International Journal of Industrial Engineering: Theory, Applications and Practice

AN EXPLORATORY OBSERVATION INTO THE STATUS OF COLLABORATIVE MANUFACTURING FOR TOOL AND DIE DEVELOPMENT IN MALAYSIA

Suman Selvarajoo, Raja Ariffin Raja Ghazilla, Azuddin Mamat — Thu, 18 Apr 2024 00:00:00 -0700

This research explores the current status of collaborative manufacturing for tool and die development in the Malaysian manufacturing industry. Collaborative manufacturing is one of the characteristics that contribute to Industrial Revolution 4.0. Therefore, it is crucial to improve the current status of collaborative manufacturing in the Malaysian industry. Factory visits were conducted to observe the practices of manufacturing companies that use tools and dies, and the data collected from the observations were analyzed and compared to understand the present status of the industry. The findings suggest that the local industries are not significantly different from the overseas industries in most areas. However, some issues need to be addressed, particularly in terms of technology, quality, and management, to further improve the current status of collaborative manufacturing. Most importantly, the management’s participation and initiatives are crucial in establishing an ideal collaborative environment.

MULTIPLE-OBJECTIVE SCHEDULING FOR BATCH PROCESS SYSTEMS USING STOCHASTIC UTILITY EVALUATION

Hongsuk Park, Youngchul Shin, Ilkyeong Moon — Thu, 18 Apr 2024 00:00:00 -0700

Most research studies in the batch process control problem are focused on optimizing system performance. The methods address the problem by minimizing a single criterion, such as cycle time and tardiness, or bi-criteria such as cycle time and tardiness, and earliness and tardiness. We demonstrate the utilization of the Stochastic Utility Evaluation (SUE) function approach to the performance of batch process systems using multiple criteria. Tri-criteria problem is used as an example to illustrate the use of the SUE function. That is, we explore how SUE function with stochastic information can be implemented to improve existing approaches to the batch process control problem. The simulation results demonstrate that strategies based on the SUE function perform better than existing strategies based on static utility values.

EVALUATING THE WORK DESIGN READINESS FOR INDUSTRY 4.0 BASED ON PERSONAL CHARACTERISTICS OF PRODUCTION WORKERS

Thu, 18 Apr 2024 00:00:00 -0700

The design of work is a crucial consideration for production workers in terms of performance, satisfaction, and motivation. This aspect has garnered increasing attention in research, particularly with the rise of automation and the transition to Industry 4.0-enabled smart factories. Work design is influenced by both internal and external performance-shaping factors (PSFs). Internal factors include human, machine and task elements and traits, which HR specialists can structure to enhance efficiency and productivity in manufacturing. External PSFs, such as physical, psychological, social and organizational factors, also play a significant role in shaping production work. This study aims to understand the impact of personal characteristics of production workers in an automated factory. The research findings are extrapolated on an Industry 4.0 framework to establish a productive work design. Data collection is conducted through a quantitative survey, and non-parametric analysis of variance tests are employed to assess significant relationships. The results indicate a better perception of the pace of operations (µ = 4.19) and feedback (µ = 4.47) among elder workers. Additionally, educated workers express significant perceptions regarding external PSFs such as supervision (µ = 4.39), health and safety (µ = 4.25), job rotation (µ = 3.91) and pay and welfare (µ = 3.58). Aspiration, motivation and organizational commitment emerge as crucial work design attributes for experienced and married workers. These findings contribute to understanding the structure of modern work design in the context of Industry 4.0, aiming to enhance performance and worker productivity. The study proposes an advanced Industry 4.0 work design framework, empowering HR specialists to develop effective strategies and optimize work distribution.

DUAL-OBJECTIVE ORDER SEQUENCING IN A PUT-WALL-BASED PICKING STATION

Seung Won Park, Kwang-Tae Kim, Kyung Min Kim — Thu, 18 Apr 2024 00:00:00 -0700

The growing e-commerce market has forced distribution companies to provide faster and more accurate delivery services. The order-picking process is the most time-consuming and labor-intensive of the logistics processes carried out in a distribution center. This study considers an order sequencing problem in a put-wall-based picking station. We introduce the problem of distributors aiming to optimize the order-picking sequence to minimize the total dwell time of SKUs as well as the maximum number of SKUs dwelling at the picking station. To tackle this problem, we propose a three-solution approach involving mixed integer nonlinear programming (MINLP), mixed integer linear programming (MILP), and NSGA-II, a non-dominated sorting genetic algorithm. Additionally, we conduct a comparative analysis and verify the performance capabilities of each approach through numerical experiments.

