Design Problem Research Articles

An investigation of PLLA hybrid stent design to overcome thick strut problems

Biodegradable polymer-based stents simultaneously provide scaffolding, drug release, and biodegradation to eliminate chronic inflammation. The most important factors hindering the wide use of these stents are thick struts, low radial strength, and large footprints formed on the inner wall of the artery as a result of stent expansion. Negative Poisson’s Ratio (NPR), also known as the Auxetic design, has shown great potential to provide radial strength with less strut thickness. However, a detailed mechanical evaluation proving improvement in stent performance parameters is not available in the literature. In this study, the performance parameters of two stent designs based on the Auxetic geometry with PLLA were analyzed under in-vivo conditions using an in-silico model consisting of the artery, crimper, and expander FE model. For this purpose, one design utilizes Auxetic unit cell, which is already available in the literature, while the other uses a newly proposed Hybrid design combining Auxetic and Chevron type geometries. Additionally, a specially heated coaxial balloon-catheter system was considered as a deployment tool between glass transition and body temperature, and carried out for thin-strut stent simulations. The Hybrid design is shown to resolve the foreshortening problem of Auxetic design and collapse pressure of commercial PLLA stents. In this present study validates the potential of Hybrid design to overcome problems for polymer-based biodegradable stents.

Practical output regulation of uncertain Euler–Lagrange systems

This paper deals with the problem of robust output regulation of uncertain Euler–Lagrange systems. An internal model controller is synthesized by a systematic framework that considers polynomial mappings of the steady-state trajectory. The closed-loop stabilization is guaranteed by using a descriptor differential–algebraic representation of the system. This methodology allows the controller design problem to be cast as a convex optimization problem subject to linear matrix inequalities. Simulations considering a two-link robotic manipulator illustrate the method.

Piranha predation optimization algorithm (PPOA) for global optimization and engineering design problems

A new nature-inspired optimization algorithm, Piranha predation optimization algorithm (PPOA), is proposed based on the unique foraging and predation behaviors of piranhas. Briefly, PPOA consists of three optimization operations, i.e., narrowing down to tear prey, swimming in a straight line, and swimming in a spiral. In this paper, various mathematical models for simulating the behavioral operators are presented in detail to solve different optimization challenges effectively. In this paper, the performance of PPOA is rigorously tested on 23 benchmark optimization functions, CEC2017 competition test set, CEC2020 real-world engineering optimization problems and four engineering design applications to show the applicability of the algorithm in different applications. Comparison experiments with other good and advanced competitive algorithms are conducted to reveal the advantages and performance of PPOA by using performance metrics such as Wilcoxon rank sum test and Friedman mean rank. The comparative results of this paper demonstrate the effectiveness of the proposed algorithmic strategy and its potential in applying it to solving optimization real-world engineering optimization problems.

Design of unreliable flow lines: How to jointly allocate buffer space and spare parts

The design of flow lines is an extensive area of research whereby most publications focus on buffer allocation to cope with random influences like breakdowns or random differences in processing times. In addition, spare parts can directly increase the machines’ availability. However, only a few papers consider the simultaneous optimization of buffer space and the possible number of spare parts in stock. The literature hardly even considers the evaluation of such systems. We are the first to introduce the joint optimization of buffer space and spare parts for flow lines of arbitrary length. First, we aim to allocate buffer capacities and spare parts efficiently. Since the buffer allocation problem is NP-hard, we can expect only to find near-optimal solutions. Second, we demonstrate the algorithmic behavior of different greedy and metaheuristics on this design problem. We illustrate how to exploit the problem structure to solve it almost optimally. Third, we generate managerial insights into allocating spare parts in manufacturing systems with buffers. We show that spare parts tend to be more effective when arranged at or near the center of a flow line, as it is already known for buffers. Moreover, we provide details on the combined buffer and spare part allocations.

