Currently practiced formulations for the assembly line balance problem

New formulations for the assembly line balance problem are proposed based on interviews and surveys of practicing engineers. These formulations are the basis for a model in goal programming form. A branch-and-bound algorithm is developed which can solve the model for an optimum solution. Computational studies show that computer run time is very modest for moderate size problems.Interviews and a survey of practicing engineers were used to develop a list of goals or constraints germane to the assembly line balance problem. These included minimizing working areas or employees, making sure tasks assigned to a station do not exceed the cycle time, and adhering to sequence constraints. These constraints are included in most traditional models. However, additional goals were mentioned. These included avoiding changes in workload assigned to a work area, adhering to layout requirements of the plant, making combinations of tasks interesting, avoiding the combination of physiologically demanding tasks, etc.The additional goals above are incorporated in a new formulation for the assembly line balance problem. This formulation is in goal programming form. The goal programming model attempts to minimize deviations from goals. If deviations are necessary, lowest ranked goals are violated first. The objective function of the model is based on an ordinal ranking of goals only. The survey mentioned above showed that engineers did not find it difficult to rank the importance of goals.The proposed goal programming model is a mixed integer linear program. Previous studies have shown that cutting plane and implicit enumeration techniques are inferior to branch-and-bound algorithms. A branch-and-bound method, called GoalOriented Algorithm for Line Balance (GOAL), is developed to solve the formulations proposed in the paper.GOAL was computationally tested with 50 problems. These problems varied in size from 4 to 35 tasks. One of the problems was an engine cradle assembly problem encountered by an automobile manufacturer. Computer run times appeared reasonable. For example, the engine cradle problem 35 tasks) required only 16.3 seconds CPU on the DEC PDF at California State University, Northridge. GOAL's execution time appeared linearly proportional to the number of tasks required for most problems.