1,720,984 research outputs found
A stepped approach to support preassembly tasks assignment in bus production
No full textThis paper proposes a structured approach to assign operations related to material and subassembly preparation before line assembly, to a group of multiskilled operators. It first characterizes the preparation tasks and their compatibility with the skill level of operators. Then, it formulates and solves the corresponding assignment problem. In case of infeasibility or not optimal solutions, the approach permits also to identify appropriate training measures in order to minimize the number of involved operators or maximize the number of assigned preparation tasks. This approach is the outcome of an action research case study which dealt with a bus assembly line. The results showed that the approach was relatively easy to implement and effectively led some crucial decisions which were mostly experience-based and not made in a structured manner
Considering greenhouse gas emissions in a single vendormultiple buyer coordinated supply chain
No full textA periodic review policy for a coordinated single vendor-multiple buyers supply chain with controllable lead time and distribution-free approach
No full textIn this paper, we study a single vendor-multiple buyer integrated inventory model with controllable lead time and backorders-lost sales mixture. Each buyer adopts a periodic review policy in which the review period is an integer fraction of the production cycle time of the vendor. To reflect the practical circumstance characterized by the lack of complete information about the demand distribution, we assume that only the first two moments of the demand during the protection interval are known. The long-run expected total cost per time unit is derived, which includes stockout costs. The problem is to determine the length of the inventory cycle of the vendor, the produce-up-to level for the vendor, the replenishment policy of each buyer, and the length of lead times that minimize the cost function under the minimax distribution-free approach. Two alternative heuristics are proposed. Numerical experiments have been carried out to investigate the performance of the heuristics and to study the sensitivity of the model
Stochastic joint replenishment problem under a fill rate constraint with controllable lead times and shared cost allocation
No full textIn a family of items under coordinated inventory replenishments, some products may be replenished at the same time, which in turn implies that some lead time components and costs may be shared among them. This paper investigates this aspect in the context of the joint replenishment problem under the class of cyclic policies, assuming random demands and controllable lead times, and imposing a fill rate constraint for each item.& COPY; 2023 Elsevier B.V. All rights reserved
A Reinforcement Learning approach in Industry 4.0 enabled production system
No full textModern manufacturing systems require a high degree of production flexibility to adapt to more personalized market demands in a timely and cost-effective manner, which the Industry 4.0 paradigm's technologies enable. As a result, in today's manufacturing environment, it is critical to optimize the use of these technologies. Simultaneously, to remain competitive, firms must commit to addressing external and/or internal restrictions in the manufacturing system. As a result, considering t he growing interest in artificial intelligence (AI) and the promising results of its industrial application, this paper proposes a novel approach to production control based on Reinforcement Learning (RL) for resolving production scheduling difficulties of varying complexity. In this way, human intervention in production scheduling can be reduced, while planning and decision-making capabilities are improved at the same time. To support this claim, a simulation study was conducted that aims to assess the behaviour of an automated factory regarding various external and internal operational constraints. Consider a Flow Shop production line working in an Industry 4.0 environment capable of adopting Cyber-Physical Systems (CPS) and the Internet of Things (IoT); this study provides a novel flexible dispatching rule based on production line performance monitoring. The performances of the proposed new approach are compared to that of previously suggested dispatching regulations in the scientific literature. The simulation results revealed several intriguing conclusions, emphasizing the rule's flexibility and practical use is given certain practical assumptions
Assessing the performances of a novel decentralised scheduling approach in Industry 4.0 and cloud manufacturing contexts
No full textThe increasing globalisation process has led to a radical change in the production concept, moving from a mass production paradigm towards one of mass customisation (MC), and focusing on value creation by pursuing customers’ needs and increasing responsiveness. The rapid development of information technologies has also made it possible to create new manufacturing paradigms, such as Industry 4.0 and cloud manufacturing, in which the increased level of autonomy is one of the key concepts for tackling new market challenges. This paper proposes a decentralised scheduling approach that improves the performance of production systems while minimising the usually high work-in-progress (WIP) requirements of the classic centralised scheduling and inventory production control system. Using a semi-heterarchical Manufacturing Planning and Control (MPC) architecture and integrating the Industry 4.0 innovation in a cloud manufacturing environment, this work contributes to the design of the lower level of the MPC architecture. The resulting production controller can allocate jobs following different dispatching rules dynamically. The performances of the proposed approach were assessed for different production scenarios and control parameter settings through an exhaustive experimental campaign based on hybrid simulation tools. The results showed that the proposed low-level controller led to a productivity increase while delivering increased responsiveness
A deep reinforcement learning approach for the throughput control of a flow-shop production system
No full textThis paper proposes a new method for controlling a flow shop in terms of throughput and Work In Process (WIP). In order to achieve a throughput target, a Deep Q-Network (DQN) is used to define the constant WIP quantity in the system. The main contribution of this paper is the novel approach used to formulate the state, action space, and reward function. An extensive preexperimental campaign is conducted to determine the best network structure and appropriate hyperparameter values. Finally, the system's performance is compared to the known results of an analytical model from the literature (Practical Worst Case, PWC)
On the modelling of a decentralized production control system in the Industry 4.0 environment
No full textThe paper deals with a decentralized production control in an Industry 4.0 environment. In such a kind of systems, the capability to deliver a high level of product customization together with reduced response time is crucial to maintain competitiveness and to increase profit. A semi-heterarchical architecture, formed by three levels, in which the first is responsible for meeting business objectives, the second to maintain target system general performances, and the third to tackle operative scheduling problems, is first discussed as a framework for the future implementation in an Industry 4.0 environment. Successively, the problem to model the system form a dynamic point of view is addressed directly at the second architectural level. This paper, in particular, contributes to the semi-heterarchical architecture development, by proposing a first mathematical model of the shop-floor of a such a system, involving the use of the population dynamic modelling. Finally, the results of the first implementation in a simulated environment are reported
On the advances of the Industry 4.0 Manufacturing Planning and Control system architectures
No full textDuring the last decades, the Material Resource Planning system has been considered an essential management tool for facing the manufacture of complex and highly customised products. Nowadays, the recent innovations brought from the Industry 4.0 push for a strong evolution of the Manufacturing Planning and Control System (MPC) architectures, aiming to a new class of control architectures. Among these, the intermediate (i.e., the semi-heterarchical and oligarchical) ones are taking considerable interest from the manufacturing firms due to their increased flexibility degree and productivity enhancement. However, the current scientific literature is still focused on the 'hierarchical' approach of these architectures while the 'horizontal' bargaining among entities and architecture modules need to be further investigated. After a narrative literature review of MPC architecture, this paper will focus on the development of such intermediate architectures. In particular, referring to a semi-heterarchical MPC architecture, this work extends the contributes to the design of the horizontal aspect of the higher level, evaluating the possible advantages of such an application
Optimizing product assortment, joint replenishments, and storage capacity allocation in a deteriorating inventory system
Full text linkIn this paper, we consider a single retailer who sells multiple products subject to decay, and that implements coordinated inventory replenishments among them. The overall available surface at the retailer is limited and is partitioned into two areas: the backroom facility and the display area. The demand rate of each product is a function of displayed quantity and location, and it also depends on the cross-elasticity among items. The objective is to find the product assortment, the replenishment policy of each product, the quantity to be displayed, and the surfaces assigned to the backroom and display areas that maximize the total profit per time unit. We develop the mathematical model and formulate the optimization problem. Finally, we investigate the model response by means of numerical experiments considering several problem instances. Copyright © 2022 IFAC
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