Multi-objective low-carbon hybrid flow shop scheduling via an improved teaching-learning-based optimization algorithm

Abstract

In this article, for achieving an effective and environmental-friendly production scheduling, we investigate a multi-objective low-carbon hybrid flow shop scheduling problem (MLHFSP) with the consideration of machines with varied energy usage ratios. The problem is formulated by a multi-objective mathematical model with two optimization objectives, i.e., minimizing total carbon emission (TCE) and makespan (C max ). We primarily analyse on the formation of TCE and derive its mathematical expression. MLHFSP is NP-hard, therefore, to tackle the model, an improved multi-objective teaching-learning-based optimization (ITLBO) algorithm is proposed. The ITLBO algorithm mainly contains global search based teaching phase and local search based learning phase. In ITLBO, a solution is represented by two vectors, i.e., job sequence vector and machine assignment vector. Sigma method is utilized to quantify each individual, and to avoid local optimum, sequential neighbourhood search (SNS) method is also adopted. Experimental results validate the feasibility and effectiveness of proposed ITLBO in addressing MLHFSP. The research findings help manufacturing engineers to seek a sophisticated balance between carbon emission reduction and makespan reduction.

Environmentally friendly strategy for hybrid flow shop scheduling with the unrelated parallel machines

“…Spacing Metric sp was used to measure the standard deviation of the minimum distance from each solution to others. The small value of spacing means that the distribution of the solutions in the Pareto solution set is more balance [49] .…”

Optimizing Product Life Cycle Systems for Manufacturing in a Circular Economy

“…For example, Jiang et al's optimization models minimize the environmental impact of remanufacturing [13]. Other studies have explored optimizing disassembly lines from the point of view of profit, disassembly time, and energy consumption [14,15]. Mathur et al [16] used a hybrid optimization approach to balance economic and environmental metrics in the context of EoU photovoltaics.…”

An Integrated Supplier Selection and Order Allocation with Quantity Discounts under Uncertainty

“…This study develops a metaheuristic algorithm using a continuous search space. The proposed metaheuristic algorithm at each iteration selects a set of solutions randomly from the search space [54][55]. To show how a random solution is created and how this random solution transforms into an integer solution meeting the constraints of our optimization model [56,57,25],…”