Energy-efficient multi-objective scheduling algorithm for hybrid flow shop with fuzzy processing time

Abstract

Increasing costs of energy and environmental pollution is prompting scholars to pay close attention to energy-efficient scheduling. This study constructs a multi-objective model for the hybrid flow shop scheduling problem with fuzzy processing time to minimize total weighted delivery penalty and total energy consumption simultaneously. Setup times are considered as sequence-dependent, and in-stage parallel machines are unrelated in this model, meticulously reflecting the actual energy consumption of the system. First, an energy-efficient bi-objective differential evolution algorithm is developed to solve this mixed integer programming model effectively. Then, we utilize an Nawaz-Enscore-Ham-based hybrid method to generate high-quality initial solutions. Neighborhoods are thoroughly exploited with a leader solution challenge mechanism, and global exploration is highly improved with opposition-based learning and a chaotic search strategy. Finally, problems in various scales evaluate the performance of this green scheduling algorithm. Computational experiments illustrate the effectiveness of the algorithm for the proposed model within acceptable computational time.

A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems

“…Fuzzy numbers were formulated to consider the uncertainty in the scheduling problem and a genetic algorithm was applied to solve it. In [57], the fuzzy numbers were used to consider the uncertainty of processing time; in this paper, the minimization of the total weighted delivery penalty and of total energy consumption was defined as an objective function, and a multi-objective differential evolution algorithm was provided. The flow line was considered as a Bernoulli serial line with the aim of improving energy efficiency by scheduling machine shutdowns [58].…”

Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme

“…Other works addressed energy utilization associated with machine setup (e.g., [140][141][142]). In this sense, the authors of [143] pointed out that production planning should also consider setup energy due to its significance in practice.…”

“…Basically, the same operation can be performed on more than one machine and processing time and energy demand depend on the chosen production resource. Taking into account such differences between parallel production resources is more realistic than assuming identical parallel machines or factories (see [142]). Typically, both old and new machines are part of the shopfloor and differ in operating speed and energy utilization (see [162]).…”

Scheduling problems with energy consideration: State of the art and opportunities

“…An approached tool based on the NSGA-II was applied to solve the considered problem. Zhou et al [23] have solved the HFS scheduling problem with fuzzy processing time by proposing a multi-objective formulation that minimizes simultaneously total energy consumption andtotal weighted delivery penalty. In their study they have considered Setup times as sequencedependent and in-stage parallel, resources are unrelated.…”