CONTEXT-TREE APPROACH FOR MONITORING THE MULTI-WAY CONTINGENCY TABLES-BASED PROCESSES WITH DEPENDENCE BETWEEN NEIGHBORHOOD CELLS

Reza Kamranrad, Azadeh Golshan, Farnoosh Bgheri — Thu, 18 Apr 2024 00:00:00 -0700

In recent years, some statistical process monitoring (SPM) approaches have been used to control contingency table-based processes. The common assumption in this research is that the Neighborhood cells of the contingency table are temporally independent. This paper develops a new approach based on the Context-Tree method and Kullback-Leibler (KL) statistic to monitor the multi-way contingency tables by considering the dependence between Neighborhood cells in Phase II. The proposed approach is evaluated by using some simulation studies. In addition, the efficiency of the proposed approach has been approved using other sensitivity analyses in some numerical examples by contingency tables with more rows and columns and contingency tables with more categorical variables. Results show that proposed statistics have suitable performance in detecting the out-of-control condition under different shifts in a multi-way contingency table.

WORKER SCHEDULING OF BANBURY PROCESS USING GOAL PROGRAMMING: A TIRE MANUFACTURING COMPANY CAES STUDY

Srisawat Supsomboon, Chonnipa Chaihanit, Anan Butrat — Thu, 18 Apr 2024 00:00:00 -0700

In today’s competitive business landscape, organizations must streamline their planning and management processes for efficiency and profitability. Effective resource management, especially in the case of human resources, is crucial. Scheduling challenges in human resource management become more complex when considering worker skills. Balancing fairness for workers and the company necessitates the inclusion of additional constraints and variables, making manual problem-solving more time-consuming and complex. This study proposes a Goal Programming Method, a mathematical model that addresses both primary and goal constraints, to optimize solutions and minimize deviation variables. The case study of a real manufacturer’s Banbury process department with 51 workers, 24 tasks, and two shifts has been analyzed. The mathematical model is utilized to allocate tasks, optimize worker skill utilization, and identify positions to close when worker availability falls short.

INTEGRATED ARTIFICIAL IMMUNE SYSTEM AND TAGUCHI APPROACH FOR PRODUCTION SCHEDULING IN THE GARMENT INDUSTRY

Chia-Nan Wang, Tram Thi Mai Nguyen — Thu, 18 Apr 2024 00:00:00 -0700

Presently, Vietnam is the third-largest garment exporter in the world, with a 6.1% market share. Nevertheless, Vietnam's apparel industry faces fierce competition from other countries making low-cost garments. Enterprises must enhance many aspects to survive in a competitive market, including ensuring product availability at optimal prices. To achieve this, all production managers shall schedule production while factoring in early and late production costs. This study presents a novel integer nonlinear programming model to minimize the cost of earliness and tardiness, considering weight for storage. The metaheuristic utilized to solve the problem in this work is the integrated artificial immune system (AIS) algorithm and the Taguchi technique. Subsequently, implementing a sensitivity analysis to ascertain the weight for storage is a crucial decision faced by production managers. Finally, this study compares the proposed method to the Vietnamese garment industry's current technique and shows its suitability for production scheduling in this sector.

THE IMPACT OF A VENDOR-MANAGED INVENTORY POLICY ON THE CASH-BULLWHIP EFFECT

Jaehun Sim — Thu, 18 Apr 2024 00:00:00 -0700

It is essential to secure a sustainable flow of cash along the supply chain in the modern corporate environment. Due to the growing significance of cash flow, the concept of the cash-flow bullwhip effect has recently drawn academic attention to finding solutions to the cash shortage. The supply chain’s response to the bullwhip effect, known as the cash-flow bullwhip effect, causes cash-flow volatility to be amplified from downstream to upstream. A number of research studies have looked into the sources and effects of the cash-flow bullwhip effect, but none have concentrated on solutions. This study investigated for the first time the impacts of the vendor-managed inventory policy as a tool for mitigating the cash-flow bullwhip effect. The findings show that the vendor-managed inventory policy typically shortens the cash-conversion cycle of each supply chain member and can, therefore, be implemented as a policy for mitigating the cash-flow bullwhip effect.

MODELING OF CROSS-NETWORK COLLABORATION SUPPLY CHAIN AND SOLUTION VIA REINFORCEMENT LEARNING-BASED EVOLUTIONARY ALGORITHM

Thu, 18 Apr 2024 00:00:00 -0700

Despite the increasing importance of collaboration in achieving cost-related advantages for companies, existing studies lack a systematic framework for determining how multiple supply chains can collectively facilitate strategic decision-making. In this study, we propose a multi-network collaborative mixed integer programming model and a reinforcement learning enhanced evolutionary algorithm to optimize cross-enterprise supply chains. The model facilitates collaborative decision-making among supply chains of different enterprises by incorporating collaborative cost management, partner sharing, collaborative transportation, and horizontal logistics. Our algorithm integrates an adaptive reinforcement learning process and an evolutionary structure with multi-branch tree encoding, allowing for the effective accumulation of solving experiences in different states to enhance the efficiency and accuracy of the solving process. We conducted extensive experiments using real data collected from electric automotive manufacturing supply chains. The experimental results show that the obtained solution quality is close to optimal with negligible margin for both small- and large-scale instances. Overall, our proposed approach enables the joint optimization of cross-enterprise collaborative supply chains and holds the potential for improving supply chain management in various industries.

