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Industrial Mathematics in Management and Engineering
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Industrial Mathematics in Management and Engineering

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 20126

Special Issue Editors

Prof. Dr. Aldina Correia

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Guest Editor
ESTG - School of Management and Technology, P.PORTO - Polytechnic of Porto, CIICESI – Center for Research and Innovation in Business Sciences and Information Systems, Rua do Curral, Casa do Curral, Margaride, 4610-156 Felgueiras, Portugal
Interests: multivariate data analysis; direct search optimization; nonlinear programming
Dr. Eliana Costa e Silva

E-Mail Website
Guest Editor
CIICESI, ESTG, Politécnico do Porto, 4610-156 Felgueiras, Portugal
Interests: robotic; optimization; multivariate data analysis and industrial mathematics applications
Dr. Ana Isabel Borges

E-Mail Website
Guest Editor
ESTG - School of Management and Technology, P.PORTO - Polytechnic of Porto, CIICESI – Center for Research and Innovation in Business Sciences and Information Systems, Rua do Curral, Casa do Curral, Margaride, 4610-156 Felgueiras, Portugal
Interests: longitudinal and survival data analysis and industrial mathematics applications

Special Issue Information

Dear Colleagues,

Industrial mathematics focuses on solving problems that emerge in the most diverse organizations, such as industries, companies, government sectors, etc. It aims to find the best solution, helping in the decision-making process, whether in terms of financial, environmental sustainability or efficiency of any kind. With the increasing complexity and sophistication of modern industry, people who are able to understand technical issues, who are able to formulate precise and exact mathematical models, who know how to implement solutions, using the latest technological techniques, and who are able to transmit in a simple way these ideas to workers, managers, engineers, etc., are sorely needed by many organizations and companies.

Industrial mathematics is an interdisciplinary field. In addition to knowledge of mathematics, statistics and optimization, it includes knowledge from other areas, such as business, computer science and engineering, and it is a branch of applied mathematics.

These skills are increasingly valued in the industry but are rare both in research centers and in recent graduates.

This Special Issue intends to disseminate research and applications of Mathematics and Statistics, with particular interest in applications of mathematical techniques in industrial and/or real problems.

Topics of interest include but are not limited to the following:

  • Data analysis;
  • Statistical applications;
  • Optimization models and methods;
  • Applied mathematical methods;
  • Success case studies of industrial mathematics applications;
  • Applied mathematical methods in management, sciences and engineering.

Prof. Dr. Aldina Correia
Dr. Eliana Costa e Silva
Dr. Ana Isabel Borges
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multivariate real-data analysis
  • statistical applications
  • optimization models and methods
  • industrial mathematics applications
  • mathematics for management, sciences and engineering

Published Papers (12 papers)

