Heuristics for Multiobjective Optimization of Two-Sided Assembly Line Systems

Abstract

Products such as cars, trucks, and heavy machinery are assembled by two-sided assembly line. Assembly line balancing has significant impacts on the performance and productivity of flow line manufacturing systems and is an active research area for several decades. This paper addresses the line balancing problem of a two-sided assembly line in which the tasks are to be assigned at L side or R side or any one side (addressed as E). Two objectives, minimum number of workstations and minimum unbalance time among workstations, have been considered for balancing the assembly line. There are two approaches to solve multiobjective optimization problem: first approach combines all the objectives into a single composite function or moves all but one objective to the constraint set; second approach determines the Pareto optimal solution set. This paper proposes two heuristics to evolve optimal Pareto front for the TALBP under consideration: Enumerative Heuristic Algorithm (EHA) to handle problems of small and medium size and Simulated Annealing Algorithm (SAA) for large-sized problems. The proposed approaches are illustrated with example problems and their performances are compared with a set of test problems.

Heuristics for solving a multi-model robotic assembly line balancing problem

“…The results from heuristic can act a benchmark for a problem that is proposed for the first time in the literature. These are the few reasons why heuristics have been selected to solve the problem of this nature (Jawahar, Ponnambalam, Sivakumar, & Thangadurai, 2014). The assembly line balancing problem has been classified as an NP-hard problem , and the problem considered in this paper also falls under this category due to additional constraints included because of the multi-batch production and robots used for the assembly.…”

Using the Efficient Frontier to Obtain the Best Solution for the Storage Location Assignment Problem

“…In systems with multiple objectives, the objectives are often in conflict with one another where one objective function value must be "traded off " for another. There are two approaches to solve these problems: the first approach combines all of the objectives into a single composite function and moves all but one objective to the constraint set and the second approach determines the Pareto-optimal solution set or an efficient frontier, which is a set of points in the objective function space that is often referred to as a tradeoff curve [40,41]. The efficient frontier is defined as the locus of achievable joint evaluations from which no joint gains are possible [42].…”