An Assembly Line Balancing Application on Oven Production Line with Hyper-Heuristics

Abstract

In this study, an oven assembly line that is planning to re-establish manufacturing to increase the efficiency of the assembly process. The importance of the problem emerges from a real-world application consisting of product-oriented restrictions. These multiple restricted problems address the single model assignment restricted ALB problem with positional constraints. A cost-based objective function is used to cope with this problem. The number of platformed and non-platformed stations, the number of direction changes in a station, the number of stations in which both connector and combiner are used are the cost factors of the objective function. Also, the main objective of the problem is to minimize the total number of stations while satisfying the restrictions. A simulated annealing-based hyper-heuristic is adapted and applied to the balancing problem of oven manufacturing process with assignments and operational restrictions with multiple objectives. The results show that better solutions can be found in the current line balance level while satisfying more restrictions. It is also observed that line balance can be improved depending on the relaxation of the restrictions.

An artificial bee colony based-hyper heuristic algorithm with local search for the assembly line balancing problems

“…Due to the advantages of hyper-heuristics, many studies utilised the hyper-heuristic algorithms to tackle a wide range of optimisation problems such as university course timetabling (Bai et al ., 2007; Soria-Alcaraz et al ., 2016), knapsack problems (Gölcük and Ozsoydan, 2021), exam timetabling problems (Hao et al ., 2020), vehicle crash-worthiness problem (Li et al ., 2017), quadratic assignment problem (Dokeroglu and Cosar, 2016) and scheduling problems (Koulinas et al ., 2014; Hart and Sim, 2016; Lin et al ., 2017; Deliktaş, 2021). Furthermore, a few studies have explored the application of hyper-heuristic algorithms in addressing different assembly line variations, such as aircraft final ALB (Bao et al ., 2023), two-sided ALB (Rong et al ., 2023), stochastic parallel disassembly line balancing (Hu et al ., 2023), parallel ALB (Seçme and Özbakır, 2019; Özbakır and Seçme, 2022), mixed-model ALB (Ebrahimi et al ., 2023; Cano-Belmán et al ., 2010) and robotic parallel with type-II (Çil et al ., 2017). The existing literature lacks sufficient studies that investigate the use of hyper-heuristic algorithms for solving the simple assembly line problem with type-I.…”