problem solving engineer

• Oct 13, 2019

10 Steps to Problem Solving for Engineers

Updated: Dec 6, 2020

With the official launch of the engineering book 10+1 Steps to Problem Solving: An Engineer's Guide it may be interesting to know that formalization of the concept began in episode 2 of the Engineering IRL Podcast back in July 2018.

As noted in the book remnants of the steps had existed throughout my career and in this episode I actually recorded the episode off the top of my head.

My goal was to help engineers build a practical approach to problem solving.

Have a listen.

Who can advise on the best approach to problem solving other than the professional problem solvers - Yes. I'm talking about being an Engineer.

There are 2 main trains of thought with Engineering work for non-engineers and that's trying to change the world with leading edge tech and innovations, or plain old boring math nerd type things.

Whilst, somewhat the case what this means is most content I read around Tech and Engineering are either super technical and (excruciatingly) detailed. OR really riff raff at the high level reveling at the possibilities of changing the world as we know it. And so what we end up with is a base (engineer only details) and the topping (media innovation coverage) but what about the meat? The contents?

There's a lot of beauty and interesting things there too. And what's the centrepiece? The common ground between all engineers? Problem solving.

The number one thing an Engineer does is problem solving. Now you may say, "hey, that's the same as my profession" - well this would be true for virtually every single profession on earth. This is not saying there isn't problem solving required in other professions. Some problems require very basic problem solving techniques such is used in every day life, but sometimes problems get more complicated, maybe they involve other parties, maybe its a specific quirk of the system in a specific scenario. One thing you learn in engineering is that not all problems are equal. These are

 The stages of problem solving like a pro:

Is the problem identified (no, really, are you actually asking the right question?)

Have you applied related troubleshooting step to above problem?

Have you applied basic troubleshooting steps (i.e. check if its plugged in, turned it on and off again, checked your basics)

Tried step 2 again? (Desperation seeps in, but check your bases)

Asked a colleague or someone else that may have dealt with your problem? (50/50 at this point)

Asked DR. Google (This is still ok)

Deployed RTFM protocol (Read the F***ing Manual - Engineers are notorious for not doing this)

Repeated tests, changing slight things, checking relation to time, or number of people, or location or environment (we are getting DEEP now)

Go to the bottom level, in networking this is packet sniffers to inspect packets, in systems this is taking systems apart and testing in isolation, in software this is checking if 1 equals 1, you are trying to prove basic human facts that everyone knows. If 1 is not equal to 1, you're in deep trouble.At this point you are at rebuild from scratch, re install, start again as your answer (extremely expensive, very rare)

And there you have it! Those are your levels of problem solving. As you go through each step, the more expensive the problem is. -- BUT WAIT. I picked something up along the way and this is where I typically thrive. Somewhere between problem solving step 8 and 10. 

The secret step

My recommendation at this point is to try tests that are seemingly unrelated to anything to do with the problem at all.Pull a random cable, test with a random system off/on, try it at a specific time of the day, try it specifically after restarting or replugging something in. Now, not completely random but within some sort of scope. These test are the ones that when someone is having a problem when you suggest they say "that shouldn't fix the problem, that shouldn't be related" and they are absolutely correct.But here's the thing -- at this stage they have already tried everything that SHOULD fix the problem. Now it's time for the hail mary's, the long shots, the clutching at straws. This method works wonders for many reasons. 1. You really are trying to try "anything" at this point.

2. Most of the time we may think we have problem solving step number 1 covered, but we really don't.

3. Triggering correlations.

This is important.

Triggering correlations

In a later post I will cover correlation vs causation, but for now understand that sometimes all you want to do is throw in new inputs to the system or problem you are solving in order to get clues or re identify problems or give new ways to approach earlier problem solving steps. There you have it. Problem solve like a ninja. Approach that extremely experienced and smart person what their problem and as they describe all the things they've tried, throw in a random thing they haven't tried. And when they say, well that shouldn't fix it, you ask them, well if you've exhausted everything that should  have worked, this is the time to try things that shouldn't. Either they will think of more tests they haven't considered so as to avoid doing your preposterous idea OR they try it and get a new clue to their problem. Heck, at worst they confirm that they do know SOMETHING about the system.

