Multi-objective optimization for energy-efficient flow shop scheduling problem with blocking and collision-free transportation constraints

In this paper, the energy-efficient flow shop scheduling problem with blocking and collision-free transportation constraints (EFSP-BCFT) with sequence dependent setup times (SDST), scheduling of automated guided vehicles (AGV), transportation speed control and battery management is investigated. An enhanced multi-objective ant colony algorithm (EMOACA) is developed to minimize makespan and total energy consumption simultaneously. Several enhancement techniques including a novel low-high resolution search strategy, an effective AGV dispatching heuristic information and critical-path-based energy reduction improvement procedure are proposed. Extensive experiments spanning various scenarios of the problem are conducted and computational results demonstrate the effectiveness of the proposed method and enhancements to obtain high quality solutions compared to standard and state- of-the-art metaheuristics. The results also showcase the significance of considering transportation speed control, battery management and AGV idle power consumption in the modelling of the problem where better system performance can be achieved both in terms of shorter schedules and smoother transportation flow.

Automated summary

This paper investigates the energy-efficient flow shop scheduling problem with various constraints and proposes an enhanced multi-objective ant colony algorithm to solve this problem. Extensive experiments demonstrate the effectiveness of the proposed method and enhancements in obtaining high quality solutions, and showcase the significance of considering transportation speed control, battery management, and AGV idle power consumption.