الفهرس 
Classification of The Assembly Line Balancing Problem
CONCLUSION AND RECOMMENDATIONSOnly 14 pages are availabe for public view
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Abstract
Assembly lines have been widely used in various production systems. An assembly line consists of a series of work stations arranged along with a material handling system. The components are assembled with a certain sequence depending on the precedence diagram for given cycle time.
The decision problem regarding balancing the assembly process optimally is known as assembly line balancing problem (ALBP). Assembly line configuration is always critical to carry out a cost-efficient production system. The configuration planning, throughout all time, consists of all elements and decisions that are related to equipment and adjusting production units for a certain production system.
Simple Assembly Line Balancing Problem (SALBP) considers only one-sided single model with deterministic time. The complexity of SALBP can be reduced by some assumptions like mass-production of only one homogenous product, no restriction besides the precedence relations, equipping stations in equal manner…etc.
Seeing that, ALBP is NP-hard problem, exact methods will need exhaustive enumerations to find the optimal solution especially with multi-objectives. Hence, leading the researchers to use Meta-heuristics methods like Genetic algorithms (GA) and simulated annealing (SA).
The aim of the research is to solve SALBP using Meta-Heuristics e.g. GA and SA, with two objectives: minimizing the number of work stations as a first objective and minimizing smoothing index as a secondary objective, and to compare the obtained results with those obtained by exact, heuristic and Meta-heuristic methods from the literature.
The Enhanced Genetic Algorithm (EGA) is based on generating population by non-traditional random method. As well, solutions generated from heuristic methods were added to the population to increase the diversity of the generated population. On the other hand, the initial solution of the Enhanced Simulated Annealing (ESA) is obtained using Ranked Positional Weight (RPW) method.
The two algorithms were tested using well-known benchmark problems available in the literature. The two algorithms showed the capability of solving optimally more than 75% of 171 benchmark problems, varying in size between 11 work elements and 111 work elements. What is more, EGA and ESA outperformed Simple Assembly Line Optimization Method of type-1 (SALOME-1) for most of the benchmark problems, from Smoothing Index and efficiency perspectives.
In order to test the applicability of the proposed algorithms on the real life, the EGA was tested on an assembly line for car seats. Indeed, the results were promising in improving the overall performance of the assembly line.
A sensitivity analysis test was carried out on the proposed algorithms for the purpose of studying different parameters that could affect the quality of the solution obtained. Moreover, statistical tests were carried out to estimate the deviation of algorithms under study from the optimal solution and to test whether there is a significant difference between algorithms or not.
Concerning future work, it is recommended that studies may focus on stochastic times. Also, it is recommended to take into consideration ALBP with realistic constraints like zoning constraints and to develop more hybrid heuristic and Meta-heuristic algorithms.