Ebook: Industrial Engineering and Applications

This proceedings is a collection of papers presented at the 10th International Conference of Industrial Engineering and Applications (ICIEA), which was held in Phuket, Thailand, from 4 to 6 April 2023. On behalf of my international colleagues in the international advisory, organizing and technical committees, I would like to thank all the authors for their support and contributions that have led to the successful publication of this proceedings.

The 10th ICIEA was conducted in hybrid mode with close to 100 delegates attending in person and about 50 participants attending online. It was co-sponsored by the Science and Engineering Institute (SCIEI) and Chulalongkorn University, Thailand, with support from the National University of Singapore, Macau University of Science and Technology and Asia University (Taiwan). In fact, ICIEA has become a signature event for the industrial engineering (IE) community in Asia. Before the pandemic, it usually received more than 350 submissions of which over 200 papers were accepted for publications. This year, with the pandemic coming to an end, we received 272 submissions of which 120 were accepted for presentation and of which 83 were published as full papers. I would like to thank all the committee members, particularly members of the Technical Committee who worked very hard in handling the reviews of these papers. Special thanks also goes to the conference organizer who has made a special effort in securing the nice beach-front hotel in Phuket. I believe all delegates who attended the conference in person had an enjoyable stay in Phuket, where we forged new friendships and reinforced old ties. I would also like to thank our keynote and invited speakers who have shared their expertise generously through their presentations in person and online.

The papers in this proceedings cover a wide range of topics in Industrial and Systems Engineering; including but not limited to supply chain and logistics, quality and reliability, advanced manufacturing, production scheduling to ergonomics and man- machine systems interfaces. In particular, there is a large number of papers on machine learning techniques and applications beyond the traditional manufacturing sector to include healthcare, sustainability assessment and other social issues. We believe that this is an encouraging trend which indicates that not only our researchers are in the forefront of IE techniques, but they have also expanded their reach beyond the traditional domains of applications, to areas that matter most to society at large.

We believe that this trend will continue, and we hope to receive more innovative and interesting applications for this conference in the coming years.

Happy reading and hope to see you in person next year!

General Chair, ICIEA 2023

Loon Ching TANG, PhD

Professor and Fellow, Academy of Engineering, Singapore

In this work, we optimize manufacturing configurations, i.e., the set of necessary assets, using the known capabilities and capacities of manufacturing equipment. In particular, the work provides an Object-Oriented data model and the translation of the Object-Oriented data model into a Mathematical data model for efficiently utilizing optimization algorithms. The main contribution is developing an optimization strategy for adapting to varying demand periods with the requested capability and capacity.

The proposed methodology is validated in optimizing the planning for small-box hinged product assemblies in aerospace manufacturing, which can be assembled in a reconfigurable environment with common pick and place, drilling, fastening, and inspection procedures.

In Unmanned Manufacturing Factory (UMF), we consider a workstation consisting of a machine and several workbenches, each assigned an AGV to perform the parts transporting service. Every workpiece requires multiple processes at the same workbench, and the time for the AGV to transport a part is greater than the time for the machine to assemble it. This operating model is complex, as several workpieces corresponding to several workbenches are in service at the same time. We extract the production system into a Semi-Open Queuing Networks (SOQNs) model. We use four parameters to describe the states of the system and construct the transition rate matrix. We find that the matrix has a particular structure that enables us to solve it with Matrix Geometric Method (MGM). Following a First-Come-First-Served (FCFS) policy, the performance of this production system with unlimited queuing space is evaluated in terms of service intensity, queue length, sojourn time, and throughput. The numerical experiments demonstrate a significant reduction in the sojourn time of the workpiece in the system when the number of workbenches increases to a certain value. Our work can provide important suggestions for designing UMF.

In this paper, a unique implant containing Gentamicin sulfate with biocompatible poly-lactide powders was developed by using such 3D printing (3D printing) process. The implantable drug delivery system prototypes, which were constructed with matrix structure; double-layer structure and sandwich structure, were manufactured with different processing parameters. The cross-sectional morphology of the implant was characterized by three dimensional video microscopic system and environmental scanning electron microscope. The microscopic morphologies and the in vitro releasing experiments of the implants fabricated by both 3D printing technique and conventional technique were investigated to evaluate the performance of the implant devices. At about 60-day release of the implants in vitro, the drug concentration was measured and the profiles were made. The release behavior and the microstructure were subsequently compared between the samples prepared using the 3D printing technology and the conventional technology. The as-described 3D printing technology in this work allows for the design and fabrication of implants with a sophistically micro- and macro-architecture, and thus having unambiguous advantage over the conventional technology.

