Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.8
3.9
Journals
Sustainability
Special Issues
Sustainable Intelligent Manufacturing and Logistics Systems
sustainability-logo
Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sustainable Intelligent Manufacturing and Logistics Systems

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 5273

Special Issue Editors

Prof. Dr. Miguel A. Salido

E-Mail Website
Guest Editor
Institute of Control Systems and Industrial Computing, Universitat Politècnica de València, Camino de Vera s/n, 46071 Valencia, Spain
Interests: constraint satisfaction problem; scheduling; robust scheduling; distributed constraints; railway scheduling; green/sustainable manufacturing; logistics; metaheuristics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Adriana Giret

E-Mail Website
Guest Editor
Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Valencia, Spain
Interests: multiagent systems; intelligent manufacturing systems; agent-supported simulation for manufacturing systems; applications of multiagent systems; sustainable intelligent manufacturing and logistics systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The urgent need for sustainable development is imposing radical changes in the way manufacturing and logistics systems are designed and implemented. This urgent requirement has arisen due to several established and emerging causes: Environmental concerns, diminishing nonrenewable resources, stricter legislation and inflated energy costs, increasing consumer preference for environmentally friendly products, etc. Moreover, the overall sustainability in industrial activities of manufacturing companies must be achieved at the same time that they face unprecedented levels of global competition.

Many research works have been reported in the Intelligent Manufacturing and Logistics Systems literature, proposing different approaches to tackling sustainability at different levels of the whole manufacturing and logistics system. Nevertheless, there are open problems that still remain unsolved and require urgent attention from academia and industry practitioners.

The objective of this Special Issue is to provide a snapshot of the status, potential, challenges, and recent developments of intelligent solutions for sustainable manufacturing and logistics systems. We invite researchers to contribute original research articles as well as review articles that will stimulate the continuing efforts to improve the current state-of-the-art within the field.

Prof. Dr. Miguel A. Salido
Prof. Dr. Adriana Giret
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sustainable optimization techniques for manufacturing and logistics system operations
  • intelligent approaches for green supply chains
  • heuristics and metaheuristics algorithms
  • developments for sustainable intelligent manufacturing and logistics systems
  • intelligent theoretical models for sustainable manufacturing and logistics systems
  • application of distributed intelligent models and solutions for sustainable manufacturing and logistics systems

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 2757 KiB  
Article
Supply Chain Management Optimization and Prediction Model Based on Projected Stochastic Gradient
by Mohammed Alkahtani
Sustainability 2022, 14(6), 3486; https://doi.org/10.3390/su14063486 - 16 Mar 2022
Cited by 6 | Viewed by 2535
Abstract
Supply chain management (SCM) is considered at the forefront of many organizations in the delivery of their products. Various optimization methods are applied in the SCM to improve the efficiency of the process. In this research, the projected stochastic gradient (PSG) method was [...] Read more.
Supply chain management (SCM) is considered at the forefront of many organizations in the delivery of their products. Various optimization methods are applied in the SCM to improve the efficiency of the process. In this research, the projected stochastic gradient (PSG) method was proposed to increase the efficiency of the SCM analysis. The key objective of an efficient supply chain is to find the best flow patterns for the best products in order to select the suppliers to different customers. Hence, the focus of this research is on developing an efficient multi-echelon supply chain using factors such as cost, time, and risk. In the convex case, the proposed method has the advantage of a weakly convergent sequence of iterates to a point in the set of minimizers with probability one. The developed method achieves strong sequence convergence to the unique optimum, with probability one. The SCM dataset was utilized to assess the proposed method’s performance. The proposed PSG method has the advantage of considering the holding cost in the profit analysis of the company. The results of the developed PSG method are analyzed according to the product’s profit, stock, and demand. The proposed PSG method also provides the prediction of demand to increase profit. Full article
(This article belongs to the Special Issue Sustainable Intelligent Manufacturing and Logistics Systems)
►▼ Show Figures

Figure 1

19 pages, 1686 KiB  
Article
Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability
by Dong Yang, Qidong Liu, Jia Li and Yongji Jia
Sustainability 2020, 12(18), 7733; https://doi.org/10.3390/su12187733 - 18 Sep 2020
Cited by 11 | Viewed by 2145
Abstract
Cloud manufacturing is an emerging service-oriented paradigm that works by taking advantage of distributed manufacturing resources and capabilities to collaboratively perform a manufacturing task, with the consideration of QoS (Quality of Service) requirements such as cost, time and quality. Incorporating environmental concerns and [...] Read more.
Cloud manufacturing is an emerging service-oriented paradigm that works by taking advantage of distributed manufacturing resources and capabilities to collaboratively perform a manufacturing task, with the consideration of QoS (Quality of Service) requirements such as cost, time and quality. Incorporating environmental concerns and sustainability into cloud manufacturing to produce a much greener product has become an urgent issue since there is fierce market competition and an increasing environment consciousness from customers. In this paper, we present a multi-objective optimization approach to selecting and scheduling cloud manufacturing services from the viewpoints of the economy and environment including carbon emissions and water resource. Subject to the carbon cap regulation, a multi-objective model for a cloud manufacturing task is built with the aim of minimizing total costs, carbon emissions, and water resource use. Transportation mode selections and carbon emissions from both cloud manufacturing services and transportation activities are taken into account in this model. The ε-constraint method is employed to obtain the exact Pareto front of optimal solutions. A case study from automobile cloud manufacturing is used to illustrate the effectiveness of the presented approach. Numerical experiments are conducted to compare the presented approach and the simple additive weighting method. The results show that the presented ε-constraint method can obtain a better and more diverse Pareto set of solutions and that it can solve the models in a reasonable time. Full article
(This article belongs to the Special Issue Sustainable Intelligent Manufacturing and Logistics Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop