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EN
The problem of sequencing jobs on a single machine to minimize total cost (earliness and tardiness) is nowadays not just important due to traditional concerns but also due to its importance in the context of Collaborative Networked Organizations and Virtual Enterprises, where precision about promptly responses to customers’ requests, along with other important requirements, assume a crucial role. In order to provide a contribution in this direction, in this paper the authors contribute with an applied constructive heuristics that tries to find appropriate solutions for single machine scheduling problems under different processing times and due dates, and without preemption allowed. In this paper, two different approaches for single-machine scheduling problems, based on external and internal performance measures are applied to the problem and a comparative analysis is performed. Computational results are presented for the problem under Just-in-Time and agile conditions on which each job has a due date, and the objective is to minimize the sum of holding costs for jobs completed before their due date and tardiness costs for jobs completed after their due date. Additional computational tests were developed based on different customer and enterprise oriented performance criteria, although preference is given to customer-oriented measures, namely the total number of tardy jobs and the maximum tardiness.
EN
Recent rapid developments in information and network technology have profoundly influenced manufacturing research and its application. However, the product’s functionality and complexity of the manufacturing environments are intensifying, and organizations need to sustain the advantage of huge competitiveness in the markets. Hence, collaborative manufacturing, along with computer-based distributed management, is essential to enable effective decisions and to increase the market. A comprehensive literature review of recent and state-of-the-art papers is vital to draw a framework and to shed light on the future research avenues. In this review paper, the use of technology and management by means of collaborative and cloud manufacturing process and big data in networked manufacturing system have been discussed. A systematic review of research papers is done to draw conclusion and moreover, future research opportunities for collaborative manufacturing system were highlighted and discussed so that manufacturing enterprises can take maximum benefit.
EN
Scheduling is one of the most important decisions in production control. An approach is proposed for supporting users to solve scheduling problems, by choosing the combination of physical manufacturing system configuration and the material handling system settings. The approach considers two alternative manufacturing scheduling configurations in a two stage product oriented manufacturing system, exploring the hybrid flow shop (HFS) and the parallel flow shop (PFS) environments. For illustrating the application of the proposed approach an industrial case from the automotive components industry is studied. The main aim of this research to compare results of study of production scheduling in the hybrid and the parallel flow, taking into account the makespan minimization criterion. Thus the HFS and the PFS performance is compared and analyzed, mainly in terms of the makespan, as the transportation times vary. The study shows that the performance HFS is clearly better when the work stations’ processing times are unbalanced, either in nature or as a consequence of the addition of transport times just to one of the work station processing time but loses advantage, becoming worse than the performance of the PFS configuration when the work stations’ processing times are balanced, either in nature or as a consequence of the addition of transport times added on the work stations’ processing times. This means that physical layout configurations along with the way transport time are including the work stations’ processing times should be carefully taken into consideration due to its influence on the performance reached by both HFS and PFS configurations.
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