Farouk Yalaoui

Solving a robotic assembly line balancing problem using efficient hybrid methods

In this paper we are studying a robotic assembly line balancing problem. The goal is to maximize the efficiency of the line and to balance the different tasks between the robots by defining the suitable tasks and components to assign to each robot. We are interested in a robotic line which consists of seizing the products on a moving conveyor and placing them on different location points. The performances evaluations of the system are done using a discret event simulation model. This latter has been developed with C++ language. As in our industrial application we are bounded by the execution time, we propose some resolution methods which define the suitable component and point positions in order to define the strategy of pick and place for each robot. These methods are based on the ant colony optimization, particle swarm optimization and genetic algorithms. To enhance the quality of the developed algorithms and to avoid local optima, we have coupled these algorithms with guided local search. After that, an exact method based on full enumeration is also developed to assess the quality of the developed methods. Then, we try to select the best algorithm which is able to get the best solutions with a small execution time. This is the main advantage of our methods compared to exact methods. This fact represents a great interest taking in consideration that the selected methods are used to manage the functioning of real industrial robotic assembly lines. Numerical results show that the selected algorithm performs optimally for the tested instances in a reasonable computation time and satisfies the industrial constraint.

Buffers sizing in assembly lines using a Lorenz multiobjective ant colony optimization algorithm

In this paper, a new multiobjective resolution approach is proposed for solving buffers sizing problems in assembly lines. The considered problem consists of sizing the buffers between the different stations in a line taking in consideration that the size of each buffer is bounded by a lower and an upper value. Two objectives are taken in consideration: the maximization of the throughput rate and the minimization of the total size of the buffers. The resolution method is based on a multiobjective ant colony algorithm but using the Lorenz dominance instead of the well-known Pareto dominance relationship. The Lorenz dominance relationship provides a better domination area by rejecting the solutions founded on the extreme sides of the Pareto front. The obtained results are compared with those of a classical Multiobjective Ant Colony Optimization Algorithm. For that purpose, three different measuring criteria are applied. The numerical results show the advantages and the efficiency of the Lorenz dominance.

Hybrid optimisation method for the facility layout problem

Due to increased competition, the manufacturers are brought to quickly respond to the needs of customers for on-time delivery of high quality products. Many studies have been made to realize these objectives such as he studies on the facility layout problem (FLP). It is to find a good configuration of the machines, equipments or other resources in a given facility in order to optimize the production flows while minimizing the total cost. The FLP arises in many industry cases, such as facility reorganization, construction of new production units, or equipment assignment. To solve this problem, we develop an ant colony optimization algorithm combined with a local search method. This chapter presents the algorithm, its performance evaluation et application to an industrial case. The remainder of this chapter is organised as follows: In section 2, the layout problem is described. In section 3, we present the general framework of the ACO algorithm and its enhancement by guided local search. Computational results on the performance evaluation of the algorithm using a set of benchmark instances are presented in section 4, and the application of the algorithm to an industrial case is reported in section 5. In section 6, we conclude this chapter with some remarks.