A multi-objective lead time control problem in multistage assembly systems using genetic algorithms

Perkgoz Cahit

Azaron Amir

Katagiri Hideki

Kato Kosuke

Sakawa Masatoshi

European Journal of Operational Research

In this paper, we develop a multi-objective model to optimally control the lead time of a multistage assembly system, using genetic algorithms. The multistage assembly system is modelled as an open queueing network. It is assumed that the product order arrives according to a Poisson process. In each service station, there is either one or infinite number of servers (machines) with exponentially distributed processing time, in which the service rate (capacity) is controllable. The optimal service control is decided at the beginning of the time horizon. The transport times between the service stations are independent random variables with generalized Erlang distributions. The problem is formulated as a multi-objective optimal control problem that involves four conflicting objective functions. The objective functions are the total operating costs of the system per period (to be minimized), the average lead time (min), the variance of the lead time (min) and the probability that the manufacturing lead time does not exceed a certain threshold (max). Finally, we apply a genetic algorithm with double strings using continuous relaxation based on reference solution updating (GADSCRRSU) to solve this multi-objective problem, using goal attainment formulation. The results are also compared against the results of a discrete-time approximation technique to show the efficiency of the proposed genetic algorithm approach.

Queueing

Genetic algorithms

Multiple objective programming

Production

Biology [ 460 ]

eng

Elsevier Science B.V.

Copyright (c) 2006 Elsevier B.V.

[ISSN] 0377-2217

[DOI] 10.1016/j.ejor.2006.04.024

[NCID] AA0017802X

[DOI] http://dx.doi.org/10.1016/j.ejor.2006.04.024