Abstract
The problem of assigning assembly tasks to the stations arranged along a material handling device is known as assembly line balancing. This paper aims to address the U-shaped assembly line balancing problem (UALBP) which arises when a U-shaped assembly line has to be configured. It is widely known that this problem is NP-hard. Accordingly, different meta-heuristics based on a single solution (such as Simulated Annealing) or a population of solutions (such as Genetic Algorithms) have been proposed in the literature. Meanwhile, it has been argued that either of these meta-heuristics with a fixed number of solutions cannot maintain efficient search progress and thus can lead to premature convergence. Thus, this study aims at adopting a novel meta-heuristic algorithm with dynamic population sizes, namely Water Flow-like Algorithm (WFA), inspired by the behaviour of water flows in nature, to address the UALBP. The line efficiency and variation of workload are considered as the primary and the secondary objective, to be optimized, respectively. To verify the efficiency and robustness of the proposed WFA, a real case study taken from an automobile manufacturer as well as a set of standard problems are solved and the results compared with the existing approaches in the literature. The computational results demonstrate the superiority of the WFA, particularly in addressing medium to large-sized problems.
Highlights
Manufacturing industry is nowadays witnessing the fourth industrial revolution that is commonly referred to as Industry 4.0
This paper deals with the U-shaped assembly line balancing problem
Since the existing meta-heuristics rely on a fixed number of solutions, there is no guarantee that either of these algorithms can search the solution space efficiently while avoiding the local traps and premature convergence
Summary
Manufacturing industry is nowadays witnessing the fourth industrial revolution that is commonly referred to as Industry 4.0. U-shaped ALs can be considered as a consequence of Lean Manufacturing implementation, as they can facilitate the assignment of tasks to the stations (operators and / or machines) by providing more flexibility in dealing with tasks on both sides of the ALs. In addition, by allowing the interaction between tasks/stations in U-shaped lines, the line efficiency, workload equalization between stations (operators), space utilization, operators’ communication, job-enrichment, and work-in-process (WIP) reduction can be further enhanced [7]. One of the most important features of meta-heuristics affecting their search efficiency and effectiveness while avoiding local traps and premature convergence is having a dynamic population size [24] In this regard, the water flow-like algorithm (WFA), inspired by the behavior of water flows traversing from higher to lower altitudes, was introduced by Yang and Wang [24].
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