Industrial Engineering Problems Research Articles

Pied kingfisher optimizer: a new bio-inspired algorithm for solving numerical optimization and industrial engineering problems

Pied kingfisher optimizer: a new bio-inspired algorithm for solving numerical optimization and industrial engineering problems

Optimization of Facility Layout Problems Using Genetic Algorithm

The facility layout problem (FLP) is one of the most important classic industrial engineering and production management problems that have attracted the attention of many researchers over the last few decades. Poor production facility layout planning can result in additional operational costs; one of them is the cost of material handling. Although crucial, FLP is a challenging issue to resolve. A unique method is needed depending on the constraint, case study, and layout type. This research was conducted in order to improve the existing layout of PT. XYZ to minimize material handling costs. The layout type in this case is the Open-field layout problem (OFLP). A genetic algorithm is proposed to optimize the layout. The result is 18.1% material handling costs can be reduced.

Backtracking search algorithm driven by generalized mean position for numerical and industrial engineering problems

Backtracking search algorithm driven by generalized mean position for numerical and industrial engineering problems

Group-individual multi-mode cooperative teaching-learning-based optimization algorithm and an industrial engineering application

This paper modifies the original Teaching-Learning-based Optimization (TLBO) algorithm to present a novel Group-Individual Multi-Mode Cooperative Teaching-Learning-based Optimization (CTLBO) algorithm. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher-learner cooperation strategies in teaching and learning processes. In the preparation phase, teacher-learner interaction and teacher self-learning mechanism are applied. In the teaching phase, class-teaching and performance-based group-teaching operators are applied. In the learning phase, neighbor learning, student self-learning and team-learning strategies are mixed together to form three operators. Experiments indicate that CTLBO has significant improvement in accuracy and convergence ability compared with original TLBO in solving large scale problems and outperforms other compared variants of TLBO in literature and other 9 meta-heuristic algorithms. A large-scale industrial engineering problem—warehouse materials inventory optimization problem is taken as application case, comparison results show that CTLBO can effectively solve the large-scale real problem with 1000 decision variables, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO, revealing that CTLBO can far outperform other algorithms. CTLBO is an excellent algorithm for solving large scale complex optimization issues.

A novel improved teaching and learning-based-optimization algorithm and its application in a large-scale inventory control system

PurposeThe purpose of this paper is to propose a novel improved teaching and learning-based algorithm (TLBO) to enhance its convergence ability and solution accuracy, making it more suitable for solving large-scale optimization issues.Design/methodology/approachUtilizing multiple cooperation mechanisms in teaching and learning processes, an improved TBLO named CTLBO (collectivism teaching-learning-based optimization) is developed. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher–learner cooperation strategies in teaching and learning processes. Applying modularization idea, based on the configuration structure of operators of CTLBO, six variants of CTLBO are constructed. For identifying the best configuration, 30 general benchmark functions are tested. Then, three experiments using CEC2020 (2020 IEEE Conference on Evolutionary Computation)-constrained optimization problems are conducted to compare CTLBO with other algorithms. At last, a large-scale industrial engineering problem is taken as the application case.FindingsExperiment with 30 general unconstrained benchmark functions indicates that CTLBO-c is the best configuration of all variants of CTLBO. Three experiments using CEC2020-constrained optimization problems show that CTLBO is one powerful algorithm for solving large-scale constrained optimization problems. The application case of industrial engineering problem shows that CTLBO and its variant CTLBO-c can effectively solve the large-scale real problem, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO and CTLBO-c, revealing that CTLBO and its variants can far outperform other algorithms. CTLBO is an excellent algorithm for solving large-scale complex optimization issues.Originality/valueThe innovation of this paper lies in the improvement strategies in changing the original TLBO with two-phase teaching–learning mechanism to a new algorithm CTLBO with three-phase multiple cooperation teaching–learning mechanism, self-learning mechanism in teaching and group teaching mechanism. CTLBO has important application value in solving large-scale optimization problems.

Indication, solution, prevention: a holistic approach to financial, industrial engineering, and business problem analysis

Indication, solution, prevention: a holistic approach to financial, industrial engineering, and business problem analysis

Improved reptile search algorithm with novel mean transition mechanism for constrained industrial engineering problems

Improved reptile search algorithm with novel mean transition mechanism for constrained industrial engineering problems

Commentary on: “STOA: A bio-inspired based optimization algorithm for industrial engineering problems” [EAAI, 82 (2019), 148–174] and “Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization” [EAAI, 90 (2020), no. 103541

This commentary concerns two recently developed metaheuristic algorithms, namely the Sooty Tern Optimization Algorithm, and the Tunicate Swarm Algorithm. Both of these algorithms claim computational superiority over other methods based on experimental results on a certain benchmark set. The aim of this note is to aware researchers that this claim is not valid: the proposed algorithms use a zero-bias operator and many of the studied benchmark functions on which they were found superior have optimal solutions located in the zero vector. Moreover, the codes for the methods provided by the authors are not achieving the results reported in the respective publications.

