Production Planning Research Articles

Knowledge-assisted hybrid optimization strategy of large-scale crude oil scheduling integrated production planning

As modern refinery crude oil scheduling scales up, traditional manual operations and independent optimization methods struggle with complexity and dynamics. This study proposes an empirically assisted integrated planning and scheduling optimization model.The integrated model is solved using a hybrid optimization algorithm combining mathematical programming and particle swarm optimization (MP/PSO). Long-term planning aims to minimize operational and transportation costs while maximizing refinery profits; short-term scheduling, based on initial long-term plans, aims to minimize unit switchovers. In the short-term scheduling phase, heuristic rules based on empirical operational knowledge generate a high-performing initial population to accelerate convergence. This strategy is crucial for enhancing refinery emergency response capabilities, ensuring stable operations, and improving economic benefits. Experimental results show that within a reasonable time frame, MP/PSO performs better than PSO and manual scheduling in large-scale crude oil scheduling scenarios.

Including Lifetime Hydraulic Turbine Cost into Short-Term Hybrid Scheduling of Hydro and Solar

In traditional short-term hydropower scheduling problems, which usually determine the optimal power generation schedules within one week, the off-design zone of a hydraulic turbine is modeled as a forbidden zone due to the significantly increased risk of turbine damage when operating within this zone. However, it is still plausible to occasionally operate within this zone for short durations under real-world circumstances. With the integration of Variable Renewable Energy (VRE) into the power system, hydropower, as a dispatchable energy resource, operates complementarily with VRE to smooth overall power generation and enhance system performance. The rapid and frequent adjustments in output power make it inevitable for the hydraulic turbine to operate in the off-design zone. This paper introduces the operating zones associated with various production costs derived from fatigue analysis of the hydraulic turbine and calculated based on the turbine replacement cost. These costs are incorporated into a short-term hybrid scheduling tool based on Mixed Integer Linear Programming (MILP). Including production costs in the optimization problem shifts the turbine’s working point from a high-cost zone to a low-cost zone. The resulting production schedule for a Hydro-Solar hybrid power system considers not only short-term economic factors such as day-ahead market prices and water value but also lifetime hydraulic turbine cost, leading to a more comprehensive calculation of the production plan. This research provides valuable insights into the sustainable operation of hydropower plants, balancing short-term profits with lifetime hydraulic turbine costs.

Comparative Analysis of Human and Artificial Intelligence Planning in Production Processes

Artificial intelligence (AI) has found applications in enterprises′ production planning processes. However, a critical question remains: could AI replace human planners? We conducted a comparative analysis to evaluate the main task of planners in an intermittent process: planning the duration of production orders. Specifically, we analysed the results of a human planner using master data and those of an AI algorithm compared to the actual realisation. The case study was conducted in a large production company using a sample of production products and machines. We were able to confirm two of the three research questions (RQ1 and RQ3), while the results of the third question (RQ2) did not meet our expectations. The AI algorithms demonstrated significant improvement with each iteration. Despite this progress, it is still difficult to determine the exact threshold at which AI outperforms human planners due to the unpredictability of unexpected events. Even though AI significantly improves prediction accuracy, the inherent variability and incomplete input data pose a major challenge. As progress is made, robust data collection and management strategies need to be integrated to bridge the gap between the potential of AI and its practical application, fostering the symbiosis between human expertise and AI capabilities in production planning.

Automated methodology for calculating key production indicators of oil field development for business planning model

Background: In order to secure the stable and efficient operations of oil-producing companies, it is essential to forecast the profitability and ROI of the enterprise, as well as to regulate development for the next 5 years. Accurate forecasts facilitate more informed planning and decision-making, directly influencing the economic sustainability and competitiveness of the enterprise. Aim: The purpose of this study is to develop an innovative approach to automate the methodology for calculating key development indicators in a business planning model. Materials and methods: The study utilizes methods for collecting and analyzing production and geological data, empirical forecasting models, and statistical analysis techniques to enchance the accuracy and reliability of forecasts. This approach employs modern algorithms and technologies to process large volumes of data, which allows for more accurate and reasonable forecasts of key production indicators of the field development. Results: This methodology can be applied to forecast a five-year business plan and evaluate its expected implementation. It is integrated into the ‘Production planning and monitoring’ of the ABAI information system, which allows direct data export from the database, automates the monthly monitoring of production indicators, and generate reports for further export. Conclusion: The proposed method for automated planning of key production indicators of the development enhances the accuracy and efficiency of forecasting, thereby improving the quality of planning and evaluating the implementation of the business plan. This contributes to more informed and strategically validated management of oil production processes. Automation of planning processes reduces the labor costs traditionally associated with manual analysis and calculations, freeing up resources for more strategic purposes. This enables rapid responses to changes in production conditions and prompt adjustments of plans. As a result, managers can allocate resources more efficiently, minimize risks, and increase the overall productivity of oil production operations.

