International Journal of Industrial Engineering: Theory, Applications and Practice

Optimal Resource Allocation Model for Multi-Function Radar on Navy Warships

Jinho Lee, Wonseok Choi, Duehee Lee, Young-Man Park, Hyunjin Cho, Suk-Moon Chung — Wed, 16 Oct 2024 00:00:00 -0700

Efficiently detecting targets using radar on a warship is critical for defending the warship itself at the initial stage of engagement. To cope with various uncertain threats (or targets), radar resources must be used efficiently because of their limited availability in terms of power, dwell time, and bandwidth. In this study, we develop an optimal multi-function radar resource allocation model in which we maximize the survival probability of a warship from all detected targets under limited resources allocated to the subsystems. The proposed problems are convex optimization models with concave objective functions. We propose two exact (polynomial and pseudo-polynomial time) algorithms that optimally solve the problem via the well-known KKT conditions. In addition, we establish special conditions for problem instances in which resources are allocated equally to all subsystems. Computational experiments show that the algorithms not only guarantee optimality but are also computationally intensive. Finally, we perform regression analysis to investigate the relationship between resource allocation and targets’ threat levels, and it shows that threat level influences more on the resource allocation than the exponent of the detection probability function.

Using The Flexible Analytic Hierarchy Process Method to Solve The Emergency Decision-Making of Public Health Emergency of International Concern (PHEIC)

Hsiang-Yu Chung, Kuei Hu Chang — Wed, 16 Oct 2024 00:00:00 -0700

The occurrence of a public health emergency of international concern (PHEIC) can lead to massive deaths, economic recession, and changes in the lifestyles of people in various countries. Addressing the problem of a public health emergency involves multiple experts and criteria, making it a multi-expert and multi-criteria decision-making problem. The assessment information of the criteria simultaneously includes complete, incomplete and hesitant fuzzy linguistic information in PHEIC problems. However, typical calculation methods cannot process the incomplete information and hesitant fuzzy linguistic information associated with PHEIC problems. In order to overcome these issues, this paper proposed a novel flexible AHP method to solve PHEIC problems. A numerical case study on public health emergency decision-making for COVID-19 was adopted to verify the effectiveness and correctness of the proposed flexible analytic hierarchy process (AHP) method. The numerical simulation results were also compared with the simple additive weighting (SAW) method, the traditional AHP method, the fuzzy set method, and the fuzzy AHP method. The simulation results show that the proposed method can provide a more reasonable and flexible decision analysis.

Risk Assessment of Distributed Network Data Security Based on SimHash Algorithm

Yanbin Tang — Wed, 16 Oct 2024 00:00:00 -0700

Distributed network data has the characteristics of distribution and concurrency, which leads to the complexity of data processing and reduces the effectiveness of security risk assessment. Therefore, a security risk assessment method for distributed network data based on the SimHash algorithm is proposed. The actual support of the distributed network data set is reconstructed by probability distortion technology, and the data mining results after probability transformation are obtained by using the data mining method of random disturbance. In order to avoid the existence of duplicate information and redundant data, duplicate distributed network data is removed by calculating text similarity. Finally, the SimHash algorithm is used to calculate the hash value before and after the distributed network data attack, calculate the security risk assessment value of the distributed network data, and complete the security risk assessment. The analysis of the experimental results shows that the proposed method effectively improves the reliability of risk assessment of distributed network data and reduces the communication overhead of the assessment, with the maximum communication overhead not exceeding 10 bits. Therefore, the research method has high effectiveness and practicability.

