Decision making in operator-machine assignment problems: an optimization approach in U-shaped production lines

ABSTRACTThe solution to the assembly line balancing with the hierarchical worker assignment problem (ALBHWP) provides the optimal allocation of workers and tasks to the stations that minimise the total worker cost. In the ALBHWP, tasks differ in terms of the qualification requirements of workers, and the qualification levels of workers are hierarchical. In the hierarchical workforce structure, a lower qualified worker can be replaced by higher qualified ones with higher costs, while the vice versa is not applicable. This problem has only been studied for straight assembly lines so far. In this paper, we introduce the ALBHWP for U-shaped assembly lines. We developed integer and constraint programming models for solving the ALBHWP and compared their effectiveness using an extensive set of benchmark instances. We solved the ALBHWP for straight and U-shaped assembly lines comparatively. Constraint programming models have been statistically proven to provide better quality solutions faster than integer programming models. Besides, the CP model outperforms the only available metaheuristic in the literature for the S-ALBHWP in almost all problem sizes. Another observation is that a U-shaped line design is more cost-effective than a straight line design, but solving the ALBHWP for U-shaped lines is more difficult regarding computational complexity.

We deal with a U-shaped production line with multiple machines and a multifunction worker. The worker visits machines in a cyclic fashion and operates an item on each machine. The operation consists of detaching the processed item from the machine, sending it to the next machine, putting the new item on the machine and switching it on. Walking times of the worker and processing and operation times are i.i.d. random variables for each machine. We show that the sum of the firstn cycle times is stochastically the same as that in the reversed system where machines are arranged in the reversed order. We give examples where the distributions of waiting and cycle times depend on the machines, called starting ones, where the worker begins operation at time 0. A sufficient condition is derived under which the limiting expected waiting and cycle times do not depend on the starting machines.

This paper discusses the optimum efficiency of a U-shaped flexible production line (U-FPL) and the relationship between the efficiency and cycle time of a U-FPL. In the literature, few researches have been done in this area. However, designing a U-FPL scheme, many designers decrease the cycle time of a U-FPL to increase its productivity and overlook the efficiency of a U-FPL. Thus it causes the increasing of equipment costs. This paper describes a model of the U-FPL that can present most U-FPLs and deduces an equation to calculate the efficiency of a U-FPL. From analyzing the equation, the optimum efficiency of a U-FPL and several rules for guiding the design of a U-FPL scheme are obtained

U-shaped Line (UL) utilizes many manufacturing industries. It applies the just-in-time principle, the lean manufacture, and the quality improvement. It reduces the waste in the manufacturing operation. This paper presents two UL models which consist of eight certain tasks and three uncertain tasks. Two UL models include UL No.1 and UL No.2. The uncertain tasks assume the time distribution that is discrete. The uncertain task time of UL No.1 classifies to three types. The uncertain task time of UL No.2 classifies to five types. The objective function of two UL models is the minimum workstations. Two UL models formulate on a zero-one integer linear programming. This academic work applies MATLAB R2014a which solves the solution of two models. The procedure is run by INTLINPROG toolbox. The solution of the uncertain tasks for UL No.1 is the optimistic time. The solution of the uncertain tasks for UL No.2 is the most optimistic time. The number of workstations of UL No.2 is less than the number of workstations of UL No.1. The most early finish tasks which assign along the UL line affect the minimum workstations.

Digital twin technology plays a crucial role in completing the virtual mapping of the physical workshop, enabling production monitoring and process optimization. New energy batteries are a form of green energy with advantages such as high energy density, environmental protection, and beneficial safety. The assembly line for new energy batteries is essential in efficiently obtaining battery packs of diverse varieties and high quality. This paper presents three assembly design schemes for square lithium iron phosphate battery packs as an example. By comparing the characteristics of different schemes, scheme C was selected as the preferred option for conducting three-dimensional modeling of the assembly line. This paper utilizes digital twin technology to create a digital representation of the battery pack assembly process. The process flow of the designed production line can be tested. The loading time, equipment idle time, fault time, blocking waiting time, material shortage waiting time, etc., of each workstation in the production line can be accurately calculated. Optimize the production process flow, reduce resource occupation, and save a large amount of human and material resources.