Integrating CLSP and GLSP models for optimizing production batch sizes in vulcanized component manufacturing

This study addresses the complex issue of optimizing production batch sizes within a company specializing in manufacturing rubber, thermoplastic, and polyurethane components, all of which undergo vulcanization prior to sale. Through meticulous analysis of the production planning process, it was observed that certain activities could be logically grouped into two sequential stages without compromising information integrity or detail. The initial stage encompasses press activities involving vulcanization and curing, while the next one involves greenhouse operations for post-curing. The commencement of the second stage is contingent upon the completion of all activities in the first stage. Consequently, an effective production plan must concurrently consider both stages. To tackle this, a mathematical model is proposed to address the batch sizing problem within the company, integrating both stages and drawing inspiration from the CLSP (Capacitated Lot Sizing Problem) for the first stage and the subperiod approach derived from GLSP (General Lot-sizing and Scheduling Problem) models for the second stage. The model is solved utilizing the CPLEX solver version 12.8. It incorporates 74 items, spans 20 periods, each with specific demands, and delineates 16 sub-periods within each planning horizon period. Experimentation involving a hypothetical example and subsequent tests utilizing real data from the case study company demonstrate the successful resolution of the problem, yielding enhanced production plans, particularly in terms of cost reduction objectives.

Optimized Economic Production Quantity Inventory Model with Weibull Decay, Dynamic Holding Cost Under Varied Demand Scenario and Demand Series Analysis

This paper presents a sophisticated modification of the economic production quantity (EPQ) model. It incorporates Weibull-distributed progressive deterioration, dynamic holding costs, variable demand, and strong strategies for effectively handling shortages and backlogs. Targeting products that have a short-term expiration but can still be stored for a long time without significant deterioration, the model aims to increase production quantities while lowering overall variable expenses. The extended EPQ model utilizes Maclaurin series approximations to solve ordinary differential equations, effectively capturing the dynamics of inventory under three different demand patterns. The focus of this handle’s situations when the demand for a product changes in a predictable manner over time and considers the likelihood of defective goods during the manufacturing process. This approach provides useful insights for optimizing inventory management and production planning in the manufacturing of goods that are used up or consumed. The usefulness of the model is confirmed in the study through the use of two illustrative instances. Additionally, sensitivity analysis is utilized to suggest potential areas for additional investigation.

Bi-objective optimization modeling of a three-level supply chain in production planning and scheduling considering price-dependent demand: a case study of a soap factory

Bi-objective optimization modeling of a three-level supply chain in production planning and scheduling considering price-dependent demand: a case study of a soap factory

ПЛАНИРОВАНИЕ ПРОИЗВОДСТВЕННОГО ПРОЦЕССА МЕХАНИЧЕСКОЙ ОБРАБОТКИ БРУСОВЫХ ЗАГОТОВОК КАК ЭЛЕМЕНТ ПОВЫШЕНИЯ ЭКОНОМИЧЕСКОЙ БЕЗОПАСНОСТИ ПОДРАЗДЕЛЕНИЙ УГОЛОВНО-ИСПОЛНИТЕЛЬНОЙ СИСТЕМЫ

The article examines the issue of planning production activities at woodworking enterprises in the industrial sector of the penal system. The organization of planning of production and economic activities based on the requirements of the penal enforcement legislation is considered. A conceptual scheme for planning the process of mechanical processing of timber blanks has been developed. The place of effective planning in the overall strategy of ensuring the economic security of the industrial sector of the penal system is shown.

