Manufacturing cell formation with flexible processing capabilities and worker assignment: Comparison of constraint programming and integer programming approaches

2017 ◽  
Vol 232 (11) ◽  
pp. 2054-2068 ◽  
Author(s):  
Adil Baykasoğlu ◽  
Şeyda Topaloğlu ◽  
Filiz Şenyüzlüler
Keyword(s):  
Integer Programming ◽  
Constraint Programming ◽  
Manufacturing Systems ◽  
High Efficiency ◽  
Programming Model ◽  
Cell Formation ◽  
Programming Approach ◽  
Cell Formation Problem ◽  
Formation Problem ◽  
Complex Constraints

Cell formation deals with grouping of machines and parts in manufacturing systems according to their compatibility. Manufacturing processes are surrounded with an abundance of complex constraints which should be considered carefully and represented clearly for obtaining high efficiency and productivity. Constraint programming is a new approach to combinatorial optimization and provides a rich language to represent complex constraints easily. However, the cell formation problems are well suited to be solved by constraint programming approach since the problem has many constraints such as part-machine requirements, availabilities in the system in terms of capacity, machine and worker abilities. In this study, the cell formation problem is modeled using machine, part processing and worker flexibilities via resource element–based representation. Resource elements define the processing requirements of parts and processing capabilities of machines and workers, which are resource-independent capability units. A total of 12 case problems are generated, and different search phases of constraint programming are defined for the solution procedure. The cell formation problem is modeled in both constraint programming and integer programming, and a comparative analysis of constraint programming and integer programming model solutions is done. The results indicate that both the models are effective and efficient in the solution of the cell formation problem.

scholarly journals Solving the Cubic Cell Formation Problem Using Simulated Annealing

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Simulated Annealing ◽  
Manufacturing Systems ◽  
Cell Formation ◽  
Cell Formation Problem ◽  
Major Objective ◽  
Minimum Value ◽  
Formation Problem ◽  
Neighbourhood Search ◽  
Problem Instances ◽  
Solution Methodology

The Cubic Cell Formation Problem (CCFP) in cellular manufacturing systems consists in decomposing a production system into a set of manufacturing cells, and assigning workers to cells besides parts and machines. The major objective is to obtain manageable cells. Manageable cells mean cells with a minimum value of inter-cell moves of parts and workers and a minimum value of heterogeneity within cells. In this paper, a solution methodology based on a modified simulated annealing heuristic with a proposed neighbourhood search procedure is proposed. The methodology allows building multiple configurations by giving to the decision-maker the ability to control some parameters. Experimental results show that the proposed algorithm gives a promising performance for all problem instances found in the literature.

scholarly journals A Mathematical Programming Model for Cell Formation Problem with Machine Replication

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Reza Raminfar ◽  
Norzima Zulkifli ◽  
Mohammadreza Vasili
Keyword(s):  
Mathematical Programming ◽  
Programming Model ◽  
Cell Formation ◽  
Performance Measure ◽  
Cell Formation Problem ◽  
Mathematical Programming Model ◽  
Solution Quality ◽  
Formation Problem ◽  
Proposed Model ◽  
Size Limits

Cell formation (CF) is a crucial aspect in the design of cellular manufacturing (CM) systems. This paper develops a comprehensive mathematical programming model for the cell formation problem, where product demands, cell size limits, sequence of operations, multiple units of identical machines, machine capacity, or machine cost are all considered. In this model, the intercell moves are restricted to be unidirectional from one cell to the downstream cells, without backtracking. The proposed model is investigated through several numerical examples. To evaluate the solution quality of the proposed model, it is compared with some well-known cell formation methods from the literature, by using group capability index (GCI) as a performance measure. The results and comparisons indicate that the proposed model produces solution with a higher performance.

Sign in / Sign up

Export Citation Format

Share Document