Go out and problem solve ! As always, thanks for reading and good luck with all of your side hustles.

If you prefer to listen to learn we got you covered with the Engineering IRL show!

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FREE K-12 standards-aligned STEM

curriculum for educators everywhere!

Find more at TeachEngineering.org .

• TeachEngineering
• Problem Solving

Lesson Problem Solving

Grade Level: 8 (6-8)

(two 40-minute class periods)

Lesson Dependency: The Energy Problem

Subject Areas: Physical Science, Science and Technology

• Print lesson and its associated curriculum

Curriculum in this Unit Units serve as guides to a particular content or subject area. Nested under units are lessons (in purple) and hands-on activities (in blue). Note that not all lessons and activities will exist under a unit, and instead may exist as "standalone" curriculum.

• Energy Forms and States Demonstrations
• Energy Conversions
• Watt Meters to Measure Energy Consumption
• Household Energy Audit
• Light vs. Heat Bulbs
• Efficiency of an Electromechanical System
• Efficiency of a Water Heating System
• Solving Energy Problems
• Energy Projects

TE Newsletter

Engineering connection, learning objectives, worksheets and attachments, more curriculum like this, introduction/motivation, associated activities, user comments & tips.

Scientists, engineers and ordinary people use problem solving each day to work out solutions to various problems. Using a systematic and iterative procedure to solve a problem is efficient and provides a logical flow of knowledge and progress.

• Students demonstrate an understanding of the Technological Method of Problem Solving.
• Students are able to apply the Technological Method of Problem Solving to a real-life problem.

Educational Standards Each TeachEngineering lesson or activity is correlated to one or more K-12 science, technology, engineering or math (STEM) educational standards. All 100,000+ K-12 STEM standards covered in TeachEngineering are collected, maintained and packaged by the Achievement Standards Network (ASN) , a project of D2L (www.achievementstandards.org). In the ASN, standards are hierarchically structured: first by source; e.g. , by state; within source by type; e.g. , science or mathematics; within type by subtype, then by grade, etc .

Ngss: next generation science standards - science.

View aligned curriculum

Do you agree with this alignment? Thanks for your feedback!

International Technology and Engineering Educators Association - Technology

State standards, national science education standards - science.

Scientists, engineers, and ordinary people use problem solving each day to work out solutions to various problems. Using a systematic and iterative procedure to solve a problem is efficient and provides a logical flow of knowledge and progress.

In this unit, we use what is called "The Technological Method of Problem Solving." This is a seven-step procedure that is highly iterative—you may go back and forth among the listed steps, and may not always follow them in order. Remember that in most engineering projects, more than one good answer exists. The goal is to get to the best solution for a given problem. Following the lesson conduct the associated activities Egg Drop and Solving Energy Problems for students to employ problem solving methods and techniques. 

Lesson Background and Concepts for Teachers

The overall concept that is important in this lesson is: Using a standard method or procedure to solve problems makes the process easier and more effective.

The specific process of problem solving used in this unit was adapted from an eighth-grade technology textbook written for New York State standard technology curriculum. The process is shown in Figure 1, with details included below. The spiral shape shows that this is an iterative, not linear, process. The process can skip ahead (for example, build a model early in the process to test a proof of concept) and go backwards (learn more about the problem or potential solutions if early ideas do not work well).

This process provides a reference that can be reiterated throughout the unit as students learn new material or ideas that are relevant to the completion of their unit projects.

Brainstorming about what we know about a problem or project and what we need to find out to move forward in a project is often a good starting point when faced with a new problem. This type of questioning provides a basis and relevance that is useful in other energy science and technology units. In this unit, the general problem that is addressed is the fact that Americans use a lot of energy, with the consequences that we have a dwindling supply of fossil fuels, and we are emitting a lot of carbon dioxide and other air pollutants. The specific project that students are assigned to address is an aspect of this problem that requires them to identify an action they can take in their own live to reduce their overall energy (or fossil fuel) consumption.

