Multi-level Production System Research Articles

Clusters in industrial ecosystems – new innovative trends in the development of post-war Ukraine

The article discusses the importance of creating and developing clusters in the context of the digital economy and socio-ecological transformation. The main goal is to unite competencies and resources through common strategies and projects to increase the innovative potential of clusters and their participants. The article emphasizes the need to build and support industrial-technological partnerships worldwide as a key task for industrial clusters. The importance of developing the production sector and supporting one's own technological level for the development of the economy and ensuring national well-being, particularly in Ukraine, is also highlighted in the article.Different ways and motives are proposed for strategic partnerships between clusters, including cooperation in management training, identification of international innovations, exploration of new markets, and initiation of international scientific research projects.The need to create a professional management team of clusters was emphasized in times of digitalization, economic and socio-ecological transformation, as only clusters cover various areas of knowledge and will closely cooperate with other market participants. Moreover, increasing the innovation potential of clusters and their participants is becoming more and more important. This facilitates the pooling of competences and resources through joint strategies and joint projects.The importance of identifying structural gaps in industrial ecosystems to maximize the benefits of potential collaborations and promote innovative policies for upgrading business processes and equipment is considered. The geographical boundaries of the industrial ecosystem are determined by the interdependencies that connect organizations within its boundaries and new ties that strengthen beyond its boundaries. Industrial ecosystems are defined as multi-level production systems that include heterogeneous agents involved in value chains, as well as those who contribute to the realization of their potential and the country's productive capacity. The value chain can be divided into five main types of activities: inbound logistics, production, outbound logistics, marketing and sales, and after-sales services, and may also contain secondary or supporting activities that contribute to the effectiveness of the main activities. The article emphasizes that defining the real boundaries of industrial ecosystems is critical for governments interested in supporting them and transforming them into closed-loop innovation systems.

Designing an adaptive and deep learning based control framework for modular production systems

AbstractIn today’s rapidly changing production landscape with increasingly complex manufacturing processes and shortening product life cycles, a company’s competitiveness depends on its ability to design flexible and resilient production processes. On the shop-floor, in particular, the production control plays a crucial role in coping with disruptions and maintaining system stability and resilience. To address challenges arising from volatile sales markets or other factors, deep learning algorithms have been increasingly applied in production to facilitate fast-paced operations. In particular deep reinforcement learning frequently surpassed conventional and intelligent approaches in terms of performance and computational efficiency and revealed high levels of control adaptability. However, existing approaches were often limited in scope and scenario-specific, which hinders a seamless transition to other control optimization problems. In this paper, we propose a flexible framework that integrates a deep learning based hyper-heuristic into modular production to optimize pre-defined performance indicators. The framework deploys a module recognition and agent experience sharing, enabling a fast initiation of multi-level production systems as well as resilient control strategies. To minimize computational and re-training efforts, a stack of trained policies is utilized to facilitate an efficient reuse of previously trained agents. Benchmark results reveal that our approach outperforms conventional rules in terms of multi-objective optimization. The simulation framework further encourages research in deep-learning-based control approaches to leverage explainability.

Multi-level lot-sizing and job shop scheduling with lot-streaming: Reformulation and solution approaches

This paper addresses a multi-level lot-sizing and job shop scheduling problem with lot-streaming. In the multi-level production system, workstations receive materials from the lower level, and after some operation, materials are shipped to the next level. Hence, establishing a material balance between the different levels is the most challenging part of multi-level production planning and scheduling. The material balance can be performed with or without lot-streaming. Lot-streaming effectively enables consecutive operations to overlap by splitting a processing lot into several sub-lots. In small-bucket time models, this capability is taken into account by establishing the material balance in each small unit of time (micro-period), which makes the models computationally expensive. In the present work, a novel and much less complicated big-bucket time formulation has been developed, which incorporates lot-streaming considering sequence-dependent setup times and capacitated machines. Computational experiments affirm the promising results of the proposed model compared to the well-known models in the literature. Moreover, two efficient heuristics have been developed for solving larger-size problems. First, the fix-and-relax algorithm as a constructive heuristic is combined with the fix-and-optimize algorithm as an improvement heuristic. Next, a decomposition heuristic is proposed using mixed-integer programming (MIP) and constraint programming (CP) in the master and sub-problem, respectively. The computational results show that the proposed heuristics are very efficient, even in solving large-sized problems.

