Studying unbalanced workload and buffer allocation of production systems using multi-objective optimisation

Optimal Buffer Allocation in Multi-Product Repairable Production Lines Based on Multi-State Reliability and Structural Complexity

“…Sabry and Dmitriev studied the assembly line function having the characteristic of reliable, unpaced merging and asymmetric buffer storage sizes [10]. Oriented for mutiple key performance measures of production systems, Shaaban et al put forward a MOO method to search and analyze the supreme patterns of buffer allocation and workload imbalance [12]. Aiming at the optimization of unreliable and imperfect production system, Radhoui et al developed an integrated model based on quality control, preventive maintenance and buffer allocation sizing [45].…”

“…The problem that how to make the buffer stations have enough space to store the WIP (Work in Process), and minimize the equipment cost and maintenance cost brought by the intermediate buffers, while avoiding time waste and logistics interruption due to excessive travel of WIP in intermediate buffers, and maintaining the reliable, controllable and stable operations has always been a key issue in the design of production systems [3][4][5][6][7][8][9][10]. Among them, system modeling and analysis are the basis for the buffer capacity distribution problem, and the optimization method is the means to obtain reasonable allocation solutions [11,12]. Therefore, based on the analysis of multi-state reliability and structural complexity, it is of great significance to study the allocation planning method and optimization algorithm of buffer capacity for production system design.…”

Multiobjective optimization of buffer capacity allocation in multiproduct unreliable production lines using improved adaptive NSGA-II algorithm

“…It has always been a key issue in production line design to create enough space in buffer stations for the storage of Work in Process and minimize the equipment cost and maintenance cost brought about by the intermediate buffers while avoiding time waste and logistics interruption due to excessive travel of Work in Process in intermediate buffers, and maintaining the reliability, controllability, and stability of operations (Sabuncuoglu et al, 2006;Staley et al, 2012;Gan et al, 2013 and. Among these aspects, system modeling and evaluation lay the basis for buffer capacity distribution, while reasonable allocation solutions are obtained by means of the optimization method (Ng et al, 2017). Therefore, it is of great significance to study the allocation method and optimization algorithm of buffer capacity on account of analyzing multistate reliability and structural complexity for production line design.…”

“…Kose et al (2015) developed a hybrid simulation optimization approach based on evolutionary algorithm to optimize two conflicting objectives, which included the maximization of average system production rate and the minimization of total buffer size. Sabry et al (2017) studied the performance of reliable, unpaced merging assembly lines with asymmetric buffer storage sizes. Radhoui et al (2009) developed an integrated model considering preventive maintenance, quality control, and buffer sizing for unreliable and imperfect production systems.…”

Maintenance policy structure investigation and optimisation of a complex production system with intermediate buffers

“…Roda and Macchi 27 adopted a simulation approach to estimate the factor-level performance of a multistate production system with buffers. The simulation based approach was also adopted by Ng et al 28 to jointly optimise the workload and buffer capacity in a flow line system. Rivera-Gómez et al 29 optimised the control policy of subcontracting, production, and maintenance by combining the simulation and optimisation methods, where the discounted overall cost was used as the objective function.…”