Multi-objective Assembly Line Balancing And Sequencing Problems With The Application Of Abc Algorithm

Assembly line balancing is significant for efficient and effective production of the products and is therefore gaining popularity in recent years. Different type of assembly lines are used to assemble different products and these includes, single model assembly lines, mixed model assembly lines and multi model assembly lines. Single model assembly lines are bound to produce single type of product and in recent years, mixed model assembly lines are gaining popularity to produce variety of models on the single assembly lines. Mixed model assembly lines have two types of problems which include sequencing of models on the line and balancing of line. These two problems can affect the performance of mixed model assembly line. Mixed model assembly lines can be single line or multi lines to fulfill the customer demands. Single model assembly lines and mixed model assembly line problems are significant but some companies have more than one assembly line and multi lines to fulfill the demand of customers. The integration of multi-mixed model assembly line sequencing and balancing has significant practical importance. Current research is focused on different assembly line problems which are described one by one as under:(1) As for the single model assembly line balancing problem with uncertain task times, the mathematical model is established to minimize cycle time of the line, maximize the probability to ensure that the completion time of tasks on stations will not exceed the cycle time, and minimize the smoothness index. A Pareto based artificial bee colony (PBABC) algorithm is proposed to get Pareto solutions of the multiple objectives. Experiments are performed to solve standard assembly line balancing problems taken from operations research (OR) library and the performance of proposed PBABC algorithm is compared with a famous multi-objective optimization algorithm NSGA Ⅱ, in literature.(2) As for the single model assembly line balancing problem with uncertain task times, the mathematical model is formulated to minimize cycle time, maximize the average probability of stations to ensure that the completion time will not exceed the cycle time, and maximize the overall probability of the whole assembly line to ensure that the completion time of tasks on different stations will not exceed the cycle time. A hybrid Pareto artificial bee colony (HPABC) algorithm is proposed to solve this problem. Computational experiments are performed to solve standard assembly line benchmarks from operations research (OR) library. The performance of HP ABC is compared with the performance of SPEA2algorithm.(3) As for simultaneous balancing and sequencing of mixed model assembly lines, the mathematical model is established to balance workload of different models on each station, reduce the deviation of workload of a station from the average workload of all the stations, and minimize total flow time of models on different stations simultaneously. A multi-objective artificial bee colony algorithm (Multi-ABC) is presented for this problem. Two kinds of mixed model assembly line problems, including standard assembly line problems from operation research library (OR) and a practical problem taken from a manufacturing company in China, are analyzed. The performance of Multi-ABC algorithm is tested against the performance of famous algorithm in literature i.e., the NSGA II algorithm.(4) As for order oriented multi mixed model assembly line balancing and sequencing problem, the mathematical model is formulated to level the material usage on different lines in multi-mixed model assembly lines, minimize maximum makespan between the lines and minimize the penalty cost on the later models from different orders. A multi-objective artificial bee colony algorithm (MABC) for simultaneous sequencing and balancing problem in multi-mixed model assembly lines is presented. Two kinds of multi-mixed model assembly line problems, including standard assembly line problems from operation research library (OR) and a practical problem taken from a manufacturing company in China, are analyzed. The performance of the proposed MABC algorithm is compared to a famous multi-objective algorithm (SPEA2) in literature.Computational result shows that proposed PBABC algorithm for the single model assembly line balancing problem with uncertain task times outperforms NSGA II in terms of both the quality of Pareto solutions obtained and the corresponding computational time needed. Moreover, the results of HP ABC indicate that the presented HP ABC algorithm outperforms a famous multi-objective algorithm (known as SPEA2) in most of the instances tested. Furthermore, computational results for the mixed model assembly line balancing and sequencing problem indicates that the proposed Multi-ABC algorithm outperforms the NSGA Ⅱ algorithm and gives better Pareto solutions on different demand scenarios of models. Moreover, Computational results of MABC indicates that the proposed MABC algorithm out performs SPEA2algorithm and gives better Pareto solutions for the tested problems for order oriented multi mixed model assembly line balancing and sequencing problem.