Resource Allocation Embedded Assembly Line Balancing Problem and Ant Colony Optimization Methods
Date Issued
2010
Date
2010
Author(s)
Wang, Ya-Chin
Abstract
This research introduces a resource allocation embedded assembly line balancing problem. The problem involves operations of task sequencing and grouping and operations of operator selection and assignment, for a given number of workstations. The goal of the problem is to minimize the cycle time of assembly line and the labor cost incurred from the operator assignments. The mathematical model is rigorously defined and ACO techniques for solving the problem are presented. A single objective optimization mode is adopted to accommodate the two primary evaluation terms, cycle time and labor cost, using weighting factors. For the solution construction, we propose a dynamic cycle time threshold and a dynamic heuristic value to guide the solution search toward the optima. This paper proposes two solution construction methods: the RAF ACO method deals with operations of operator selection and assignment first and then operations of assembly task sequencing and grouping; and conversely, the LBF ACO method deals with line balance related operations first, then the resource allocation related operations. Therefore, within these methods an ant is committed with two missions in different orders: (1) sequentially assign a list of assembly tasks to each workstation and (2) sequentially select and assign an operator to each workstation. Several operational options related to the evaluations of heuristic values use in probability calculation, estimation of cycle times, updates of cycle times and heuristic values and also proposed. A prototype system namely, RAELBP Solver, that implements the proposed methods is constructed for numerical tests and to verify the proposed methods. To facilitate numerical tests, a data generator for the introduced problem is developed by integrating the numerical data from line balance problems and automatically generate data about the operators. Three sample problems with different scales are therefore generated and used for the tests. Numerical results from different settings for different problems are separately compared against the objective values, and associations of cycle time and labor cost. Results show that the proposed methods are all able to solve the problems with different achievements of non-dominated solutions. In general, the LBAF ACO method covers a large area of Pareto frontier than that of the RAF ACO method.
Subjects
Task sequencing and grouping
Operator selection and assignment
Pareto frontier
Non-dominated solutions
Assembly line balancing problem
Resource allocation problem
Ant Colony Optimization
Type
thesis
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