Recursive Heuristic Algorithm for Balancing Mixed–Model Assembly Line Type–II in Stochastic Environment

Abstract

Straight assembly line and U-shaped assembly line usually required to be extended and updated in order to solve line balancing problems in the real-world based on computational intelligence. In the literature, most models are presented for solving assembly line balancing (ALBP) assume deterministic processing time. This paper is extended solving ALBP with the stochastic environment using fuzzy theory as one of the main pillars of computational intelligent, it’s called “Worker–Task Stochastic Assigned to Workstation Heuristic” (W–TSAWH) is adopted. The framework can be structured by creating stochastic work environment (SWE) and assigning process. Firstly, SWE is adopted, with three fuzzy logic models as fuzzy skill level, fuzzy work stability and dynamic fuzzy processing time models. These models are used in order to represent uncertainty associated with task processing time in real assembly system. Secondly, a heuristic algorithm is developed to obtain best solution. The algorithm organized by sequence vector, and a mathematical model that assigns task and worker that subjected to some constraints into constant number of workstations to minimize cycle time. Finally, the performance validation of the methodology is proved using a numerical example.