Balancing of cost-oriented U-type general resource-constrained assembly line: new constraint programming models

• Published in: Soft computing (Berlin, Germany) Vol. 27; no. 24; pp. 18667 - 18680
• Main Authors: Alakaş, Hacı Mehmet, Pınarbaşı, Mehmet
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2023; Springer Nature B.V
• Subjects: Artificial Intelligence; Assembly lines; Balancing; Computational Intelligence; Constraint modelling; Control; Cycle time; Datasets; Engineering; Genetic algorithms; Heuristic; Literature reviews; Mathematical Logic and Foundations; Mathematical programming; Mechatronics; Optimization; Robotics; Task complexity; Conjunctive constraints; Assembly line balancing; Constraint programming; U-type; General resource constraints
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-023-09105-9

In simple assembly line balancing problems, it is assumed that the resources required to perform the tasks are available at the relevant station for task assignment. However, each task may need different resource types depending on the difficulty, complexity and technical requirements of the tasks in real life. For this reason, tasks and resources belonging to these tasks must be assigned to the relevant station while balancing the line. In this study, the resource-constrained U-shaped assembly line balancing problem (U-GRCALBP) is discussed. According to the literature research, there is no study dealing with U-GRCALBP. Two different constraint programming (CP) models that define the resource constraints as “and/or” constraint types with concurrent resource types have been developed. In these models, the sum of resource usage costs and station opening cost minimization is aimed. The models are explained with an illustrative example and the efficiency of the models is tested by deriving new resource constraints on five different data set each taking into account four different cycle times and the number of two and four resource types. The number of stations obtained, the total number of resources used, total station opening and resource usage costs, and CPU time are used as performance criteria The results are compared with the traditional U-type assembly line balancing problem results. The proposed CP models give superior performance on all data sets, especially in terms of total cost. The numerical results show that both CP models are effective in solving the problem. Furthermore, some managerial implications are presented to be useful for professionals, organizations, and society.