International Journal of Industrial Engineering & Production Research
Year:2024 | Volume:35 | Issue:3|Papers Count:14

This study focuses on evaluating potential raw material providers (RMPs) as one of the critical tasks of the logistics managers. In this regard, the literature showed that the simultaneous consideration of resilience, digitalization, and circular economy in the RMP selection problem (RMPSP) has been ignored by previous studies. Therefore, to cover the mentioned gap, this research attempts to study the RMPSP by considering other crucial concepts namely resilience and Circular Economy (CE). For this purpose, by considering a real-world case study in the steel industry, the current work first specifies the indicators of the research problem. Then, the indicators’ weights are measured using the stochastic Best-Worst Method (BWM). In the next step, the RMPs are prioritized by developing a novel approach called the stochastic Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In general, the main objective of this study is to evaluate the performance of the RMPs in the steel industry based on the CE, resilience, and digitalization aspects. According to the achieved results, “Reliability”, “Price”, “Quality”, “Reverse logistics and Waste management”, “Information systems usage”, and “Restorative Capacity”, are identified as the most desirable indicators. Moreover, the results confirm the effectiveness and validation of the developed method.

One method to diagnose retinal diseases is by using the Optical Coherence Tomography (OCT) scans. Annually, it is estimated that around 30 million OCT scans are performed worldwide. However, the process of analyzing and diagnosing OCT scan results by an ophthalmologist requires a long time so machine learning, especially deep learning, can be utilized to shorten the diagnosis process and speed up the treatment process. In this study, several pre-trained deep learning models are compared, including EfficientNet-B0, ResNet-50V2, Inception-V3, and DenseNet-169. These models will be fine-tuned and trained with a dataset containing OCT scanned images to classify four retinal conditions, namely Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal. The models that have been trained are then tested to classify the test set and the results are evaluated using a confusion matrix in terms of accuracy, recall, precision, and F1-score. The results show that the model with the best classification results in the batch size of 32 scenario is the ResNet-50V2 model with an accuracy value of 98.24%, precision of 98.25%, recall of 98.24%, and F1-score of 98.24%. While for the batch size of 64, the EfficientNet-B0 model is the model with the best classification results with an accuracy value of 96.59%, precision of 96.84%, recall of 96.59%, and F1-score of 96.59%.

The increasing demand for food delivery services driven by technological innovations has led to a surge in online shopping and food ordering. Efficient logistics play a crucial role in connecting customers with restaurants seamlessly. In this context, the practical application of graphical networks is explored in this article to streamline food delivery operations. We introduce a novel parameter eternal m-certified domination number denoted by γmcer∞(G) , which represents the minimum number of guards needed to handle any sequence of single orders using multiple-guard movements, ensures that the guard arrangement consistently constitutes a certified dominating set. A case study is presented, illustrating how this concept can be employed to de-crease human resources in a food delivery start-up. This research contributes to optimizing food delivery logistics and reducing operational costs, thereby enhancing the efficiency of the food delivery industry.

Maintenance plan efficacy traditionally prioritizes long-term predicted maintenance cost rates, emphasizing performance-centric approaches. However, such criteria often neglect the fluctuation in maintenance costs over renewal cycles, posing challenges from a risk management perspective. This study challenges conventional solutions by integrating both performance and robustness considerations to offer more suitable maintenance options. The study evaluates two representative maintenance approaches: a block replacement strategy and a periodic inspection and replacement strategy. It introduces novel metrics to assess these approaches, including long-term expected maintenance cost rate as a performance metric and variance of maintenance cost per renewal cycle as a robustness metric. Mathematical models based on the homogeneous Gamma degradation process and probability theory are employed to quantify these strategies. Comparative analysis reveals that while higher-performing strategies may demonstrate cost efficiency over the long term, they also entail greater risk due to potential cost variability across renewal cycles. The study underscores the necessity for a comprehensive evaluation that balances performance and resilience in maintenance decision-making. By leveraging the Monte Carlo Method, this research offers a critical appraisal of maintenance strategies, aiming to enhance decision-making frameworks with insights that integrate performance and robustness considerations.

