Production Planning Research Articles

Integrating Six Sigma Principles towards Multi-Objective Production Planning for Enhanced Quality in the Al Kharj Manufacturing Sector

Effective production planning and creating high-quality items are two of the most critical factors in establishing long-term success in the manufacturing business. This study presents Goal Programming (GP), a Multi-Objective Optimization technique for decision-making issues, incorporating Six Sigma concepts to tackle the industrial sector's complex production planning problems. The aim is to provide a systematic framework for decision-making, ensuring a comprehensive approach to quality engineering in manufacturing processes. The current study entails considerable practical implications regarding the fridge industry in specific practical ways. Production planning was enhanced by evaluating market demand, manufacturing costs, and sales data using LINGO. Deploying Six Sigma to find the limits on demand, helped reduce production costs while making additional revenue from sales. Sensitivity analysis revealed that by the following year, the firm is expected to have decreased manufacturing costs by 13.13% to minimize expenses, while it is also anticipated to have reduced sales by 1.12% to maximize revenue. The GP technique demonstrated that the Al Kharj fridge industry could optimize sales revenue and costs. The suggestions provided based on these findings are actionable and have the potential to be effectively implemented in the industrial sector.

Optimized Scheduling Model Considering the Demand Response and Sequential Requirements of Polysilicon Production

The polysilicon production process has significant potential to be made adjustable, and actively changing its production schedule to participate in grid dispatch can effectively alleviate the pressure on the power supply and balance demand while promoting renewable energy consumption. Considering the complex inter-coupling relationship between the sequential requirements and adjustable potential of polysilicon production, this paper analyzes the electricity consumption characteristics of various stages of a polysilicon production process that uses the improved Siemens method as its primary approach to production. The interaction between the polysilicon production process, equipment, and materials is modeled through the state–task network method, and the production timing requirements are transformed into constraint expressions. An optimized scheduling model that includes the production’s sequential requirements within a time-of-use electricity pricing context is established. Our analysis shows that the proposed model can formulate a feasible production plan with the lowest power purchase cost for polysilicon plants while meeting the production’s sequential requirements and product order demands.

A reinforcement learning hyper-heuristic algorithm for the distributed flowshops scheduling problem under consideration of emergency order insertion

Large enterprises are composed of several subproduction centers. The production plan is changed based on the procedure of the manufacturing system. The distributed flowshop scheduling problem under consideration of emergence order insertion is challenging as the assignment of the product, and the scheduling of the process are coupled with each other. A general distributed flow shop scheduling problem regarding emergency order insertion (DFSP_EOI) is addressed under rescheduling circumstances in this paper. The Q-learning hyper-heuristic algorithm with dynamic insertion rule (QLHH_DIR) is proposed to solve the DFSP_EOI. Eight low-level heuristics (LLHs) for static job assignment. A dynamic insertion rule based on the state of each production center is designed for emergency order insertion. The Q-learning mechanism at high-level space selects appropriate LLH through learning the experience from the optimization process. The computational simulation is carried out, and the results confirm that the proposed algorithm is superior to the competitors in solving the distributed flow shop rescheduling problem. The results of the 720 problem instances show that the proposed algorithm is highly efficient in rescheduling problems.

Circular economy-based integrated closed-loop farming system: A sensitivity analysis for profit optimization

This study proposes and constructs a model for a closed-loop farming system (CLFS) for pig manure waste reuse under circular economy principles (CEPs) to optimize economic benefits. Pig farming produces waste that harms the ecological environment in Taiwan. However, few studies have focused on pig farming waste within the context of CEPs, where the resource utilization of pig manure waste and CLFS are the main concerns. This study introduces a CLFS with a farming production plan that enhances systematic efficiency and builds a framework for resource reuse, supporting a local living circle. The resource utilization of pig manure waste is integrated into the CLFS, ensuring that the entire pig raising production process operates within the system, maximizing waste utilization to increase economic benefits. A sensitivity analysis is also presented. The results confirm that all counties and municipalities in Taiwan achieved self-sufficiency in pork production, reaching a 100% satisfaction rate for pork demand, and feed prices did not impact the overall CLFS.