The ecological niche of an enterprise is composed of ecological niche factors, which affect the long-term development of the enterprise.This paper aims to identify the factors that influence the ecological niche of manufacturing enterprises in the service ecosystem, and explores the constitutive dimensions of ecological niche factors of manufacturing enterprises in the context of digital servitization through the research method of rooting analysis. Four types of ecological niche factors are identified and divided into internal and external dimensions according to whether the ecological niche factors have a direct impact on the business activities of the enterprise. The development history of the digital service business and the current status of its ecological niche are analyzed in the context of the case company, and the future changes of the ecological niche are predicted by fitting the numerical curves of the ecological niche factors for ten years. The study enriches the existing research results of ecological niche theory and provides a measurement basis for the evaluation of ecological niche factors of manufacturing enterprises in the transformation of digital services.

Since the framework conditions of manufacturing companies change dynamically, production control must react to this and be adaptive and dynamically designed. Our article addresses the status quo of industrial production management systems in the context of advancing digitization. The aim is to examine the extent to which traditional systems for controlling and optimizing production systems have been supplemented by Industry 4.0 concepts. In the course of the scarcity of resources and the shortage of labor, the human factor is once again taking a central role. Against this background, the interaction between users / humans and artificial intelligence applications will be the main focus. The result should give an indication on whether or how this connection must be considered in the future and whether this interaction will play a central role. Furthermore, the possibilities and limitations of AI-based production control systems should be clarified. In addition, the questions of what can and what should artificial intelligence do in the context of production control arise. The findings will be the basis for future considerations of a smart production management system, which can be used for decision support as well as for auto-control.

Collective contribution of Small and Medium Enterprises (SMEs) to economic growth is evident in many countries worldwide. On the other hand, however, it contributes also to environmental burdens the world already suffered today. This paper suggests that sustainable SMEs growth is attainable through Open Innovation (OI) concept. A model is developed with the aim to evaluate the role of OI in reducing SMEs environmental impact. The model development is conducted in two stages; first is identification of relevant variables. This is done by reviewing relevant OI models and select variables based on statistical characteristics; second is building logical relationships between the variables based on interviews and literature reviews. The development is iterative so each iteration can bring new information and update the model. Final model is presented in a causal loop diagram (CLD) and discussed.

Today industrial companies are subject to major and profound changes. It is constantly confronted with a world of ruthless competition that continually aims to improve quality, cost, and lead time. Each company works to satisfy the requirements of its customers and to do this, it continually improves its performance and controls its manufacturing processes from the reception of raw materials from the suppliers to the shipment of the final product to the customer. In this paper, the study focuses on the assembly area of an electrical wiring harness production line and adopts a Lean Manufacturing approach to reduce the time waste and improve the efficiency of this line of engine wiring harness using Line balancing and the work allowed an eliminate of Waiting Muda and increase the efficiency.

This paper studies the joint production/inventory and condition-based maintenance control for a multi-product manufacturing system with setup and maintenance times under stochastic product demands. The problem is modelled as a semi-Markov decision process (SMDP). The objective is to find a joint production and maintenance policy that minimizes the long run expected discounted cost including setup, holding, lost sales, preventive and corrective maintenance costs. A Q-learning method with state aggregation (QLA) is proposed to find near-optimal policies for large-scale problems that cannot be solved to optimality due to the curse of dimensionality. The numerical results show that QLA provides well-performing policies in a reasonable computational time.

