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A Multiobjective Optimization Approach for Multiobjective Hybrid Flowshop Green Scheduling with Consistent Sublots

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Bibliographic Details
Title: A Multiobjective Optimization Approach for Multiobjective Hybrid Flowshop Green Scheduling with Consistent Sublots
Authors: Weiwei Wang, Biao Zhang, Baoxian Jia
Source: Sustainability, Vol 15, Iss 3, p 2622 (2023)
Publisher Information: MDPI AG
Publication Year: 2023
Collection: Directory of Open Access Journals: DOAJ Articles
Subject Terms: hybrid flowshop scheduling problem, green scheduling, consistent sublots, multiobjective evolutionary algorithm, Environmental effects of industries and plants, TD194-195, Renewable energy sources, TJ807-830, Environmental sciences, GE1-350
Description: Hybrid flowshop scheduling problems are encountered in many real-world manufacturing scenarios. With increasingly fierce market competition, the production mode of multiple varieties and small batches has gradually been accepted by enterprises, where the technology of lot streaming is widely used. Meanwhile, green criteria, such as energy consumption and carbon emissions, have attracted increasing attention to improving protection awareness. With these motivations, this paper studies a multiobjective hybrid flowshop green scheduling problem with consistent sublots (MOHFGSP_CS), aiming to minimize two objectives, i.e., makespan and total energy consumption, simultaneously. To solve this complex problem, we first formulate a novel multiobjective optimization model. However, due to the NP-hard nature of the problem, the model is computationally prohibitive as the problem scale increases. Thus, a multiobjective discrete artificial bee colony algorithm (MDABC) based on decomposition is proposed. There are three phases in this algorithm: the VND-based employed bee phase, the adjustment weight onlooker bee phase, and the population interaction scout bee phase. In the experimental study, various small-scale and large-scale instances are collected to verify the effectiveness of the multiobjective optimization model and the MDABC. Comprehensive computational comparisons and statistical analysis show that the developed strategies and MDABC show superior performance.
Document Type: article in journal/newspaper
Language: English
Relation: https://www.mdpi.com/2071-1050/15/3/2622; https://doaj.org/toc/2071-1050; https://doaj.org/article/9e7370b8fff940978e25656ac11e64c6
DOI: 10.3390/su15032622
Availability: https://doi.org/10.3390/su15032622
https://doaj.org/article/9e7370b8fff940978e25656ac11e64c6
Accession Number: edsbas.46180A39
Database: BASE
Description
Abstract:Hybrid flowshop scheduling problems are encountered in many real-world manufacturing scenarios. With increasingly fierce market competition, the production mode of multiple varieties and small batches has gradually been accepted by enterprises, where the technology of lot streaming is widely used. Meanwhile, green criteria, such as energy consumption and carbon emissions, have attracted increasing attention to improving protection awareness. With these motivations, this paper studies a multiobjective hybrid flowshop green scheduling problem with consistent sublots (MOHFGSP_CS), aiming to minimize two objectives, i.e., makespan and total energy consumption, simultaneously. To solve this complex problem, we first formulate a novel multiobjective optimization model. However, due to the NP-hard nature of the problem, the model is computationally prohibitive as the problem scale increases. Thus, a multiobjective discrete artificial bee colony algorithm (MDABC) based on decomposition is proposed. There are three phases in this algorithm: the VND-based employed bee phase, the adjustment weight onlooker bee phase, and the population interaction scout bee phase. In the experimental study, various small-scale and large-scale instances are collected to verify the effectiveness of the multiobjective optimization model and the MDABC. Comprehensive computational comparisons and statistical analysis show that the developed strategies and MDABC show superior performance.
DOI:10.3390/su15032622