An Improved Discrete Migrating Birds Optimization Algorithm for the No-Wait Flow Shop Scheduling Problem

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. INDEX TERMS Improved discrete migrating birds optimization algorithm, no-wait flow shop scheduling problem, hybrid multi-neighborhood strategy, population reset mechanism.

A discrete whale optimization algorithm for the no-wait flow shop scheduling problem

“…The performance of the intelligent optimization algorithm is heavily affected by parameters involved, 26 so it is crucial to finding a better set of parameters by executing the parameter calibration experiments. The DWOA has five main parameters, which are NP (population size), t max (the maximum evolutionary algebras), limit (the given old enough age), u _ max (maximum cycle of SNS) and d (the degree of destruction in DC), needed to set by preliminary testing.…”

“…When calculating the makespan of the neighborhood individual, considering only the impact of the changed part will greatly decrease lots of calculation time. The accelerate methods of insertion and swap have been described in detail, 11,26 and the accelerate methods of the first three mentioned above is given in increment. 19 Moreover, the specific description of the accelerate methods for above-mentioned structures except for DC has presented by Zhang et al 26 The DC performs to generate the neighborhood solution, where the computing time mainly spends on the insert operation.…”

“…HMM-FPA 22 is provided to solve the NWFSSP, in which it takes advantage of the hormone modulation mechanism to improve the exploring ability of FPA. Recently, Some swarm-based intelligence optimization algorithms with better performance, such as a discrete version algorithm based on water wave optimization (DWWO), 23 a particle swarm optimization mixed the adaptation mechanism, 24 and a hybrid biogeographybased optimization 25 and an improved discrete version of migrating bird algorithm, 26 have presented to solve NWFSSP. It is worth noting that the variable neighborhood search (VNS) is embedded in the above four algorithms [23][24][25][26] directly and indirectly for generating neighborhood solutions and getting remarkable result.…”

“…Recently, Some swarm-based intelligence optimization algorithms with better performance, such as a discrete version algorithm based on water wave optimization (DWWO), 23 a particle swarm optimization mixed the adaptation mechanism, 24 and a hybrid biogeographybased optimization 25 and an improved discrete version of migrating bird algorithm, 26 have presented to solve NWFSSP. It is worth noting that the variable neighborhood search (VNS) is embedded in the above four algorithms [23][24][25][26] directly and indirectly for generating neighborhood solutions and getting remarkable result. Besides the makespan and total flow time criterion, the total earliness and tardiness criterion are often considered when solving the NWFSSP.…”

A Simple and Effective Approach for Tackling the Permutation Flow Shop Scheduling Problem

“…According to the significant success achieved by those algorithms, they are extensively used in tackling the FSSP. Zhang et al [7], presented an improved discrete migrating birds optimization for overcoming the no-wait FSSP (NWFSSP) using the makespan criterion. A decision tree in Govindan et al [8] is further combined with scatter search (SS) algorithms for solving the permutation FSSP (PFSSP) by reducing the make-span.…”