Retail chain stores commonly experience dead stock inventory accumulation due to the absence of indicators and decision rules in the inventory management system to track down the impact of potential dead stocks when left “unattended” or “unmanaged” in the warehouse. Potential dead stocks are inventory items that are either near-expiry, near its end-of-season, near the end of its product market life cycle, or simply slow moving which will soon become dead stocks in the warehouse if not managed in a timely manner. Most retail systems have focused on fighting the dead stock fire rather than developing a standardized process to manage inventories and prevent potential dead stocks from becoming dead stocks. The systematic management perspective is to identify the potential dead stocks first and then apply the best strategies to prevent the occurrence of dead stocks. This research aims to develop a standardized potential dead stock identification and prioritization framework that will provide the level of priority for management intervention using decision rules. Literature review is performed to develop the indicators required. The framework is then validated through hypothetical data sets. As a result, the classification phase shows that the data sets produced similar industry findings on dead stock composition as a percentage of total inventory. Next, the prioritization phase shows that considering a 10-4-1 risk weight produces more discriminating ranks than a 9-3-1, adopted from the House of Quality (HOQ) framework. The rank discrimination is an important metric for this to address the primary research objective as it represents the ability of the framework to prioritize intervention given urgency based on criteria and resource constraints. Further research may be performed on enhancing the decision rules used in producing the prioritization output of the developed framework.

PET (Polyethylene Terephthalate) waste is a crucial problem because of its high consumption as disposable packaging. Even used PET plastic is the most accessible type of plastic to recycle. So, recycling PET plastic waste is a solution to the problem of plastic waste that contributes to environmental safety while providing economic benefits towards a circular economy. In a circular economy, the consumer is the initial entity producing waste to be recycled. So that consumers can act as suppliers in reverse logistics, this study aims to analyze consumer behavior after PET consumption in terms of economic, social, environmental, and regulatory aspects. This study was conducted with a survey on household consumers with residence, education, and income variables. The survey results were presented using descriptive statistics, and the relationship between the variables was tested using the Chi-Square test. The survey results show an association between residence, education, and income variables with post-consumption consumer behavior. This research contributes to determining consumer behavior, leading to increased consumer engagement in the return of waste products in reverse logistics through implementing policies.

The food industry is one of the main players in the development of the Peruvian economy. However, new challenges and changes are continuously presented in order to better adapt to the needs of the consumer, and in the process to overcome these challenges, problems such as poor process management, long production times and low efficiency are often encountered. In this way, the aforementioned drawbacks generate as a consequence a low rate of order fulfilment, which generates a negative economic impact. In this research, a model using Lean manufacturing tools such as SMED, Kanban and Standard Work was proposed. The model mainly seeks to increase to 95% the current order fulfilment rate in a Peruvian SME food company, which after the implementation of the model obtained a value of 95.86%. In addition, other improvements were obtained such as a 14.45% reduction in penalties, a 19% reduction in the main process (pack arming) cycle time and a 10.33% reduction in moving times. These results proved the effectiveness of the proposed model, which was validated through a simulation in the Arena 16 Software.

This paper seeks to address the sugar crisis supply chain management problem of the Philippines using a mixed-integer linear programming model. Despite the topic on resource scarcity being a widely taken subject in the literature, little to no research works have satisfactorily provided an objective means to allocate scarce resources. As such, the paper formulates a mixed-integer linear programming model to represent the allocation problem across multiple levels of echelons in the supply chain. Key results indicate that there may be a certain monopolistic characteristic of the allocation which happens as expected in reality.

The main objective of this research article is to optimize costs and logistic KPIs applying an economic order quantity (EOQ) inventory model in a metal-mechanic MSE with intermittent demand. Firstly, the forecast model with the lowest MAD and ECM is selected. The object under study, after ABC classification, belongs to the family of products located in class A due to its valuation and participation in the inventory. The Croston method is considered the most effective forecast model. Secondly, an aggregate planning is developed to satisfy the projection. Then, the EOQ or Wilson model is implemented to reduce inventory costs. Finally, to validate the calculated data, a simulation model is built in Arena with 50 replications. As a result, the inventory costs were reduced to 22.6%.

In order to further promote the trade development of western China towards the “Belt and Road”, identify the countries for building international logistics hub, and construct the cross-border logistics hub network of western China along the “Belt and Road” are urgently needed. This paper proposes a two-stage method to study the construction of cross-border logistics hub network. In the first stage, 27 nations are initially selected using the fuzzy C-means clustering approach. These 27 nations enjoy better trading conditions and higher trade volumes with China than other the “Belt and Road” cooperation nations. In the second stage, this paper puts forward the maximum criterion of logistics gravity based on the gravity model, and objectively determines 14 countries for building international logistics hub. These nations, which include China, Russia, the United Arab Emirates, and others, have a high degree of international logistics development and robust logistics interconnection. According to the logistics connecting countries of these 14 countries, the logistics circle is divided. This paper connects China with other hub countries in the logistics circle, then the cross-border logistics hub network is constructed.

