Material Flow Optimization Research Articles

Study of Friction Stir Welding effects on the Microstructure & Mechanical Properties of AA3003 Pipes

Abstract In the present study, similar Aluminum (AA3003) alloy pipes with a thickness of 5 mm were friction stir welded using a high-speed steel (HSS) tool with a cylindrical pin profile. Welding was done at three different rotational speeds and three traverse speeds corresponding to pipes of three different diameters. Six combinations of rotational speed and traverse speed were used to observe the effect of energy input on the properties, specifically tensile strength and microhardness, as well as grain size of the resulting microstructure. This systematic variation in welding parameters was designed to assess how changes in energy input influence both the mechanical performance and microstructural attributes of the welded joints. The tensile test results indicated that an ultimate tensile strength (UTS) of 84% relative to the base metal was achieved at a rotational speed of 900 rotations per minute(rpm) and a traverse speed of 131.94 mm/min. The impact energy corresponding to these parameters was found to be 38% more than base metal which is a remarkable contribution. This combination of welding parameters facilitated optimal material flow and bonding, resulting in improved tensile properties of the welded joint. Additionally, the tensile results showed a consistent pattern in the tensile failure of the welded samples, where the joint failure occurred at the location with the lowest hardness in the welded region. This correlation highlights the critical influence of hardness distribution on the structural integrity of the welds, indicating that areas with reduced hardness are more prone to failure under tensile loading of FSWed joints.

Optimizing municipal solid waste management in industrial ports: A case study of source separation implementation

ABSTRACT With the acceleration of industrialization and population growth, there has been a notable surge in municipal solid waste (MSW) production globally. This necessitates the development of efficient waste management strategies that align with sustainable economic, environmental, and social goals. Implementing sorting and source separation policies serves as foundational steps toward optimizing material flow and resource utilization. In industrial areas, waste generation around a primary source may improve policy implementation and participation. Given the unique strategic context of port environments, Bahregan Port in Iran was selected as a case study to evaluate the proposed methodology. Three integrated scenarios were formulated and prioritized using the ELECTRE-I model: currently practiced collection method, and segregated collection followed by source separation of MSW into two or three groups. The analysis identified the third scenario as the most effective approach. Remarkably, when evaluating the results under optimistic and pessimistic conditions, the ranking of preferences remained consistent across scenarios in the optimal state. Under pessimistic conditions, all scenarios received identical scores, indicating significant uncertainty in criteria weights. Furthermore, sensitivity analysis revealed that criteria such as the raw material sources protection, emissions in the waste separation sector, and greenhouse gas emissions were highly influential. Interestingly, criteria with the highest weighting did not always dictate the final scenario selection. External factors, such as expert opinions on specific criteria, played a pivotal role in scenario prioritization. This research serves as a foundational study for source separation initiatives, offering practical and technically viable strategies to enhance environmental sustainability within industrial settings. Implications Statement Rising industrialization and population growth have led to increased municipal solid waste (MSW) production worldwide. To address this, efficient waste management strategies are essential, focusing on sustainability and resource optimization through sorting and source separation policies. By prioritizing the scenario of segregated collection followed by source separation – industrial areas can optimize material flow and resource utilization. The study highlights the influence of criteria like raw material protection, emissions, and greenhouse gases, making this research foundational for enhancing environmental sustainability in industrial settings. This study provides practical strategies for enhancing environmental sustainability in industrial settings through source separation initiatives.

Design of an improved layout for a steel processing facility using SLP and lean Manufacturing techniques

Implementing lean manufacturing practices in the systematic layout planning (SLP) process for improving facility layouts in various industries is essential. This approach enhances material handling processes and effectively manages space utilization. An efficient plant employing systematic layout planning leads to improved resource utilization and facilitates the integration of lean manufacturing techniques such as value stream mapping, 5 S, Ohno’s 7 wastes, and cellular manufacturing. This research article presents a case study on a steel processing plant in Dubai with an area of 160 m × 160 m, where systematic layout planning and lean manufacturing techniques were applied. The factors considered included space and material flow optimization, incorporating layout alternatives based on systematic layout planning principles along with lean manufacturing techniques. The study revealed a 34% reduction in material flow and an efficient overall space utilization improvement of 26%. Additionally, cost analysis validation was conducted.