The Seven Steps of Problem Solving

1.  Identify the problem

Clearly state the problem. (Short, sweet and to the point. This is the "big picture" problem, not the specific project you have been assigned.)

2.  Establish what you want to achieve

• Completion of a specific project that will help to solve the overall problem.
• In one sentence answer the following question: How will I know I've completed this project?
• List criteria and constraints: Criteria are things you want the solution to have. Constraints are limitations, sometimes called specifications, or restrictions that should be part of the solution. They could be the type of materials, the size or weight the solution must meet, the specific tools or machines you have available, time you have to complete the task and cost of construction or materials.

3.  Gather information and research

• Research is sometimes needed both to better understand the problem itself as well as possible solutions.
• Don't reinvent the wheel – looking at other solutions can lead to better solutions.
• Use past experiences.

4.  Brainstorm possible solutions

List and/or sketch (as appropriate) as many solutions as you can think of.

5.  Choose the best solution

Evaluate solution by: 1) Comparing possible solution against constraints and criteria 2) Making trade-offs to identify "best."

6.  Implement the solution

• Develop plans that include (as required): drawings with measurements, details of construction, construction procedure.
• Define tasks and resources necessary for implementation.
• Implement actual plan as appropriate for your particular project.

7.  Test and evaluate the solution

• Compare the solution against the criteria and constraints.
• Define how you might modify the solution for different or better results.
• Egg Drop - Use this demonstration or activity to introduce and use the problem solving method. Encourages creative design.
• Solving Energy Problems - Unit project is assigned and students begin with problem solving techniques to begin to address project. Mostly they learn that they do not know enough yet to solve the problem.
• Energy Projects - Students use what they learned about energy systems to create a project related to identifying and carrying out a personal change to reduce energy consumption.

The results of the problem solving activity provide a basis for the entire semester project. Collect and review the worksheets to make sure that students are started on the right track.

Learn the basics of the analysis of forces engineers perform at the truss joints to calculate the strength of a truss bridge known as the “method of joints.” Find the tensions and compressions to solve systems of linear equations where the size depends on the number of elements and nodes in the trus...

Through role playing and problem solving, this lesson sets the stage for a friendly competition between groups to design and build a shielding device to protect humans traveling in space. The instructor asks students—how might we design radiation shielding for space travel?

A process for technical problem solving is introduced and applied to a fun demonstration. Given the success with the demo, the iterative nature of the process can be illustrated.

The culminating energy project is introduced and the technical problem solving process is applied to get students started on the project. By the end of the class, students should have a good perspective on what they have already learned and what they still need to learn to complete the project.

Hacker, M, Barden B., Living with Technology , 2nd edition. Albany NY: Delmar Publishers, 1993.

Other Related Information

This lesson was originally published by the Clarkson University K-12 Project Based Learning Partnership Program and may be accessed at http://internal.clarkson.edu/highschool/k12/project/energysystems.html.

Contributors

Supporting program, acknowledgements.

This lesson was developed under National Science Foundation grants no. DUE 0428127 and DGE 0338216. However, these contents do not necessarily represent the policies of the National Science Foundation, and you should not assume endorsement by the federal government.

Last modified: August 16, 2023

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Critical Thinking and Problem Solving for Engineers

Critical Thinking and Problem Solving for Engineers Training by Tonex

This comprehensive course, “Critical Thinking and Problem Solving for Engineers,” offered by Tonex, equips engineering professionals with essential skills to enhance their critical thinking capabilities and problem-solving acumen. Participants will engage in practical exercises and case studies tailored to the engineering context, fostering a deep understanding of analytical approaches and innovative problem-solving techniques.

Tonex’s “Critical Thinking and Problem Solving for Engineers” training is a dynamic program designed to empower engineering professionals with advanced cognitive skills. This intensive course, spanning diverse modules, delves into the core aspects of critical thinking and innovative problem-solving tailored specifically for engineering contexts.

Participants will explore analytical techniques, creative problem-solving methodologies, and decision-making frameworks crucial for navigating complex engineering challenges. With a focus on real-world case studies and practical applications, this training equips engineers at all levels with the essential tools to enhance their problem-solving acumen, fostering a culture of strategic thinking and informed decision-making within the engineering domain.