In recent years, the trading industry in Peru has experienced a remarkable growth. However, they face several problems such as varying demand, delays in order delivery and having to offer high quality products quickly at low cost. Small and medium-sized enterprises (SMEs) account for 95% of companies, so it is necessary to implement productivity tools. In the study, the proposed model is to improve the level of service, first we will collect data through value stream map, Pareto diagram and problem tree, then we will implement the proposed methodologies and at the end we will reevaluate the indicators. Inventory processes were also simulated to reduce waste and costs.

This paper mainly proposes a coordination mechanism for a supply chain system containing multiple retailers and one supplier. According to the proposed coordination mechanism, lateral transshipments are performed among retailers while revenue-sharing contract is used to allocate the revenue caused by lateral transshipment. Meanwhile, buy-back contract is used to coordinate the retailer and supplier. We use a supply chain system with a supplier and two retailers as an example to demonstrate the effectiveness of the proposed coordination mechanism. The numerical example shows that the proposed coordination mechanism can effectively coordinate the discussed supply chain.

This study focuses on the roadmap for sustainable fashion supply chain management and examines the specific measures adopted by luxury fashion brands to achieve sustainability in their supply chain management. The findings of this study may offer valuable insights and guidance for other brands looking to implement sustainable supply chain management practices.

In recent years, the market of electric vehicles (EVs) has developed rapidly across the world, and recycling a large number of their spent power batteries has become an urgent challenge today. The resulting closed-loop supply chain (CLSC) have been considerably studied under different aspects. However, there is a lack of research investigating electric vehicle batteries (EVBs) network design under uncertainty. This paper focuses on the issues of quantitative modelling for the network design of a CLSC of used EVBs consisting of power battery manufacturers, EV retailers, collection centers, recycling centers, echelon utilization centers and disposal centers, where power battery manufacturers can remanufacture used EVB products. We investigate a two-stage stochastic mixed-integer programming (SMIP) model to design the network and the model is solved using the Benders Decomposition (BD) method to derive optimal solutions. Numerical experiments show that the SMIP model can effectively hedge against high uncertainty.

The online meal delivery business in Thailand has grown substantially. Online platforms, customers, and riders are the three main stakeholders in this service system. While the platform aims to minimize total operation cost, customers would like to receive a high-quality meal. Moreover, the riders who are responsible for the pickup and delivery of meal to the customer’s house focus on maintaining their income. To address the issue, this research develops and validates a multi-objective model. The three objectives are to 1) minimize total operation costs, 2) minimize meal waiting time, and 3) maximize the number of riders receiving orders. From the experiments, the results indicate that when operation cost is the primary concern, only a small number of riders will be assigned the order. Alternatively, if the meal quality is the most important factor, orders must be distributed to all riders, which will maximize the number of riders who receive assignments at the same time.

This study aims at developing and validating a model of causal factors potentially affecting long-term development of Laem Chabang Deep-Sea Port in Thailand. The data collected by a questionnaire survey has been analyzed by a measurement model of latent variables. Several key hypotheses have also been tested by the proposed model. According to the model analysis, it is found that the empirical data analysis is consistent with the theoretical measurement model. The prosperous development is positively influenced with statistical significance by four latent variables – as represented by the following statistics: CMIN/P = 0.068, CMIN/df = 1.282, GFI = 0.976, and RMSEA = 0.027. Additionally, results from the Second Confirmatory Factor Analysis (SCFA) shows that there are two factors considered as key success factors for smart port implementation at the port. It is found that all four factors are rated at high-level of importance with operation, environment, safety and security, and energy, respectively.

The covariance-driven stochastic subspace modal parameter identification method has been widely used in the field of engineering structures. Effective determination of the model order of the structural system is the key to applying this method to identify the modal parameters. It is particularly difficult to determine the model order for unstable systems affected by noise disturbances and computational errors. In order to effectively determine the model order, an exponential eigenvalue entropy incremental covariance-driven stochastic subspace identification (EE-COV-SSI) algorithm is proposed. The condition number of the state matrix is used to determine the degree of perturbation of the response signal to the system stability. Meanwhile, the identification accuracy of the modal parameters is reflected by calculating the modal frequency coefficient of variation. Finally, the method is applied to the modal analysis of a four-story frame structure. The results show that the method can accurately identify the model order and improve the identification accuracy of the modal parameters.