Abstract
The paradigm of the cyber-physical manufacturing system is playing an increasingly important role in the development of production systems and management of manufacturing processes. This paper presents an optimization model for solving an integrated problem of production planning and manufacturing control. The goal is to create detailed production plans for a complex manufacturing system and to control the skilled manual workers. The detailed optimization model of the problem and the developed approach and algorithms are described in detail. To consider the impact of human workers performing the manufacturing primary operations, we elaborated an extended simulation-based procedure and new multi-criteria control algorithms that can manage varying availability constraints of parallel workstations, worker-dependent processing times, different product types and process plans. The effectiveness of the proposed algorithms is demonstrated by numerical results based on a case study.
Highlights
Due to the increasing globalization of production networks, developments aimed at the cost-effective operation of production systems are becoming more and more important for companies to increase their competitiveness
Assignments of jobs and workers were performed according to the Fist Suitable Job Selection (FSJS) strategy, which means that the worker selects the first suitable item from the queue of waiting jobs
We focused on modelling and solving an integrated production planning, scheduling and control problem of on-demand flexible manufacturing
Summary
Due to the increasing globalization of production networks, developments aimed at the cost-effective operation of production systems are becoming more and more important for companies to increase their competitiveness. Companies aim to satisfy the customers’ requirements at the highest possible level while meeting the rigorous order deadlines. The large number and diverse composition of production orders require an increasing degree of flexibility in production systems considering all levels of flexibility. Shivanand (2006) and Kumar et al (2017) presented these levels of flexibility in detail. Flexible manufacturing systems (FMSs) integrate manufacturing and logistics resources (e.g. machining cells, workstations, automated material handling and storage equipment). Michalos et al (2010) presented the effect of system flexibility and degree of automation on product type variants and series sizes through the example of an automotive assembly system
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