Learning Objectives:

• Develop advanced critical thinking skills applicable to engineering challenges.
• Enhance problem-solving capabilities through structured methodologies.
• Apply logical reasoning and analysis to technical problem domains.
• Cultivate creativity and innovation within engineering problem-solving.
• Improve decision-making processes through systematic evaluation.
• Master effective communication of complex technical solutions.

Audience: This course is designed for engineers at all levels, including professionals, project managers, and team leaders seeking to elevate their critical thinking and problem-solving skills within an engineering framework.

Course Outline:

Module 1: Introduction to Critical Thinking in Engineering

• Importance of Critical Thinking
• Role in Problem Identification
• Significance in Solution Development
• Critical Thinking Models
• Integration with Engineering Practices
• Critical Thinking in Decision Making

Module 2: Analytical Techniques for Engineers

• Structured Analysis Methods
• Application in Problem Solving
• Root Cause Analysis
• Data-driven Decision Making
• Quantitative Analysis Techniques
• Analytical Tools for Engineers

Module 3: Creative Problem Solving in Engineering

• Fostering Creativity in Engineering
• Innovation in Problem Solving
• Creative Thinking Models
• Brainstorming Techniques
• Design Thinking in Engineering
• Overcoming Creative Blocks

Module 4: Decision Making in Engineering

• Decision Matrices in Engineering
• Multi-Criteria Decision Analysis
• Risk Assessment in Engineering Decisions
• Decision Support Systems
• Ethical Considerations in Decision Making
• Adaptive Decision Making in Engineering

Module 5: Communication Skills for Technical Problem-Solving

• Effective Communication Strategies
• Tailoring Communication to Audiences
• Writing Technical Reports
• Documentation Best Practices
• Visualization of Technical Information
• Communicating Results and Solutions

Module 6: Real-world Case Studies and Practical Applications

• Hands-on Problem Solving
• Analysis of Engineering Scenarios
• Application of Critical Thinking Tools
• Lessons Learned from Case Studies
• Group Problem-Solving Exercises
• Discussion and Reflection on Practical Challenges

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Process Support Engineer

• Rensselaer, New York, United States of America
• Industrial Operations & Product Supply

Job Description

Regeneron is currently looking for a Process Support Engineer to join our Integrated Manufacturing Support (Formulated Bulk, Drug Product, and Fill/Finish) team. A Process Support Engineer is passionate about problem solving and performing scientific and technical investigations. They will serve as a subject matter expert (SME) to support biopharmaceutical production and Fill/Finish activities across the Industrial Operations and Product Supply (IOPS) Rensselaer site. This is a non-lab based position.

As a Process Support Engineer, a typical day might include the following:

• Lead, author, and/or review scientific root cause investigations and develop corrective action plans to resolve complex deviations related to process control and product quality.
• Perform data analysis and apply statistical process control tools for trending and analyzing process-related data to drive consistency and improvements.
• Serve as a Subject Matter Expert (SME) by utilizing a science-based approach and through analysis of historical data to support production, purification, and fill/finish activities.
• Present process data and investigational findings at group and department meetings.
• Monitoring of production processes to proactively acquire vital data in support of preventing issues and tracking efficiency of changes.
• Interact with Manufacturing, Quality Assurance, Quality Control, Manufacturing Sciences & Technology, and External Manufacturing teams to drive root cause investigations and implement process improvements.