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Research

34 pages, 1583 KiB  
Article
A Study to Identify Long-Term Care Insurance Using Advanced Intelligent RST Hybrid Models with Two-Stage Performance Evaluation
by You-Shyang Chen, Ying-Hsun Hung and Yu-Sheng Lin
Mathematics 2023, 11(13), 3010; https://doi.org/10.3390/math11133010 - 06 Jul 2023
Cited by 1 | Viewed by 1033
Abstract
With the motivation of long-term care 2.0 plans, forecasting models to identify potential customers of long-term care insurance (LTCI) are an important and interesting issue. From the limited literature, most past researchers emphasize traditional statistics techniques to address this issue; however, these are [...] Read more.
With the motivation of long-term care 2.0 plans, forecasting models to identify potential customers of long-term care insurance (LTCI) are an important and interesting issue. From the limited literature, most past researchers emphasize traditional statistics techniques to address this issue; however, these are lacking in some areas. For example, intelligent hybrid models for LTCI are lacking, performance measurement of components for hybrid models is lacking, and research results for interpretative capacities are lacking, resulting in a black box scenario and difficulty in making decisions, and the gap between identifying potential customers and constructing hybrid models is unbridged. To solve the shortcomings mentioned above, this study proposes some advanced intelligent single and hybrid models; the study object is LTCI customers. The proposed hybrid models were used on the experimental dataset collected from real insurance data and possess the following advantages: (1) The feature selection technique was used to simplify variables for the purpose of improving model performance. (2) The performance of hybrid models was evaluated against some machine learning methods, including rough set theory, decision trees, multilayer perceptron, support vector machine, genetic algorithm, random forest, logistic regression, and naive Bayes, and sensitivity analysis was performed in terms of accuracy, coverage, rules number, and standard deviation. (3) We used the C4.5 algorithm of decision trees and the LEM2 algorithm of rough sets to extract and provide valuably comprehensible decisional rules as decision-making references for the interested parties for their varied benefits. (4) We used post hoc testing to verify the significant difference in groups. Conclusively, this study effectively identifies potential customers for their key attributes and creates a decision rule set of knowledge for use as a reference when solving practical problems by forming a structured solution. This study is a new trial in the LTCI application field and realizes novel creative application values. Such a hybrid model is rarely seen in identifying LTCI potential customers; thus, the study has sufficient application contribution and managerial benefits to attract much concern from the interested parties. Full article
(This article belongs to the Special Issue Industrial Mathematics in Management and Engineering)
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23 pages, 635 KiB  
Article
Optimizing Vehicle Repairs Scheduling Using Mixed Integer Linear Programming: A Case Study in the Portuguese Automobile Sector
by Fátima Pilar, Eliana Costa e Silva and Ana Borges
Mathematics 2023, 11(11), 2575; https://doi.org/10.3390/math11112575 - 04 Jun 2023
Cited by 1 | Viewed by 1352
Abstract
This study investigates the scheduling of mechanical repairs performed at a Portuguese firm in the automobile sector. The aim is to reduce the amount of time that vehicles spend inactive between interventions by developing a mathematical model that takes into account the available [...] Read more.
This study investigates the scheduling of mechanical repairs performed at a Portuguese firm in the automobile sector. The aim is to reduce the amount of time that vehicles spend inactive between interventions by developing a mathematical model that takes into account the available resources and mechanics, the necessary interventions, and the time required for each repair. To accomplish this, a mixed-integer linear programming (MILP) model was employed, incorporating various variables to schedule interventions, allocate resources, and determine start times for each vehicle. The problem was formulated using the AMPL modeling language, and real-world instances of the problem, derived from data provided by the company, were solved using the Gurobi solver. Results show that the developed model significantly improves the scheduling of the vehicles’ repairs at the firm, leading to a reduction of 67% on average in the downtime of the vehicles and allowing an automatic correct schedule of the mechanical interventions. Moreover, the comparison of the scheduling obtained from the developed model and the firm’s procedure shows that interventions on vehicles arriving at the repair shop are mostly repaired on the day of entry, allowing for quicker delivery to the customer. Full article
(This article belongs to the Special Issue Industrial Mathematics in Management and Engineering)
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19 pages, 861 KiB  
Article
Divide and Conquer: A Location-Allocation Approach to Sectorization
