Production Planning Research Articles

A fuzzy multi-objective model for aggregate production planning considering energy consumption, carbon emission, chemical, solid waste, and wastewater

With a growing focus on environmental conservation, enterprises continuously reevaluate their conventional profit-driven business models. This shift has led to the development of green production planning models, attracting considerable attention in recent years. Previously, energy consumption or carbon emission were the most considered green parameters in the production planning problems. Nevertheless, several other essential green aspects such as reductions in chemical, solid waste, and wastewater are usually neglected. Therefore, this study proposes a multi-objective model for environmentally conscious aggregate production planning, simultaneously considering the above-mentioned green objectives. Additionally, the uncertain parameters of the model, including the green parameters, were modelled through fuzzy programming. Furthermore, this study incorporated a contradicting relationship between energy consumption cost and associated carbon emission, which was previously presented as non-contradicting. This relationship was illustrated by utilizing the time-of-use electricity tariff scheme. To solve the model, the initial step involved the defuzzification of uncertain attributes, followed by the transformation of the multi-objective model into a single-objective model. Finally, the IBM ILOG CPLEX Optimization Studio solver was utilized to solve the model based on the data collected from a precision engineering company. The generated production plan achieved an overall satisfaction level of 71%, which is reasonably acceptable to the decision maker. Moreover, sensitivity analysis has been conducted for the degree of optimism of the objective functions and the feasibility degree of the constraints. A general overview of the results associated with the performed sensitivity analysis indicated that the optimum values of the objective functions deteriorate as the parameters increase, though all remain within an acceptable range. The proposed study will assist decision makers in generating production plans that are both economically and environmentally feasible.

Automated generation of digital models for manufacturing systems: The event-centric process mining approach

Digital and simulation models support the design and management of complex systems. However, system modelling is a time-demanding and knowledge-intensive activity. Moreover, modern manufacturing systems are subjected to frequent changes in production plans and subsequent reconfigurations. Therefore, the quick regeneration of the digital models is necessary to align digital twins and cyber-physical systems. This paper proposes a novel event-centric process mining paradigm, a process discovery algorithm, and a set of Key Performance Indicators for the fast and automated generation of digital models and their benchmarking. The discovery algorithm is based on the Event Relationship Graph of the conceptual model of the physical line. The algorithm is tested in four realistic systems of increasing complexity to verify the accuracy in modelling multi-product systems with re-entrant flows and random reworks in the presence of the assembly, disassembly, and split processes beyond the processing operations, and multi-operation workstations. The Event Relationship Graphs of the four systems are presented through the equivalent Petri nets models. The proposed approach is suitable for systems where the sensor positions are known and meaningful, like manufacturing systems, and it is effective for the quick automated generation of digital models for the activities of production planning and control as it requires a few seconds of computation time and a few hours of system observation.

A novel framework for production planning and class-based storage location assignment: Multi-criteria classification approach

Efficient warehouse management is essential for optimizing inventory, minimizing transportation costs, and enhancing overall performance. This research introduces a novel Mixed-Integer Nonlinear Programming (MINLP) model to address the Storage Location Assignment Problem (SLAP) in warehouse management. Integrating multi-criteria decision-making with strategic production planning, our model advances warehouse operations by allocating storage locations to products strategically, focusing on reducing transportation distances and maximizing storage efficiency. The distinctive innovation of this study is the nuanced application of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) results to strategic storage location assignments, enhancing the model's capability to consider a comprehensive evaluation of inventory attributes, including physical characteristics and perishability. This approach evolves TOPSIS's application in warehouse management, enabling it to consider both physical characteristics and perishability of products. The outcomes of TOPSIS, including product classifications and preferences, serve as vital inputs to the mathematical model, facilitating a comprehensive evaluation of storage locations that encompasses spatial, demand-related, and physical aspects of inventory. Additionally, our research introduces a versatile decision support system, adaptable to various operational requirements. This system enhances practical decision-making in warehouse management, accommodating scenarios based on single or multiple criteria, including the cube-per-order index (COI). The research results highlight the significant impact of this innovative approach in enhancing warehouse management. By addressing the complexities of storage location assignment and integrating multiple criteria, we achieve more efficient and cost-effective warehouse operations. The approach has been shown to be adaptable and practical, making it a valuable contribution to the field of logistics and warehouse management.