This role might be for you if you:

• Enjoy working in a fast-paced environment and you have the innate ability to balance multiple projects, prioritize them and execute them independently.
• Have fundamental knowledge of cell culture, purification/chromatography operations, analytical methods (i.e. UPLC, ELISA, PCR), material sciences, and/or drug product fill/finish operations, and statistical analyses.
• Have a strong attention to detail; experience in a GMP environment is a plus.
• Can take on new and sometimes ambiguous challenges and learn quickly.
• Can lead and manage large technical reports and projects.
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A metaheuristic-based comparative structure for solving discrete space mechanical engineering problem

• Original Research
• Published: 05 June 2024

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• Mohammad Ali Arjomandi   ORCID: orcid.org/0000-0001-6101-3962 1 ,
• Seyed Sajad Mousavi Asl   ORCID: orcid.org/0000-0002-5008-6020 2 ,
• Behzad Mosallanezhad   ORCID: orcid.org/0000-0002-5380-004X 3 &
• Mostafa Hajiaghaei-Keshteli   ORCID: orcid.org/0000-0002-9988-2626 3  

Composite materials have become widespread in various industries due to their exceptional properties of strength and flexibility, which creates an entirely new area of design opportunities. However, optimizing structures containing elements made of composite material is a complicated challenge in mechanical engineering due to the natural characteristics of the material. Especially, the way that two different laminates connect together needs meticulous attention. Bolt-nut joints are one such fasteners, characterized by the high concentration of stresses and skewed stress distribution along the bolt plane. To avoid mentioned problems in bolt-nuts, adhesive-bonded joints are commonly used in composite structures. But these joints are potentially vulnerable to other defects like delamination on free ends that reduction of its risk is the core of this paper. Most traditional optimization methods, such as finite element analysis or experimental approaches are characterized by numerous variables and restrictions, and complex relations described by controlling equations. So, it is crucial to seek more powerful and sound alternatives such as metaheuristic optimization techniques which can yield a reliable solution to challenging problems in a reasonable amount of time. In this study, the performance of eight well-known metaheuristic algorithms in the optimization of two distinct multilayer adhesively-bond joints is analyzed for the first time to tackle the strength against delamination which is one of the major concerns in the design of composite material structures. The performance of metaheuristic algorithms is also evaluated using two non-parametric tests of Friedman and Wilcoxon signed rank as well as interval plots. According to the findings, the three algorithms namely the Simulated Annealing, Harmony Search, and Particle Swarm Optimization offer the most reliable performance for finding the solution. Harris Hawks Optimization, Genetic Algorithm, and Bees Algorithm, on the other hand, have the worst performance in solving such problems.

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Abuiziah, & Shakraneh, N. (2013). A review of genetic algorithm optimization: Operations and applications to water pipeline systems. International Journal of Physical, Natural Science and Engineering , 7 (12). https://www.researchgate.net/publication/260035091

Alemi-Ardakani, M., Milani, A. S., Yannacopoulos, S., & Shokouhi, G. (2016). On the effect of subjective, objective and combinative weighting in multiple criteria decision making: A case study on impact optimization of composites. Expert Systems with Application, 46 , 426–438. https://doi.org/10.1016/j.eswa.2015.11.003

Article   Google Scholar  

Alrof. (2023). A survey of recently developed metaheuristics and their comparative analysis. Engineering Applications of Artificial Intelligence, 117 , 105622. https://doi.org/10.1016/j.engappai.2022.105622

Arhore, E. G., Yasaee, M., & Dayyani, I. (2021). Comparison of GA and topology optimization of adherend for adhesively bonded metal composite joints. International Journal of Solids and Structures, 226–227 , 111078. https://doi.org/10.1016/j.ijsolstr.2021.111078

Arjomandi, M. A., Shishehsaz, M., Ghanbarzadeh, A., Mosallanezhad, B., & Akrami, M. (2022). Application of particle swarm optimization for improvement of peel strength in a laminated double-lap composite joint. Applied Sciences, 12 , 6997. https://doi.org/10.3390/app12146997

Bavi, O., Bavi, N., & Shishesaz, M. (2013). Geometrical optimization of the overlap in mixed adhesive lap joints. Journal of Adhesion, 89 , 948–972. https://doi.org/10.1080/00218464.2013.782813

Calzavara, G., Iori, M., Locatelli, M., Moreira, M. C. O., & Silveria, T. (2021). Mathematical models and heuristic algorithms for pallet building problems with practical constraints. Annals of Operations Research . https://doi.org/10.1007/s10479-021-04349-w