Exploring the Design Guidelines of Large Screen UI for Optimal Viewing Experience on Smart TV

Donghyun Kim, Youngjae Im — Wed, 16 Oct 2024 00:00:00 -0700

With the prevalence of large-screen smart TVs, viewing experiences are becoming more interactive. This technological advance makes an increase in information density and diverse layouts. This study aims to derive ergonomic design guidelines for TV UI to optimize the viewing experience. Fifteen subjects participated in the main test, performing tasks consisting of visual navigation and remote manipulation according to twelve different alternatives. The experimental variables were selected as screen size, information quantity and layout. The dependent measures were collected on performance time, visual cognitive satisfaction and manipulation satisfaction. The results revealed that screen size, information quantity, and layout had significant effects on visual satisfaction. Larger screens with more information enhanced visual cognitive satisfaction. Screen size and information quantity influenced on manipulation satisfaction. Meanwhile, information quantity and layout had greater impacts on user performance. This research provides appropriate design guidelines for smart TV UI and practical implications for product developers.

Integrating BIM (Building Information Modeling) into Lean Project Construction: A Novel Fuzzy Methodology

Cheikh Moustapha Djamil Seck, Aidin Delgoshaei, Mohd Zuhri Bin Mohamed Yusoff — Wed, 16 Oct 2024 00:00:00 -0700

The construction industry has recognized the potential benefits of Lean methodology for improving project outcomes. However, the full utilization of Lean principles remains challenging in the industry. This study aims to address this issue by exploring integrating Building Information Modeling (BIM) into Lean Construction practices to enhance project success. The concept of success in construction projects encompasses various dimensions, including sustainability, time efficiency, cost-effectiveness, and budget adherence. This study focuses on measuring success through the lens of sustainability, emphasizing the need to make construction projects more sustainable and effective. To achieve this, a survey was conducted to collect data on sustainable factors that influence project success. The research analysis focused on a selected company's capacity to utilize its existing BIM software to improve critical sustainable factors. The sustainable factors identified as crucial for project success were the relationship with subcontractors, site cleanliness and tidiness, safety, and solid waste production. Projects can enhance their overall success and sustainability by effectively managing these factors. To evaluate different BIM software options, a comprehensive review of available software was undertaken based on their ability to address the identified sustainable factors. The Fuzzy Weighted LC-BIM TOPSIS was proposed as the evaluation method to determine the best BIM software that could maximize project success. The findings of this study highlight the importance of considering sustainable factors in construction projects and leveraging BIM software to manage them effectively. Integrating BIM into Lean Construction practices can improve project success by enhancing collaboration, streamlining processes, and optimizing resource allocation. The implications of this study suggest that construction industry stakeholders should prioritize adopting lean construction principles and leveraging BIM as a valuable tool to enhance project success. Integrating BIM and Lean methodologies can drive sustainable practices, optimize project performance, and contribute to the long-term success of the construction industry.

Interval Robust Design on Quality Improvement for Non-Normal and Contaminated Responses

Atakan Baydar, Melis Zeybek, Elif Kozan, Agah Kozan — Wed, 16 Oct 2024 00:00:00 -0700

The basis of robust parameter design is the creation of a design that can resist the negative effects caused by uncontrollable or difficult-to-control external and environmental factors, which affect the product parameters in achieving product design during product realization activities. Robustness is the ability of a product or process to be least affected by variabilities caused by external factors. The success of the response surface methodology generally depends on a model chosen to fit the data distribution. Making incorrect assumptions regarding data distribution when creating response surface models can affect the effectiveness of the quality improvement strategy used. Non-normal or contaminated data is a common phenomenon in quality improvement applications. Although non-normal data is common in robust parameter applications, it is often the case that users ignore the underlying distribution shape of the data at the modeling stage and use normal theory techniques naively. This study proposes a dual response surface approach based on robust confidence intervals for cases where the experimental data do not meet normality assumptions or have contaminated data distribution. A new dual response surface methodology is proposed based on modeling the confidence interval, confidence interval, and confidence interval formulations with the response surface methodology. All the proposed methods make the process median unbiased for the mean using the skewness of the experimental data. Two well-known experimental design studies are used to demonstrate the procedure and its advantages.