Generative neural network models for synthetic solar irradiance sequences

With the significant increase in the amount of solar energy sources, the variation in the production caused by the stochastic nature of these sources becomes a larger problem for the electrical grid. Having a large percentage of installed units, therefore, requires some type of both short- and long-term planning of energy production. Most planning and forecasting tools require a large amount of solar energy data for the location in question and oftentimes that data is difficult to obtain. In this paper, we propose a method for generating synthetic solar irradiance data and test its applicability on forecasting and long-term planning problems. Synthetic data are generated using various generative adversarial networks and masked autoencoder based models, which are all trained on a dataset of solar irradiance data measured on the territory of the city of Banja Luka in Bosnia and Herzegovina. The synthetic data are evaluated both on different types of short-term forecasting tools and a long-term solar plant simulation tool, and the obtained results are compared with the cases where real data is used. By comparing the results, we have concluded that by using generative models we are able to generate synthetic data which is not only realistic, but applicable for both of these uses.

A MILP-based approach to address the production and distribution planning of large industrial gas supply chains

This paper focuses on addressing the Production Routing Problem (PRP) of industrial gas supply chains (SC). The work introduces a Mixed-Integer Linear Programming (MILP) based approach to effectively tackle this challenging problem. The proposed framework aims to increase company profits by optimizing both production and distribution activities simultaneously. For the distribution part, a route generation algorithm is employed to initially generate a set of feasible routes, ensuring that only routes with practical significance are considered. Subsequently, the mathematical model is developed considering only the set of routes generated. The formulation includes decisions associated with plants production levels, plant selection to supply the different consumers, clients to be visited per trip and day, quantity to be delivered and routes to be used. As part of the problem, it is considered that trucks can make several trips per day, as well as trips of several days. To take into account the last feature, the approach accounts for the explicit effect of the lead time on production and distribution decisions. In this way, the work allows for different delivery times for customers included in the same route, according to their inventories and usage levels. The model considers fleet unavailability until they return to the plants after finishing a trip. Finally, two case studies, one motivating and the other industrial size, are presented and solved. The results obtained demonstrate the effectiveness of the proposed approach to solve the case studies in reasonable CPU times.

Research on reconfigurable systems in discrete manufacturing workshops

Abstract With the development of smart manufacturing, the demand for personalized production has made reconfigurable systems in the manufacturing industry a focal point of research and application in high-end manufacturing. The paper analyzes the current status of the manufacturing industry and the challenges of reconfigurable technology. Furthermore, it designs and develops a reconfigurable system for discrete manufacturing of automotive components. The paper focuses on issues such as creating reconfigurable models, designing adaptive reconfiguration algorithms, accomplishing the creation of multi-layered models, the design of rule libraries, and algorithm implementation. The reconfiguration result scheme can be input into commercial software for performance simulation, and can directly integrate with production systems to complete production planning. This demonstrates the rationality of the reconfigurable models in the system, the correctness of the reconfiguration algorithms, and the effectiveness of the system, providing a theoretical basis for reconfigurable production lines.

Solving distributed assembly blocking flowshop with order acceptance by knowledge-driven multiobjective algorithm

In the era of Industry 4.0, industrial artificial intelligence technologies make production planning and scheduling systems more flexible. A new distributed assembly blocking flowshop problem with order acceptance and scheduling decisions (DABFSP_OAS) was investigated in this paper. Specifically, three objectives—the makespan, total energy consumption (TEC), and total profit (TP)—were addressed simultaneously. To address this problem, we established a knowledge-driven non-dominated sorting genetic algorithm-II (KDNSGAII). First, three initialization schemes based on the problem-specific property were introduced to generate diverse initial population. Then, to accelerate the convergence process, we developed multiple Pareto-based crossover and mutation operators. In addition, two novel destructive reinsertion strategies based on product and job sequence length were implemented to enhance the development ability of the algorithm. Finally, the designed strategies were evaluated. Comparisons and discussions showed that the KDNSGAII outperformed the other state-of-art multi-objective algorithms in solving DABFSP_OAS.

