Cell formation and scheduling of part families for reconfigurable cellular manufacturing systems using Tabu search

Abstract

A reconfigurable cellular manufacturing system (RCMS) consists of multiple reconfigurable machining cells, each of which has one or more reconfigurable machine tools (RMTs), a setup station, and an automatic material handling and storage system. As part of the RCMS design process, similar parts must be grouped into part families and the RMTs must be arranged to form parallel cell configurations. A RCMS is designed at the outset for rapid changes in its components, allowing the production of multiple part families in each parallel cell. This paper proposes a new approach to simultaneously solve the cell formation and the scheduling of part families for an effective working of a RCMS. A new mixed integer linear programming model is used to represent both problems at the same time with the objective of minimizing production costs. Two types of production costs are considered: reconfiguration (i.e. setup) costs for changing from one family to the next one, and under-utilization costs for not using the RMT resources. A small size example is used to illustrate this integrated methodology. Computational experiments have been carried out adapting some larger instances from the literature on cellular manufacturing systems. Solving large instances optimally becomes prohibitive in terms of computational effort. That is why an approximate method, based on a Tabu search (TS) algorithm, has also been developed. Results show the ability of this algorithm to find good-quality production schedules of part families in a RCMS without requiring long computing times. It can be concluded that a RCMS can attain manufacturing flexibility without losing cost-effectiveness and that the approach proposed in this paper can efficiently solve real-world problems.

Strategic Outcome Using Fuzzy-AHP-Based Decision Approach for Sustainable Manufacturing

“…Artificial intelligence and meta-heuristic techniques have also been utilized in the literature for the design and analysis of RCMS. For example, Xing et al [33] elaborated an artificial neural network and Eguia et al [42] used a tabu search algorithm to figure out the cell formation and scheduling of part families in RCMS. A systematic design method for RCMS should provide users with the required knowledge and tools to realize its benefits and utility.…”

Group scheduling in a cellular manufacturing shop to minimise total tardiness and nT: a comparative genetic algorithm and mathematical modelling approach

“…In terms of the recent works, a group scheduling problem in a two-machine flow shop with a bicriteria objective and carryover sequence-dependent setup times was studied by Yazdani et al (2013). Additionally, case of reconfigurable manufacturing systems (Eguia et al 2013), sequence dependent set-up times and skilled workforce assignment (Costa et al 2014), case of robotic cells (Elmi and Topaloglu 2014) and case of batch production with minimizing the total number of changeovers (Alfieri and Nicosia 2014) are other critical works published in recent years.…”

Grouping and sequencing product variants based on setup similarity

“…Grouping results are used to find the best sequence of product variants. In this algorithm, products with higher coefficient of similarity are grouped together (Vakharia and Wemmerlöv 1995;Galan et al 2007aGalan et al , 2007bEguia et al 2011Eguia et al , 2013. A submatrix consisting of grouped and ungrouped products is then constructed.…”