In today’s world, supply chains must adopt new and intelligent technologies to achieve objectives such as enhancing productivity and performance, competitiveness, and overcoming challenges. The Internet of Things (IoT), as an emerging and transformative technology, is considered one of the most significant technology areas today and has garnered considerable attention across various industries. However, the implementation of IoT at the supply chain (SC) level faces numerous challenges and obstacles, and its acceptance at this level requires specific drivers. To date, no specific classification has been provided for drivers at the SC level, and existing classifications for challenges also need to be reviewed and updated. Given the importance of IoT in SC management, a systematic review at this level is necessary. This article provides a systematic literature review to identify and classify the challenges and drivers of IoT at the SC level. The study reviewed articles published from 2004 to 2023, ultimately identifying and categorizing 92 challenges into 16 categories: financial, standards and government regulations, privacy and security, energy consumption, health issues, hardware and software issues, culture in the SC, lack of knowledge and awareness, poor IT management, coordination in the SC, perception, the Challenge of uncertainty, lack of Plan and Strategy, incompatibility with existing technology, supply Problems, and user acceptance and trust in technology. Additionally, the study identified 4 antecedent drivers (pressures, understanding the benefits, government regulations, government incentives) and 10 consequent drivers (production benefits, improving competitive advantage, inventory management, cost management, improving transparency, efficiency of information flow, development of responsiveness and agility, sustainable development, facilitation of management, and development of cooperation and coordination). Finally, a model for implementing IoT technology in the SC is presented. This model synthesizes the findings from the literature review and offers a practical roadmap for organizations seeking to leverage IoT in their supply chains. By addressing the identified challenges and utilizing the drivers, organizations can effectively integrate IoT technology, thereby enhancing the efficiency, transparency, and overall performance of their SC operations.

The production of food-grade salt from crude solar salt has been examined through a techno-economic evaluation. This study aimed to investigate a salt factory to analyze its technical and economic aspects to determine the precise parameters for improving the quality of food-grade salt. The primary process of this factory involves grinding, washing, draining, drying, and fortification, supported by equipment like brine management, conveyors, sieves, and packaging. The proposed salt plant, designed for a 3-ton daily output over 15 years, requires 30 months for construction and a 4-month startup. The total capital outlay is USD 1,921,000, with USD 310,000 for technology and equipment. Economic indicators, including a Net Present Value (NPV) of USD 7,862,000, an Internal Rate of Return (IRR) of 46.48%, payback in 1.56 years, and a Return on Investment (ROI) of 64.28%, demonstrate feasibility. Establishing a salt plant in Indonesia supports food-grade salt production, stabilizes solar salt prices and enhances the welfare of traditional salt farmers. Ultimately, the results of this study can provide valuable insights for evaluating the feasibility of establishing a food-grade salt production plant in Indonesia.

Technological advancements have fueled heightened competition in manufacturing, compelling companies to adopt strategies prioritizing swift, timely, and high-quality customer service. This necessitates seamless integration of supportive systems such as resources, equipment, facilities, and workforce, underscoring the criticality of scheduling in aligning activities and resources for on-time task completion. Scheduling, inseparable from sequencing, is pivotal in optimizing manufacturing and service industries' operations. However, challenges arise when tasks converge with limited facility capacities, necessitating effective resource allocation. By leveraging mathematical techniques and heuristic methods, scheduling optimizes resource utilization, minimizes production costs, and enhances service quality. Despite its significance, existing models often overlook critical aspects like identical job consideration and sequence-dependent setup times, limiting real-world applicability. This research addresses these gaps by proposing robust mathematical models for intricate scheduling requirements. The proposed approach seeks to optimize manufacturing operations by effectively handling complex scheduling needs, thereby minimizing production costs and enhancing operational efficiency. This research endeavours to advance scheduling optimization strategies through real-world implementation and evaluation and contribute to the manufacturing industry's sustainable growth.

Seablite salt (Suaeda maritima) was a unique product currently under development. Seablite is a low-sodium salt essential for modern society, particularly for those who prioritize their health. This investigation aims to employ a dynamic system approach to evaluate the revenue and profit generated by the salt production system. The dynamic systems approach steps: the construction of the causal loop diagram, the development of the stock-and-flow model, the parameterization of the model, the simulation to analyze the system's behavior under various conditions, the verification and validation, the development of policy recommendations, and the conclusion with a summary of the core findings. The model was developed using four submodels: (1) demand, (2) supply, (3) production cost, and (4) revenue. The moderate scenario demonstrates that the salt flow requirements can be satisfied by utilizing the dynamic system to protect the revenue and production costs. It was consistent with the escalating production expenses. According to the optimistic scenario, the salt demand can be satisfied until 2026. The company's revenue is insufficient to cover production costs due to the rise in raw material prices. Farmers begin to reap the rewards in this scenario. It's because the overall revenue exceeds the production costs.

Cooperative performance efficiency describes the level of cooperative ability to utilize resources to generate profits. Efficient performance will increase productivity and strengthen business competitiveness. This study was conducted on 34 secondary cooperatives at the provincial level throughout Indonesia. Data was analyzed from 2019 - 2021 using the DEA (Data Envelopment Analysis) method. The results of the analysis show that the efficiency of cooperative performance in Indonesia is very low because the use of input resources exceeds the target needed to generate optimal profits. By using the DEA method, the level of achievement of input use in generating optimum profits is described in detail, starting from the number of cooperative members, utilization of own capital, utilization of external capital, utilization of assets, and the level of business volume developed by the cooperative. The results of this study greatly contribute to improving cooperative performance by evaluating the use of input resources in generating optimum profits according to the capacity of the cooperative

This research investigated the problem of joint inventory control and pricing for non-instantaneous deteriorating products; while, the quantity dependent trade credit is allowed. It was observed here that the buyer order amount is equal or more than the amount specified by the seller. The Shortage was not permitted in the system. It was aimed in present study to find a procedure for achieving the optimal selling price and replenish cycle and to be able to maximize the system's profit. To do so, first, the system's total profit function was derived. Then, the uniqueness of the optimal replenishment cycle for a given price was proved. Next, the concavity of the total profit function concerning the price was revealed, depending on the trade credit policy. Thereafter, an algorithm was provided to fulfill the optimal solution and eventually a dual-purpose numerical analysis was carried out both to show the model performance and to evaluate the sensitivity of the main parameters.

