A Hybrid Multi-Objective Teaching-Learning Based Optimization for Scheduling Problem of Hybrid Flow Shop With Unrelated Parallel Machine

A Hybrid Multi-Objective Teaching-Learning Based Optimization for Scheduling Problem of Hybrid Flow Shop With Unrelated Parallel Machine

Blog Article

Efficient scheduling benefits productivity promotion, energy savings and the customer’s satisfaction.In recent years, with a growing concern about the energy saving and environmental impact, energy oriented scheduling is going to be a hot issue for sustainable manufacturing.In this study, we investigate an energy-oriented scheduling problem deriving from the hybrid flow shop with unrelated parallel machine.First, we formulate the scheduling problem with a mixed integer linear Sweatpeants programming (MILP) model, which considers two objectives including minimizing the completion time and energy consumption.

Second, a hybrid multi-objective teaching-learning based optimization (HMOTLBO) algorithm based on decomposition is proposed.In the proposed HMOTLBO, a new solution presentation and five decoding rules are designed for mining the optimal solution.To reduce the standby energy consumption BOTTLE OPENER and turning on/off energy consumption, a greedy shifting algorithm is developed without changing the completion time of a scheduling.To improve the converge speed of the algorithm, a weight matching strategy is designed to avoid randomly matching weight vectors with students.

To enhance the exploration and exploitation capacities of the algorithm, A teaching operator based on crossover and a self-learning operator based on a variable neighborhood search(VNS) are proposed.Finally, fourth different experiments are performed on 15 cases, the comparison result verified the effectiveness and the superiority of the proposed algorithm.