by Cristina Lopes, Ana Maria Rodrigues, Valeria Romanciuc, José Soeiro Ferreira, Elif Göksu Öztürk and Cristina Oliveira
Mathematics 2023, 11(11), 2553; https://doi.org/10.3390/math11112553 - 02 Jun 2023
Viewed by 1143
Abstract
Sectorization is concerned with dividing a large territory into smaller areas, also known as sectors. This process usually simplifies a complex problem, leading to easier solution approaches to solving the resulting subproblems. Sectors are built with several criteria in mind, such as equilibrium, [...] Read more.
Sectorization is concerned with dividing a large territory into smaller areas, also known as sectors. This process usually simplifies a complex problem, leading to easier solution approaches to solving the resulting subproblems. Sectors are built with several criteria in mind, such as equilibrium, compactness, contiguity, and desirability, which vary with the applications. Sectorization appears in different contexts: sales territory design, political districting, healthcare logistics, and vehicle routing problems (agrifood distribution, winter road maintenance, parcel delivery). Environmental problems can also be tackled with a sectorization approach; for example, in municipal waste collection, water distribution networks, and even in finding more sustainable transportation routes. This work focuses on sectorization concerning the location of the area’s centers and allocating basic units to each sector. Integer programming models address the location-allocation problems, and various formulations implementing different criteria are compared. Methods to deal with multiobjective optimization problems, such as the ϵ-constraint, the lexicographic, and the weighted sum methods, are applied and compared. Computational results obtained for a set of benchmarking instances of sectorization problems are also presented. Full article
(This article belongs to the Special Issue Industrial Mathematics in Management and Engineering)
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15 pages, 794 KiB  
Article
Automatic Completion of Data Gaps Applied to a System of Water Pumps
by Ricardo Enguiça and Filipa Soares
Mathematics 2023, 11(7), 1707; https://doi.org/10.3390/math11071707 - 03 Apr 2023
Viewed by 701
Abstract
We consider a time series with real data from a water lift station, equipped with three water pumps which are activated and deactivated depending on certain starting and halting thresholds. Given the water level and the number of active pumps, both read every [...] Read more.
We consider a time series with real data from a water lift station, equipped with three water pumps which are activated and deactivated depending on certain starting and halting thresholds. Given the water level and the number of active pumps, both read every 5 min, we aim to infer when each pump was activated or deactivated. To do so, we build an algorithm that sets a hierarchy of criteria based on the past and future of a given interval to identify which thresholds have been crossed during that interval. We then fill the gaps between the 5 min time steps, modeling the water level continuously with a piecewise linear function. This filling takes into account not only every water level reading and every previously identified change of status, but also the fact that activation and deactivation of a pump has no immediate effect on water level. This allows for the fulfillment of the ultimate objective of the problem in its real context, which is to provide the water management company an estimate of how long each pump has been working. Additionally, our estimates correct the errors contained in the time series regarding the number of active pumps. Full article
(This article belongs to the Special Issue Industrial Mathematics in Management and Engineering)
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17 pages, 5093 KiB  
Article
Application of the k-Prototype Clustering Approach for the Definition of Geostatistical Estimation Domains
by Heber Hernández, Elisabete Alberdi, Aitor Goti and Aitor Oyarbide-Zubillaga
Mathematics 2023, 11(3), 740; https://doi.org/10.3390/math11030740 - 01 Feb 2023
Viewed by 2409
Abstract
The definition of geostatistical domains is a stage in the estimation of mineral resources, in which a sample resulting from a mining exploration process is divided into zones that show homogeneity or minimal variation in the main element of interest or mineral grade, [...] Read more.
The definition of geostatistical domains is a stage in the estimation of mineral resources, in which a sample resulting from a mining exploration process is divided into zones that show homogeneity or minimal variation in the main element of interest or mineral grade, having geological and spatial meaning. Its importance lies in the fact that the quality of the estimation techniques, and therefore, the correct quantification of the mineral resource, will improve in geostatistically stationary areas. The present study seeks to define geostatistical domains of estimation for a mineral grade, using a non-traditional approach based on the k-prototype clustering algorithm. This algorithm is based on the k-means paradigm of unsupervised machine learning, but it is exempt from the one-time restriction on numeric data. The latter is especially convenient, as it allows the incorporation of categorical variables such as geological attributes in the grouping. The case study corresponds to a hydrothermal gold deposit of high sulfidation, located in the southern zone of Peru, where estimation domains are defined from a historical record of data recovered from 131 diamond drill holes and 37 trenches. The characteristics directly involved were the gold grade (Au), silver grade (Ag), type of hydrothermal alteration, and type of mineralization. The results obtained showed that clustering with k-prototypes is an efficient approach and can be used as an alternative or complement to the traditional methodology. Full article