Design of modelling and online simulation for energy systems in papermaking mill

This paper presents a general framework of modelling and online simulation for papermaking mill’s energy systems. The energy flow models of thermal power plant, DIP and paper machine for a papermaking mill was designed and implemented an online simulation. The traditional offline, closed and scattered modelling and simulation methods were changed into online, open and integrated forms. Through the analysis of energy systems of papermaking mills, the framework has implemented the followings. The unit (equipment) models were established, it can analyse the energy of energy flow material. The corresponding material flow models library was developed. The craftwork models were designed and the energy flow models library was developed of thermal power plant, DIP and paper machine. The mathematical models of unit (equipment), energy flow, exergy flow, thermal power plant, DIP and paper machine were built. The material properties database was developed. General simulation software architecture and online simulation software architecture were designed. The framework implemented online simulation, analysis, diagnosis and optimization for energy systems of whole papermaking mill. The practical application results are fine when the framework was applied in Guangzhou Paper Group Co., Ltd., and Gold East Paper (Jiangsu) Co., Ltd. The practical application results indicate that the emulation, simulation, analysis, diagnosis and optimization of material flow and energy flow for papermaking mill whole production process can be implemented by online simulation, and the purposes of maximizing the utility efficiency of material and energy resource, reducing resource, reusing and recycling can be achieved.

A new lean tool to enhance internal logistics in Engineer and Design-to-Order industrial environment: plan for every order

ABSTRACT The paper presents a new Lean Logistics tool specifically designed for Engineer and Design-To-Order (ETO and DTO) industrial environments. The new tool, namely Plan For Every Order (PFEO), conceptually derives from the well-known Plan For Every Part used in the automotive serial production context. PFEO is proposed as a tool for industrialization, focusing on the design and logistics optimization of internal material flows at ETO and DTO plants. In the paper, the principles, and concepts behind PFEO are presented and developed. The tool collects information on both suppliers and materials, improving both the management of materials and spaces and communication between the company and suppliers. The operability of the tool is proven in a real business case involving an shipyard. The PFEO has provided a solid database for the development of auxiliary monitoring and visualization tools, the application of which increases order in the warehouse and assembly areas and reduces costs and errors.

Combining material flow simulation and optimization for sustainable manufacturing – application in automotive paint shops

The necessity for sophisticated decision support in industrial manufacturing has been described manifoldly and simulation approaches as well as optimization methods are continuously developed. This can be especially promising when it comes to decision-making with multidimensional requirements of economic and ecological efficiency. However, due to the required expert knowledge, there is still a gap between theoretically applicable and industrially applied methods. The paper therefore presents results of two use cases as part of the European project ECOFACT where different approaches for the combination of simulation and (heuristic) optimization were applied with industry partners. An overview of recent works and systematic literature reviews of sustainable manufacturing and the approaches for combining simulation and optimization is given. Correspondingly, the investigated use cases of automobile paint shops refer to the reduction of environmental impacts by optimized heating processes and an improved batching process to minimize paint waste. In the first case, setup times for heating color pools and ovens were shifted to avoid load peaks in a calculation time of less than one minute. In the second use case batch size was increased by around 25%.

Optimal material flow channels of cross flow dryers

Reducing the moisture content of harvested grains is crucial for their subsequent processing or storage. Given the substantial energy requirements and potential environmental consequences associated with artificial drying, it is imperative to optimize the efficiency of this process. The quality of the final product is significantly impacted by inadequate or excessive drying, which is heavily influenced by the duration of grain exposure to the drying environment. Advances in dynamic modeling, facilitated by enhanced computational capabilities, have empowered researchers to intricately examine particle movement phenomena within drying equipment. In our study, we investigate variations in particle residence times under diverse geometric and tribological boundary conditions.

Nonlinear modeling and interior point algorithm for the material flow optimization in petroleum refinery

<abstract> <p>This paper established a mathematical model with nonconvex bilinear terms. It formulated the complex material flow in the petroleum refinery scenario based on the concept of the "P model". The mathematical model described the nonlinear constraints such as linear and nonlinear mass and volume intersection flow blending of crude and middle material physical properties. Additionally, it described the complex inflow and outflow in secondary devices as nonlinear constraints such as delta-base structure and physical property transfer. It is highly difficult to determine the direction and quantity of each material in the network of refineries. An improved interior point algorithm with an initial point strategy was proposed to find a high-quality feasible solution in a short time. The real instances from the petroleum refinery were employed to compare and analyze the solutions from the improved algorithm and commercial solver. The experimental results show that the proposed algorithm framework can balance the solution quality and computational efficiency and perform well in different scenarios of refinery material flow networks.</p> </abstract>

Resource saving and carbon footprint reduction potential of urban symbiosis strategy in express packaging waste recycling network

