Improved Spider Monkey Optimization Algorithm for Hybrid Flow Shop Scheduling Problem with Lot Streaming

Abstract

This paper investigates the hybrid flow shop scheduling problem with lot streaming, which integrates the order lot problem (OLP), order sequence problem (OSP), and lots assignment problem (LAP), with the objective of minimizing both the maximum completion time (Cmax) and the total tardiness (TT) simultaneously. An improved spider monkey optimization (I-SMO) algorithm is proposed by combining the advantages of crossover and mutation operations of a genetic algorithm (GA) with the spider monkey optimization algorithm. The contribution value method is employed to select both global and local leaders. Experimental comparisons with classical optimization algorithms, including particle swarm optimization (PSO) and differential evolution (DE), were conducted to demonstrate the superiority of the proposed I-SMO algorithm.

Keywords

• spider monkey optimization algorithm
• multi-objective scheduling optimization
• lot streaming
• hybrid flow shop scheduling