Boosted Harris Hawks gravitational force algorithm for global optimization and industrial engineering problems

Boosted Harris Hawks gravitational force algorithm for global optimization and industrial engineering problems

An Archive-Based Multi-Objective Arithmetic Optimization Algorithm for Solving Industrial Engineering Problems

An Archive-Based Multi-Objective Arithmetic Optimization Algorithm for Solving Industrial Engineering Problems

Критический анализ обобщенной модели строительной системы

Introduction. The established modeling paradigm of a building system as a branch of economy considers individual building processes or groups of interconnected processes, ignoring any previous or subsequent processes or those processes and transactors that are indirectly related to the building system. A critical analysis of a generalized model of the construction system has been performed. The subject of the study is a detailed examination of the construction process. This project takes account of the influence of related, earlier and upcoming stages of the construction process. The research is focused on reducing the number of cost overruns, unmet deadlines in terms of facilities and particular processes, as well as improving the overall organizational and technological reliability of the entire construction system. The purpose of the study is to propose a more progressive and comprehensive vision of the construction system as a branch of economy, the system that takes account of all stakeholders. Materials and methods. The review of studies on this topic was made; current restrictions were identified. The author has found that the most widely spread approach focuses on individual processes or a group of related processes. Results. The author has found that an objective assessment of the influence of previous processes on subsequent ones, an evaluation of the reliability of the whole system, and a correct identification of risks at early stages are unavailable. Further in-depth research using methods of mathematical statistics is needed to provide an objective solution to global engineering problems in the construction industry, which will allow to reduce deviations in the cost and duration of the implementation of facilities and individual processes. Conclusions. To date, a sufficiently comprehensive approach to the analysis of risks/failures in construction systems as a whole has not been presented. The need for a deeper vision of a complex building model that identifies risk factors and chain links has been identified. The use of techniques tested by the author is proposed as a mathematical tool.

Construction of the curriculum for developing the ability to solving complex engineering problems in the field of biotechnology industry

Accurately understanding the features and connotations of complex engineering problems is an important prerequisite for setting graduation requirements, constructing curriculum and designing teaching contents. By discussing the characteristics of complex engineering problems in the biological industry, this paper explored the demands for undergraduates in Yangtze river delta region, summarized the typical jobs and their requirements, and expounded the connotation of complex engineering problems contained in various typical tasks. On this basis, a gradual curriculum system was constructed, which included multiple stages of conceiving, formation and application, to cultivate the ability to solve complex engineering problems in the major of bioengineering. The curriculum coordinated the implementation of deep integration of industry and education, research feed back course construction, course team and advanced courses building up, professional associations covered all crews and students, supporting the ability training of solving complex engineering problems.

A coupled finite volume and material point method for two-phase simulation of liquid–sediment and gas–sediment flows

Mixtures of fluids and granular sediments play an important role in many industrial, geotechnical, and aerospace engineering problems, from waste management and transportation (liquid–sediment mixtures) to dust kick-up below helicopter rotors (gas–sediment mixtures). These mixed flows often involve bulk motion of hundreds of billions of individual sediment particles and can contain both highly turbulent regions and static, non-flowing regions. This breadth of phenomena necessitates the use of continuum simulation methods, such as the material point method (MPM), which can accurately capture these large deformations while also tracking the Lagrangian features of the flow (e.g. the granular surface, elastic stress, etc.).Recent works using two-phase MPM frameworks to simulate these mixtures have shown substantial promise; however, these approaches are hindered by the numerical limitations of MPM when simulating pure fluids. In addition to the well-known particle ringing instability and difficulty defining inflow/outflow boundary conditions, MPM has a tendency to accumulate quadrature errors as materials deform, increasing the rate of overall error growth as simulations progress. In this work, we present an improved, two-phase continuum simulation framework that uses the finite volume method (FVM) to solve the fluid phase equations of motion and MPM to solve the solid phase equations of motion, substantially reducing the effect of these errors and providing better accuracy and stability for long-duration simulations of these mixtures.

An adaptive sampling surrogate model building framework for the optimization of reaction systems

Many industrial engineering problems involve complex formulations and are assisted by simulation tools. Although these tools provide highly accurate solutions, they may not be suitable for large scale problems and for optimization applications. Looking for alternatives to complex formulations that often lead to convergence issues and to time consuming solutions, the use of surrogate modeling for reaction systems is addressed herein. We propose a novel adaptive sampling algorithm that iteratively explores the solution space and incorporates ideas from adaptive sampling, trust region methods, and successive linear programming approaches. The surrogates are iteratively embedded into optimization problems to check feasibility and to collect insights to the following adaptive sampling iteration. The methodology is applied to a reaction system network and the surrogates are built to predict the reactor outputs. The adaptive sampling algorithm builds highly accurate surrogates that can be embedded into the reaction system optimization leading to near optimal solutions.

