Optimizations and Operations Management of Modern Logistic Systems and Supply Chains

A special issue of Logistics (ISSN 2305-6290).

Deadline for manuscript submissions: 22 March 2025 | Viewed by 1714

Special Issue Editors


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Guest Editor
Laboratory of Informatics Engineering, Production and Maintenance (LGIPM), Logistic and Maintenance departemnt, University of lorraine, 57070 Metz, France
Interests: sustainable supply chain systems; transport and delivery; optimization methods and operations research; production and maintenance planning; risk assessment; assembly/disassembly systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Laboratory of Informatics Engineering, Production and Maintenance (LGIPM), Logistic and Maintenance department, University of lorraine, 57070 Metz, France
Interests: manufacturing; process mechanics; production monitoring; digital twins; dynamic scheduling; predictive maintenance; analytics; optimization;sustainable supply chain systems; logistic systems; transport; optimization methods and operations research; production and maintenance planning; assembly/disassembly systems

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Guest Editor
Laboratory QUARTZ EA 7393, University Paris VIII Vincennes, 93100 Montreuil, France
Interests: evolutionary computation; linear programming; discrete optimization; optimization modeling; integer programming; optimization theory; nonlinear optimization; logistics; operations management; production planning; AHP; inventory management; supply chain management; modeling and simulation; process simulation

Special Issue Information

Dear colleagues,

Due to the potential economic growth at both regional and global levels, the transformation of the market and business have become one of the most crucial and competitive assets since their turnover is the main source of profit for companies. Thus, the field of logistics and supply chain management plays a pivotal role in today's globalized economy.

An efficient and effective management of supply chains is essential for businesses to meet customer demands, reduce costs, and gain a competitive edge. To achieve this goal, optimization and operations management are the key disciplines as they can contribute toward enhancing the performance of modern logistic systems and supply chains. In fact, optimization involves finding the best possible solutions to complex problems, often characterized by multiple variables, constraints, and objectives. Operations management focuses on designing, managing, and improving processes that transform inputs into goods and services. Together, these disciplines help to streamline processes, minimize waste (reverse logistic), and improve resource allocation.

In recent years, many advanced technologies and digital networks have emerged, opening new ways to find credible solutions for the problems in smart logistic systems, thereby changing services, activities, skills, and management operations. However, the emergence of smart technologies and e-commerce networks also generate a multiplicity of challenges and opportunities for logistics managers and notably disrupts the organizational ecosystem due to the unprecedented technological change. Thus, firm managers are now trying to devise ways to provide novel and smart solutions to fulfil the requirements of this new era.

The field of smart and sustainable logistic systems have also drawn considerable attention of the academia and manufacturers in recent times due to their immense potential for increasing profits in the competitive and dynamic markets. The central focus of smart logistics lies in the optimization of supply chain processes. This optimization encompasses route optimization to minimize transportation costs and time, inventory management to balance stock levels efficiently, and resource allocation using advanced technologies and real-time data analysis. The goal is to streamline operations, reduce waste, and enhance overall efficiency, ultimately delivering cost savings and improved customer satisfaction in an increasingly interconnected and competitive global marketplace. Smart logistics leverages cutting-edge technologies, data analytics, and automation to make supply chain and transportation processes more intelligent and adaptive. Moreover, optimization is applied in smart logistics in several areas such as route optimization (dynamic route planning, multi-modal optimization, last-mile delivery optimization), inventory management, warehouse optimization, load planning and capacity optimization, data analytics and machine learning. To sum up, smart logistics optimization relies on data analytics, real-time data streams, and advanced algorithms to continuously improve operations. It not only enhances efficiency, but also contributes to cost savings, reduced environmental impact, and a better customer experience.

Given this context, this Special Issue aims to collate papers highlighting the cutting-edge advancements in the intersection of optimization techniques and operations management principles being applied to smart logistics and supply chains. We welcome submissions of both original research articles and reviews.

Some potential topics of this Special Issue include (but not limited to) the following:

  • Supply logistic systems: optimization and operations management;
  • Green logistics;
  • Industry 4.0: strategies, models, and technologies;
  • Data mining in smart logistics;
  • Supply chains and operations management;
  • Digital supply chains transformation;
  • Supply network resilience;
  • Digital supply chains twins;
  • Predictive models in smart logistics and supply chains;
  • Design and optimization of transport networks;
  • Reverse logistic and closed loop supply chains;
  • Sustainable logistics and delivery;
  • Carbon footprint and carbon trading;
  • Transport and delivery;
  • Manufacturing and remanufacturing systems.

We look forward to receiving your contributions.

Dr. Sadok Turki
Dr. Ayoub Chakroun
Dr. Yasmina Hani
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. Logistics is an international peer-reviewed open access quarterly 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 1400 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

  • supply logistic systems: optimization and operations management
  • industry 4.0: strategies, models, and technologies
  • data mining in smart logistics
  • digital supply chains transformation
  • predictive models in smart logistics and supply chains
  • design and optimization of transport networks
  • reverse logistic and closed loop supply chains
  • sustainable logistics and green logistics
  • carbon footprint and carbon trading
  • transport and delivery

Published Papers (1 paper)

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Research

38 pages, 10153 KiB  
Article
A Closed Queueing Networks Approach for an Optimal Heterogeneous Fleet Size of an Inter-Facility Bulk Material Transfer System
by Mohamed Amjath, Laoucine Kerbache and James MacGregor Smith
Logistics 2024, 8(1), 26; https://doi.org/10.3390/logistics8010026 - 04 Mar 2024
Viewed by 1304
Abstract
Background: This study addresses optimising fleet size in a system with a heterogeneous truck fleet, aiming to minimise transportation costs in interfacility material transfer operations. Methods: The material transfer process is modelled using a closed queueing network (CQN) that considers heterogeneous nodes and [...] Read more.
Background: This study addresses optimising fleet size in a system with a heterogeneous truck fleet, aiming to minimise transportation costs in interfacility material transfer operations. Methods: The material transfer process is modelled using a closed queueing network (CQN) that considers heterogeneous nodes and customised service times tailored to the unique characteristics of various truck types and their transported materials. The optimisation problem is formulated as a mixed-integer nonlinear programming (MINLP), falling into the NP-Hard, making exact solution computation challenging. A numerical approximation method, a modified sequential quadratic programming (SQP) method coupled with a mean value analysis (MVA) algorithm, is employed to overcome this challenge. Validation is conducted using a discrete event simulation (DES) model. Results: The proposed analytical model tested within a steel manufacturing plant’s material transfer process. The results showed that the analytical model achieved comparable optimisation of the heterogeneous truck fleet size with significantly reduced response times compared to the simulation method. Furthermore, evaluating performance metrics, encompassing response time, utilisation rate, and cycle time, revealed minimal discrepancies between the analytical and the simulation results, approximately ±8%, ±8%, and ±7%, respectively. Conclusions: These findings affirm the presented analytical approach’s robustness in optimising interfacility material transfer operations with heterogeneous truck fleets, demonstrating real-world applications. Full article
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