Multi-objective resistance-capacitance optimization algorithm: An effective multi-objective algorithm for engineering design problems

Focusing on practical engineering applications, this study introduces the Multi-Objective Resistance-Capacitance Optimization Algorithm (MORCOA), a new approach for multi-objective optimization problems. MORCOA uses the transient response behaviour of resistance-capacitance circuits to navigate complex optimization landscapes and identify global optima when faced with many competing objectives. The core approach of MORCOA combines a dynamic elimination-based crowding distance mechanism with non-dominated sorting to generate an ideal and evenly distributed Pareto front. The algorithm's effectiveness is evaluated through a structured, three-phase analysis. Initially, MORCOA is applied to five benchmark problems from the ZDT test suite, with performance assessed using various metrics and compared against state-of-the-art multi-objective optimization techniques. The study then expands to include seven problems from the DTLZ benchmark collection, further validating MORCOA's effectiveness. The final phase involves applying MORCOA to six real-world constrained engineering design problems. Notably, the optimization of a honeycomb heat sink, which is crucial in thermal management systems, is a significant part of this study. This phase uses a range of performance measures to assess MORCOA's practical application and efficiency in engineering design. The results highlight MORCOA's robustness and efficiency in both real-world engineering applications and benchmark problems, demonstrating its superior capabilities compared to existing algorithms. The effective use of MORCOA in real-world engineering design problems indicates its potential as an adaptable and powerful tool for complex multi-objective optimization tasks.

An enhanced Modelling approach for warehouse sharing platform system designing problem

With the increasing importance of sustainability, warehouse sharing arises as a possible way to improve the logistics system efficiency. This paper studied the warehouse sharing platform systems (WSPS) and proposed an enhanced modelling approach for the WSPS design problem (WSPSDP) using the multi-allocation hub location routing problem framework. New elements, such as inter-warehouse transportation and multi-allocation scheme, were considered compared to the existing WSPS model. Then, an adaptive large neighbourhood decomposition search heuristic was applied to solve our problem. Computational experiments were conducted on different-sized instances to compare the WSPS model without inter-warehouse transportation (WSPSDP-WI) and the WSPS model with single-allocation scheme (WSPSDP-SA). The results suggested that our proposed WSPSDP model is more cost-efficient than the existing WSPS models, reducing 15.38% and 1.43% operation costs compared to the WSPSDP-WI and WSPSDP-SA models. Finally, our proposed WSPSDP model also has the potential to promote the utilisation of existing cheap idle warehouses.

Automated shot-to-shot optimization of the plasma start-up scenario in the TCV tokamak

Plasma start-up is typically achieved manipulating poloidal magnetic fields, gas injection and possibly auxiliary heating. Model-based design techniques have been gaining increasing attention in view of future large tokamaks which have more stringent constraints and less room for trial-and-error. In this paper, we formulate the tokamak start-up scenario design problem as a constrained optimization problem and introduce a novel shot-to-shot correction algorithm, based on the Iterative Learning Control concept, to compensate for unavoidable modeling errors based on experimental data. The effectiveness of the approach is demonstrated in experiments on the TCV tokamak showing that the target ramp-up scenario could be obtained in a small number of shots with a rough electromagnetic model.

Game-Theoretic Adversarial Interaction-Based Critical Scenario Generation for Autonomous Vehicles

Ensuring safety and efficiency in the rapidly advancing autonomous vehicle (AV) industry presents a significant engineering challenge. Comprehensive performance evaluations and critical scenario testing are essential for identifying situations that AVs cannot handle. Thus, generating critical scenarios is a key problem in AV testing system design. This paper proposes a game-theoretic adversarial interaction method to efficiently generate critical scenarios that challenge AV systems. Initial motion prediction for adversarial and surrounding vehicles is based on kinematic models and road constraints, establishing interaction action spaces to determine possible driving domains. A novel evaluation approach combines reachability sets with adversarial intensity to assess collision risks and adversarial strength for any state, used to solve behavior values for each interaction action state. Further, equilibrium action strategies for the vehicles are derived using Stackelberg game theory, yielding optimal actions considering adversarial interactions in the current traffic environment. Simulation results show that the adversarial scenarios generated by this method significantly increase incident rates by 158% to 1313% compared to natural driving scenarios, while ride comfort and driving efficiency decrease, and risk significantly increases. These findings provide critical insights for model improvement and demonstrate the proposed method’s suitability for assessing AV performance in dynamic traffic environments.

Epigenetics, Microbiome and Personalized Medicine: Focus on Kidney Disease.