Промислові екосистеми в глобальних ланцюжках створення вартості та поставок: кластери, інноваційні та екоіндустріальні парки як чинник сталого розвитку

The article provides a comprehensive analysis of the existing forms of clustering of production processes in the process of the formation of industrial ecosystems on an international scale, highlighting the role of global supply chains in the value chain in the context of the transformation of economic development models as a factor of reindustrialization, taking into account the latest imperatives of innovation, greening and constancy. It has been established that the ways of creating value do not just depend on the features of the technological process, but, more importantly, they are induced by the structure of the ecosystem in which they are built on the principles of interdependence. The geographic boundaries of an industrial ecosystem are defined by the interdependencies that link organizations within the ecosystem, and by the new bonds that are being strengthened outside of it. The article highlights the need to identify structural gaps in the ecosystem early in order to maximize the benefits of potential collaboration and encourage innovation. It is critically important for governments to define the real boundaries of industrial ecosystems in order to promote their transformation into closed loops of innovation. The definition of an industrial ecosystem is proposed as a multi-level production system, including heterogeneous agents involved in industry value chains and contributing to the realization of the potential of the ecosystem (and its participants) and the development of the country's productive capacity. The place of the supply chain in the value chain has been established, which made it possible to assert that the supply chain is increasingly associated with business functions and processes that go beyond logistics within and between companies. The concepts of clusters and industrial areas are analyzed, which made it possible to single out the role of industrial parks in the development of an industrial cluster. It has been proven that the development of industrial parks contributes to the creation of clusters of competitive industries and the expansion of production chains, which contributes to the balanced economic development of the regions through the mobilization of all available resources. Factors are identified, the combination of which allows the transformation of an industrial zone or a park into an eco-industrial park. The definition of an eco-industrial park is proposed and its alternative names are given, such as: permanent economic zone; low-carbon technological zone; green investment cluster; circular industrial park. The potential of eco-industrial parks in the implementation of the 2030 Sustainable Development Goals has been established. This led to the conclusion that regional systems are evolving from places where firms are located that do not know about each other and simply benefit from economies of scale, to systems that include dynamic training and coordination to enhance regional competitive advantage.

Multilevel Production Systems with Dependent Demand with Uncertainty of Lead Times

This study considers a multilevel assembly system with several components in each sublevel. It is assumed that actual lead time for all components is probabilistic; and periodic order quantity (POQ) policy for ordering is utilized. If at a certain level a job is not received at the expected time, a delay is incurred at the delivery of production at this level and this may result in backorders of the finished product. It is assumed in this case that a fixed percentage of the shortage is backlogged and other sales are lost. In the real situation, some but not all customers will wait for backlogged components during a period of shortage, such as for fashionable commodities or high-tech products with the short product life cycle. The objective of this study is to find the planned lead time and periodicity for the total components in order to minimize the expected fixed ordering, holding, and partial backlogging costs for the finished product. In this study, it is assumed that a percentage of components at each level are scrap. A general mathematical model is suggested and the method developed can be used for optimization planned lead time and periodicity for such an MRP system under lead time uncertainties.

Planned lead time optimization in material requirement planning environment for multilevel production systems

This paper deals with the problem of planned lead time calculation in a Material Requirement Planning (MRP) environment under stochastic lead times. The objective is to minimize the sum of holding and backlogging costs. The proposed approach is based on discrete time inventory control where the decision variables are integer. Two types of systems are considered: multi-level serial-production and assembly systems. For the serial production systems (one type of component at each level), a mathematical model is suggested. Then, it is proven that this model is equivalent to the well known discrete Newsboy Model. This directly provides the optimal values for the planned lead times. For multilevel assembly systems, a dedicated model is proposed and some properties of the decision variables and objective function are proven. These properties are used to calculate lower and upper limits on the decision variables and lower and upper bounds on the objective function. The obtained limits and bounds open the possibility to develop an efficient optimization algorithm using, for example, a Branch and Bound approach. The paper presents the proposed models in detail with corresponding proofs and several numerical examples. Some advantages of the suggested models and perspectives of this research are discussed.

The investment model in preventive maintenance in multi-level production systems

In this research, cost/benefit models for investments made in quality improvements are developed to measure the impact of quality programs and to predict the return of an investment in these programs in a multi-level assembly system. Using these models, the decision makers can decide whether and how much to invest in quality improvement projects. The relationship between the investment and return on investment can be developed based on the tangible variables. The investment model in preventive maintenance is developed in a multi-level assembly system. The investment in preventive maintenance is to reduce the variance and the deviation of the mean from the target value of the quality characteristic, and hence to reduce the proportion of defectives and also to increase reliability. The proportion of defectives can be linked to manufacturing cost, inventory cost, and profit loss. The reliability is linked to warranty cost. The total costs in this investment model include manufacturing cost, setup cost, holding cost, profit loss, and warranty cost.

Scheduling mixed-model multi-level just-in-time production systems

Many companies are converting their mixed-model multi-level production systems to just-in-time systems. This requires reducing set-up times so that small-lot production can be run. In this paper a theoretical basis is developed for scheduling these systems, and scheduling algorithms and heuristics are developed. The only other known scheduling method (the goal-chasing method), developed and used by Toyota, is shown to be a special case of these heuristics.