Optimizing the System Life Cycle in Manufacturing Industry Through Implementation of IoT

This paper aims to explore how the implementation of the Internet of Things (IoT) can optimize the system life cycle in the manufacturing industry, enhancing efficiency, sustainability, and profitability. A qualitative research method with a case study approach was employed. Data collection techniques included observation, documentation, and in-depth interviews with management, supervisors, engineers, and operators. The data were analyzed using an interactive model, consisting of data reduction, data presentation, and conclusion drawing/verification. It was found that the implementation of IoT in the manufacturing industry's production process significantly improves production planning and control, asset and equipment maintenance, production cost reduction, product quality, and work environment safety. IoT-enabled real-time monitoring and data analytics facilitate better decision-making, predictive maintenance, and optimized resource utilization. It was found that. the implementation of IoT in the manufacturing industry's production process significantly improves production planning and control, asset and equipment maintenance, production cost reduction, product quality, and work environment safety. IoT-enabled real-time monitoring and data analytics facilitate better decision-making, predictive maintenance, and optimized resource utilization

Assembly Line Integration as a Lever for Efficiency and Space Utilization in Automotive Parts Production

Optimizing space in manufacturing is essential because space is both limited and expensive. Efficient use of space improves productivity, efficiency, and factory layout planning. The aim is to reduce the area used while increasing the value generated per unit of space, aligning with lean manufacturing principles that focus on value-adding activities and minimizing waste, including wasted space. In manufacturing, space optimization involves analyzing areas that add value, support operations, or cause waste. Key considerations include workspace design, safety clearances, and the overlap of functional areas, which can complicate space utilization. Effective floor space planning compares different layout options and measures space usage across categories to find the most efficient arrangement. Simple layout changes can greatly improve space efficiency by up to 80% without requiring large investments. This is crucial for boosting production efficiency and adapting to challenges, like those presented by the COVID-19 pandemic, which highlighted the need for flexible and efficient use of space to maintain safety and operations.

침략전쟁 시기(1931~1945) 朝鮮 馬政計劃의 추이와 건마보국운동

This study focuses on Colonial-Korean horse administration during the period of aggressive war (1931-1945) and analyzes: ①The progress of the 'Second Phase Korean Horse Production Plan' which began after the Manchurian War(1931).②The twists and turns surrounding the establishment of the 'First Phase Korean Horse Administration Plan' led by the Governor-General of Chosen before and after the Sino-Japanese War.③ The failure of the privately-driven horse production policies during the Pacific War, including the emotional movement such as the “Healthy Horse National Service Movement” and the process of frugal mobilization. These analyses confirm the deceptive nature of the colonial horse administration. To summarize the findings: Following the Manchurian Incident, the demand for military horses increased, leading the Governor-General’s office to plan ‘the first phase of horse production’ starting in 1932. This plan aimed to increase the number of military-eligible horses to 30,000, focusing on the three northern provinces of Korea. However, from 1935, Japan urged the Governor-General to expand the number of horses to 40,000 over an 18-year period. This was known as Japan's 'Second Phase Horse Administration Plan (1932-1936)'. Japan dismissed the Governor-General’s request for financial support and demanded an increase of 50,000 horses over the 18 years. In response, the Governor- General claimed that the 'reality of Korea' justified a target of increasing 30,000 horses over 15 years. After about a year of negotiation, the Korean Horse Administration 15-Year Plan was settled, aiming to increase the number of horses by 40,000 over 15 years. However, due to financial issues, the plan did not fully begin implementation until 1939. In the case of Hamnam, the second horse administration area, the plan was to secure approximately 16,000 horses from 1939 to 1951, primarily through the importation of about 9,000 horses from Japan. The plan emphasized importation over domestic production. In reality, the increase in the number of horses did not proceed as planned. The number of horses showed a decreasing trend up until 1938, and the numbers of donkeys and mules sharply declined until 1943. After 1938, Japan reduced Korea's role in its horse administration plans and increased the focus on Manchuria, leading to a stagnation of the plan. The Governor-General attributed this failure not to policy issues but to a lack of knowledge or enthusiasm among private breeders. With the outbreak of the Pacific War, the Governor-General anticipated a surge in demand for military horses. Consequently, the responsibilities of the existing 'Korean Horse Administration 15-Year Plan' were transferred to the semi-official private organization, the Korean Horse Society, and the National Mobilization Association, in an attempt to increase the voluntary efforts of horse production. The Korean Horse Society was established on April 1, 1942. However, its establishment undermined the plan to increase military-eligible horses through breeding with high-quality horses from Japan. Instead, the focus shifted to a so-called 'Korean Horse Mobilization Policy', which aimed to convert existing Korean horses into military horses. Yet, by early 1944, the increase in military horses had not been achieved according to the plan. As noted in the Korean Horse Society Chairman’s address on March 1, 1944, during the Healthy Horse National Service Movement, many of the horses in the horse administration area were 'useless broken-down horses.' In summary, while the Governor-General boasted about increasing the number of military horses to 40,000, the priority from the beginning was mobilizing Korean horses and importing Japanese horses rather than actual production. Even the Korean military did not trust Korean horses and preferred mobilization and importation over production.