The booming express delivery industry corresponds to the environmental challenges caused by massive express packaging waste (EPW). An efficient logistics network is necessary link to support EPW recycling. This study, therefore, designed a circular symbiosis network for EPW recycling based on urban symbiosis strategy. The treatment of EPW in this network includes reuse, recycling and replacing. An optimization model with multi-depot collaboration combining material flow analysis and optimization methods was developed and a hybrid non-dominated sorting genetic algorithm-II (NSGA-II) was designed as technical support for designing the circular symbiosis network while quantitatively assessing the economic and environmental benefits of the network. The results show that the designed circular symbiosis option has better resource saving and carbon footprint reduction potential than both the business as usual option and circular symbiosis option without service collaboration. In practice, the proposed circular symbiosis network can save EPW recycling costs and reduce carbon footprint. This study provides a practical guideline for the application of urban symbiosis strategies to help urban green governance and the sustainable development of express companies.

Вплив середовища на кон’юнктуру ринку автомобільних перевезень України

One of the most promising markets, which is protected from cyclical changes that occur in the economy and shows growth in the conditions of military operations, is the market of road transport, which is developing rapidly, which includes the goods of basic necessity. Maintaining competitiveness and strengthening the advantages of motor transport enterprises of this industry is largely determined by the ability to optimize all processes of value creation that continuously circulate in their supply chains - from the supply of raw materials to the service of the end user. The success of these tasks largely depends on the correct configuration of the logistics system of the enterprise itself, as well as on the synchronization of its work with other, no less important areas - the study of the market conditions of road transportation, customer and producer service. This largely explains the relevance of the study of the road transport market situation, the use of the network logistics system in the road transport market, as one of the progressive scientific and applied directions of flow process management. The road transport market of Ukraine is increasingly becoming the subject of research and development as a form of optimization, automation, integration and management of material and information flows circulating within business units. The paper proposes a methodology for determining the state of the road transport market of Ukraine and the efficiency of delivery in the modern conditions of the functioning of transport systems. The methodology of the influence of micro and macro factors of the market environment is given. This made it possible to determine the main factors of permanent and indirect effect on the situation of the road transportation market of Ukraine.

ОГЛЯД ІСНУЮЧИХ ПІДХОДІВ ТА МОДЕЛЕЙ ДО ОРГАНІЗАЦІЇ МАТЕРІАЛЬНО-ТЕХНІЧНОГО ЗАБЕЗПЕЧЕННЯ, АНАЛІЗ ЇХ ПЕРЕВАГ ТА НЕДОЛІКІВ

In the course of writing this scientific article, it was determined that the organization of material and technical support (MTS) is a critical aspect of resource management of a modern enterprise. The article analyzes the concept of logistics in the current conditions of functioning of domestic enterprises. The key aspects of the influence of the external environment on the process of rational use of production and resource potential for the effective solution of the problem of logistics of modern enterprises are generalized. In addition, in view of the purpose of the study, the most effective approaches to the organization of logistics are identified, in particular, the traditional one, based on the preservation of stocks to meet production needs; process-based, aimed at integrating and optimizing management processes; logistics, which systematizes the optimization and control of material flows. It is found that each of these approaches has its advantages and disadvantages, which should be taken into account when choosing the optimal model for a particular enterprise. The study also found that models such as economic order size, material requirements planning, just-in-time system and supply chain are key management tools for organizing the MTP. It is determined that such models help to optimize production processes, reduce storage costs and increase transparency in supply chains. In addition, it is noted that the models have their limitations, such as the need for accurate demand and cost data, complexity in application, and dependence on supply stability. The article also notes that the introduction of modern approaches and models in the organization of logistics allows enterprises to ensure effective resource management, which contributes to sustainable development and competitiveness, as well as ensures their readiness for changes in the economic environment.

Modeling of cargo flow processes in the logistics of the transport system

The management of cargo flows and transport activities is one of the most significant challenges currently. This article aims to discuss optimizing material flows in both transport and cargo management. It also develops mathematical software and material flow structures to facilitate management and production functions in supply enterprises. The article solves regulatory and forecasting issues using material flows and information models to optimize vehicle operational forms.