A mathematical model for potash supply chain management with a strategic logistics perspective

Abstract Accepted by: M. Zied Babai This paper introduces a novel Integer Linear Programming model designed to enhance the efficiency and sustainability of the potash supply chain, a crucial element supporting global agriculture. The developed mathematical optimization model focuses on fleet selection (private/outsource) and incorporates the concept of ‘inter-warehouse collaboration’, which addresses key logistics considerations. Integrating mining, processing, storage and transportation, the model encompasses decision variables like extracted carnallite amount, production, storage levels and shipped potash amount. Illustrated through a case study on the Arab Potash Company in Jordan, the results showcase the model’s proficiency in meeting local and international market demands. The model ensures resilient and sustainable supply chain performance by emphasizing logistics optimization, particularly in fleet selection. The study attains the highest ‘warehouse-to-warehouse’ support for Standard and Granular potash types in the international demand scenario, contributing to efficient production planning and fleet management. In conclusion, the presented mathematical model is a valuable tool for potash industry stakeholders, offering insights for strategic decision-makers involved in production planning and fleet management.

Research on the Injection–Production Law and the Feasibility of Underground Natural Gas Storage in a Low-Permeability Acid-Containing Depleted Gas Reservoir

Depleted gas reservoirs are important places for the rebuilding of gas-storage reservoirs. In order to demonstrate the feasibility of constructing and operating such underground gas storage, a low-permeability gas-storage seepage model considering fracture development was developed and established. The model was solved using semi-analytical methods, and the pressure–response characteristics during natural gas injection were analyzed. The impact of gas injection volume on formation pressure has been clarified, and the calculation method for ultimate injection pressure has been determined. Additionally, through numerical simulation methods, the migration law of acidic gas during gas injection, the variation law of produced acidic gas concentration, and the main control factors affecting the concentration of the produced acidic gas were studied. Furthermore, measures to reduce the concentration of the acidic gas produced were proposed. Finally, injection and production plans were designed for typical depleted acidic gas reservoirs, simulating the operation of gas storage for 12 cycles. The results indicate that the quality of natural gas produced in the third cycle can meet the Class II standard for commercial natural gas. Through this study, the feasibility of constructing gas-storage facilities for acidic depleted gas reservoirs has been demonstrated, and injection and production strategies for this type of gas reservoir have been proposed.

ECOBRICK SOFA DESIGN IS ENVIRONMENTALLY FRIENDLY USING THE HOUSE OF QUALITY METHOD AND THE BOSTON CONSULTING GROUP (BCG) METHOD

Plastic is one of the longest decomposing types of waste with a decomposition time of up to 10 centuries. Plastic waste that is difficult to decompose can be minimized by using ecobrick techniques. This ecobrick technique aims to turn plastic waste that was initially worthless (zero value) into a product that has added value. This study uses four methods, namely the House of Quality (HOQ), Quality Function Deployment (QFD), Slovin, and Boston Consulting Group (BCG) methods. These methods are used to determine product planning and development, consumer desires, product position in market share, and systematic evaluation of its ability to produce products to satisfy consumers. This study involved 100 respondents obtained from the slovin method. Where the respondents are positioned as research subjects. The respondent data was obtained from distributing questionnaires through google forms. The data collected is calculated and analyzed for unique and minimalist sofa House of Quality (HOQ). The results of this study inform that customer feedback on the product or Voice of Customer can affect quality control and improvement by using the statistical method or Statistical Quality Control. Furthermore, unique and minimalist sofa companies each have different product development focuses. Following up on the findings in this study, the company can adjust the needs and desires of these consumers to the design and service standards as well as the obstacles that may occur.

Investigating cross-docking criteria for raw materials with hybrid MCDM

The cross-docking process involves unloading raw materials from incoming vehicles and immediately loading them onto outgoing vehicles. This can greatly improve supply chain operations and expedite transportation to the factory. By minimizing warehouse storage expenses, space requirements, and inventory management, the process can be streamlined effectively. In industries where production profiles need to be responsive to market demands, the location of the cross-docking facility and transportation planning are crucial. This article aims to investigate the criteria for selecting a cross-docking location, using a case study of Arta Profile Company in Ardabil. The research population includes all industrial engineers and production planners in the company, with 10 industry experts selected for this study. The analytic network process (ANP) and grey complex proportional assessment (COPRAS-G) methods were used to compare and prioritize cross-docking locations. Additionally, the fuzzy DEMATEL method was used to determine the strength of connections between different locations. The COPRAS-G approach helps decision-makers make informed decisions by evaluating different options and their importance. The industry experts identified 28 criteria for this study. The results from the network analysis method showed that the most important criterion is the vehicle routing problem (VRP), with a value of 0.46. According to the ANP technique, strategic decision-making is the best option. The fuzzy DEMATEL method revealed that distribution centers are the most important factor, followed by reduced delivery/distribution operations, and finally, the cost of late or early delivery.

