In a typical assembly line, workstations can be classified into two sets: A) Fully loaded ( i.e. , bottleneck) and B) Partially loaded, with respect to the total processing times of their tasks and the cycle time. Collaborative Control Theory (CCT) is applied in this paper for efficient Assembly Line Balancing (ALB). Collaboration is carried out through: 1) Dynamic Tool Sharing (TS) between workstations in Sets A and B, and 2) Dynamic Best Matching (BM) between tasks and workstations. The purpose: Minimize the number of workstations and the cycle time, thus maximizing the line utilization/efficiency and the production throughput, and increase capability in dealing with demand variability. Collaboration between workstations is enabled, under TS protocol, in order for the workstations in Set A to make use of idle tools of workstations in Set B, thus minimizing the total processing time of the bottleneck workstation(s) ( i.e. , cycle time). A dynamic BM protocol is designed for optimal matching of tasks to workstations with respect to their processing times, precedence relations, and the cycle time. A bi-objective Mixed-Integer Programming (MIP) formulation is developed for modeling and analyzing the ALB-TS-BM decisions, minimizing the number of workstations ( i.e., Type -I ALB), and the average cycle time ( i.e. , Type -II ALB). A Fuzzy Goal Programming (FGP) method with imprecise goal hierarchy is applied for solving the bi-objective MIP model. The ALB-TS-BM model is original and first ALB solution method to consider CCT by TS, generalizable to and implementable in various instances of ALB problems through proper application of CCT principles. An illustrative example is used to exemplify the concept and its advantages over conventional ALB methods.
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