Currently, companies are trying to optimize production, even more so as the economic situation is gradually deteriorating in many countries around the world, which is currently caused mainly by the COVID 19 pandemic. A specific type of production is that on production lines, which are individual workplaces where various production and assembly operations take place. The aim of the article is to analyze the impact of the choice of the control method for the production process on the optimization of costs and the process. The subject of the research is the production of fire-fighting throttles on a production line that has three workplaces. The problem of waste on the production line was identified by direct measurements of time consumption at the individual workplaces and by using images of the working day schedule. The article compares three approaches to production line management and waste reduction. The first approach was based on counting the number of finished products on the production line. The second approach analyzed in detail the duration of the individual operations into which the workplaces were divided. In the third approach, a method of balancing workplaces was used, which brought time and cost savings. The achieved results, using different methods of production management, are analyzed in detail, and the conclusions evaluated. The article contributes a comparison of the achieved results of cost optimization through the course of production of fire dampers. The research results were applied in practice. The research used methods of industrial engineering in the field of ergonomics, technical and economic analysis, and production management. Visualization of hourly performances, downtimes and failures, execution of work procedures, Shop Floor Management and many others, are recommended as ways to implement waste elimination when using the methods of industrial engineering.

This article explores the impacts of digital transformations and new technologies in industrial sector (particularly through the Fourth Industrial Revolution) on optimizing production processes. Characterized by key technologies such as the Internet of Things (IoT), big data analytics, artificial intelligence (AI), blockchains, and advanced robotics, Industry 4.0 has significantly shaped modern manufacturing management. IoT enables autonomous communications between machines and equipment, providing real-time insights into production parameters and enabling predictive maintenance, and big data plays a vital role by analyzing the large volumes of data that are generated by these devices, thus supporting informed management decisions. AI and machine learning help automate complex tasks, optimize production schedules, and improve product quality through real-time adjustments. Blockchain enables decentralized and secure data recording, which is particularly useful in supply-chain management. Advanced robotics increases production speed and accuracy, thus reducing labor costs and mitigating any risks that are associated with hazardous tasks. Integrating these technologies requires strategic planning, including identifying key challenges, conducting pilot projects, integrating with existing IT and OT systems, and managing organizational change. Measuring the effectiveness of Industry 4.0 implementation should involve well-defined key performance indicators (KPIs) and return-on-investment (ROI) analysis. The primary challenges that are associated with adopting Industry 4.0 include the alignment of technology with specific business needs, employee resistance to change, and hidden costs of implementation. In summary, industrial transformation offers opportunities for companies to optimize production processes, reduce costs, and increase competitiveness in the global marketplace. However, a careful approach is necessary to maximize efficiency, foster innovation, and secure long-term success in an increasingly digitalized world.

Reducing scrap and improving an air conditioning pipe production line

Technological innovation is profoundly transforming industrial production, notably through automated systems like robotic production lines. To harness their full potential, effective monitoring and evaluation are essential, facilitated by Key Performance Indicators (KPIs). This study conducts a thorough analysis of KPIs within industrial automation, focusing on a robotic production line assembling heat exchangers for refrigeration equipment. The aim is to measure, evaluate, and optimize performance in a smart, adaptable manner, creating a robust database that is not currently available as intended. Identified KPIs include standards such as cycle time, utilization, efficiency, and tailor-made metrics for this application, aligning with Industry 4.0 principles and sustainable manufacturing by monitoring energy and resource consumption as well. A granular data analysis approach captures the daily performance of each robot, enabling comprehensive assessment. Operational, daily, and line-level indicators provide insights into specific improvement areas. The methodology’s scalability allows for easy adaptation to larger production lines, highlighting its versatility and potential applicability across various industrial settings. By proposing a granular and scalable KPIs framework, this research underscores the critical role of detailed performance monitoring in industrial robotics, driving continuous improvement and innovation while enhancing operational efficiency and competitiveness, in an ever-growing landscape of automated manufacturing environments.