Distributionally robust CVaR optimization for refinery integrated production–maintenance scheduling under uncertainty

In the petroleum refining industry, efficient production planning and maintenance scheduling are crucial for economic performance and operational efficiency. Moreover, the production processes face significant uncertainties stemming from market fluctuations and equipment failures. However, traditional optimization methods often treat production and maintenance independently and neglect the risk management associated with uncertainties in the production process, leading to unreliable plans and suboptimal execution. To address these issues, this paper proposes an innovative data-driven distributionally robust conditional value-at-risk (DRCVaR) method to tackle the integrated production–maintenance optimization problem under crude oil price uncertainty. By constructing confidence sets with L2 norm constraints based on historical data, our approach directly links the model’s conservatism to the amount of available data, effectively managing risk. In addition, we propose robust linear transformation to simplify the min–max nonlinear problem into a conic constraint problem, enhancing solution efficiency and ensuring better operational stability. Refinery case studies demonstrate that the proposed DRCVaR consistently achieves a practical and acceptable solution, significantly outperforming state-of-the-art approaches.

Analysis of the Concentration and Specialization of Vegetable Production in Colombian Municipalities between 2007 and 2021

In 2021, vegetable crops in Colombia accounted for 3.7% of national agricultural production, reaching a total production of 2.71 million tons, of which 81.2% came from 8 departments, the most representative being Boyacá (20.3%), Antioquia (17.3%), Cundinamarca (12.0%), Nariño (8.1%) and Santander (7.4%). Of the country’s 1,121 municipalities, 796 reported vegetable production. This article aims to analyze the concentration and specialization of vegetable production in Colombian municipalities between 2007–2021. The methodology analyzed two statistical databases applying the Gini coefficient, Location Coefficient (LQ), Herfindahl-Hirschman Index (HHI) and Moran’s index of spatial autocorrelation to identify the agricultural clusters (CA) in the Colombian vegetables sector, which are complemented with thematic cartography. Measurements were made in 2007, 2014 and 2021 (start–mid–end). Additionally, the sensitivity of five vegetable crops (the most important for national production) is estimated using OLS, with respect to the average annual temperature (AAT) and total annual precipitation (TAP). The results reveal a regional Gini of 0.7496, 0.7674 and 0.7072 in 2007, 2014 and 2021, respectively. By 2021, 398 municipalities were identified with LQ greater than 1.0, of which 66 were among the 20% of the highest values (between 12.03 and 27.02). Likewise, 51 municipalities were found where vegetables accounted for more than 50% of total agricultural production. Additionally, in 2021, the municipalities with the highest level of specialization HHI were Sáchica (1.00), Cajicá (0.77), Aquitania (0.65), Tibasosa (0.605) and Cota (0.619). The OLS between production, AAT and TAP identified statistical significance only in tomato and green onion. In conclusion, the findings indicate a significant regional concentration of vegetable production in the last fifteen years. A variation of 34.7% in the number of producing municipalities was observed at the between 2007 and 2021. The most representative crops in the years analyzed were tomato, green onion, bulb onion and carrot, which in 2021 were 64.0% of national production. These results contribute to the guilds institutions, regional governments, municipal administrations, and public policymakers because they constitute an input to regional productive planning in rural areas, especially where peasant family farming is prevalent.

Zero-inventory plans, constant workforce, or hybrid approach? Analysing pure production strategies for enhancing factory resilience with demand variability

Historically the notion of resilience capability has primarily been conceptualised as a holistic construct in the domain of supply chain networks – referred to as SCRES. The field of SCRES has since evolved gaining prominence among scholars and practitioners. However, the existing SCRES literature inadequately delves into the embedded resilience harboured by their constituent components, practices, or internal routines, including the aggregate production planning (APP) process. Therefore, the purpose of this article is to examine the interplay between medium-term pure strategies for APP and the resilience of manufacturing facilities, assuming demand to be the sole source of uncertainty. To accomplish this, a realistic Monte-Carlo simulation model integrated with a robust heuristic procedure for pure strategies is merged with a factory resilience index-based Cobb–Douglas function. The results of this research suggest that manufacturing facilities achieve a higher level of resilience when certain ‘zero-inventory plans’ are implemented. Based on this key finding, production/demand planners might incorporate the resilience dimension alongside the customary manufacturing success factors. Thus, to the best of the authors’ knowledge, this study represents the first attempt to establish a clear linkage between the implementation of pure APP strategies and a quantitative measure of factory resilience.

