An Improved Mayfly Method to Solve Distributed Flexible Job Shop Scheduling Problem under Dual Resource Constraints

Abstract

Aiming at the distributed flexible job shop scheduling problem under dual resource constraints considering the influence of workpiece transportation time between factories and machines, a distributed flexible job shop scheduling problem (DFJSP) model with the optimization goal of minimizing completion time is established, and an improved mayfly algorithm (IMA) is proposed to solve it. Firstly, the mayfly position vector is discrete mapped to make it applicable to the scheduling problem. Secondly, three-layer coding rules of process, worker, and machine is adopted, in which the factory selection is reflected by machine number according to the characteristics of the model, and a hybrid initialization strategy is designed to improve the population quality and diversity. Thirdly, an active time window decoding strategy considering transportation time is designed for the worker–machine idle time window to improve the local optimization performance of the algorithm. In addition, the improved crossover and mutation operators is designed to expand the global search range of the algorithm. Finally, through simulation experiments, the results of various algorithms are compared to verify the effectiveness of the proposed algorithm for isomorphism and isomerism factories instances.

A Strengthened Dominance Relation NSGA-III Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

“…In future work, we plan to further enhance the algorithm and expand its applications. We consider incorporating a dynamic resource allocation mechanism into the algorithm to solve the flexible job shop scheduling problem [39], or introducing a workshop allocation mechanism to address the distributed job shop scheduling problem [40].…”

“…FT06 has processing time in the range [1,10], and FT10 and FT20 have processing time in the range [1,99]. The ABZ dataset is of size 20 × 15, with processing time in the range [10,40]. SWV dataset includes three different sizes: 20 × 10, 20 × 15, and 50 × 10.…”

A Data Security Model for Mobile Ad Hoc Network Using Linear Function Mayfly Advanced Encryption Standard

“…Algorithm 1: Mayfly Optimization Algorithm [27] Input The mayfly algorithm was used in this research work for an optimal secret key generation because itcombined the concept and advantages of three optimization algorithms which are Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Fire Fly Algorithm (FA) which has made it to be one of the best optimization techniques in recent times [28]. Though mayfly has been found suitable for solving complex optimization problems, however, it is known to have inherited the challenges facing the three (PSO, GA, and FA) combined algorithms and thereby developed its own drawbacks which include, suffering from insufficient search capability and low convergence speed [29] stagnation in local optima, and an unsuitable balance between the exploration and exploitation [30].…”

An effective hybrid algorithm for joint scheduling of machines and AGVs in flexible job shop

“…12 This assumption is clearly unrealistic. Furthermore, transport time constraints can be used for different problems, as these constraints often occur for practical problems in the industry such as: distributed job shops, 13,14 flow shops, 15 and multi-objective scheduling problems. 16 The introduction of AGVs will increase production costs, so the number of AGVs in the actual workshop is limited.…”