Construction of a Digital Twin System and Dynamic Scheduling Simulation Analysis of a Flexible Assembly Workshops with Island Layout

Assembly Workshops with Island Layout (AWIL) possess flexible production capabilities that realize product diversification. To cope with the complex scheduling challenges in flexible workshops, improve resource utilization, reduce waste, and enhance production efficiency, this paper proposes a production scheduling method for flexible assembly workshops with an island layout based on digital twin technology. A digital twin model of the workshop is established according to production demands to simulate scheduling operations and deal with complex scheduling issues. A workshop monitoring system is developed to quickly identify abnormal events. By employing an event-driven rolling-window rescheduling technique, a dynamic scheduling service system is constructed. The rolling window decomposes scheduling problems into consecutive static scheduling intervals based on abnormal events, and a genetic algorithm is used to optimize each interval in real time. This approach provides accurate, real-time scheduling decisions to manage disturbances in workshop production, which can enhance flexibility in the production process, and allows rapid adjustments to production plans. Therefore, the digital twin system improves the sustainability of the production system, which will provide a theoretical research foundation for the real-time and unmanned production scheduling process, thereby achieving sustainable development of production.

The Critical Role of Product Managers and Their Responsibilities in Software Startups: A Systematic Literature Review

In the high-paced, uncertain, and resource-constrained environment of software startups, achieving product–market fit and acquiring initial customers are critical yet challenging tasks. These challenges are often exacerbated by the absence of strategic planning, inefficient requirements selection processes, inadequate resource allocation, and insufficient market research, leading to an alarming 63% failure rate among software startups. The Product Manager (PM), responsible for product strategy, planning, and results monitoring, plays a pivotal role in navigating these complexities; however, the PM role is often undervalued and lacks a universally accepted definition. This study aims to enhance the understanding of the PM's role within software startups by conducting a comprehensive Systematic Literature Review (SLR). Employing a rigorous selection methodology, we reviewed 134 studies and identified 662 distinct PM tasks across 122 activities. Notably, only 7 studies (5.73%) specifically addressed the software startup context, revealing a substantial gap in the literature. Our findings indicate that while product planning and requirements prioritization are dominant topics, many critical activities remain underrepresented, underscoring the need for targeted research in these areas. By addressing the unique challenges faced by software startups, we pave the way for more targeted and effective PM strategies. Future research should consider adopting mixed-method approaches to deepen the understanding of PM practices and investigate underexplored areas such as go-to-market strategies, roadmap creation, and requirements selection within the startup context.

Decision-Making Model to Support Agricultural Policies in Realizing Economic and Social Sustainability

Abstract Background Achieving economic and social sustainability is the goal of any policy when defining measures. We focus on the beef sector, where many challenges have arisen due to its structural characteristics, such as an unfavourable scale structure, high costs, low efficiency, and a low environmental footprint. This paper presents an example of the support provided by a mathematical programming model in the development of a Common Agricultural Policy Strategic Plan for the period 2023-2027. Methods/approach It is a model based on linear programming that allows such an ex-ante analysis by calculating production plans at the farm level and aggregating the results at the sector level. Objectives When defining the interventions, the question arose as to what the reform of the Common Agricultural Policy will bring and to what extent the sector should be supported in meeting these challenges. These were the concerns of agricultural policy that we sought to support by modelling different scenarios. Results The results show that the situation of the sector will worsen, especially for larger farms, but they also show the great importance of production-related payments to mitigate the negative trend. Conclusions The applied approach proves to be suitable for supporting the design of agricultural policy and achieving greater economic and social sustainability in the sector.

Ultra-Short-Term Wind Farm Power Prediction Considering Correlation of Wind Power Fluctuation

Accurate ultra-short-term power prediction for wind farms is challenging under rapid wind speed fluctuations, complicating production planning and power balancing. This paper proposes a new method considering spatial and temporal correlations of wind fluctuations among adjacent wind farms. The method first calculates the time difference between power fluctuations based on wind speed, direction, and relative positions, determining the prior information period. The variational Bayesian model is then used to extract implicit relationships between power fluctuations of adjacent wind farms, enabling power prediction during the prior information period. Finally, the non-prior information period is predicted to complete the ultra-short-term power prediction. Using measured data from three wind farms in Fujian Province, compared to other models, the method demonstrates improved accuracy by effectively leveraging the power fluctuation characteristics of adjacent wind farms, and it has a certain amount of generalizability.