(This article belongs to the Special Issue Industrial Mathematics in Management and Engineering)
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11 pages, 282 KiB  
Article
Resource Allocation Scheduling with Position-Dependent Weights and Generalized Earliness–Tardiness Cost
by Yi-Chun Wang, Si-Han Wang and Ji-Bo Wang
Mathematics 2023, 11(1), 222; https://doi.org/10.3390/math11010222 - 02 Jan 2023
Cited by 6 | Viewed by 1054
Abstract
Under just-in-time production, this paper studies a single machine common due-window (denoted by CONW) assignment scheduling problem with position-dependent weights and resource allocations. A job’s actual processing time can be determined by the resource assigned to the job. A resource allocation model is [...] Read more.
Under just-in-time production, this paper studies a single machine common due-window (denoted by CONW) assignment scheduling problem with position-dependent weights and resource allocations. A job’s actual processing time can be determined by the resource assigned to the job. A resource allocation model is divided into linear and convex resource allocations. Under the linear and convex resource allocation models, our goal is to find an optimal due-window location, job sequence and resource allocation. We prove that the weighted sum of scheduling cost (including general earliness–tardiness penalties with positional-dependent weights) and resource consumption cost minimization is polynomially solvable. In addition, under the convex resource allocation, we show that scheduling (resp. resource consumption) cost minimization is solvable in polynomial time subject to the resource consumption (resp. scheduling) cost being bounded. Full article
(This article belongs to the Special Issue Industrial Mathematics in Management and Engineering)
20 pages, 6438 KiB  
Article
Research on a Risk Early Warning Mathematical Model Based on Data Mining in China’s Coal Mine Management
by Kai Yu, Lujie Zhou, Pingping Liu, Jing Chen, Dejun Miao and Jiansheng Wang
Mathematics 2022, 10(21), 4028; https://doi.org/10.3390/math10214028 - 30 Oct 2022
Cited by 6 | Viewed by 2167
Abstract
The degree of informatization of coal mine safety management is becoming higher and higher, and a large amount of information is generated in this process. How to convert the existing information into useful data for risk control has become a challenge. To solve [...] Read more.
The degree of informatization of coal mine safety management is becoming higher and higher, and a large amount of information is generated in this process. How to convert the existing information into useful data for risk control has become a challenge. To solve this challenge, this paper studies the mathematical model of coal mine risk early warning in China based on data mining. Firstly, the coal mine risk data was comprehensively analyzed to provide basic data for the risk prediction model of data mining. Then, the adaptive neuro-fuzzy inference system (ANFIS) was optimized twice to build the coal mine risk prediction model. By optimizing the calculation method of the control chart, the coal mine risk early warning system was proposed. Finally, based on the coal mine risk early warning model, the software platform was developed and applied to coal mines in China to control the risks at all levels. The results show that the error of the optimized ANFIS was reduced by 66%, and the early warning error was reduced by 57%. This study aimed to provide implementation methods and tools for coal mine risk management and control, and data collected has reference significance for other enterprises. Full article
(This article belongs to the Special Issue Industrial Mathematics in Management and Engineering)
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22 pages, 2399 KiB  
Article
Working Capital Management as Crucial Tool for Corporate Performance in the Transport Sector: A Case Study of Slovakia and the Czech Republic
by Jaroslav Mazanec
Mathematics 2022, 10(15), 2584; https://doi.org/10.3390/math10152584 - 25 Jul 2022
Cited by 2 | Viewed by 2555
Abstract
Working capital management is one of the decisive factors in increasing business performance through the efficient use of current assets such as inventories, receivables, funds, and current liabilities. The primary aim is to identify how working capital management using a wide range of [...] Read more.
