A New Multi-Objective Hybrid Flow Shop Scheduling Method to Fully Utilize the Residual Forging Heat

Abstract

This paper aims to solve the problem of high energy consumption in forging production through energy-saving scheduling. By analyzing the flow shop characteristics of a forging workshop, an energy-efficient hybrid flow shop scheduling problem with forging tempering (EEHFSP-FT) is proposed. An energy-efficient scheduling model is established to simultaneously minimize both the completion time and energy consumption. In the scheduling model, constraints such as heating furnace capacity, required forging temperature, and required quenching temperature are taken into consideration. An energy-saving strategy of heat treatment with residual forging heat is adopted to address the problem of energy underutilization after forging. In order to use multi-objective optimization algorithms to solve the scheduling problems of charging and machine selection in forging production, encoding and decoding rules and evolutionary search strategies are designed. Finally, a case study on the flow shop of an automated forging center is analyzed. The validity of the proposed model is demonstrated by testing cases of different scales in conjunction with three different evolutionary algorithms. By analyzing the performance of the three algorithms, the algorithm suitable for solving the proposed model is determined. INDEX TERMS Energy saving scheduling; Forging planning; Multi-objective optimization; Temperature constraint.

Reinforcement Learning-Based Multi-Objective of Two-Stage Blocking Hybrid Flow Shop Scheduling Problem

“…The parameter settings are as follows: n = 15, m 1 = 2, m 2 = 3, p sj and t ik are generated at random between [1,50], and SP i and TP ik are generated at random between [1,10].…”

“…The hybrid flow shop scheduling problem (HFSP), which combines the features of traditional flow shop scheduling and parallel machine scheduling, is widely employed in the auto industry, food processing, steel forging [1], and other industries. The HFSP buffer is always intended to be infinite; however, owing to product processes and technological restrictions, the buffer is sometimes non-existent or confined.…”

Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives

“…Apart from that, the probability values used more were the higher ones (i.e., in the Q4 range), as expected. EDA GA [52] Own algorithm -- [53] 2-echelon iMOEA/D MOEA/D NSGA-II; MOGLS [90] HEA -NSGA-II; NNIA [59] iIMOALO ALO NSGA-II; MOPSO [60] NMA MA MA [73] NSGA-II NSGA-II SPEA2 [74] NSGA-II NSGA-II - [75] EE-VBIH; EE-IG; IG-ALL [76] MDSS-MOGA-DE MOABC; MOACO; MOCS [77] NSGA-II; SPEA-2 NSGA-II; SPEA-II NSGA-II; SPEA-2 [78] NSGA-II NSGA-II [81] PSO_SWS; PSO_LWR PSO PSO_SWS; PSO_LWR; PSO [72] HPSO PSO i NSGA-II; HPSO-LS…”

Optimization of Heat Treatment Scheduling for Hot Press Forging Using Data-Driven Models

“…Accordingly, numerous efforts have been made to develop methods for optimizing heat treatment scheduling. A previous study [7] dealt with scheduling the batch annealing process in steel coil production, where heating and cooling equipment are moved to the workpieces by cranes, while in most cases workpieces are moved to fixed [4,8,9] solved the problem of heat treatment scheduling in which workpieces were not grouped to form a batch but were sequentially loaded into a furnace. The common goal of these studies was to find an optimal order of loading.…”