The LTAR Grazing Land Common Experiment at Platte River High Plains Aquifer.

The Long-Term Agroecosystem Research (LTAR) network of the United States Department of Agriculture (USDA) consists presently of 18 sites within the contiguous United States that are managed by the Agricultural Research Service (ARS) and its partners. The LTAR network focuses on developing national strategies for more efficient, resilient, and profitable agricultural production systems, improved environmental quality, and enhanced rural prosperity. The Platte River High Plains Aquifer (PRHPA) LTAR site is managed jointly by the University of Nebraska-Lincoln (UNL) and USDA-ARS and is one of the LTAR sites that conduct research on both integrated cropping and grazing systems. The PRHPA region encompasses multiple land resource areas and diverse agricultural production systems. The PRHPA sites, predominantly located in eastern Nebraska, are designated as an integrated system focused specifically on the region's dominant production practices of row crop (corn and soybean), managed pastures, and beef cattle production. Here, we focus on C3 cool-season smooth bromegrass (Bromus inermis Leyss.) pasture grazing systems under prevailing and alternative management practices for the region. The sites evaluate continuous and rotational grazing with and without pasture fertilization (prevailing practices). In an additional treatment, cattle are supplemented with dry distillers grains plus solubles, while manure supplies fertilization (alternative practice). Main measurements at the site evaluate plant and animal productivity, forage quality, greenhouse gas fluxes, and soil physical, chemical, and biological properties. This paper describes the regional characteristics of the PRHPA site, ongoing LTAR research related to pasture and livestock production, stakeholder engagement, and future research plans.

Модели цепей поставок и их классификация

The article discusses various models of supply chains of goods. The classification and models of supply chains are considered. The basic concepts and elements of supply chain management are revealed. The theoretical basis of the article is research conducted in such fields of knowledge as logistics and supply chain management. Logistics supply chains are an important element in the activities of any company. They include the planning, production and management of goods, services and information from the point of origin to the point of consumption. Supply chains coordinate complex patterns of movement and transportation, shipment and receipt, import and export operations, warehousing, inventory management, procurement, production planning and customer service.

Application of lean methodology in the optimisation of the production process of entrepreneurs

This paper provides a detailed overview of how Lean philosophy can be applied in the context of electric bicycle production to improve efficiency and reduce losses. The central part of the paper deals with the detailed application of these methods and techniques to the production of electric bicycles. Via a detailed analysis of existing processes and losses, the paper provides specific proposals for optimising the production process, improving quality control, better production planning, and encouraging continuous improvement (Kaizen). The case study provides a comparison between conventional and Lean production of electric bicycles, including a detailed quantification of losses and their ranking according to the magnitude of the impact on overall efficiency. Via specific application of the Lean philosophy, its tools and techniques, this paper shows how to achieve significant improvement in the efficiency of the production process, reduce losses and increase the competitive advantage in the market of electric bicycles.

Digital twin-based multi-granularity synchronisation for production-warehousing under batch processing mode

Batch production is a cost-effective way that boosts efficiency in manufacturing systems. As customer demands become increasingly customised, there is a growing need to consolidate numerous small-batch orders. However, if batch production exceeds the optimal level, it may lead to the premature completion of customer orders, causing temporary congestion in the finished goods warehouse and prolonged resource occupation. Consequently, an essential focus of this study pertains to coordinating decision-making units like workshops and warehouses within batch production setups to ensure stable system operation amidst dynamic customer demands. The proposed solution encompasses a Digital Twin-based Multi-level Synchronised Control System (DTMCS) framework, featuring the Multi-granularity Synchronised Control Mechanism (MgSCM) and a collaborative optimisation model for batch production and warehouse planning. By examining a practical case study from a paint manufacturing company, the research delves into the effects of four synchronised control patterns on various system performance metrics, such as operating costs, equipment and warehouse utilisation rates, and system stability. Overall, this study provides an adaptable and cost-effective solution for enhancing production-logistics synchronisation.