Journal Article10.1007/S11590-011-0344-9
Bees Algorithm for constrained fuzzy multi-objective two-sided assembly line balancing problem
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TL;DR: Two-sided Assembly Line Balancing Problem is considered more realistically by employing positional, zoning and synchronous task constraints and by utilizing fuzzy approaches so as to maximize work slackness index and line efficiency, and minimize total balance delay.
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Abstract: Bees Algorithm is one of the swarm intelligence based heuristics which tries to model natural behaviour of honey bees in food foraging and used to solve optimization problems. On the other hand, Two-sided Assembly Line Balancing Problem is a generalization of simple Assembly Line Balancing Problem where different assembly tasks are carried out on the same product in parallel at both left and right sides of the line. Two-sided assembly lines are generally employed for the assembly of large-sized products such as buses and trucks. Furthermore, many real life problems contain imprecise objectives and Fuzzy Multi-objective Programming gives an opportunity to handle such situations. In this study, Two-sided Assembly Line Balancing Problem is considered more realistically by employing positional, zoning and synchronous task constraints and by utilizing fuzzy approaches so as to maximize work slackness index and line efficiency, and minimize total balance delay. For solving this problem Bees Algorithm is used as a search mechanism for obtaining good solutions and extensive computational results are presented.
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References
A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
Dervis Karaboga,Bahriye Basturk +1 more
TL;DR: Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm that is used for optimizing multivariable functions and the results showed that ABC outperforms the other algorithms.
The bees algorithm, a novel tool for complex optimisation problems
Duc Truong Pham,Afshin Ghanbarzadeh,Ebubekir Koç,Sameh Otri,S. Rahim,M. Zaidi +5 more
- 01 Jan 2006
TL;DR: This chapter presents a new population-based search algorithm called the Bees Algorithm, which mimics the food foraging behavior of swarms of honeybees and can be used for both combinatorial optimization and functional optimization.
Fuzzy goal programming- an additive model
TL;DR: An additive model to solve Fuzzy Goal Programming (FGP) is formulated that uses arithmetic addition to aggregate the fuzzy goals to construct the relevant decision function.
530
A survey of recent developments in multiobjective optimization
TL;DR: Recent developments in Multiobjective Optimization are discussed, including optimality conditions, applications, global optimization techniques, the new concept of epsilon Pareto optimal solution, and heuristics.
342
Balancing two-sided assembly lines: a case study
TL;DR: The design and use of a computer program is described that embodies a balancing algorithm that emphasizes speed over accuracy for the interactive, rapid refinement of solutions in two-sided assembly lines.
293