Optimizing production in the plastic extrusion industry is a pivotal task for small scale industries. To enhance the efficiency in today’s competitive market being a small-scale manufacturer over their peers is challenging. With the limited resources, having constraints on manpower, capital, space, often facing fluctuations in demand and production, simultaneously maintaining high quality became very important for the success. Among the plethora of KPIS used in manufacturing, Overall Equipment Effectiveness (OEE) stands out as corner stone. In this study, we collected real-world data from a plastic extrusion company. i.e., an HDPE Pipe manufacturing company. It serves as the backdrop for our study, this is based on the plastic extrusion sector and set out a goal of enhancing OEE through a comparative investigation of various ML models.  To forecast and estimate OEE values, we used various Machine Learning models and examine each algorithm’s performance using metrics like Mean Squared Error (MSE) and model comparisons using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), creating a comprehensive picture of each algorithm’s strength which enables the small businesses to make informed decisions and empowers them to stay agile and adapt to the changes in the manufacturing environment.

Refrigerated trucks in the cold chain enhance the shelf-life of food. In the fruit supply chain (FSC), if each different fruit necessitates its dedicated fleet of refrigerated vehicles, the total cost of the supply chain would increase. On the other hand, if there are several fruits in a single compartment, the quality and freshness of the fruits will be impacted since each fruit requires a different operating temperature. Therefore, partitions are necessary within the container. While the use of cold chain infrastructure will result in a reduction in food loss and an enhancement in food security, it will also incur an increase in the overall cost of the supply chain. Therefore, this paper aims to create a mixed integer non-linear programming (MINLP) mathematical model considering multi-compartment reefer trucks (MCRTs) to minimize the total cost in the FSC. To assess the efficiency of the model, a case study is carried out in India, and the formulated mathematical model is solved using a heuristic approach. The findings indicate that utilizing MCRTs leads to a reduction in the number of vehicles required and a drop in total supply chain cost. Three-compartment reefer trucks offer a more significant cost-saving advantage in the FSC compared to two-compartment reefer trucks. Furthermore, it is noted that operating three distribution centers (DCs) results in a reduction in the overall cost. The decrease in total supply chain costs enhances the affordability of fruits for low-income populations and contributes to the enhancement of food security. In addition to cost reduction, implementing MCRT has also beneficial environmental impacts such as decreased emissions due to a decrease in the number of trucks utilized and reduced food waste.

The reliability of each component in a system plays a crucial role, as any malfunction can significantly reduce the system's overall lifespan. Optimizing the arrangement and sequence of heterogeneous components with varying lifespans is essential for enhancing system stability. This paper addresses the redundancy allocation problem (RAP) by determining the optimal number of components in each subsystem, considering their sequence, and optimizing multiple criteria such as reliability, cost uncertainty, and weight. A novel approach is introduced, incorporating a switching mechanism that accommodates both correct and defective switches. To assess reliability benefits, Markov chains are employed, while cost uncertainty is evaluated using the Monte-Carlo method with risk criteria such as percentile and mean-variance. The problem is solved using a modified genetic algorithm, and the proposed method is benchmarked against alternative approaches in similar scenarios. The results demonstrate a significant improvement in the Model Performance Index (MPI), with the best RAPMC solution under a mixed strategy achieving an MPI of 0.98625, indicating superior model efficiency compared to previous studies. Sensitivity analysis reveals that lower percentiles in the cost evaluations correlate with reduced objective function values and mean-variance, confirming the model's robustness in managing redundancy allocation to optimize reliability and control cost uncertainties effectively.

This study examines the role of forensic accounting in enhancing financial transparency and reducing fraud in Jordanian institutions. Using a mixed-method approach, data were collected from 150 respondents including chartered accountants, auditors, financial managers. through a structured questionnaire. The findings reveal that forensic accounting significantly contributes to fraud prevention by supporting government investigations, providing courtroom testimony), and developing financial management systems. Additionally, forensic accountants play a crucial role in preparing key reports for government activities. The correlation analysis shows strong interdependencies between forensic accounting’s roles in arbitration and fraud detection. While most hypotheses were confirmed, challenges were noted in applying forensic accounting within the public sector. The study concludes by recommending that policymakers strengthen the integration of forensic accounting into Jordan's financial regulatory framework to enhance its effectiveness, particularly in the public sector. This research highlights the vital role of forensic accounting in maintaining financial integrity and provides a foundation for future studies.