Chunsheng, S., Erwei, S., Xiangyang, H., & Jinguang, Z. (2018). Monitoring the cohesive damage of the adhesive layer in CFRP double-lapped bonding joint based on non-uniform strain profile reconstruction using dynamic particle swarm optimization algorithm. Measurement, 123 , 235–245. https://doi.org/10.1016/j.measurement.2018.03.063

Cikan, M., & Kekezoglu, B. (2022). Comparison of metaheuristic optimization techniques including equilibrium optimizer algorithm in power distribution network reconfiguration. Alexandria Engineering Journal, 61 , 991–1031. https://doi.org/10.1016/j.aej.2021.06.079

Di Franco, G., & Zuccarello, B. (2014). Analysis and optimization of hybrid double lap aluminum-GFRP joints. Composite Structures, 116 , 682–693. https://doi.org/10.1016/j.compstruct.2014.05.044

Foroutan, K., Varedi-Koulaei, S. M., Duc, N. D., & Ahmadi, H. (2022). Non-linear static and dynamic buckling analysis of laminated composite cylindrical shell embedded in non-linear elastic foundation using the swarm-based metaheuristic algorithms. European Journal of Mechanics /a Solids, 91 , 104420. https://doi.org/10.1016/j.euromechsol.2021.104420

Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony search. SIMULATION, 76 (2), 60–68. https://doi.org/10.1177/003754970107600201

Gupta, S., Abderazek, H., Yildiz, B. S., Yildiz, A. R., Mirjalili, S., & Sait, S. M. (2021). Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems. Expert Systems with Applications, 183 , 115351. https://doi.org/10.1016/j.eswa.2021.115351

Haddock, J., & MittenThal, J. (1992). Simulation optimization using simulated annealing. Computers Industrial Engineering, 22 (4), 387–395.

Hassan Vand, M., Abbaszadeh, H., & Shishesaz, M. (2021). Optimization of adhesive single-lap joints under bending moment. The Journal of Adhesion . https://doi.org/10.1080/00218464.2021.1932485

Heidari, A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97 , 849–872. https://doi.org/10.1016/j.future.2019.02.028

Henderson, D., Jacobson, S. H., & Johnson, A. W. (2006). The theory and practice of simulated annealing. Handbook of Metaheuristics . https://doi.org/10.1007/0-306-48056-5_10

Her, S.-C. (1999). Stress analysis of adhesively-bonded lap joints. Composite Structures, 47 , 673–678. https://doi.org/10.1016/S0263-8223(00)00052-0

Hernandez-Lara, D., Rodriguez-Canizo, R. G., Merchan-Cruz, E. A., Santiago-Miguel, A. M., Juarez-Velazquez, E. T., & Trejo-Villanueva, C. A. (2022). Optimal Design of a foot prosthesis insole with composite materials applying metaheuristic algorithms. Results in Engineering, 13 , 100322. https://doi.org/10.1016/j.rineng.2021.100322

Holland, J. H. (1992). Genetic algorithms. Scientific American, 267 (1), 66–73.

Huang, X., Liu, X., Song, C., Jiang, F., Jiang, Y., & Zhou, Z. (2019). Damage detection of carbon fiber reinforced plastic π adhesive-bonded joints based on the peeling stress reconstruction with hybrid artificial fish swarm and frog leaping algorithm. Advances in Mechanical Engineering, 11 , 1–9. https://doi.org/10.1177/1687814019841783

Jafari, M., Bayati Chaleshtari, M. H., Khoramishad, H., & Atenbach, H. (2023). Minimization of thermal stress in perforated composite plate using metaheuristic algorithms WOA, SCA and GA. Composite Structures, 304 , 116403. https://doi.org/10.1016/j.compstruct.2022.116403

Katoch, S., Chauhan, S. S., & Kumar, V. (2021). A Review on Genetic algorithm: Past, present, and future. Multimedia Tools and Applications, 80 , 8091–8126. https://doi.org/10.1007/s11042-020-10139-6

Kaveh, S. B., & Hashemi, R. S. (2013). Optimal design of laminated composite structures via hybrid charged system search and particle swarm optimization. Asian Journal of Civil Engineering, 14 , 4.

