International Journal of Industrial Optimization

Joint production and human replacement optimization policy for a deteriorating manufacturing system

2024-08-05T02:15:45+00:00

This article examines the integration of production and human resource and human resource management, considering the operator as a production unit whose efficiency decreases over time, in an unreliable production system marked by significant deterioration. This deterioration impacts the reliability and continuity of the production unit in two main ways. To mitigate the impact of this deterioration, a replacement action can be implemented based on the system's current state. The objective of this study is to establish an effective production policy and replacement strategy to meet customer demand. We employ a combination of stochastic dynamic programming and numerical methods to solve this optimal control problem. Additionally, a numerical example is presented to demonstrate the applicability of the proposed approach and to explore the interaction between a specific production strategy and human resource management. The main contribution of this research lies in the development of innovative methods and solutions aimed at optimizing the performance of a complex system through stochastic optimal control. The impact of the new approach, based on a logical implementation, is discussed following a sensitivity analysis of the numerical example. The results include a comparative study between recent research and the proposed policy. Lastly, an implementation chart is created to assist decision-makers in determining production rates and managing human resources effectively to meet customer demand.

A simulation framework for emergency evacuation, considering navigation errors

2024-08-24T23:57:42+00:00

The rapidly growing tourism industry has brought forth substantial safety concerns, particularly in the context of emergency evacuations necessitated by natural and human-induced disasters. Tourists often lack the necessary orientation, information, and preparedness, rendering them vulnerable during such crises. While research has extensively explored tourist behavior and evacuation procedures independently, the intersection of these two fields remains underexamined. This study introduces a simulation model utilizing MATSim to characterize the behaviors of tourists during emergencies, highlighting navigation errors and decision-making processes. Two novel routers - “Random Walk” and “Landmark Assisted” - have been developed to better reflect tourist navigation challenges. A case study in Conegliano, Italy, demonstrates the effectiveness of these routers under two evacuation policies: predefined and undefined destinations. Results indicate significant disparities in evacuation times: optimal routes average 50 minutes, while random navigation extends this to 544 minutes. The Landmark Assisted router improves evacuation to 73 minutes, underscoring the importance of identifiable landmarks. Additionally, managing intersections further reduces evacuation times. This simulation framework serves as a decision-making tool for evaluating evacuation policies, providing insights into optimizing resource allocation and enhancing overall efficacy in emergency scenarios. Future research should focus on developing optimization algorithms for intersection management selection, reinforcing the practical applicability of this model in real-world contexts.

Collaborative digital marketing and supply chain management for micro, small and medium enterprises

2024-10-29T00:19:23+00:00

This study explores specific supply chain challenges faced by Batik Ayu Arimbi, a small-scale business in the batik industry, particularly how the manual calculations in its make-to-stock system impact its financial accuracy and operational efficiency. The company has been experiencing financial losses due to these inaccuracies. To address these issues, this research proposes improvements in supply chain management by implementing Business Process Model Notation (BPMN) to streamline process visualization and coordination among the artisans, showrooms, and production houses. The use of digital marketing platforms, particularly Instagram, is also suggested to optimize marketing efforts and achieve sales targets. This study contributes to the literature by emphasizing novel BPMN implementation aspects and demonstrating the effectiveness of digital marketing for MSMEs in the textile sector. The qualitative data was collected through semi-structured interviews, which provided insights into current practices and areas for improvement. The findings show that increased cooperation between supply chain actors reduces inventory errors by 20% and increases the accuracy of financial tracking by 15%, thereby reducing operational risks. Furthermore, the digital marketing strategies increased customer engagement rates by 25%, directly contributing to sales growth. The findings further suggest that the proposed solutions not only resolve the identified problems but also provide a scalable model for enhancing resilience in Batik Ayu Arimbi’s supply chain operations as one of MSMEs. In conclusion, boosting sustainability and performance in MSMEs requires improved supply chain collaboration and the strategic application of digital tools.

Application of deep learning for predicting ignition delay in hydrogen combustion engines

2025-01-07T00:35:14+00:00

This study investigates the use of deep learning techniques to forecast ignition delays in hydrogen combustion systems, with a focus on optimizing hydrogen combustion processes in industrial applications such as stationary power generation and the automotive industry. The work utilizes experimental data from a rapid compression machine (RCM) and a shock tube. Two large datasets were created through 0-D simulations and experimental measurements, covering a wide range of conditions. The study involves the development of two artificial neural network (ANN) models, one for RCM and another for shock tube data, each with distinct architectures. The ANN models were trained, tested, and evaluated using thoughtfully divided datasets. The results demonstrate the effectiveness of the developed ANN models in predicting ignition delays with remarkable accuracy. Comparative analyses with 0-D simulations and experimental measurements reveal that the ANN models predict ignition delays "1000 times faster" than traditional simulation methods. This speed improvement is crucial for real-time industrial applications, allowing engineers to quickly optimize combustion parameters, adjust engine settings, and make operational decisions in a fraction of the time. The study highlights the potential of these ANN models to optimize hydrogen combustion processes, improving combustion efficiency, reducing operational costs, and enhancing resource utilization in industrial settings. This progress can play a significant role in optimizing hydrogen-powered internal combustion engines by increasing fuel efficiency, reducing emissions, and enhancing overall engine performance. In the automotive and power generation sectors, the quick predictive abilities of ANN models can support more effective energy production, decrease operational expenses, and lessen environmental harm.

