An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer

Abstract

Flow shop scheduling optimization is one important topic of applying artificial intelligence to modern bus manufacture. The scheduling method is essential for the production efficiency and thus the economic profit. In this paper, we investigate the scheduling problems in a flexible flow shop with setup times. Particularly, the practical constraints of the multi-queue limited buffer are considered in the proposed model. To solve the complex optimization problem, we propose an improved compact genetic algorithm (ICGA) with local dispatching rules. The global optimization adopts the ICGA, and the capability of the algorithm evaluation is improved by mapping the probability model of the compact genetic algorithm to a new one through the probability density function of the Gaussian distribution. In addition, multiple heuristic rules are used to guide the assignment process. Specifically, the rules include max queue buffer capacity remaining (MQBCR) and shortest setup time (SST), which can improve the local dispatching process for the multi-queue limited buffer. We evaluate our method through the real data from a bus manufacture production line. The results show that the proposed ICGA with local dispatching rules and is very efficient and outperforms other existing methods.

Flow Shop Providing Frequency Regulation Service in Electricity Market

“…The scheduling goal is to minimize makespan and total tardiness in [26,27]. Besides, in [28], idle time for total machine, total device availability, total machine setup times and total job blocking time are also evaluation indexes of the scheduling result. Production cost is considered as an available evaluation index in [29].…”

“…Analysis and discussion are presented below. Give that most research [26][27][28][29][30][31][32][33][34] defines both optimizations to be nonlinear programming and addresses them by heuristic algorithms, we also adopted genetic algorithm to solve them.…”

Reactive Power Optimization of Large-Scale Power Systems: A Transfer Bees Optimizer Application

“…The TBO for RPO was assessed by the IEEE 118-bus system and the IEEE 300-bus system and compared with those of ABC [8], GSO [11], ACS [12], PSO [10], GA [9], quantum genetic algorithm (QGA) [36], and ant colony based Q-learning (Ant-Q) [37]. Furthermore, the main parameters of other algorithms were obtained through trial and error and were set according to reference [38], while the weight coefficient µ applied to eq (7) assigned the active power loss and the deviation of the output voltage.…”

“…In the past decades, artificial intelligence (AI) [8][9][10][11][12][13][14][15][16][17][18] has been widely used as an effective alternative because of its high independence from an accurate system model and strong global optimization ability. Inspired by nectar gathering of bees in wild nature, the artificial bee colony (ABC) [19] has been applied to optimal distributed generation allocation [8], global maximum power point (GMPP) tracking [20], multi-objective UC [21], and so on, and has the merits of simple structure, high robustness, strong universality, and efficient local search.…”

“…However, the ABC mainly depends on a simple collective intelligence without self-learning or knowledge transfer, which is a common weakness of AI algorithms such as genetic algorithm (GA) [9], particle swarm optimization (PSO) [10], group search optimizer (GSO) [11], ant colony system (ACS) [12], interactive teaching-learning optimizer (ITLO) [13], grouped grey wolf optimizer (GGWO) [14], memetic salp swarm algorithm (MSSA) [15], dynamic leader-based collective intelligence (DLCI) [16], and evolutionary algorithms (EA) [17]. Thus, a relatively low search efficiency may result, particularly while considering a new optimization task of a complex industrial system [22], e.g., the optimization of a large-scale power system with different complex new tasks.…”