Personalized medicine, which involves modifying treatment strategies/drug dosages based on massive laboratory/imaging data, faces large statistical and study design problems. The authors believe that the use of continuous multidimensional data, such as those regarding gut microbiota, or binary multidimensional systems properly transformed into a continuous variable, such as the epigenetic clock, offer an advantageous scenario for the design of trials of personalized medicine. We will discuss examples focusing on kidney diseases, specifically on IgA nephropathy. While gut dysbiosis can provide a treatment strategy to restore the standard gut microbiota using probiotics, transforming epigenetic omics data into epigenetic clocks offers a promising tool for personalized acute and chronic kidney disease care. Epigenetic clocks involve a complex transformation of DNA methylome data into estimated biological age. These clocks can identify people at high risk of developing kidney problems even before symptoms appear. Some of the effects of both the epigenetic clock and microbiota on kidney diseases seem to be mediated by endothelial dysfunction. These "big data" (epigenetic clocks and microbiota) can help tailor treatment plans by pinpointing patients likely to experience rapid declines or those who might not need overly aggressive therapies.

Optimization of truss structures using multi-objective cheetah optimizer

In this study, a multi-objective version of the recently proposed cheetah optimizer called multi-objective cheetah optimizer (MOCO) has been proposed. MOCO draws inspiration from the targeted hunting strategy employed by cheetahs, which involves a sequence of actions: searching for prey, patiently waiting for the right moment to attack, swiftly launching the attack, and then retreating from the prey and returning to their habitat. MOCO is the result of modification and enhancement from its single-objective counterpart, utilizing a Pareto dominance-based approach. This adaptation allows MOCO to efficiently handle multiple objectives, explores and exploits promising areas in the optimization landscape, and identifies non-dominated solutions, offering valuable tradeoff choices for decision-makers. To demonstrate its practical applications, the MOCO method has been employed to address five intricate structural design problems. These problems involve a pair of competing objectives: the minimization of structural weight and the reduction of maximum nodal displacement. To gauge the efficacy and efficiency of the proposed algorithm, a comparative analysis is conducted against three alternative state-of-the-art multi-objective algorithms. Furthermore, a rigorous evaluation is carried out utilizing hypervolume testing. The findings reveal that the MOCO algorithm surpasses the performance of the other algorithms, underscored by its capacity to uncover a diverse array of non-dominated solutions. To delve deeper into the experimental results, statistical analysis employing Friedman’s rank test is employed. The solutions generated and the convergence patterns exhibited by the MOCO approach underscore its exceptional proficiency in resolving intricate design problems.

Metaheuristic algorithm inspired by enterprise development for global optimization and structural engineering problems with frequency constraints

In this study, a novel metaheuristic optimization algorithm inspired by the enterprise development (ED) process was developed. This process encompasses tasks, structure, technology, and human interactions. A mechanism for switching activities was utilized to determine each step by updating the searched solutions. The ED optimizer underwent evaluation against 50 mathematical functions and 54 IEEE Congress on Evolutionary Computation (CEC) functions, and it was compared with six up-to-date algorithms, three winners of CEC 2020 in real-world single-objective constrained optimization, and ten well-known algorithms. The evaluation results revealed that the ED optimizer outperformed the algorithms in mathematical tests. Using finite element simulation, this novel method was applied to minimize steel structure component weights under various frequency constraints. The engineering designs included a 37-bar planar truss, a 52-bar dome, a 72-bar space truss, a 600-bar dome, and a 1,410-bar dome. Optimization results for these structures showed that the developed ED optimizer yielded the optimal solution and required fewer function evaluations. In conclusion, the ED optimizer is a superior metaheuristic algorithm that can effectively solve complex structural design problems.