Exploring the development trajectory of single-machine production scheduling

AbstractThe Single-Machine Scheduling Problem (SMSP) serves as the cornerstone of scheduling theory. Almost all developments in production planning and control were initially introduced and tested within single-machine production settings. Exploring the literature on SMSPs illuminates the entire development trajectory of scheduling theory. This study employs the Main Path Analysis (MPA) for a systematic review that is the first of its kind in the literature on SMSPs. By analyzing 2904 articles, the main path and key branches that highlight the most significant documents in the development trajectory as well as the specifics in different development stages of the field are identified. The literature is analyzed in four stages of development, following a chronological order. Within this framework, the focus of the studies, key technologies, and characteristics of each period are discussed. Cluster and keyword analysis are employed alongside MPA to identify and explore the predominant themes in the field, including SMSPs with deterioration and/or learning effects, solution methods for SMSPs with various constraints, solving SMSPs integrated with maintenance-related variables and constraints, advanced solution methods and constructive heuristics for minimizing delays, as well as agent-based methods for single-machine scheduling. The review outcomes consist of directions for future research based on the development trajectories of SMSPs.

Study on the Impact of Spatiotemporal Changes in the Ecological Environment on Grain Crops in the Subtropical Monsoon Climate Zone

Global warming and land-use type shifting lead to the degradation of natural habitats. The research on the ecological and environmental impact of the subtropical monsoon climate zone on food crop cultivation is not systematic enough. An Integrated Valuation of Ecosystem Services and Trade-offs (InVEST)–Patch-generating Land Use Simulation (PLUS)–Maximum Entropy (MaxEnt) model was created to provide a comprehensive assessment of the spatiotemporal variations for food crop habitat quality (HQ) in China’s subtropical monsoon climate zone from 2010 to 2030. The HQ degradation trend was obvious during 2010–2030 under the influence of land-use change. The expansion of lower habitat areas was larger than that of medium and higher habitat areas. The shared socio-economic pathways SSP-CP and SSP-EP increased the mean total area of suitable areas compared with the SSP-ND scenario for food crops by 9% and 17.8%, respectively. Land-use shifts increased the suitable range of food crops and mitigated the negative impacts of urban expansion on food growth. This research has theoretical guidance for land-use planning for food crop production in subtropical monsoon climate zones.

USO DE MODELOS DE PREDIÇÃO PARA MELHORAR O PLANEJAMENTO DE ESTOQUE DE ALTA ROTAÇÃO - ESTUDO EM UMA EMPRESA NO SETOR DE BEBIDAS

This project develops a prediction model for inventory planning of high-turnover products, specifically draft beer, in a company in the beverage sector. Using neural network and time series techniques, the aim is to minimize waste and guarantee product availability, taking into account the short shelf life of draft beer and the need for advance orders. The research involved collecting sales data from previous years, processing the data and developing a user-friendly interface. The model was trained with machine learning algorithms to predict weekly sales. The results show that the model is effective at predicting sales patterns, allowing for adjustments and future integrations, such as the inclusion of climate and demographic data. This work provides a solid basis for optimizing inventory management and formulating more efficient commercial strategies.

USO DE MODELOS DE PREDIÇÃO PARA MELHORAR O PLANEJAMENTO DE ESTOQUE DE ALTA ROTAÇÃO - ESTUDO EM UMA EMPRESA NO SETOR DE BEBIDAS

This project develops a prediction model for inventory planning of high-turnover products, specifically draft beer, in a company in the beverage sector. Using neural network and time series techniques, the aim is to minimize waste and guarantee product availability, taking into account the short shelf life of draft beer and the need for advance orders. The research involved collecting sales data from previous years, processing the data and developing a user-friendly interface. The model was trained with machine learning algorithms to predict weekly sales. The results show that the model is effective at predicting sales patterns, allowing for adjustments and future integrations, such as the inclusion of climate and demographic data. This work provides a solid basis for optimizing inventory management and formulating more efficient commercial strategies.