Working capital management is one of the decisive factors in increasing business performance through the efficient use of current assets such as inventories, receivables, funds, and current liabilities. The primary aim is to identify how working capital management using a wide range of liquidity and activity indicators affects the corporate performance of transport companies broken down by company size into small, medium, large, and very large companies in Slovakia and the Czech Republic using multiple linear regression analysis with achieving competitive R-square as a relevant statistical metric compared to other models from previous research. Our research focuses on a different industry than the traditional production industry. Descriptive statistics show that more than half of the assets are impelled assets in the corporate finances of transport companies. We deal with the impact of working capital management on corporate performance, considering the corporate size. This output delivers specific findings for small, medium, large, and very large businesses separately. All multiple linear regression models for estimating corporate performance are proposed for transport companies in the Czech and Slovak Republics. The results show that liquidity has a negative impact, in contrast to activity indicators except for DPO, on corporate performance in Czech transport companies. On the other hand, Slovak small, medium, and large enterprises must effectively manage free cash and cash equivalents, too. However, activity indicators, except DRO for an aggregated group of large and very large enterprises, also harm business performance. These outputs are beneficial for business management and making relevant decisions to increase business performance, the models identify the strengths and weaknesses of working capital management. In general, this research helps to make specific decisions focused on receivables, inventory management, and cash management as part of working capital management. Full article
(This article belongs to the Special Issue Industrial Mathematics in Management and Engineering)
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17 pages, 2329 KiB  
Article
A Robust Single-Machine Scheduling Problem with Two Job Parameter Scenarios
by Gang Xuan, Win-Chin Lin, Shuenn-Ren Cheng, Wei-Lun Shen, Po-An Pan, Chih-Ling Kuo and Chin-Chia Wu
Mathematics 2022, 10(13), 2176; https://doi.org/10.3390/math10132176 - 22 Jun 2022
Cited by 2 | Viewed by 1200
Abstract
In many real-world environments, machine breakdowns or worker performance instabilities cause uncertainty in job processing times, while working environment changes or transportation delays will postpone finished production for customers. The factors that impact the task processing times and/or deadlines vary. In view of [...] Read more.
In many real-world environments, machine breakdowns or worker performance instabilities cause uncertainty in job processing times, while working environment changes or transportation delays will postpone finished production for customers. The factors that impact the task processing times and/or deadlines vary. In view of the uncertainty, job processing times and/or job due dates cannot be fixed numbers. Inspired by this fact, we introduce a scenario-dependent processing time and due date concept into a single-machine environment. The measurement minimizes the total job tardiness in the worst case. The same problem without the presence of processing time uncertainty has been an NP-hard problem. First, to solve this difficult model, an exact method, including a lower bound and some dominance properties, is proposed. Next, three scenario-dependent heuristic algorithms are proposed. Additionally, a population-based iterated greedy algorithm is proposed in the hope of increasing the diversity of the solutions. The results of all related algorithms are determined and compared using the appropriate statistical tools. Full article
(This article belongs to the Special Issue Industrial Mathematics in Management and Engineering)
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18 pages, 803 KiB  
Article
Evaluating E-Teaching Adoption Criteria for Indian Educational Organizations Using Fuzzy Delphi-TOPSIS Approach
by Tsun-Yu Huang, Wen-Kuo Chen, Venkateswarlu Nalluri and Thao-Trang Huynh-Cam
Mathematics 2022, 10(13), 2175; https://doi.org/10.3390/math10132175 - 22 Jun 2022
Cited by 8 | Viewed by 1640
Abstract
Due to advances in information and communication technology, e-teaching has become increasingly popular and is in high demand by educational organizations. During the lockdown period of COVID-19 especially, e-teaching provided prior solutions to address the pressing need for monitoring students’ learning progress. However, [...] Read more.