SYSTEM DYNAMICS ANALYSIS FOR IMPACT OF CLOUD-BASED INFORMATION-SHARING ON SUPPLY CHAIN PERFORMANCE

Yueran Zhang, Zhanwen Niu, Chao Liu, Chaochao Liu, Kaixuan Hou — Thu, 18 Apr 2024 00:00:00 -0700

Cloud computing has emerged as a vital tool for facilitating information-sharing and collaboration in supply chain operations. However, how the information is shared in the cloud-based supply chain has not been thoroughly investigated. Thus, adopting a system dynamics approach, this research deepens the understanding of information transmission within the supply chain by proposing a general stock and flow model and endorsing the model in an equipment manufacturing setting in China. Building upon the general model, this paper establishes a conceptual framework for cloud-based information-sharing mechanisms both within and across supply chains. The impact of cloud-based information-sharing on supply chain performance is evaluated by inventory level and customer satisfaction. The findings indicate a positive influence of information-sharing on supply chain performance. Notably, sharing information across supply chains enhances customer satisfaction to a greater extent than within the supply chain. However, it does not lead to a significant increase in inventory levels. This research expounds on the impact of information-sharing on the supply chain in quantitative forms, which is of theoretical and practical significance to the field of supply chain management.

A NETWORK-BASED METHOD FOR CONSTRUCTING A TECHNOLOGY ROADMAP

Jia-Yen Huang, Chun-Lin Lee — Thu, 18 Apr 2024 00:00:00 -0700

Technology roadmaps are essential for technology management, but current roadmaps that use text mining to build new processes are time-consuming and laborious. In particular, the identification of relationships between different layers of roadmaps mainly relies on experts. To remedy this problem, this study proposes a systematic and objective process for constructing a technology roadmap. First, by integrating the Stanford parser and a co-word analysis method, we develop a modified Stanford parser-based method to select two-word technological terms, which is suitably comprehensive and complete for building a technology roadmap. Second, we innovatively integrate patents, products, and keywords into the same matrix, thus generating an incidence matrix of products and technologies. Based on this matrix, products and technologies can be directly linked to construct a two-layer product technology roadmap without the assistance of experts. We recommend applying a technology lifecycle to estimate the development time of technologies on the technology roadmap timeline, which is an important but often ignored issue. Finally, we provide an example of cloud computing-related products to demonstrate how the proposed technology roadmap approach can provide managers with a competitive technology and product development strategy.

A NEW BI-OBJECTIVE MODEL TO OPTIMIZE SOLID TRANSPORTATION UNDER UNCERTAINTY TO FACILITATE CATASTROPHE VICTIMS: A CASE STUDY

Jency Leona Edward, Palanivel Kaliyaperumal — Thu, 18 Apr 2024 00:00:00 -0700

This study addresses the crucial challenge of efficient resource allocation during emergencies, aiming to minimize loss of life and property. We analyzed a case study for the 2015 floods in Tamil Nadu, India, where local emergency centers in Chennai and surrounding cities distributed relief materials. Additionally, employing a Bi-objective Solid Transportation Problem (BOSTP) model, minimizes both cost and time of delivery. Further, the traditional methods like Row Maximum, Least Cost, and Vogel's Approximation are compared to BOSTP, which shows that Column Maximum yields an optimal result based on cost and time constraints. However, introduced a novel algorithm for BOSTP using trapezoidal neutrosophic numbers(TNN) to account for inherent uncertainties in logistics, specifically shipment cost and time. Lastly, performance analysis of the BOSTP model and sensitivity analysis under various scenarios demonstrate its effectiveness in enhancing rapid humanitarian assistance during emergencies. This case study contributes to developing efficient bi-objective models for future disaster response efforts.

PROJECT SELECTION REVISITED: CUSTOMIZED TYPE-2 FUZZY ORESTE APPROACH FOR PROJECT PRIORITIZATION

Meryem Uluskan, Büşra Beki — Thu, 18 Apr 2024 00:00:00 -0700

In this study, a customized version of a less-preferred methodology in decision-making processes, i.e., the interval type-2 fuzzy ORESTE (IT2F-ORESTE), is proposed, and its effectiveness for selecting the most viable projects is demonstrated. The findings are evaluated against those of fuzzy TOPSIS, which is among the most preferred methods, to provide evidence that the proposed method achieves comparable and even superior results. To this end, multicriteria decision-making studies conducted between 2016 and 2021 were examined. Subsequently, 30 automotive manufacturing projects were evaluated over seven criteria using the fuzzy TOPSIS and customized IT2F-ORESTE methods. The results revealed that IT2F-ORESTE assigned the highest ranks to projects with high earning potential, low cost, low number of operations, and high production capacity, whereas fuzzy TOPSIS failed to select the best project. To the best of the authors’ knowledge, this is the first study to utilize this new IT2F-ORESTE method in project evaluation within the automotive industry and demonstrate its superiority over that of conventional methods.