Quantifying carbon footprint of salmon fillet processed in Vietnam: impact of transportation and improvement opportunities.

The carbon footprint of a product represents the amount of greenhouse gas (GHG) emissions released during its production, transportation, and consumption and is calculated as carbon dioxide equivalent (CO2-eq). It should be integrated into different existing and future seafood awareness campaigns to create more holistic yardsticks by which consumers, retail businesses, and producers can assess the environmental impacts of seafood. This study used the life cycle assessment (LCA) method for the first time to quantify the carbon footprint of salmon fillet products processed in Vietnam for export. The carbon footprint of 1-kg salmon fillet at the factory gate ranges between 7.20 and 15.05kg CO2-eq, depending on transportation modes of head-on-gutted (HOG) salmon from Norway to Vietnam. Transportatiton by airfreight doubled carbon footprint of salmon fillet products processed in Vietnam compared to sea freight. Feed and electricity were identified as the two most respective contributing factors during the stage of cultivation, processing fresh salmon in Norway, and the stage of salmon fillet processing in Vietnam. They accounted for about 95% and 50% of the total carbon footprint in these stages of the production chain, respectively. To reduce the carbon footprint of the salmon fillet products processed in Vietnam, the company should (i) make a careful production plan to use sea freight transportation instead of airfreight and (ii) use more electricity from renewable energy sources. Furthermore, the carbon footprint of these products can be reduced by improving the cultivation process via changing feed ingredients and enhancing the feed conversion ratio (FCR).

AI Research for Power Grid Transmission and Environmental Energy Saving Using Fuzzy Evaluation

Given the increasing adverse impacts of climate change on ecosystems, the power sector needs to improve rapidly. One solution is to utilize efficient resources for energy production and processing. In terms of resource efficiency, although AI has not yet been fully utilized, AI-6G can play a key role in developing environmentally friendly and efficient energy saving systems that support a more environmentally friendly environment. This paper focuses on the utilization of 6G technology for power transmission, environmental protection and energy saving projects. The advantage of 6G technology is the faster communication between devices within the network. Fuzzy Integrated Assessment (FIA) is an effective assessment methodology applicable to power transmission, grids, retrofitting processes, eco-security and energy efficiency measures that can be used to assess the effectiveness of these programs. This paper focuses on sustainable energy production by integrating AI-6G with emphasis on maintenance, production planning, fault detection and incorporating green AI.