Data-Driven Surface Classification for Differential Drive Autonomous Guided Vehicles

As a result of the digital transformation, the degree of automation in production environments is constantly increasing. The automation of logistics processes offers great potential for optimizing material flows within production. However, there are strong requirements regarding the reliability of automated guided vehicles (AGVs). This paper investigates if it is possible to identify the surface of an AGV based on control and measurement parameters compared to target parameters. The vehicle concept is based on differential drive and is used for the autonomous transport of goods within and between production halls. The information about the surface can be used to ensure a better and safer interaction with the environment. The creation of a dataset consisting of a low and a high friction surface is described. Furthermore, the pre-processing of the data which covers the time window length, resampling and data scaling is described. Finally, different algorithms for multivariable time series classification are benchmarked on the created dataset. Experiments show that the state of the art algorithm HIVE-COTE 2.0 provides the highest accuracy on the test dataset with 97.5%. Finally, a validation is performed by reviewing sequences with low confidence levels.

Multi-objective optimization and analysis of material and energy flows in a typical steel plant

The integrated optimization and analysis of material and energy flows are effective ways to reduce energy consumption and costs in the iron and steel sector. There is still a lack of research on the multi-objective optimization of the integrated steel production process and the auxiliary energy process. To fill this gap, this paper presented a single- and multi-objective optimization model for material and energy scheduling and utilization of the steelworks. This model considered physical and chemical conversion mechanisms in the production process, as well as operating characteristics and production-consumption relations of equipment in the gas-steam-electricity network. The objective function was constructed using comprehensive energy consumption (CEC) and economic cost (EC), and fuzzy sets were used to find a compromise solution and the trade-off operation strategies between the two goals. The CEC and EC effectively dropped by 5.09% and 7.29%, respectively, in comparison to the actual enterprise value, through the optimization of the material ratio, product structure, energy utilization, and other parameters of the steelworks. Additionally, a quantitative analysis of the effects of variables such as the ratio of pellets, the ratio of scrap, the hot charging temperature of the slab, and the utilization of surplus gases was conducted.

Multiperiod refinery optimization for mitigating the impact of process unit shutdowns

Process unit shutdowns, whether planned or unplanned, disrupt the normal operation of process plants. The negative impact of such disruptions can often be mitigated by the strategic use of material inventories, coupled with redistribution of material flows. In this paper, an optimization framework is proposed for a multiperiod refinery planning model in which a system of inventory tanks is used to mitigate the impact of process unit shutdowns. An economic objective is optimized over a planning horizon, subject to operating, design, and product quality constraints. Two formulation paradigms are considered - an operating problem in which optimal material flow trajectories are determined in response to a shutdown scenario, and a retrofit design problem in which the optimal location and sizing of inventory tanks are determined, the latter formulated as a two-stage stochastic programming problem. The utility of the formulation to shutdown planning is shown through case studies of the operational and design problems applied to a simple refinery scheme.

Modelling and optimization of material flows in the wood pellet supply chain

To reduce the use of fossil fuels, forest-based biomass appears as an engaging source of energy. Nevertheless, logistics costs represent one of the main barriers in the use of forestry residues and wood waste for bioenergy and biofuels production. The relatively low energy density of biomass contributes to a higher transportation cost per unit of energy content compared with fossil fuels. Other issues, such as raw material availability, seasonality, production and storage capacity constraints, and transportation distances for product distribution also are critical in the management of biomass supply chain. The interconnectedness of these factors directly impacts into the movement of materials along the logistics chain. In this way, this paper researches the alternative of using forest residues and wood waste as raw material sources for wood pellet production through the modelling and optimization of all logistics activities performed in the supply chain. The objective is the minimization of the total operative cost, which in turn allows determining the minimum profitable selling price of produced pellets. Besides production and inventory decisions, the proposal determines the more profitable routes for biomass collection and pellets distribution. A standard decomposition technique, consisting in designing routes first and later computing products and raw material flows, is employed to solve the optimization problem. The proposal is tested on a large-size case study designed with real data from the forest industry in the Argentinean Mesopotamia. Due to the close interaction between all logistics decisions in the supply chain, several scenarios are assessed to estimate the impact on the solutions of some changes in the problem configuration.

Indoor Positioning System Based on Bluetooth Low Energy Technology and a Nature-Inspired Optimization Algorithm

Warehousing is one of the most important activities in the supply chain, enabling competitive advantage. Effective management of warehousing processes is, therefore, crucial for achieving minimal costs, maximum efficiency, and overall customer satisfaction. Warehouse Management Systems (WMS) are the first steps towards organizing these processes; however, due to the human factor involved, information on products, vehicles and workers may be missing, corrupt, or misleading. In this paper, a cost-effective Indoor Positioning System (IPS) based on Bluetooth Low Energy (BLE) technology is presented for use in Intralogistics that works automatically, and therefore minimizes the possibility of acquiring incorrect data. The proposed IPS solution is intended to be used for supervising order-picker movements, movement of packages between workstations, and tracking other mobile devices in a manually operated warehouse. Only data that are accurate, reliable and represent the actual state of the system, are useful for detailed material flow analysis and optimization in Intralogistics. Using the developed solution, IPS technology is leveraged to enhance the manually operated warehouse operational efficiency in Intralogistics. Due to the hardware independence, the developed software solution can be used with virtually any BLE supported beacons and receivers. The results of IPS testing in laboratory/office settings show that up to 98% of passings are detected successfully with time delays between approach and detection of less than 0.5 s.