Smart door lock design and development using the pahl and beitz approach

2025-03-13T01:11:12+00:00

Security vulnerabilities in conventional locks and existing smart locks necessitate innovative solutions that integrate robust authentication mechanisms. This study addresses the research gap by developing a smart door lock system that uniquely combines Indonesia's government-issued e-KTP (embedded with an RFID chip) and a capacitive touch sensor for multi-factor authentication, enhancing security while ensuring universal accessibility. The design process employs the Pahl and Beitz systematic engineering methodology, emphasizing iterative optimization through planning, conceptual design, embodiment design, and detail design phases. Key specifications, including e-KTP compatibility, cost-effectiveness <IDR 1 million, and energy efficiency, were prioritized. Prototype evaluations revealed that the final design achieved superior functionality, scoring 87/100 in a multi-criteria assessment. The assessment considered components, space, aesthetics, cost, and manufacturability. The system integrates an Arduino Nano Microcontroller, a 9V battery with a 17-day lifespan, and IoT connectivity for real-time feedback. Comparative analysis demonstrates a 40–60% cost reduction compared to commercial alternatives, alongside tamper-resistant advantages from e-KTP integration system, modularity potential, and rechargeable battery. This study underscores the viability of leveraging national ID systems in IoT security frameworks, offering policymakers and manufacturers actionable insights for scalable, standardized smart home solutions.

A smart city infrastructure implementation framework – insights from smart street lighting implementation optimization

2025-05-23T00:45:45+00:00

In recent years, the concept of smart cities and infrastructure has gained momentum as a solution to challenges such as population growth, resource management, and environmental sustainability. Rapid urbanization in many developing countries highlights the need for efficient infrastructure planning and management. This framework offers a structured approach for decision-making and resource allocation, enabling prioritization of investments to maximize limited resources while supporting development goals. The framework is tested through an analysis of the Smart Street Lighting Systems (SSLS) in Surabaya, Indonesia, addressing the city's intention to upgrade street lighting to reduce maintenance costs and energy consumption. Currently, the street lighting system faces issues including a high rate of broken or damaged lights and inefficiencies in handling complaints. However, limited funding and varied regional needs constrain any comprehensive upgrade. The proposed framework integrates the Analytical Hierarchy Process (AHP) to prioritize regions as weighting inputs, Mixed Integer Goal Programming (MIGP) to optimize the distribution of SSLS and conventional LED lighting across regions, and Cost-Benefit Analysis (CBA) to evaluate financial feasibility. Results recommend purchasing 11,915 new SSLS units with region-specific distributions, achieving a financially viable Benefit-Cost Ratio (BCR) of 2.059. These findings demonstrate practical implementation of smart city principles, balancing cost-efficiency, service performance, and stakeholder priorities. Policymakers can use this framework to maximize impact within budget constraints. This framework serves as a viable template for other regions and countries embarking on smart city infrastructure implementation.

Development of an energy management system for palm oil refinery facilities: implementing a systems approach

2025-05-15T02:36:43+00:00

This study aims to develop a proactive Energy Management System (EnMS) for a palm oil refinery using a comprehensive systems-based approach implemented carefully during the plant design phase. Unlike conventional methods that rely mainly on historical operational data, this research deliberately utilizes engineering design specifications together with simulation modeling to estimate accurate energy consumption baselines and formulate an ISO 50001-compliant EnMS. A regression-based analysis is systematically applied to define reliable Energy Performance Indicators (EnPIs), using production volume and running hours as key variables influencing overall energy utilization. The resulting analytical model estimates a Specific Energy Consumption (SEC) of 2.168 MWh/MT—significantly higher than the 0.45 MWh/MT BAT benchmark—primarily due to assumptions of full-capacity, simultaneous operation under conservative conditions. To support continuous energy performance improvement, the system incorporates PDCA-based review mechanisms and establishes progressive energy-saving targets: an initial 10% reduction, followed by 1–2% annual incremental improvements. Validation through structured feedback sessions from plant management confirmed the system's strong alignment with operational needs, feasibility within industrial contexts, and readiness for phased implementation. Ultimately, this study contributes a novel, simulation-based framework for integrating EnMS during the design stage, offering a scalable and adaptable model for energy-intensive industries that aim to enhance efficiency and achieve long-term sustainability from the outset.