Carbon peaking and carbon neutrality, which are significant strategies for national sustainable development, have attracted enormous attention from researchers in the manufacturing domain. A Pareto-based discrete Jaya algorithm (PDJaya) is proposed to solve the carbon-efficient distributed blocking flow shop scheduling problem (CEDBFSP) with the criteria of total tardiness and total carbon emission in this paper. The mixed-integer linear programming model is presented for the CEDBFSP. An effective constructive heuristic is produced to generate the initial population. The new individual is generated by the update mechanism of PDJaya. The self-adaptive operator local search strategy is designed to enhance the exploitation capability of PDJaya. A critical-path-based carbon saving strategy is introduced to further reduce carbon emissions. The effectiveness of each strategy in the PDJaya is verified and compared with the state-of-the-art algorithms in the benchmark suite. The numerical results demonstrate that the PDJaya is the efficient optimizer for solving the CEDBFSP. 

A Pareto-Based Discrete Jaya Algorithm for Multiobjective Carbon-Efficient Distributed Blocking Flow Shop Scheduling Problem

Green manufacturing has attracted increasing attention under the background of carbon peaking and carbon neutrality. Distributed production has widely existed in various manufacturing industries with the development of globalization. This paper investigates an energy-efficient distributed no-wait flow-shop scheduling problem with sequence-dependent setup time (DNWFSP-SDST) to minimization of makespan and total energy consumption (TEC). A mixed-integer linear programming model of energy-efficient DNWFSP-SDST is constructed and a cooperative meta-heuristic algorithm based on Q-learning (CMAQ) is proposed to address energy-efficient DNWFSP-SDST in this paper. In CMAQ, a heuristic named RNRa is proposed to generate initial solutions. A bi-population cooperative framework based on double Q-learning is designed to further optimize the solutions. According to the properties of energy-efficient DNWFSP-SDST, an energy-saving strategy based on knowledge is proposed to improve makespan and TEC. The results of experiments show that the performance of CMAQ is superior to certain state-of-the-art comparison algorithms in solving energy-efficient DNWFSP-SDST. 

A Reinforcement Learning Driven Cooperative Meta-Heuristic Algorithm for Energy-Efficient Distributed No-Wait Flow-Shop Scheduling with Sequence-Dependent Setup Time

Solving energy-efficient fuzzy hybrid flow-shop scheduling problem at a variable machine speed using an extended NSGA-II

Improved meta-heuristics with Q-learning for solving distributed assembly permutation flowshop scheduling problems

Green manufacturing has attracted increasing attention under the background of carbon peaking and carbon neutrality. Distributed production has widely existed in various manufacturing industries with the development of globalization. This article investigates an energy-efficient distributed no-wait flow-shop scheduling problem with sequence-dependent setup time (DNWFSP-SDST) to minimization of makespan and total energy consumption (TEC). A mixed-integer linear programming model of energy-efficient DNWFSP-SDST is constructed and a cooperative meta-heuristic algorithm based on Q-learning (CMAQ) is proposed to address energy-efficient DNWFSP-SDST in this article. In CMAQ, a heuristic named RNRa is proposed to generate initial solutions. A bipopulation cooperative framework based on double Q-learning is designed to further optimize the solutions. According to the properties of energy-efficient DNWFSP-SDST, an energy-saving strategy based on knowledge is proposed to improve makespan and TEC. The results of experiments show that the performance of CMAQ is superior to certain state-of-the-art comparison algorithms in solving energy-efficient DNWFSP-SDST.