With the sharp increase in the number of products and the development of the remanufacturing industry, disassembly lines have become the mainstream recycling method. In view of the insufficient research on the layout of multi-form disassembly lines and human factors, we previously proposed a linear-U-shaped hybrid layout considering the constraints of employee posture and a Duel-DQN algorithm assisted by Large Language Model (LLM). However, there is still room for improvement in the utilization efficiency of workstations. Based on this previous work, this study proposes an innovative layout of U-shaped and circular disassembly lines and retains the constraints of employee posture. The LLM is instruction-fine-tuned using the Quantized Low-Rank Adaptation (QLoRA) technique to improve the accuracy of disassembly sequence generation, and the Dueling Deep Q-Network(Duel-DQN) algorithm is reconstructed to maximize profits under posture constraints. Experiments show that in the more complex layout of U-shaped and circular disassembly lines, the iterative efficiency of this method can still be increased by about 26% compared with the traditional Duel-DQN, and the profit is close to the optimal solution of the traditional CPLEX solver, verifying the feasibility of this algorithm in complex scenarios. This study further optimizes the layout problem of multi-form disassembly lines and provides an innovative solution that takes into account both human factors and computational efficiency, which has important theoretical and practical significance.

Diversified and complicated manufacturing sites make optimal scheduling of production lines difficult. Under current manufacturing processes, it is almost impossible for schedulers to consider all the constraints of production processes. A strategy of simulation-based advanced planning and scheduling (APS) is employed to overcome difficulties that interfere with satisfactory on-time delivery and commitment to the current status. In simulation-based scheduling, key performance indices (KPIs) are important for selecting optimal dispatching rules in scheduling. In cases involving complex processes, in which the identification of appropriate KPIs is limited to selection among existing KPIs, KPIs should be chosen and modified carefully to optimize process management and to reflect all of the existing constraints of production. However, the existing methodologies for modifying KPIs are misplaced in complex manufacturing environments such as job-shop processes. We propose a new method to design and select appropriate KPIs that meet the characteristics of any given process, and verify with empirical analysis whether or not the KPIs meet requirements from experts of production lines.

The sound production and distribution of essential medicines, from producers to distributors, retailers, and ultimately to end-users, depend on the effective and efficient application of supply chain principles. This research assessed the awareness, adoption, and impact of supply chain principles on supply chain performance in the pharmaceutical manufacturing industries of Southeastern Nigeria. A cross-sectional descriptive survey approach was adopted. Randomly selected sixty-four (64) respondents from thirty-seven pharmaceutical industries that are registered with the Pharmacy Council of Nigeria at the time of the study, with one or two product lines, were used. Data was collected using a well-designed and validated questionnaire in an interviewer-administered approach. The collected data were descriptively analysed using SPSS (version 20). The results showed that the perceived awareness of supply chain principles among pharmaceutical manufacturing industries in the southeast was moderate (50.8%), while the knowledge of these principles was notably high (71.4%). Furthermore, the findings revealed a low adoption rate of modern supply chain principles (44.4%). However, where these principles were implemented, they demonstrated a significant positive impact (mean score: 3.77 ± 0.87) on key performance indicators, particularly in enhancing customer satisfaction and improving overall operational efficiency. Economic and financial barriers, as well as organisational and management issues, were identified as the major hindrances to the adoption of supply chain principles. There is a need for pharmaceutical manufacturing industries in the Southeast to prioritise workforce training with practical applications that enhance supply chain processes during budgeting.

