Research on group search optimizers for a reconfigurable flow shop sequencing problem

Abstract

A reconfigurable manufacturing system is usually designed for quick re-adjusting of production capacity in response to market changes. In this paper, we study a flow shop sequencing problem (FSSP) with controllable processing times as a special case of reconfigurable manufacturing system. It is possible to speed up the processing times through assigning additional resources or control of machine speed. After formulating this problem mathematically, a novel evolutionary procedure, entitled group search optimizer (GSO), is devised as solution method. The adapted GSO is a population-based search tool which is devised based on the producer and scrounger behavior. GSO emphasizes on imitating searching model of real-world animals. The basic GSO with four promising improvements is elaborated and discussed for addressing the FSSP with controllable processing times. A set of computational experiments is also conducted to demonstrate the applicability of proposed FSSP and performance of improved GSOs.

Optimization of heterogeneous vehicle logistics scheduling with multi-objectives and multi-centers

“…The basic Group Search Optimizer (GSO) algorithm has been widely used in optimization problems since it was proposed in 2006 35 , 36 . Laithadualigah et al successfully applied GSO algorithm to function selection (FS) problem in machine learning field, and studied the application of improved GSO search strategy in multi-objective optimization problem 37 .…”