A hybrid learning-based meta-heuristic algorithm for scheduling of an additive manufacturing system consisting of parallel SLM machines

Abstract

S.1. Data related to the problem with 10 parts and 2 machinesTable ST.1. Part parameters related to a problem with 10 parts and 2 machines i 𝐻𝑃 𝑖 -cm 𝐴𝑃 𝑖 -𝑐𝑚 2 𝑉𝑃 𝑖 -𝑐𝑚 3 𝑀𝑇 𝑖 𝐷 𝑖 -hr 𝑇𝑃 𝑖 -$

A Heuristic Scheduling Algorithm in Cloud-Based Additive Manufacturing; an Approach to Using Critical Chain Buffer Management Technique

“…In addition, Equation ( 9) demonstrates the ordering time. In Equation (10), the packaging time is calculated.…”

“…In another research, Li et al considered a real-time order acceptance and scheduling problem in a metal additive manufacturing production environment, where the manufacturer with multiple machines makes decisions on the acceptance and scheduling of dynamic arriving part orders simultaneously [9]. Rohaninejad et al investigated a scheduling problem in an additive manufacturing system with nonidentical parallel selective laser melting machines [10]. In addition, Lee and Kim proposed a twostage meta-heuristic that can decompose the proposed problem into the part-packaging and the 2 build-scheduling stages in additive manufacturing.…”

Large-Scale Customized Production Scheduling of Multiagent-Based Medical 3D Printing

“…De Antón et al [ 12 ] used the combinatorial auction and heuristic algorithm to solve the allocation problem of 3DP parts. Rohaninejad et al [ 9 ] constructed a biobjective mathematical model targeting makespan and the total tardiness penalty and developed an efficient hybrid meta-heuristic algorithm to solve the production shop scheduling problem under heterogeneous 3D printing equipment. Che et al [ 13 ] established a mixed-integer linear programming model and applied a simulated annealing algorithm with designed packing strategies based on the skyline representation of packing pattern to solve the problem of machine scheduling with orientation selection and two-dimensional packing in a 3D production workshop.…”

“…To clarify the research status in the eld of 3D printing production scheduling, a comprehensive taxonomy covering 3DP allocation and scheduling problems is proposed (Figure 1), which is based on a hierarchy of general literature and consists of four parts: 3DP devices, material, methodology, and optimization goals. From the perspective of 3DP devices, researchers have studied the situations of machines with the same or different specifications [4,5] and of single or multiple machines [5][6][7][8][9]. From the perspective of printing materials, Rohaninejad et al [9] discussed the scheduling optimization problem of different parallel laser melting devices when producing parts with different printing materials.…”

“…From the perspective of 3DP devices, researchers have studied the situations of machines with the same or different specifications [4,5] and of single or multiple machines [5][6][7][8][9]. From the perspective of printing materials, Rohaninejad et al [9] discussed the scheduling optimization problem of different parallel laser melting devices when producing parts with different printing materials. Li et al [4] studied the scheduling cost optimization problem for different parallel devices with a single material.…”