A bay design problem in less-than-unit-load production warehouse

In this paper, we consider a bay design problem in a less-than-unit-load production warehouse, which is motivated by a real-world problem in a semiconductor company. The objective is to maximize the utilization of the vertical space in bays by considering several practical storage requirements. To solve the problem, a non-linear integer programming model is first formulated. Since the problem is similar to a two-stage cutting stock problem (CSP), a column-and-row generation (CRG) method is developed, in which the original problem is decomposed into a restricted master problem and three subproblems, including two classical column generation subproblems and a row generation subproblem. The two former subproblems are solved as unbounded knapsack problems and for the latter, a two-stage approach is applied. The results of computational experiments on randomly generated instances show that the proposed CRG method is more efficient than the classic column-generation-based method, solving the non-linear model directly and solving a cut model in the literature directly. The results of a case study show that our strategy can improve the utilization of the existing warehouse storage space significantly by about 24%. The CRG method is also tested on basic two-stage two-dimensional CSP benchmarks and its performance is compared to those of other pattern-based methods. The results show its potential for effectively solving the basic two-stage two-dimensional CSPs.

Research on Electrotechnics Teaching Based on BOPPPS Teaching Mode

Electrotechnics has the characteristics of strong abstraction and comprehensiveness. In the current teaching, many teachers have the problems of single teaching method and insufficient design of teaching links, and students' enthusiasm for classroom participation are also weak. This is due to the unclear role of teachers in the teaching process and the lack of timely and effective feedback from students in the learning process. In this paper, BOPPPS teaching mode is applied to the teaching of electrotechnics. It follows the student-centered principle and is designed based on constructivist learning theory. This teaching method is highly implementable and interactive. If teachers apply this new teaching method, they can make the teaching of electrical engineering more diversified, and the number of students' participation becomes more. This paper also combines the electrical engineering course with the BOPPPS teaching mode to analyze the strategy in stages in order to improve the teaching quality of the course.

Examining an Evolving Biologically Inspired Design Professional Learning Environment through Conjecture Mapping and Design-Based Research.

Biologically inspired design (BID) in engineering is a convergent, systematic approach that uses analogies from biological organisms to develop solutions for human engineering and design problems. Based on outcomes from prior studies of integrating BID in higher education, incorporating BID into pre-college education is a logical evolution. For effective BID instruction of these convergent concepts in pre-college education, teachers need to be well-equipped with biological, engineering, and pedagogical knowledge, both in general and those unique to the convergent, still evolving discipline. In this paper, we investigate the Professional Learning (professional learning) environment designed to foster engineering teachers' understanding of BID integration in engineering and to determine to what extent the evolving professional learning environment fostered engineering teachers' conceptual knowledge of BID across the three-year project. This design study applies conjecture mapping with design-based research (DBR) to examine a professional learning environment that changed over three summers and its impact on teachers' conceptual understanding of BID integration in engineering. The analysis indicates that a combination of experiential and informal learning experiences along with engagement in a formal design challenge promoted teacher enthusiasm and a conceptual understanding of BID across the three years. Professional learning fostered teachers' understanding of BID integration in engineering and enabled them to integrate BID into their engineering teaching practice.

Knowledge graph-based representation and recommendation for surrogate modeling method

Surrogate models have been widely used in engineering design for approximating a simulation system with high computational cost. Complex system design typically is a multi-stage and multi-discipline design problem, which requires a large number of surrogate models. The choice of surrogate modeling method (SMM) is critical as it directly impacts the performance of both the surrogate models and the designed systems. With the growing variety of SMMs, designers face challenges in selecting the appropriate methods for their specific applications. To address this, we propose a representation and recommendation framework for surrogate modeling methods based on knowledge graph. Firstly, we develop an ontology to formally represent core concepts involved in the recommendation for surrogate modeling methods, including surrogate modeling method, surrogate model, and data sets,etc. Secondly, we extract 460 samples from 46 benchmark functions using Latin hypercube sampling to construct a knowledge graph with 8,343 nodes and 16,100 relationships, which involves 7,820 surrogate models generated from 17 surrogate modeling methods. Finally, we propose a knowledge graph-based recommendation method for surrogate modeling method named KGRSMM to facilitate the selection of an appropriate surrogate modeling method. We test the efficacy of KGRSMM using examples of theoretical problems and engineering problems of hot rod rolling respectively. It is shown in the results that KGRSMM is capable of recommending surrogates with appropriate accuracy, robustness, and time to satisfy designers’ preferences.

