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A Novel Collaborative Optimization Model for Job Shop Production–Delivery Considering Time Window and Carbon Emission

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  • Wenzhu Liao

    (College of Mechanical Engineering, Chongqing University, Chongqing 400044, China)

  • Tong Wang

    (College of Mechanical Engineering, Chongqing University, Chongqing 400044, China)

Abstract

The manufacturing industry is undergoing transformation and upgrading from traditional manufacturing to intelligent manufacturing, in which Internet of Things (IoT) technology plays a central role in promoting the development of intelligent manufacturing. In order to solve the problem that low production efficiency and machine utilization lead to serious pollution emissions in the workshop caused by untimely transmission of information in all links of the production and manufacturing process to whole supply chains, this study establishes an intelligent production scheduling and logistics delivery model with IoT technology to promote green and sustainable development of intelligent manufacturing. Firstly, an application framework of IoT technology in production–delivery supply chain systems was established to improve efficiency and achieve the integration of production and delivery. Secondly, an integrated production–delivery model was constructed, which takes into account time and low carbon constraints. Finally, a two-layer optimization algorithm was proposed to solve this integration problem. Through a case study, the results show this integration production–delivery model can reduce the cost of supply chains and improve customer satisfaction. Moreover, it proves that carbon emission cost is a major factor affecting total cost, and it could help enterprises to realize the profit and sustainable development of the environment. The production–delivery model could also support the last kilometer distribution problem and extension under E-commerce applications.

Suggested Citation

  • Wenzhu Liao & Tong Wang, 2019. "A Novel Collaborative Optimization Model for Job Shop Production–Delivery Considering Time Window and Carbon Emission," Sustainability, MDPI, vol. 11(10), pages 1-27, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2781-:d:231379
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    Cited by:

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    2. Chauhan, Chetna & Kaur, Puneet & Arrawatia, Rakesh & Ractham, Peter & Dhir, Amandeep, 2022. "Supply chain collaboration and sustainable development goals (SDGs). Teamwork makes achieving SDGs dream work," Journal of Business Research, Elsevier, vol. 147(C), pages 290-307.
    3. Dorota Kuchta & Ewa Marchwicka & Jan Schneider, 2021. "Sustainability-Oriented Project Scheduling Based on Z-Fuzzy Numbers for Public Institutions," Sustainability, MDPI, vol. 13(5), pages 1-17, March.
    4. João M. R. C. Fernandes & Seyed Mahdi Homayouni & Dalila B. M. M. Fontes, 2022. "Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review," Sustainability, MDPI, vol. 14(10), pages 1-34, May.
    5. Chong Wu & Jiahua Gan & Zhuo Jiang & Anding Jiang & Wenlong Zheng, 2022. "Ecological Efficiency Evaluation, Spatial Difference, and Trend Analysis of Logistics Industry and Manufacturing Industry Linkage in the Northeast Old Industrial Base," Sustainability, MDPI, vol. 14(19), pages 1-20, October.
    6. Hao Yu & Jiaqi Yang & Xipei Kang & Zhe Cong & Siwei Yao, 2022. "Empty Pallet Allocation Optimization in Shipbuilding Using a Pallet Pool System," Sustainability, MDPI, vol. 14(9), pages 1-21, May.
    7. Varun Tripathi & Somnath Chattopadhyaya & Alok Kumar Mukhopadhyay & Shubham Sharma & Changhe Li & Sunpreet Singh & Waqas Ul Hussan & Bashir Salah & Waqas Saleem & Abdullah Mohamed, 2022. "A Sustainable Productive Method for Enhancing Operational Excellence in Shop Floor Management for Industry 4.0 Using Hybrid Integration of Lean and Smart Manufacturing: An Ingenious Case Study," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    8. Magdalena Mucowska, 2021. "Trends of Environmentally Sustainable Solutions of Urban Last-Mile Deliveries on the E-Commerce Market—A Literature Review," Sustainability, MDPI, vol. 13(11), pages 1-26, May.

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