlights
The no-wait flow shop scheduling problem (NWFSSP) is an important branch of the flow shop scheduling problem, in which all jobs are processed successively in the same order on all machines; that is, the job is processed with no interruption until the processing is completed
When the age of the individual exceeded to the limit, that is, when the individual becomes old enough, a new solution is produced by performing the destruction and reconstruction of the iterated greedy (IG) algorithm [40] to the individual and put it into the population instead of the original, as well as by setting the age by zero to escape the local optimal and without affecting the optimization of the algorithm
It may be because in DPSOVND and discrete Water Wave Optimization (DWWO) algorithms, all individuals in the initial population are constructed by heuristics, while in improved discrete migrating birds optimization (IDMBO) algorithm the only one individual is generated by standard deviation heuristic (SDH)
Summary
The no-wait flow shop scheduling problem (NWFSSP) is an important branch of the flow shop scheduling problem, in which all jobs are processed successively in the same order on all machines; that is, the job is processed with no interruption until the processing is completed. Many remarkable heuristic and metaheuristic algorithms have been proposed in the literature to optimize NWFSSP with the minimization of makespan, total flow time, and other objectives. Deng et al [16] proposed an effective co-evolutionary quantum GA to balance the exploration and exploitation of algorithm, where store-with-diversity and competitive coevolution were designed; Lin and Ying [17] provided two meta-heuristic algorithms, and the computational results showed that these algorithms outperformed existing ones These algorithms can solve very hard and large NWFSSP to optimality. Shao et al [19] presented a hybrid discrete optimization algorithm based on teaching-probabilistic learning mechanism under the frame of teaching and learning, which involves neighborhood search and probabilistic learning, as well as population reconstruction It has comparable performance with some efficient algorithms in solving NWFSSP. Neighbors to be considered (β) and the number of neighbors that are shared with the solution (χ) are the parameters to be considered
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