CREATION OF A NEW METHODOLOGY FOR THE DEVELOPMENT OF DESIGN COMPETENCE AMONG FUTURE ENGINEERS

This article explores the challenge of developing design competence in engineering students, a skill set increasingly vital in the face of modern technological demands. It introduces a novel methodology aimed at enhancing the training of future engineers, equipping them with the necessary tools to solve complex design and construction problems effectively. Grounded in an in-depth analysis of current engineering education practices, the proposed methodology integrates modern technologies, project-based learning, and an interdisciplinary approach to create a comprehensive and innovative educational framework.

Development of a DSP based collaborative computing platform for power system

Abstract With the advancement of information technology, there has been tremendous progress in data production, processing, and storage technology, especially in data processing technology. With the rapid development of the power grid, its operation has become more intelligent and efficient. Traditional power system simulation and analysis tools have disadvantages, such as slow message transmission and the inability to share data, which cannot meet the requirements of modern power system simulation calculations. DSP was introduced and developed by the Southern Power Grid Research Institute and can perform various simulation calculations such as transient stability calculation, power flow calculation, and short circuit calculation. It is widely used in departments such as power planning and design. However, the data calculated by DSP is input through a data card, which is cumbersome and difficult to manage. In order to meet the new requirements of power system simulation analysis, a power system collaborative computing platform has been developed based on DSP, which makes DSP data maintenance and management more efficient and intuitive. It comprehensively improves the efficiency of power grid planning work. This article provides a detailed analysis of the architecture design and application problems of the power system collaborative computing platform, clarifies the requirements of large-scale interconnected power grids for simulation computing data management, and proposes solutions that can meet the needs of simulation computing data management in China’s power grid from two aspects: key data architecture issues and software architecture technology.

Periodic event-triggered adaptive neural control of USVs under replay attacks

This work aims to address the control issue of unmanned surface vehicles (USVs) under replay attacks, where the influence of internal/external uncertainties and actuator’s physical constraints are taken into account. In the control design, a hyperbolic tangent function is used to replace the actuator saturation nonlinearity. To solve the design problem of mismatched unknown time-varying control gain caused by replay attacks, the single-parameter-learning technique is introduced, which avoids the operation of estimating for the unknown gain directly. To compensate for the effect of lumped uncertainties including attacks, unknown dynamics and external disturbances, the adaptive neural network with the finite covering principle is involved in the kinematic and dynamic channels, respectively. Furthermore, to reduce the update rate of actuator and alleviate the actuator wear, a periodic event-triggering mechanism (PETM) is established in the controller-actuator (C-A) channel. Finally, a periodic event-triggered (PET) adaptive neural tracking control solution for USVs under replay attacks is proposed, and it is verified that the control solution can force the trajectory of USVs to follow the reference trajectory even if there exists the effect of replay attacks. In addition, all signals in the closed-loop control system of USVs under replay attacks are bounded, and simulation and comparison results demonstrate the effectiveness of the control strategy.

Synthesize Personalized Training for Robot-Assisted Upper Limb Rehabilitation With Diversity Enhancement.

For upper limb rehabilitation, the robot-assisted technique in combination with serious games requires well-specified training plans. For the best quality of the rehabilitation process, customized game levels for each user are desired, while it is labor-intensive to design and adjust game levels for different individuals. We work on generating training content for a desktop end-effector rehabilitation robot and propose a method to automatically generate individualized training plans. By modeling the search of the training motions as finding optimal hand paths and trajectories, we introduce solving the design problem with a multi-objective optimization (MO) solver. We further improve the MO solver to enhance the diversity of the solutions. With the proposed approach, our system is capable of automatically generating various training plans considering the training intensity and dexterity of each joint in the upper limb. In addition, the enhanced diversity avoids repeated training plans, which helps motivate the user in the rehabilitation. We test our method with different requirements on the training plans and validate the solutions.

Crowding in School Choice

We consider the market design problem of matching students to schools in the presence of crowding effects. These effects are salient in parents’ decision-making and the empirical literature; however, they cause difficulties in the design of satisfactory mechanisms and, as such, are not currently considered. We propose a new framework and an equilibrium notion that accommodates crowding, no-envy, and respect for priorities. The equilibrium has a student-optimal element that induces an incentive-compatible mechanism and is implementable via a novel algorithm. Moreover, analogs of fundamental structural results of the matching literature (the rural hospitals theorem, welfare lattice, etc.) survive. (JEL D47, H75, I21, I28)