دانایی و آگاهی کلنی زنبور عسل در منطقه محدود دو طرفه مساله موازنه خط مونتاژ

Bees Algorithm is a relatively new member of swarm intelligence based meta-heuristics which tries to model natural behavior of real honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new search algorithms for solving optimization problems in operational research. On the other hand, two-sided assembly lines are generally occurred in assembly of large-sized products such as buses and trucks. In a two-sided assembly line, different assembly tasks are carried out on the same product in parallel to both left and right sides of the line. In this study Bees Algorithm is adopted to solve two-sided assembly line balancing problem with zoning constraint so as to minimize the number of stations for a given cycle time. An extensive computational study is carried out and the results are compared with the results of several algorithms from the literature with the results of exact solution approaches and several algorithms from the literature such as ant colony optimization, tabu search.

A branch of nature inspired algorithms which are called as swarm intelligence focused on insect behavior in order to develop some effective meta-heuristics which can mimic insect’s problem solution abilities. Interaction between insects contributes to the collective intelligence of the social insect colonies. These communication systems between insects have been adapted to scientific problems for optimization. One of the examples of such interactive behavior is the waggle dance of bees during the food procurement. By the way of waggle dance, successful foragers share the information about the direction and distance to patches of flower and the amount of nectar within this flower with their hive mates. This is a successful mechanism in which foragers can recruit other bees in their colony to productive locations to collect various resources. The information exchange among individual insects is the most important part of the collective knowledge. Communication among bees about the quality of food sources is achieved in the dancing area by performing waggle dance. The previous studies on dancing behavior of bees show that while performing the waggle dance, the direction of bees indicates the direction of the food source in relation to the sun, the intensity of the waggles indicates how far away it is and the duration of the dance indicates the amount of nectar on related food source. Waggle dancing bees that have been in the hive for an extended time, adjust the angles of their dances to accommodate the changing direction of the sun. Therefore bees that follow the waggle run of the dance are still correctly led to the food source even though its angle relative to the sun has changed. So collective intelligence of bees based on the synergistic information exchange during waggle dance. Observations and studies on honey bees’ behaviors resulted in a new generation of optimization algorithms. Such an algorithm which is known as Bees Algorithm (BA) is used in this paper in order to solve two-sided assembly line balancing problem (two-sided ALB). The paper is organized as follows; description of natural behaviour of bees and the literature survey on foraging behaviour of bees are presented in Section 2. In Section 3, two-sided ALB is clarified and in Section 4 the modified BA for two-sided ALB is explained in detail. Lastly, computational results and comparisons are presented in Section 5.

Two-sided assembly line balancing problem is one of the hard problems in the literature. By adaptation of Bees Algorithm to without constrained and zone constrained two-sided assembly line balancing problem with simple neighborhood structures, it is observed that the proposed algorithm performs well, since it finds the best known number of station and yields the other goals effectively. Neighborhood structures are especially determined as simple shift and swap in order to evaluate the performance of Bees Algorithm itself. The performance of the algorithm is evaluated especially on large sized problems. As the problem size increases, the complexity of the problem also increases. Because of that there are a few studies performed on the large sized problems with zoning constraints in the literature. For most of the large sized problems, proposed Bees Algorithm obtained better solutions than the best known solutions recently presented in the literature. It can be concluded that the recently developed Bees Algorithm is a promising search algorithm for the complex combinatorial optimization problems. For further studies it is aimed to improve the algorithm performance by adapting complex neighborhood structures and different fitness functions. Also experimental design for parameter optimization is scheduled as a future work.