The computer-aided design optimization and vibration analysis, especially avoiding resonance are fundamental challenge to piping system design of complex products. Conventional pipe design methods commonly focus on either automatic geometry layout or vibration analysis, which usually leads to repeated modifications between both disciplines. In this paper, a multi-objective pipe routing algorithm considering vibration performance for aero-engine is presented based on Kriging model and non-dominated sorting genetic algorithm-II (NSGA-II), where pipe length, smoothness, and natural frequency are taken into account. Pipe node positions are optimized by using NSGA-II subject to several constraints. In order to improve computational efficiency, a Kriging model reflecting the relationship between the pipe node position and natural frequency is constructed, and then is applied instead of computer-aided engineering analysis program to evaluate the individual fitness during evolutionary computations. Several numerical computations are performed on a simplified piping model, and the results show that the constructed Kriging model can guarantee high modeling accuracy and the presented routing algorithm is able to obtain a set of non-dominated solutions of pipe layouts, in particular, the natural frequency of pipes are far from engine excitation frequency, which guarantees to avoid possible resonance.

This article provides practical guidance for engineering managers and policymakers to design cost-efficient and sustainable REVB-RNs. Engineering managers should adopt multi-period network structure, incorporate low-carbon regulations and address heterogeneity of REVB capacities through a collaborative framework that integrates the EVM's proprietary recycling network with 3PRs. Specifically, embedding WD of CRA at the tactical level with REVB-RN design at the strategic level can minimize the total cost under uncertainty. Capacity-based categorization of REVBs enables more specialized and granular management of REVB-RNs. For policymakers, effective inducements for responsible recycling include setting appropriate carbon caps and taxes and imposing penalties for unrecycled REVBs. Overall, the proposed collaborative framework can support a more sustainable and cost-efficient ecosystem for related departments to better recycle REVBs. This study contributes to SDGs 9, 12 and 13.

Multifactory remanufacturing is a widely adopted sustainable manufacturing approach nowadays. Its complexity lies in coordinating the dismantling, remanufacturing, and resource circulation among factories to maximize resource reuse and minimize environmental impact. Proper worker scheduling is crucial in this process to ensure efficient workflow and maximal resource utilization. This study proposes and addresses an optimization problem for multifactory remanufacturing considering worker scheduling, which is mainly divided into two parts: worker scheduling and remanufacturing process optimization (MRPO). A mixed-integer programming (MIP) mathematical model is established with the objective of profit maximization. A discrete battle royale optimization (BRO) algorithm is proposed to solve this problem, with a novel encoding structure and three soldier search strategies devised to better search for the optimal solution. The correctness of the model is validated through experiments on cases of different scales and comparisons with the IBM CPLEX optimizer. Furthermore, comparisons with the carnivorous plant algorithm (CPA), whale optimization algorithm (WAO), dingo optimization algorithm, and migrating bird optimization algorithm demonstrate the superiority and effectiveness of the proposed algorithm.

Pipe routing can be briefly formulated as seeking the shortest collision-free pipe paths while meeting certain engineering constraints. This article presents a new rectilinear pipe routing algorithm called Manhattan visibility graph (MVG) by extending the Visibility Graph method used for finding the shortest collision-free paths in Euclidean spaces to Manhattan spaces. Subsequently, the article proves that MVG can theoretically guarantee an optimal solution. Further, the article extends MVG algorithm to surface cases to meet requirements of routing pipes in aero-engine rotational spaces. Unlike previous graph methods that commonly yield more than n nodes (where n is the total number of terminals and obstacle vertices), MVG significantly reduces computation complexity because of containing only n nodes. Finally, numerical computations on a developed pipe routing system are performed to demonstrate the effectiveness and efficiency of the proposed method.

In order to solve the limitations of ant lion optimization algorithm such as local optimum slow convergence speed and so on this paper applies optimization strategy Logistic chaotic map and reverse learning to improve the selection of random ant lion update ant position and elite ant lion respectively. The improved ant lion optimization algorithm is compared and analyzed by selecting standard test functions, and the optimization accuracy and convergence speed of the algorithm are obviously improved. The improved ant lion optimization algorithm combined with a series of pipe routing engineering rules is applied to the pipe routing of aeroengine. The simulation of a pipe routing example is carried out by Siemens NX11.0 Grip and MATLAB, and the effectiveness of the proposed method is verified again.