Google Scholar  

Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the ICNN’95-international conference on neural networks, Perth, Australia, 27 November–1 December 1995 (Vol. 4) 1942–1948.

Kim, H. S., Rhee, S. Y., & Cho, M. (2008). Simple and efficient interlaminar stress analysis of composite laminates with internal ply-drop. Composite Structures, 84 , 73–86. https://doi.org/10.1016/j.compstruct.2007.06.004

Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, New Series, 220 (4598), 671–680. https://doi.org/10.1126/science.220.4598.671

Kumar, M., Sahu, A., & Mitra, P. (2020). A comparison of different metaheuristics for the quadratic assignment problem in accelerated systems. Applied Soft Computing Journal . https://doi.org/10.1016/j.asoc.2020.106927

Kurban, R., Durmus, A., & Karakose, E. (2021). A comparison of novel metaheuristic algorithms on color aerial image multilevel thresholding. Engineering Applications of Artificial Intelligence, 105 , 104410. https://doi.org/10.1016/j.engappai.2021.104410

Lara-Montano, E. D., Gomez-Castro, F. I., & Gutierrez-Antonio, C. (2021). Comparison of the performance of different metaheuristic methods for the optimization of shell-and-tube heat exchangers. Computers and Chemical Engineering, 152 , 107403. https://doi.org/10.1016/j.compchemeng.2021.107403

Li, X., Li, P., Lin, Z., & Yang, D. (2014a). Analysis and optimization of metal to composite joints for marine structures. Applied Mechanics and Materials, 556–562 , 91–95. https://doi.org/10.4028/www.scientific.net/AMM.556-562.91

Li, X., Li, P., Lin, Z., & Yang, D.-M. (2014b). Analysis and optimization of composite to steel joints for ships. Sensors and Transducers, 182 (11), 10–16.

Ma, J., Xia, D., Wang, Y., Niu, X., Jiang, S., Liu, Z., & Guo, H. (2022). A comprehensive comparison among metaheuristics (MHs) for geohazard modeling using machine learning: Insights from a case study of landslide displacement prediction. Engineering Applications of Artificial Intelligence, 114 , 105150. https://doi.org/10.1016/j.engappai.2022.105150

Ma, Z., Wu, G., Shuganthan, P. N., Song, A., & Luo, Q. (2023). Performance assessment and exhaustive listing of 500+ nature-inspired metaheuristic algorithms. Swarm and Evolutionary Computation, 77 , 101248. https://doi.org/10.1016/j.swevo.2023.101248

McCall, J. (2005). Genetic algorithms for modelling and optimization. Journal of Computational and Applied Mathematics, 184 , 205–222. https://doi.org/10.1016/j.cam.2004.07.034

Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., & Teller, A. H. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21.

Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95 , 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69 , 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

Mortensen, F. (1998). Development of tools for engineering analysis and design of high-performance FRP-composite structural elements. Ph.d. Thesis, Aalborg Universitetsforlag, Aalborg, Denmark.

Mosallanezhad, B., Hajiaghaei-Keshteli, M., Cornejo, N. R. S., & Calvo, E. Z. R. (2023). An IoMT platform for an integrated sustainable energy-efficient disaster relief supply chain to prevent severity-driven disruptions during pandemics. Journal of Industrial Information Integration, 35 , 100502. https://doi.org/10.1016/j.jii.2023.100502

Nadimi-Shahraki, M. H., Taghian, S., & Mirjalili, S. (2020). An improved grey wolf optimizer for solving engineering problems. Expert Systems with Applications, 166 , 113917. https://doi.org/10.1016/j.eswa.2020.113917

Pham, D. T., & Castellani, M. (2009). The Bees algorithm: Modelling foraging behaviour to solve continuous optimization problems. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science . https://doi.org/10.1243/09544062JMES1494

Pham, D. T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., & Ziadi, M. (2005). Bee algorithm a novel approach to function optimisation. The Manufacturing Engineering Centre, Cardiff University, Technical Note: MEC 0501. https://www.researchgate.net/publication/260985621

Pham, D. T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., & Ziadi, M. (2006). The Bees algorithm—A novel tool for complex optimisation problems. In Intelligent production machines and systems .