Due to advances in information and communication technology, e-teaching has become increasingly popular and is in high demand by educational organizations. During the lockdown period of COVID-19 especially, e-teaching provided prior solutions to address the pressing need for monitoring students’ learning progress. However, in many developing countries, it is apparent that a wide variety of issues are related to e-teaching adoption. Although the implementation issues associated with e-teaching have been addressed in the existing research literature and in practice for many years, from the available research, the evaluation of e-teaching adoption criteria and ranking using fuzzy theory has been ignored. Therefore, the present research aims to evaluate and rank the criteria for e-teaching adoption through Fuzzy Delphi and Fuzzy TOPSIS. A total of four criteria and twelve sub-criteria for e-teaching adoption were determined based on a systematic literature review and professors’ opinions in India. In addition, the Fuzzy Delphi method was employed to finalize the criteria, and the Fuzzy TOPSIS method was employed for ranking the alternatives. The assessment results showed that among the identified alternatives, the “share the technology with other organizations” and “course integration with technology” were the top-ranked alternatives for improving e-teaching adoption. An understanding of these conceptual alternatives can encourage the adoption of e-teaching in educational organizations. Full article
(This article belongs to the Special Issue Industrial Mathematics in Management and Engineering)
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21 pages, 786 KiB  
Article
On the Dynamics in Decoupling Buffers in Mass Manufacturing Lines: A Stochastic Approach
by Gilberto Pérez-Lechuga, Francisco Venegas-Martínez, Marco A. Montufar-Benítez and Jaime Mora-Vargas
Mathematics 2022, 10(10), 1686; https://doi.org/10.3390/math10101686 - 14 May 2022
Cited by 1 | Viewed by 1612
Abstract
This paper analyzes the flow of the contents of interleaved buffers with continuously operating machines in a mass production line. Under this framework, the products to be manufactured advance from station to station to receive a physical–chemical transformation that adds value as they [...] Read more.
This paper analyzes the flow of the contents of interleaved buffers with continuously operating machines in a mass production line. Under this framework, the products to be manufactured advance from station to station to receive a physical–chemical transformation that adds value as they progress in the process. The existence of decoupling buffers between operations (between two consecutive workstations) is a common practice in order to alleviate the pressure that is ahead due to the lack of synchronization between consecutive operations, which causes leisure and/or bottlenecks in the system. In this proposal, we analyze the dynamics of a mass manufacturing line with intermediate decoupling buffers. To do that, we use a regenerative stochastic process approach to build a model where the products stored in each buffer are taken all at once by the consecutive machine. In a second approach, we use a homogeneous birth–death process with constant input–output and assume that the products are taken one by one by the consecutive machine. Finally, we use a non-homogeneous birth–death process to analyze the dynamics of a system whose inputs and outputs depend on time. These proposals are accompanied by numerical examples that illustrate its practical utility. Full article
(This article belongs to the Special Issue Industrial Mathematics in Management and Engineering)
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13 pages, 302 KiB  
Article
The Dynamics between Structural Conditions and Entrepreneurship in Europe: Feature Extraction and System GMM Approaches
by Ana Borges, Aldina Correia, Eliana Costa e Silva and Glória Carvalho
Mathematics 2022, 10(8), 1349; https://doi.org/10.3390/math10081349 - 18 Apr 2022
Viewed by 1389
Abstract
Structural conditions and population characteristics of countries have been identified in the literature as factors for an individual to become, or to have intentions of becoming, an entrepreneur. However, this is still a subject under research, which has become increasingly relevant and could [...] Read more.
Structural conditions and population characteristics of countries have been identified in the literature as factors for an individual to become, or to have intentions of becoming, an entrepreneur. However, this is still a subject under research, which has become increasingly relevant and could be crucial in the current challenges of European countries. In this work, the factors for entrepreneurial intentions and entrepreneurship activity are studied. More precisely, the structural conditions of European countries, which has changed over the last two decades, is analysed. The aim is to describe this behaviour and to state the main conditions for developing entrepreneurship activities and the intentions to become an entrepreneur. To achieve this purpose, feature extraction, namely, principal component analysis and dynamic longitudinal approaches are used. In particular, we propose that the system-generalised method of moments (GMM) model is adequate in this situation. The results suggest that the structure of the European framework conditions for entrepreneurship, obtained using the Factor Analysis year by year, is quite diversified until 2008, while after 2008, it is more stable. Moreover, it is concluded that the conditions associated with entrepreneurial intentions and entrepreneurial activity differ between these two time periods. Hence, the dynamic aspect of the structural conditions that affect entrepreneurial activities or intentions should be acknowledged. Full article
(This article belongs to the Special Issue Industrial Mathematics in Management and Engineering)
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