Nowadays, the growth of industry can be seen as a nature of the world. Each company race again each other to increase productivity to produce new, high quality and product that fulfil customer demand. One can achieve the Key Performance Indicator (KPI) or targeted goal but without considering the cost, manpower, time or others elements is inefficient toward productivity. Upgrade production line in manufacturing industry needs huge investment to come out with good performance. The company can receive Return of Investment (ROI) and save more money from paying labor salary and increase productivity. However, the company also may have the risk of losing their money from the investment done. In this research, we studied the effectiveness of production line that equipped with automation usage to determine the productivity and quality of the product produced. We apply Data Envelopment Analysis (DEA) to measure efficiencies of the production line where DEA is one of an excellent tool that can evaluate efficiencies and have been using widely in many sectors. The model that will be used in this study is Two-Stage Network DEA. As a case study, this research focuses on the production line that producing a product with a high and continues demand to observe how the investment on automation can give good return or otherwise.

Purpose: This study examines the impact of climate change on global supply chain management by analyzing the influence of climate-related variables, specifically CO₂ emissions, temperature fluctuations, and flood-related economic damages, on key logistics performance indicators. These indicators include the customs score, infrastructure score, logistics performance index, and logistics competence and quality score. Methodology: A mixed-methods analytical approach was employed, incorporating regression analysis, correlation matrix evaluation, and principal components analysis (PCA), supplemented by visual analytics. This methodology enabled the identification of relationships between selected climate variables and supply chain performance metrics across various countries. Findings: The study reveals a statistically significant positive association between CO₂ emissions and improved logistics performance indicators. This suggests that regions with higher industrial output and consequently higher emissions often have more advanced logistics infrastructure and trade efficiency. Conversely, while flood-related economic damages exhibited a negative correlation with logistics performance, the relationship lacked statistical significance. The findings underscore the complex dynamic between industrial growth, environmental impact, and logistics efficiency. Unique Contribution to Theory, Practice, and Policy (Recommendations): This research contributes to the theoretical discourse by highlighting the dual role of industrial activity in enhancing logistics performance while exacerbating environmental degradation. Practically, it calls for urgent investment in climate-resilient infrastructure and adaptive logistics strategies. From a policy perspective, the study advocates for integrated climate and trade policies that align economic development with environmental sustainability. Future research should explore indirect and long-term effects of climate change on supply chains to inform more robust mitigation strategies.

Synopsis After three years in business together, Des Carpenter and Kees Schaddelee had a decision to make – should they double the size of their location, based on the opportunities and competitive threats they perceived? The startup phase took longer than expected and access to distribution channels was more difficult than expected. Nonetheless, the business gained traction with online sales that proved the concept of custom-made counters using EnvironiteTM technology was viable. As they prepared to expand the business, the owner-managers needed to decide on a growth strategy that would let them leverage their strengths. In analyzing their successes so far, they needed to evaluate their business model including their product line, target markets, marketing strategy (including the pricing strategy, product lines, and channels of distribution) and operations. Research methodology Data were collected through interviews with business owners and a review of company documents, production processes and the company website. Relevant courses and levels This case exercise will suit strategy and entrepreneurship students at both the senior undergraduate level and graduate level. The case discussion will ask students to consider operations, supply chain management, marketing and other issues, all through the lens of a holistic vision for the company. This case may be taught as an example of a growth strategy or a business model in a capstone business strategy course or higher level entrepreneurship course. It is appropriate for both undergraduate seniors and graduate students. Theoretical bases This case may be taught as an example of a growth strategy or a business model in a capstone business strategy course or higher-level entrepreneurship course. The case may be used to help students understand external and internal analysis, identifying the sources of value creation and competitive advantage, and creating an appropriate strategy for growth. It provides a rich context to discuss and apply the following conceptual tools: the application of a value chain analysis and the application of a business model canvas (key partners, key activities, key resources, value propositions, customer relationships, distribution channels, customer segments, cost structure and revenue streams). The case may also be used to reinforce the applications of growth phases in a young firm that are part of the entrepreneurial setting, for example, value proposition, ideal customer, revenue streams and key performance indicators.