A new mixed-integer linear programming formulation and particle swarm optimization based hybrid heuristic for the problem of resource investment and balancing of the assembly line with multi-manned workstations - Şahin, Murat; Kellegöz, Talip (Computers & industrial engineering, 2019)
•Resource constrained multi-manned assembly line balancing problem is addressed.•Mathematical models of the problem firstly have been improved.•An effective hybrid algorithm based on particle swarm optimization is developed.•Computational study shows the effectiveness of the proposed solution methods. Resource investment and balancing problem of an assembly line with parallel multi-manned workstations can be defined as the assignment of tasks to reduce the cost of the line, which includes the cost of opened workstations and required renewable resources. Although mentioned problem has been commonly occurred in industrial environment that produce large scale products in high volumes, there have been restricted number of studies in the literature about this field. This article proposes a new mixed-integer linear programming approach that can be used in solving small size instances of the problem. In addition, a new hybrid method has been developed to solve larger scale instances by combining particle swarm optimization algorithm with a special constructive heuristic. In the constructive heuristic, serial schedule generation scheme widely used in solving resource constrained project scheduling problems has been adapted to resource investment problem with some modifications. Proposed metaheuristic has been compared against a tabu search and cuckoo search algorithm taken from the assembly line balancing literature. Many precedence diagrams commonly used in solving various assembly line balancing problems in the literature, have been used to generate test instances for the considered problem type. After solving these test instances using each solution methods, it has been observed that the proposed hybrid metaheuristic yielded the solutions, which have acceptable deviations from the lower bounds.