Modelling and optimisation of low-indirect land used change biomass supply chains

Biofuels derived from biomass feedstocks produced following the implementation of measures that avoid indirect land used change (ILUC) have the potential of reducing dependency on fossil-based fuels without competing with food value chain. This work develops a model- and optimisation-based methodologies that address challenges related to the production of low-ILUC biomass feedstocks. Case studies investigated include planning of Iow-ILUC biomass feedstock production, biomethane production using low-ILUC biomass feedstocks, integrated production of first generation (1G) and second generation (2G) bioethanol using Miscanthus, and production of hydrotreated vegetable oil (HVO) using castor seeds. Analysis of results show that farmers interested in these models are recommended to sell low-ILUC biomass such as soybean, wheat and brassica above the breakeven price to avoid losses. The estimated selling price for the three crops are 362 €/t, 321 €/t and 381 €/t respectively. To meet the demand of 40,000 t/yr of 2G bioethanol in the UK, approximately 17,094 ha of underutilised land is required. Policy makers should consider options to support alternatives such as retrofitting, and inter-cropping to avoid or mitigate ILUC.

Analysis of Production Process Parameters Using Computer Simulation at the Planning Stage

Computer simulation is one of the tools that is often used to support activities related to the management of the production process. Computer simulation allows for the analysis of the parameters of production or logistics processes, among others. The study presents a comparative analysis of analytical methods and computer simulation (FlexSim 22.2.1 software) in relation to the subject matter. The use of computer simulation allowed for the verification of the number of production resources, determined by analytical methods, and for the proposal of improvements that would allow for shortening the duration of the production process and reducing its costs. The proposed improvements at the stage of planning the production process in terms of the number of production resources used to implement the assumed production plan allowed for the reduction of the duration of the production process and its costs. Therefore, it is justified to use computer simulation in solving problems related to various aspects of the functioning of an enterprise.

Percepción sobre la salinización del suelo en una zona vitícola de Argentina

Salt-affected soils occupy 1,000 million hectares and concentrate in arid and semi-arid regions, affecting between 20 and 30% of irrigated lands. Primary salinization is derived from natural conditions, while secondary salinization is the product of anthropic factors. Salinized agricultural plots can be abandoned and excluded from the productive system. In this context, productive restoration is an opportunity to recover these sites. Our objective is to inquire about the environmental perception of the social actors of salinization. We aim at transversally incorporating the social component in productive restoration planning. Our study case was the East Zone of the North Oasis of Mendoza, where almost half of the irrigated area is cultivated by vines, almost a quarter is abandoned land, and the rest includes olive trees, fruit trees, and vegetables. We conducted semi-structured interviews to identify the main lines of perception, which were analyzed to recognize strengths, weaknesses, opportunities, and threats for the design of productive restoration projects at a local scale. Strategic, efficient, and sustainable planning must include the social dimension.

Investigation on matching of individualised requirements and shared manufacturing resources in the context of shared factory runtime

Shared Manufacturing (SharedM) empowers individuals to engage in manufacturing via order-driven, resource-sharing processes, embodying the principles of Industry 5.0 and a human-centric approach. This study tackles the challenges of requirement-resource matching to realise Production Planning and Scheduling (PP&S), stemming from conflicts in the distribution and ownership of shared Manufacturing Resources (MRs). We introduce a factory-level runtime framework alongside a self-organising redundant matching algorithm based on sample average approximation to efficiently manage shared MRs and individualised orders. Then, an industrial case study illustrates the application from the modelling of MRs and individualised requirements, to the generation of the final matching Gantt chart. The findings demonstrate that the proposed shared factory can align idle MRs with personalised consumer demands effectively. This paper presents a viable solution for implementing shared factories in industrial settings, providing valuable insights into social manufacturing, SharedM, and novel manufacturing paradigms focused on human-centric values.

CONCEPTUAL FOUNDATIONS OF MARKETING ORGANIZATION AND LOGISTICS MANAGEMENT IN AGRIBUSINESS

Objective: This study aims to explore the impact of marketing and logistics systems on the efficiency of agricultural product production, storage, and sales, focusing on the challenges faced by agricultural producers in Uzbekistan. Methods: The research employs a systematic analysis of the agricultural supply chain, evaluating the role of marketing and logistics in managing the flow of goods, determining production volumes, and assessing market capacities. Data is gathered through literature review, case studies, and qualitative analysis of existing agricultural practices. Results: The findings highlight significant inefficiencies in the current agricultural logistics system, where the lack of proper marketing strategies and logistics infrastructure leads to high production and transportation costs, imbalances in consumer markets, and low competitiveness. The study identifies key areas where improvements in the marketing-logistics framework can reduce costs by 10-15%, enhance production planning, and better align supply with consumer demand. Novelty: This research introduces a comprehensive approach to integrating marketing and logistics systems in agricultural production, emphasizing the importance of strategic coordination between resource suppliers, producers, and market demands. It provides insights into developing a more efficient, market-responsive agricultural sector in Uzbekistan

