Nothing Special   »   [go: up one dir, main page]

You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 

New Insights in Algorithms for Logistics Problems and Management

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 1175

Special Issue Editor


E-Mail Website
Guest Editor
Department of Mathematics and Informatics, Faculty of Sciences, Vasile Alecsandri University of Bacău, 600115 Bacău, Romania
Interests: artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Logistics management is essential for the efficient delivery of goods and services, enabling organizations to meet customer needs on time and within budget. The effective use of logistics integrates planning, execution, and oversight across suppliers, facilities, and distribution channels, leveraging operations research, optimization algorithms, sensing technologies, and modeling techniques to enhance decision making. However, persistent challenges remain around coordination, transportation costs, sustainability, and regulatory compliance.

This Special Issue welcomes novel contributions on optimization algorithms and analytical models that provide new insights for advancing logistics management. Potential topics include, but are not limited to:

  • Innovative algorithms for vehicle routing, fleet optimization, and delivery scheduling;
  • Novel approaches for integrating Internet of Things (IoT) with logistics analytics;
  • New models and algorithms for sustainable supply chain management;
  • Advances in arc routing algorithms and applications;
  • Logistics optimization under uncertainty and real-time dynamics;
  • AI-based algorithms for automated logistics facilities;
  • Algorithmic approaches for resilient and reconfigurable logistics;
  • Analytics and algorithms for omnichannel logistics operations;
  • Emerging applications of optimization algorithms in logistics management.

We encourage authors to submit original research articles as well as comprehensive review articles on the state of the art and future directions. Submissions will be rigorously peer-reviewed for technical depth, innovation, and contribution to advancing logistics systems and theory.

Dr. Gloria Cerasela Crisan
Guest Editor

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. Algorithms is an international peer-reviewed open access monthly 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 1600 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

  • operations research
  • vehicle routing problem
  • arc routing problem

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

14 pages, 6820 KiB  
Article
Local Search Heuristic for the Two-Echelon Capacitated Vehicle Routing Problem in Educational Decision Support Systems
by José Pedro Gomes da Cruz, Matthias Winkenbach and Hugo Tsugunobu Yoshida Yoshizaki
Algorithms 2024, 17(11), 509; https://doi.org/10.3390/a17110509 - 6 Nov 2024
Viewed by 394
Abstract
This study focuses on developing a heuristic for Decision Support Systems (DSS) in e-commerce logistics education, specifically addressing the Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP). The 2E-CVRP involves using Urban Transshipment Points (UTPs) to optimize deliveries. To tackle the complexity of the 2E-CVRP, [...] Read more.
This study focuses on developing a heuristic for Decision Support Systems (DSS) in e-commerce logistics education, specifically addressing the Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP). The 2E-CVRP involves using Urban Transshipment Points (UTPs) to optimize deliveries. To tackle the complexity of the 2E-CVRP, DSS can employ fast and effective techniques for visual problem-solving. Therefore, the objective of this work is to develop a local search heuristic to solve the 2E-CVRP quickly and efficiently for implementation in DSS. The efficiency of the heuristic is assessed through benchmarks from the literature and applied to real-world problems from a Brazilian e-commerce retailer, contributing to advancements in the 2E-CVRP approach and promoting operational efficiency in e-commerce logistics education. The heuristic yielded promising results, solving problems almost instantly, for instances in the literature on average in 1.06 s, with average gaps of 6.3% in relation to the best-known solutions and, for real problems with hundreds of customers, in 1.4 s, with gaps of 8.3%, demonstrating its effectiveness in achieving the study’s objectives. Full article
(This article belongs to the Special Issue New Insights in Algorithms for Logistics Problems and Management)
Show Figures

Figure 1

Figure 1
<p>Illustrative example of a route in the 2E-CVRP. Adapted from [<a href="#B12-algorithms-17-00509" class="html-bibr">12</a>].</p>
Full article ">Figure 2
<p>Delivery density in the regions evaluated in the case study.</p>
Full article ">
20 pages, 3504 KiB  
Article
On the Estimation of Logistic Models with Banking Data Using Particle Swarm Optimization
by Moch. Fandi Ansori, Kuntjoro Adji Sidarto, Novriana Sumarti and Iman Gunadi
Algorithms 2024, 17(11), 507; https://doi.org/10.3390/a17110507 - 5 Nov 2024
Viewed by 408
Abstract
This paper presents numerical works on estimating some logistic models using particle swarm optimization (PSO). The considered models are the Verhulst model, Pearl and Reed generalization model, von Bertalanffy model, Richards model, Gompertz model, hyper-Gompertz model, Blumberg model, Turner et al. model, and [...] Read more.
This paper presents numerical works on estimating some logistic models using particle swarm optimization (PSO). The considered models are the Verhulst model, Pearl and Reed generalization model, von Bertalanffy model, Richards model, Gompertz model, hyper-Gompertz model, Blumberg model, Turner et al. model, and Tsoularis model. We employ data on commercial and rural banking assets in Indonesia due to their tendency to correspond with logistic growth. Most banking asset forecasting uses statistical methods concentrating solely on short-term data forecasting. In banking asset forecasting, deterministic models are seldom employed, despite their capacity to predict data behavior for an extended time. Consequently, this paper employs logistic model forecasting. To improve the speed of the algorithm execution, we use the Cauchy criterion as one of the stopping criteria. For choosing the best model out of the nine models, we analyze several considerations such as the mean absolute percentage error, the root mean squared error, and the value of the carrying capacity in determining which models can be unselected. Consequently, we obtain the best-fitted model for each commercial and rural bank. We evaluate the performance of PSO against another metaheuristic algorithm known as spiral optimization for benchmarking purposes. We assess the robustness of the algorithm employing the Taguchi method. Ultimately, we present a novel logistic model which is a generalization of the existence model. We evaluate its parameters and compare the result with the best-obtained model. Full article
(This article belongs to the Special Issue New Insights in Algorithms for Logistics Problems and Management)
Show Figures

Figure 1

Figure 1
<p>Total assets of (<b>a</b>) commercial banks and (<b>b</b>) rural banks in Indonesia in the period January 2007−January 2020. The monthly fluctuation of total assets of (<b>c</b>) commercial banks and (<b>d</b>) rural banks.</p>
Full article ">Figure 2
<p>The number of commercial and rural banks in Indonesia over the years.</p>
Full article ">Figure 3
<p>MAPE and RMSE of the obtained models for (<b>a</b>) commercial banks and (<b>b</b>) rural banks.</p>
Full article ">Figure 4
<p>Plot of the Pearl–Reed generalization model versus the data of (<b>a</b>) commercial banks and (<b>c</b>) rural banks and the Richards model versus the data of (<b>b</b>) commercial banks and (<b>d</b>) rural banks.</p>
Full article ">Figure 5
<p>The carrying capacity of the obtained models.</p>
Full article ">Figure 6
<p>The SN ratio for PSO’s parameters.</p>
Full article ">Figure 7
<p>The result of data fitting and prediction of Indonesian (<b>a</b>) commercial and (<b>b</b>) rural banking data.</p>
Full article ">
Back to TopTop