Optimization of production, distribution, natural resources and capacity planning throughout process sector worldwide supply chains with various goals

This study delves into the intricate dynamics of green transformation within global mining and other process industries, focusing on production, distribution, and capacity planning under the framework of the Asian Mineral Vision. By addressing sustainability, resource efficiency, and cost reduction, the research highlights the multifaceted challenges faced by industry participants in achieving green objectives. In addition to financial efficiency, the study emphasizes the importance of customer service quality and responsiveness. To tackle these challenges, we propose a novel multiobjective mixed-integer linear programming (MILP) approach. The model incorporates key goals such as minimizing total cost, total flow time, and total missed sales, reflecting the interconnected nature of financial efficiency, operational agility, and customer satisfaction. To further enhance the model's flexibility, we integrate discrete strategies for plant capacity expansion, recognizing the crucial role of capacity management in responding to evolving demand dynamics. The multiobjective optimization problem is addressed using the lexicographic minimax technique and the ε-constraint method. A comprehensive numerical example illustrates the practical relevance and effectiveness of our proposed model and solution methods, providing valuable insights into improving the resilience and performance of green supply chain networks in the process industry.

Evaluation of Livestock Enterprises in terms of Farmer Satisfaction and Current Situation

In addition to meeting the basic needs and nutritional needs of people, the agricultural sector is of vital importance for countries due to many factors such as its place in employment, the supply of raw materials to the industrial sector and the use of the products produced as inputs, and its share in national income. The agricultural sector needs to be supported due to its unique characteristics and the importance of the sector. Although economic factors come to the fore on the sustainability of agricultural enterprises, the current situation and satisfaction of the enterprises also have an important place. In this study, the current situation and satisfaction of livestock enterprises are evaluated. The research was conducted in Seben District of Bolu Province small ruminant breeding as well as small ruminant breeding enterprises agricultural enterprises. In determining the enterprises, fullcensus method was used. Complete census method was used to determine the enterprises. Face-to-face questionnaire application was used to collect the data and SPSS package program was used to analyze the data obtained. As a result of the study, it was concluded that 74.4% of the enterprises were not satisfied with the situation and future of their agricultural enterprises, and they wanted their children to turn to different sectors and not to continue in the agricultural sector. In addition, producers stated that agricultural supports are important for the continuity of production and sustainability of enterprises (62.8%), and that they take agricultural supports into consideration in their production planning.

Effect of Data Integration Techniques on Operational Efficiency in Manufacturing Industries in Iran

Purpose: The aim of the study was to assess the effect of data integration techniques on operational efficiency in manufacturing industries in Iran. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that by seamlessly amalgamating data from disparate sources across production lines, supply chains, and customer feedback systems, manufacturers have been able to streamline processes, optimize resource allocation, and achieve significant cost savings. For instance, real-time data integration facilitates timely decision-making, allowing for adaptive production planning and inventory management. Furthermore, the integration of advanced analytics and machine learning algorithms has enabled predictive maintenance strategies, reducing downtime and enhancing overall equipment effectiveness (OEE). These technological advancements not only bolster operational efficiency but also foster innovation, as companies leverage integrated data insights to drive continuous improvement initiatives and meet evolving consumer demands. Implications to Theory, Practice and Policy: Resource-based view (RBV), technology acceptance model and dynamic capabilities theory may be used to anchor future studies on assessing the effect of data integration techniques on operational efficiency in manufacturing industries in Iran. Manufacturing firms should prioritize strategic implementation plans for data integration technologies to ensure that investments in IoT, AI, ETL, data warehousing, data virtualization, and APIs are aligned with their overall business objectives. Policymakers should provide incentives and support for the adoption of advanced data integration technologies in the manufacturing sector.

Integrating Blockchain in Scheduling: A Comprehensive Review of Current Trends and Future Directions

This review paper offers a comprehensive exploration of blockchain technology's integration in scheduling activities across various industries. It systematically gathered and analyzed literature, identifying 133 relevant documents through rigorous screening from 2017 till 2023. The bibliometric analysis, including co-authorship and co-occurrence maps, revealed key trends, influential authors, and emerging research areas. Content analysis focused on blockchain applications in service optimization, system and resource planning, management, project and production planning, energy optimization, and security enhancement. The research acknowledged significant advancements in these areas, particularly in terms of improved efficiency, transparency, and security. The findings underscore the potential of blockchain in revolutionizing scheduling and management processes across different sectors, highlighting the need for further exploration of its applications and implications in various professional contexts. The paper aims to bridge the gap in the literature by presenting a holistic view of blockchain's role in advancing scheduling methods, driving innovation, and fostering more robust, efficient, and transparent scheduling systems across industries.