A Reinforcement Learning Driven Cooperative Meta-Heuristic Algorithm for Energy-Efficient Distributed No-Wait Flow-Shop Scheduling With Sequence-Dependent Setup Time

Carbon peaking and carbon neutrality, which are the significant national strategy for sustainable development, have attracted considerable attention from production enterprises. In this study, the energy consumption is considered in the distributed blocking flow shop scheduling problem (DBFSP). A hyperheuristic with <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-learning (HHQL) is presented to address the energy-efficient DBFSP (EEDBFSP). <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-learning is employed to select an appropriate low-level heuristic (LLH) from a predesigned LLH set according to historical information fed back by LLH. An initialization method, which considers both total tardiness (TTD) and total energy consumption (TEC), is proposed to construct the initial population. The <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>-greedy strategy is introduced to utilize the learned knowledge while retaining a certain degree of exploration in the process of selecting LLH. The acceleration operation of the job on the critical path is designed to optimize TTD. The deceleration operation of the job on the noncritical path is designed to optimize TEC. The statistical and computational experimentation in an extensive benchmark testified that the HHQL outperforms the other comparison algorithm regarding efficiency and significance in solving EEDBFSP.

A Hyperheuristic With Q-Learning for the Multiobjective Energy-Efficient Distributed Blocking Flow Shop Scheduling Problem

A reinforcement learning-driven brain storm optimisation idea (RLBSO) is proposed in this paper to solve multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem. The objectives of the problem include minimising the maximum assembly completion time ( ), minimising the total energy consumption (TEC) and achieving resource allocation balanced . Four operations, which are critical factory insert, critical factory swap, critical factory insert to other factories, critical factory swap with other factories, are designed to optimise the objective of maximum assembly completion time. Q-learning mechanism is utilised to guide the selection of operations to avoid blind search in the iteration process. The learning mechanism based on clustering mechanism in brain storm optimisation algorithm is utilised to assign products to factories in the objective space according to the processing time of products to balance the resources allocation. The speed of operations on non-critical path is slowed down to reduce TEC regarded with the characteristics of no-wait flow shop scheduling problem. The experimental results under 810 large-scale instances by RLBSO show that the RLBSO outperforms the comparison algorithm for addressing the problem.

A reinforcement learning-driven brain storm optimisation algorithm for multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem

The distributed assembly blocking flow-shop scheduling problem (DABFSP), which is a promising area in modern supply chains and manufacturing systems, has attracted great attention from researchers and practitioners. However, minimizing the total tardiness in DABFSP has not captured much attention so far. For solving the DABFSP with the total tardiness criterion, a mixed integer linear programming method is utilized to model the problem, wherein the total tardiness during the production process and assembly process are optimized simultaneously. A constructive heuristic (KBNEH) and a water wave optimization algorithm with problem-specific knowledge (KWWO) are presented. KBNEH is designed by combining a new dispatching rule with an insertion-based improvement procedure to obtain solutions with high quality. In KWWO, effective technologies, such as the re-developed destruction–construction​ operator, four local search methods under the framework of the variable neighborhood search strategy (VNS), the path-relinking method are applied to improve the performance of the algorithm. Comprehensive numerical experiments based on 900 small-scale benchmark instances and 810 large-scale benchmark instances are conducted to evaluate the performance of the presented algorithm. The experimental results obtained by KWWO are 1 to 4 times better than those obtained by the other comparison algorithms, which demonstrate that the effectiveness of KWWO is superior to the compared state-of-the-art algorithms for the considered problem. 

An effective water wave optimization algorithm with problem-specific knowledge for the distributed assembly blocking flow-shop scheduling problem

Carbon peaking and carbon neutrality, which are significant strategies for national sustainable development, have attracted enormous attention from researchers in the manufacturing domain. A Pareto-based discrete Jaya algorithm (PDJaya) is proposed to solve the carbon-efficient distributed blocking flow shop scheduling problem (CEDBFSP) with the criteria of total tardiness and total carbon emission in this article. The mixed-integer linear programming model is presented for the CEDBFSP. An effective constructive heuristic is produced to generate the initial population. The new individual is generated by the update mechanism of PDJaya. The self-adaptive operator local search strategy is designed to enhance the exploitation capability of PDJaya. A critical-path-based carbon saving strategy is introduced to further reduce carbon emissions. The effectiveness of each strategy in the PDJaya is verified and compared with the state-of-the-art algorithms in the benchmark suite. The numerical results demonstrate that the PDJaya is the efficient optimizer for solving the CEDBFSP.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ling Wang

Author Tools

Chat about Author