Wasserstein distributionally robust learning for predicting the cycle time of printed circuit board production

This paper proposes a Wasserstein distributionally robust learning (WDRL) model to predict the production cycle time of highly mixed printed circuit board (PCB) orders on multiple production lines. The PCB production cycle time is essential for optimizing production plans. However, the design of the PCB, production line configuration, order combinations, and personnel factors make the prediction of the PCB production cycle time difficult. In addition, practical production situations with significant disturbances in feature data make traditional prediction models inaccurate, especially when there is new data. Therefore, we establishe a WDRL model, derive a tight upper bound for the expected loss function, and reformulate a tractable equivalent model based on the bound. To demonstrate the effectiveness of this method, we collected data related to surface mounted technology (SMT) production lines from a leading global display manufacturer for our computational experiments. In addition, we also designed experiments with perturbations in the training and testing datasets to verify the WDRL model’s ability to handle perturbations. This proposed method has also been compared with other machine learning methods, such as the support vector regression combined with symbiotic organism search, decision tree, and kernel extreme learning machine, among others. Experimental results indicate that the WDRL model can make robust predictions of PCB cycle time, which helps to effectively plan production capacity in uncertain situations and avoid overproduction or underproduction. Finally, we implement the WDRL model for the actual SMT production to predict the production cycle time and set it as the target for production. We observed a 98–103 % achievement rate in the last 20 months since the implementation in September 2022.

AI in Manufacturing: Driving Operational Excellence

This comprehensive article explores the transformative impact of Artificial Intelligence (AI) in the manufacturing sector, focusing on key operational areas, including automated operations management, predictive maintenance, advanced production planning, and quality control enhancement. The article examines how AI technologies revolutionize traditional manufacturing processes through intelligent automation, data-driven maintenance strategies, sophisticated production planning, and enhanced quality control systems. The article demonstrates significant improvements in operational efficiency, maintenance optimization, production accuracy, and product quality across diverse manufacturing environments. Through detailed analysis of implementation cases and industry studies, this paper illustrates how AI integration drives operational excellence while creating new paradigms in intelligent manufacturing systems.

GESTÃO DA CADEIA DE SUPRIMENTOS USANDO IA

Supply Chain Management (GCS) integrates and coordinates all activities from obtaining materials to final delivery, aligning objectives to maximize efficiency, reduce costs and improve quality. Planning in the chain is crucial to guarantee resources, minimize uncertainties and add value to the customer. Logistics is essential in efficient distribution, and inventory management balances supply and demand. Performance indicators (KPIs) and process mapping are used to evaluate and improve operations, while production planning and control (PCP) ensure efficiency in production and delivery. These aspects form the basis of GCS, essential for customer success and satisfaction. Therefore, the objective of this work is to analyze the use of artificial intelligence of supply chain management in a contemporary context, identifying challenges, strategies and impacts for organizations, with a focus on optimizing processes and improving business competitiveness.

Applications of Kuz–Ram Models in Mine-to-Mill Integration and Optimization—A Review

The Mine-to-Mill (M2M) approach aims to enhance efficiency and reduce costs in the mineral processing industry by optimizing the mining and processing stages. M2M integrates orebody characterization, blasting, and downstream processes, such as grinding and flotation, demonstrating that material fragmentation directly impacts downstream efficiency. This review studies the development and applications of fragmentation models in M2M integration and optimization, finding that their study is divided into three phases. In the first, the potential of M2M is investigated through simulation models that improve fragmentation in blasting to optimize grinding. The second focuses on the practical application of these models in mines, while the third phase integrates geometallurgical data into mine block models, enhancing production planning and selective ore extraction. The M2M integration has demonstrated significant improvements in plant performance, particularly in increasing grinding efficiency through optimized blast fragmentation. The literature also emphasizes the role of optimizing crushing and grinding conditions through models and circuit adjustments to enhance performance and reducing energy consumption. Geometallurgy plays a crucial role in plant optimization by identifying areas with better processing characteristics and adjusting operating parameters to maximize efficiency. Recent studies have shown how the implementation of integrated models can increase the profitability and sustainability of mining operations.