Investigation of Non-Value-Added Activities to Reduce Lead Time in The Mass-Customized Glass Process Industry

Arunmozhi Balamurugan, Sudhakarapandian R — Wed, 16 Oct 2024 00:00:00 -0700

Increasing levels of customization in customer orders leads to numerous new challenges in the industry. One significant aspect is achieving the optimal lead time to meet customer demands. The reconfigurable hybrid permutation mass customization problem (RHPMCP), a subset of flow shop problems with significant application in the mass-customized tuff glass process industry, is the primary focus of this study. We categorize this study into two phases; the first phase investigates the non-value-added activities to identify which output parameter (i.e., Makespan, flow-time, idle-time, and efficiency) significantly affects the schedule. We employ a new decision-making method for investigation by integrating a Z-number-based Consistent Fuzzy Analytic Hierarchy Process (Z-CFAHP) and a Z-number-based fuzzy Combined Compromise Solution (Z-FCoCoSo). The second phase, using Analytical Batch Enhanced State-Action-Reward-State-Action Optimization (ABESO), reduces the identified high-impact output parameter makespan to obtain the optimal lead time. The proposed approaches are validated by sensitivity analysis for decision-making, and scheduling problems are compared to the existing scheduling rule in the real-time tuff glass process industry. This research provides a practical solution for optimizing lead time in the tuff glass process industry, demonstrating the effectiveness of the proposed method.

Neutrosophic Optimization in Transportation Networks: Navigating Transportation Challenges with and Without Warehouses

Kalaivani Kaspar, Palanivel Kaliyaperumal — Wed, 16 Oct 2024 00:00:00 -0700

Neutrosophic set theory is an extension of classical set theory that deals with uncertainty, imprecision, and indeterminacy by introducing three degrees of membership, and further it offers the researchers a wide range of applications in numerous disciplines. In general, neutrosophic sets (NS) focus on the hesitant, ambiguous, and uncertain data present in real-world mathematical challenges. They are an enhanced form of crisp, fuzzy, and intuitionistic fuzzy sets. The simplest form of the neutrosophic set is a single-valued set, and it provides better outcomes than a regular NS. Recent research enhances the applications of single-valued neutrosophic sets (SVNS) through approaches like MCDM, MADM, MCGDM, and many other extensively deployed fields. This study has proposed two different ranking methods, namely the Removal Area Method (RAM) and the Mean Interval Method (MIM), to de-neutrosify the single-valued trapezoidal neutrosophic numbers (SVTNNS). These methods offer significantly greater accuracy compared to conventional transportation problem (TP) approaches, particularly when evaluated using various ranking functions through (SVTNNS). Further, the TP has been addressed with or without warehouses to determine the optimum cost for the proposed problems, where all the source, demand, and cost representations are treated as SVTNNS. The quantitative analysis of the transportation problem, both with and without the inclusion of warehouses, yielded optimal results of 3,684.83 and 263,703.64 using the MIM and RAM ranking methods, respectively. Thus, the proposed ranking methods demonstrate their innovation and effectiveness by outperforming traditional approaches in transportation problems, as validated through comprehensive numerical examples. Additionally, a thorough comparison with conventional methods highlights the impact of the new techniques in achieving optimal solutions. The detailed results, further supported by sensitivity analysis, provide strong evidence that the optimal solutions are robust and justified, confirming the reliability and accuracy of the proposed methods.

Wholesale Price and Buyback Contracts under Fairness Concerns and Asymmetric Information

Pooria Shekasteband, Mona Ahmadirad, Shervin Asadzadeh — Wed, 16 Oct 2024 00:00:00 -0700

This paper enhances supply chain coordination by incorporating behavioural considerations into contractual mechanisms. We analyze the performance of wholesale price and buyback contracts, highlighting the impact of fairness concerns on supply chain coordination. Our findings indicate the significant influence of supplier and retailer fairness concerns on order quantity and coordination. The buyback contract facilitates closely aligned coordination, while the wholesale price contract may achieve higher utility but lacks comparable coordination. Transparency in information sharing reduces retailer concerns and improves overall utility and coordination. Incorporating fairness concerns and behavioural considerations into contractual mechanisms also enhance supply chain coordination.