Raut, N. P., Kolekar, A. B., & Gombi, S. L. (2021). Optimization techniques for damage detection of composite structure: A review. Materials Today: Proceeding, 45 , 4830–4834. https://doi.org/10.1016/j.matpr.2021.01.295

Rodriguez, R. Q., Picelli, R., Sollero, P., & Pavanello, R. (2012). Structural shape optimization of bonded joints using the ESO method and a honeycomb-like mesh. Journal of Adhesion Science and Technology, 28 , 1451–1466. https://doi.org/10.1080/01694243.2012.698112

Saleh, M. N., Saeedifar, M., Zarouchas, D., & De Freitas, S. T. (2019). Stress analysis of double-lap bi-material joints bonded with thick adhesive. International Journal of Adhesion and Adhesives, 97 , 102480. https://doi.org/10.1016/j.ijadhadh.2019.102480

Schellenberg, C., Lohan, J., & Dimache, L. (2020). Comparison of metaheuristic optimisation methods for grid-edge technology that leverages heat pumps and thermal energy storage. Renewable and Sustainable Energy Reviews, 131 , 109966. https://doi.org/10.1016/j.rser.2020.109966

Silveria, C. L. B., Tabares, A., Faria, L. T., & Franco, J. F. (2021). Mathematical Optimization versus metaheuristic techniques: A performance comparison for reconfiguration of distribution systems. Electric Power Systems Research, 196 , 107272. https://doi.org/10.1016/j.epsr.2021.107272

Suhas, T., Narayana Swamy, G., & Nagaraju, B. P. (2016). An analysis of interlaminar stress on free edges of laminated composites. International Journal of Research in Aeronautical and Mechanical Engineering, 4 (6), 22–26.

Tsai, M., & Morton, J. (1994). An evaluation of analytical and numerical solutions to the single-lap joint. International Journal of Solids and Structures, 31 , 2537–2563. https://doi.org/10.1016/0020-7683(94)90036-1

Tsai, M., & Morton, J. (2010). An investigation Into the stresses in double-lap adhesive joints with laminated composite adherends. International Journal of Solids and Structures, 47 , 3317–3325. https://doi.org/10.1016/j.ijsolstr.2010.08.011

Wang, J.-S., & Li, S.-X. (2019). An improved grey wolf optimizer based on differential evolution and elimination mechanism. Scientific Report, 9 , 7181. https://doi.org/10.1038/s41598-019-43546-3

Wang, Y., Ge, J., Miao, S., Jiang, T., & Shen, X. (2023). Application of hybrid artificial bee colony algorithm based on load balancing in aerospace composite material manufacturing. Expert Systems with Applications, 215 , 119375. https://doi.org/10.1016/j.eswa.2022.119375

Zhou, W., Zhang, R., Ai, S., He, R., Pei, Y., & Fang, D. (2015a). Load distribution in threads of porous metal-ceramic functionally graded composite joints subjected to thermomechanical loading. Composite Structures, 134 , 680–688. https://doi.org/10.1016/j.compstruct.2015.08.113

Zhou, W., Ai, S., Chen, M., Zhang, R., He, R., Pei, Y., & Fang, D. (2015b). Preparation and thermodynamic analysis of the porous ZrO2/(ZrO2+ Ni) functionally graded bolted joint. Composites. Part B, Engineering, 82 , 13–22. https://doi.org/10.1016/j.compositesb.2015.07.018

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Mohammad Ali Arjomandi

Department of Mechanical Engineering, Islamic Azad University, Dezful, Iran

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School of Engineering and Science, Tecnologico de Monterrey, Puebla, Mexico

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Arjomandi, M.A., Mousavi Asl, S.S., Mosallanezhad, B. et al. A metaheuristic-based comparative structure for solving discrete space mechanical engineering problem. Ann Oper Res (2024). https://doi.org/10.1007/s10479-024-06052-y

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DOI : https://doi.org/10.1007/s10479-024-06052-y

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