Impedance Spectroscopy Sensing Material Properties for Self-Tuning Ratio Control in Pharmaceutical Industry

Following the paradigm shift in the pharmaceutical industry from batch to continuous production, additional instrumentation and revision of control strategies to optimize material flow throughout the downstream processes are required. Tableting manufacturing is one of the most productive in terms of turnover and investment into new sensor technologies is an important decision-making step. This paper proposes a continuous solution to detect changes in material properties, and a control algorithm to aid in minimizing risk at the end-product line. Some of the sub-processes involved in tableting manufacturing perform changes in powder and liquid mixtures, granulation, density, therefore changing flow conditions of the raw material. Using impedance spectroscopy in a continuous sensing and monitoring context, it is possible to perform online identification of generalized (fractional) order parametric models where the coefficients are correlated to changes in material properties. The model parameters are then included in a self-tuning control gain used in ratio control as part of the local process control loop. The solution proposed here is easy to implement and poses a significant added value to the current state of art in pharmaceutical manufacturing technologies.

Resilient NdFeB magnet recycling under the impacts of COVID-19 pandemic: Stochastic programming and Benders decomposition

Neodymium-iron-boron (NdFeB) magnets are the most powerful magnets per unit volume sold in the commercial market. Despite the increasing demand for clean energy applications such as electric vehicles and wind turbines, disruptive events including the COVID-19 pandemic have caused significant uncertainties in the supply and demand for NdFeB magnets. Therefore, this study aims to alleviate the risk of supply shortage for NdFeB magnets and the containing critical materials, rare-earth elements (REEs), through the development of a resilient reverse supply chain and logistics network design. We develop scenarios to model the unique impact of the COVID-19 pandemic on the proposed business, incorporating both disruption intensity and recovery rate. We formulate a chance-constrained two-stage stochastic programming model to maximize the profit while guaranteeing the network resiliency against disruption risks. To solve the problem in large-scale instances, we develop an efficient Benders decomposition algorithm that reduces the computational time by 98.5% on average compared to the default CPLEX algorithm. When applied to the United States, the model suggests the optimal facility locations, processing capacities, inventory levels, and material flows for NdFeB magnet recyclers that could meet 99.7% of the demand. To the best of our knowledge, this study is the first to incorporate the impacts of the COVID-19 pandemic to design a resilient NdFeB magnet recycling supply chain and logistics network, leveraging risk-averse stochastic programming.

A lab-scale manufacturing system environment to investigate data-driven production control approaches

Controlling production and release of material into a manufacturing system effectively can lower work-in-progress inventory and cycle time while ensuring the desired throughput. With the extensive data collected from manufacturing systems, developing an effective real-time control policy helps achieving this goal. Validating new control methods using the real manufacturing systems may not be possible before implementation. Similarly, using simulation models can result in overlooking critical aspects of the performance of a new control method. In order to overcome these shortcomings, using a lab-scale physical model of a given manufacturing system can be beneficial. We discuss the construction and the usage of a lab-scale physical model to investigate the implementation of a data-driven production control policy in a production/inventory system. As a data-driven production control policy, the marking-dependent threshold policy is used. This policy leverages the partial information gathered from the demand and production processes by using joint simulation and optimization to determine the optimal thresholds. We illustrate the construction of the lab-scale model by using LEGO Technic parts and controlling the model with the marking-dependent policy with the data collected from the system. By collecting data directly from the lab-scale production/inventory system, we show how and why the analytical modeling of the system can be erroneous in predicting the dynamics of the system and how it can be improved. These errors affect optimization of the system using these models adversely. In comparison, the data-driven method presented in this study is considerably less prone to be affected by the differences between the physical system and its analytical representation. These experiments show that using a lab-scale manufacturing system environment is very useful to investigate different data-driven control policies before their implementation and the marking-dependent threshold policy is an effective data-driven policy to optimize material flow in manufacturing systems.