Exploring The Showrooming and Webrooming Effect on Hybrid Platform and Reselling Platform: The Strategic Role of In-Store Service

Bicheng Yue, Qiaoyun Yun, Bin Liu — Wed, 16 Oct 2024 00:00:00 -0700

This paper aims to examine how customers' bi-directional free-riding behavior (showrooming phenomenon and webrooming phenomenon) affects the operation of e-commerce platforms and how the service resources of different platforms can be coordinated to bring a better shopping experience to consumers. We develop a game model based on a multi-channel supply chain structure to explore competitive decisions, cooperative decisions, and coordinative decisions, respectively, where the hybrid platform and the reselling platform engage in both price and service competition. It shows that whichever platform actively enhances service efforts is beneficial to the development of the entire supply chain system, especially for its own more significant promotion. However, when there are increasing numbers of bi-directional free-riding customers in the market, both e-commerce platforms are becoming conservative, and are waiting for the other side to enhance its service efforts. On this basis, a coordination mechanism can achieve a win-win situation for both platforms, where the profit of the two platforms, total consumer surplus, and total social welfare are higher than the level before the implementation of coordination.

Multi-Objective Optimization for Sustainable Vehicle Routing Problem Under Uncertainty Using The Lagrangian Relaxation Algorithm-Case: Food Industry Company

Behnaz Aghaabdollahian, Babak Javadi, Mohammad reza Abdali — Wed, 16 Oct 2024 00:00:00 -0700

The current study indicates a multi-objective optimization model for vehicle routing problems in the sustainable supply chain under uncertainty conditions. The proposed optimization model seeks to take into consideration the economic, environmental, and social aspects. The focus research is on social indicators among the dimensions of sustainability, for which the weights of the significance of the adverse social effects, including the risk of accidents, working leaves, and the positive social effects, including impartiality between employees, more job opportunities, and heightened levels of welfare for employees, are calculated using the Group Best-Worst method (GBWM) and simultaneously included in the model. Also, the possibilistic-robust programming (PRP) approach was employed to adjust the robustness level of the outputting decisions against the uncertainty of the parameters. A single-objective model can be created by utilizing an extended ε-constraint method from a multi-objective one. and Lagrangian relaxation heuristic is used to solve the proposed model given its medium to large scale, and a case study of a food industry company is examined to verify the applicability of the proposed model for real-life data. The numerical results and the obtained optimal routes indicate that the model can greatly enhance the decision-making capacities of supply chain executives.

Supply Chain Responsiveness Evaluation Using Forecasting and An Integrated Rough Z-number-based SWARA-TODIM Method

Chia-Nan Wang, Thuy-Duong Nguyen, Ngoc-Hien Do — Wed, 16 Oct 2024 00:00:00 -0700

In a dynamic environment characterized by rapid market competition and unanticipated changes, supply chains emphasize the cost-effective implementation of responsive strategies. Moreover, enterprises are experiencing the repercussions of substantial economic transformations precipitated by the COVID-19 pandemic. This research paper aims to identify the critical enablers of responsiveness in Vietnam's jewelry supply chain and rank the supply chain responsiveness (SCR) areas. Evaluating and developing in a challenging economic context is essential to support top management in reallocating resources based on a more empirical foundation. This article presents a novel integrated method combining demand forecasting, the rough Z-number, Stepwise Weight Assessment Ratio Analysis (SWARA), and TODIM (Interactive and Multi-Criteria Decision Making in Portuguese). The approach can decrease the number of pairwise comparisons substantially and demonstrate a substantial benefit in managing ambiguous data and ensuring the dependability of the assessment.