Hydraulic Dynamic Calculation and Simulation

Hydraulic dynamics is emerging as a basic concept with applications and important implications for many industrial, civil, and environmental engineering problems [...]

Theory and Its Applications to Large-Scale Industrial Engineering Problems of Industrial Engineering Education

Industrial engineering is manufacturing and service Design, analysis of operations and systems, and related to control. Past At the time, an industrial engineer was in a manufacturing plant Working and workers and machines Engaged in functional capacity. Industrial Engineers To eliminate waste in production processes Find ways. a product or service Labor, machinery, materials, information to supply, and efficient systems for integrating energy They create. Industry Engineering every year countless international attracts students. It's great engineering and Committed to business. It is provides undergraduate students with key concepts in the design, planning and optimization of manufacturing and production processes. That is the basis of a Bachelor of Industrial Engineering degree. Petroleum engineer tops our list of highest paying engineering jobs. The primary responsibility of petroleum engineers is to design and develop ways to extract the natural resources of oil and gas from the earth. Elon Musk is a South African-born American who is an Industrial Engineer with Paypal Co-founded and Space Transportation Service He founded the company Space X. He is an electric car Among the early investors in the company Tesla Was and is one company's CEO. SPSS statistics is a SOA, SHO, GWO, PSO, MVO, SCA, GSA, GA, DE. The Cronbach's Alpha Reliability The overall Cronbach's Alpha value for the model is 0.991 which Indicates 70 % reliability. From the literature review, the above 80 % Cronbach's Alpha value model can be considered for analysis.

Организация производства продукции сезонного спроса на промышленных предприятиях

The paper states an industrial engineering problem concerning industrial enterprises manufacturing goods for seasonal industries and activities. We developed a method for selecting optimum enterprise production capacity and forming a production plan that allows for meeting seasonal increases in demand, simultaneously ensuring rational utilisation of human resources, manufacturing equipment and storage facilities. The basis of the method is seeking the production volume function by minimising total expenditures for storage and manufacturing of goods, provided the seasonal demand is fully met over the whole period. We present a numerical implementation algorithm for our method and a calculation example. The main stages of the algorithm are as follows: setting the demand function; computing the core production capacity as dictated by steady equipment utilisation; varying the production plan until the minimum total expenditure condition is met. We ran a series of simulations that allowed us to establish the function describing the optimum production capacity as a fraction of the core capacity for various ratios of manufacturing expenditures to storage expenditures, and as a function of the overpayment rate for additional capacities

STOA: A bio-inspired based optimization algorithm for industrial engineering problems

This paper presents a bio-inspired algorithm called Sooty Tern Optimization Algorithm (STOA) for solving constrained industrial problems.The main inspiration of this algorithm is the migration and attacking behaviors of sea bird sooty tern in nature. These two steps are implemented and mathematically modeled to emphasize exploitation and exploration in a given search space. The proposed algorithm is compared with nine well-known bio-inspired algorithms over 44 benchmark test functions. The analysis of convergence behaviors and computational complexity of the proposed algorithm have been evaluated. Furthermore, to demonstrate its applicability it is then employed to solve six constrained industrial applications. The outcomes of experiment reveal that the proposed algorithm is able to solve challenging constrained problems and is very competitive compared with other optimization algorithms.

Discrete simulation-based optimization methods for industrial engineering problems: A systematic literature review

In recent years, some attention has been driven to modeling, simulation, and optimization techniques capable of representing and improving discrete event systems. These techniques can support decision making helping to determine the best scenario on a combinatorial search space with stochastic variables. This paper presents findings from a systematic literature review of discrete simulation-based optimization applied to industrial engineering problems. It indicates the most frequent contexts, problems, methods, tools, and intended results of discrete-simulation based studies published in the last 25 years (1991–2016) in scientific journals and conference proceedings. The four research questions presented a scenario to help practitioners and researchers to develop simulation optimization projects for industrial engineering problems. A conclusion presented the gap and prospects found during the writing of the research.

Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems

This paper presents a novel bio-inspired algorithm called Seagull Optimization Algorithm (SOA) for solving computationally expensive problems. The main inspiration of this algorithm is the migration and attacking behaviors of a seagull in nature. These behaviors are mathematically modeled and implemented to emphasize exploration and exploitation in a given search space. The performance of SOA algorithm is compared with nine well-known metaheuristics on forty-four benchmark test functions. The analysis of computational complexity and convergence behaviors of the proposed algorithm have been evaluated. It is then employed to solve seven constrained real-life industrial applications to demonstrate its applicability. Experimental results reveal that the proposed algorithm is able to solve challenging large-scale constrained problems and is very competitive algorithm as compared with other optimization algorithms.