An innovative integrated framework for multi-level production planning and control systems in construction

The construction industry is plagued by chronic inefficiencies, such as low productivity, significant waste, cost overruns, and schedule delays. One of the most critical contributors to these issues is fragmented and ineffective planning and control methods. This research aims to address these challenges by developing an innovative integrated multi-level framework for production planning and control that harmonizes various planning methods and control metrics across different schedule levels. Employing the Design Science Research (DSR) methodology, this study systematically develops and evaluates an innovative framework that integrates internal and external project characteristics, utilizing the concept of "functionality" as a primary key for coherent integration. Furthermore, a multi-objective mathematical model is incorporated to clarify the framework's components and optimize the number of suggested planning methods and control metrics at each scheduling level, aiming to balance the functional requirements of project teams with the practicality of implementation.The application of the framework to a renovation case study demonstrates its practicality and strategic value, effectively addressing real-world project management challenges. Moreover, industry and academic evaluations highlight its potential to significantly improve construction project management practices.

Assessing the Competitive Advantage of Ecuador’s Shrimp Industry in the Global Market Using the Balassa Index for 2018–2022

Increased shrimp production in Ecuador might significantly boost the nation’s economic development while allowing for the sustainable exploitation of its natural resources. Business owners in Ecuador still require diagnostic tools for investment, marketing, and production planning. Therefore, this study aimed to evaluate the competitive position of Ecuador’s shrimp industry and direct competitors in the global shrimp market. Export data on shrimp from the International Trade Centre and Ecuador’s National Chamber of Aquaculture were used for the top five shrimp-exporting countries. The period 2018–2022 was chosen as the information was available at the start of the research. Balassa’s methodology was employed for the competitiveness analysis, and statistical analysis was conducted to validate the findings. The results show Ecuador has a significant competitive edge compared to its main rivals, Argentina, India, Indonesia, and Vietnam. This analysis indicates that Ecuador has the potential to win bigger market shares in the global shrimp trade despite being placed 70th and 97th, respectively, in the 2017–2018 Global Competitiveness Index and the 2018 Logistics Performance Index. Therefore, it is necessary to establish strategic production plans for Ecuadorian shrimp enterprises to adapt to the global shrimp trade demands and to advance production and supply chain technology so that Ecuador can access additional worldwide markets.

A Mathematical Framework Evaluation for Sustainable EOQ Considering Environmental Factors

One of the key concerns in production planning has always been determining the economic lot size. Researchers have been studying this issue for a while, and numerous models have been created to meet objectives with the least amount of expense. This study's objective is to assess a more sophisticated approach to deciding lot size by taking into account the costs associated with emissions taxes (environmental impact). It will be suggested to adopt a framework, called Sustainable Economic Order Quantity (SEOQ), for inventory control in the pump manufacturing industry. For the purpose of assisting decision-makers and policies on inventory issues, this study included a useful numerical analysis as well as a sensitivity analysis. Finally, the experimental findings demonstrated that the suggested models would solve the issues with the lowest possible overall inventory costs.

Modeling and optimization for the continuous catalytic reforming process based on the hybrid surrogate optimization model

To address the modeling and optimization challenges of the complex reaction system in the continuous catalytic reforming process, a new integrated simulation and optimization framework is presented. First, a detailed mechanism model is established based on a reaction network involving 32 components and 50 reactions, coupled with mass transfer, heat transfer, pressure drop, and catalyst deactivation equations. Then, to solve the differential-algebraic equations in the mechanism model, a multi-objective hybrid optimization method with the adaptive infill strategy is introduced. GAMS and MATLAB are integrated to perform a joint iterative solution. Finally, two cases are conducted with the proposed algorithm. Results show that the mechanism model calculation deviations are below 4 % of reactor temperature, pressure, and composition distribution, and the Pareto front of various production plans is obtained. The accurate simulation and rapid trade-off optimization among the key goals can be achieved to provide scientific decision support for enterprise production.