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Computation
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Transport and Logistics Optimization Solution
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Transport and Logistics Optimization Solution

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 7346

Special Issue Editors

Dr. Evelin Krmac

E-Mail Website
Guest Editor
Faculty of Maritime Studies and Transport, University of Ljubljana, 6320 Portoroz, Slovenia
Interests: information and communication technologies; decision support techniques in logistics and transportation
Dr. Danijela Tuljak-Suban

E-Mail Website
Guest Editor
Faculty of Maritime Studies and Transport, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: decision-making; optimization; data analysis in logistics and transport
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Optimising transport and logistics networks is a crucial step toward improving sustainability—one of society’s main goals today. Moreover, optimisation is also a key factor in improving the efficiency and effectiveness of these networks. Part of the challenge in managing logistics networks is the sheer size, complexity, and the working parts involved. The other key part is data. Now, more than ever, data are a critical factor for any business. Additionally, they are available in vast quantities, in many different types, from a variety of different sources, and in real time—you just have to know how to use them and how to get the most out of them.

Optimising all these elements is therefore necessary and goes hand in hand with the introduction of new transport solutions and network modernization.

The Special Issue is dedicated to the presentation of new solutions in these fields, particularly highlighting new methods and applications of optimisation, decision making, intelligence, forecasting and prediction.

Specific methods and fields of application include, but are not limited to:

  • Decision making;
  • Risk management in logistics and transport;
  • Queueing and stochastics in logistics;
  • Network optimization;
  • Routing problems;
  • Green supply chain;
  • City logistics;
  • Uncertainty modeling in planning and control;
  • Freight transportation;
  • Data-driven transport and logistics;
  • Intelligent transport systems and services;
  • Artificial intelligence;
  • Machine-learning methods.

Dr. Evelin Krmac
Dr. Danijela Tuljak-Suban
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. Computation 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 1800 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

  • multi-criteria decision making (MCDM)
  • vehicle routing problems (VRP)
  • queuing theory
  • fuzzy theory
  • linear optimization
  • artificial intelligence algorithms
  • big data and data science

Published Papers (3 papers)

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Research

14 pages, 4194 KiB  
Article
Modification of the Bellman–Ford Algorithm for Finding the Optimal Route in Multilayer Network Structures
by Olga Timofeeva, Alexey Sannikov, Maria Stepanenko and Tatiana Balashova
Computation 2023, 11(4), 74; https://doi.org/10.3390/computation11040074 - 07 Apr 2023
Viewed by 1995
Abstract
One of the actual tasks of the contemporary logistics business using the “just in time” supply planning concept, is to distribute manufactured goods among the objects of the distribution network in the most efficient manner at the lowest possible cost. The article is [...] Read more.
One of the actual tasks of the contemporary logistics business using the “just in time” supply planning concept, is to distribute manufactured goods among the objects of the distribution network in the most efficient manner at the lowest possible cost. The article is devoted to the problem of finding the optimal path in network structures. The problem statement for multilayer data transmission networks (MDTN), which is one of the possible representations of multimodal transport networks, is considered. Thus, each MDTN layer can be represented as a separate type of transport. The problem is solved by modifying the Bellman–Ford mathematical programming algorithm. Load testing of the modified method was performed, and a comparative analysis was given, including an assessment of speed and performance, proving the effectiveness of the results of the study. Based on the results of comparative analysis, recommendations for using a modified version of the Bellman–Ford algorithm for application in practical problems in optimizing logistics networks are proposed. The results obtained can be used in practice not only in logistics networks but also in the construction of smart energy networks, as well as in other subject areas that require optimization of multilayer graph structures. Full article
(This article belongs to the Special Issue Transport and Logistics Optimization Solution)
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18 pages, 735 KiB  
Article
A Comprehensive Decision Framework for Selecting Distribution Center Locations: A Hybrid Improved Fuzzy SWARA and Fuzzy CRADIS Approach
by Adis Puška, Anđelka Štilić and Željko Stević
Computation 2023, 11(4), 73; https://doi.org/10.3390/computation11040073 - 02 Apr 2023
Cited by 6 | Viewed by 1604
Abstract
The focus of this study is on the significance of location in establishing distribution centers. The key question when selecting a location is regarding which location would contribute the most to the growth of a company’s business through the establishment of distribution centers. [...] Read more.
The focus of this study is on the significance of location in establishing distribution centers. The key question when selecting a location is regarding which location would contribute the most to the growth of a company’s business through the establishment of distribution centers. To answer this question, we conducted research in the Brčko District of BiH in order to determine the best location for a distribution center using expert decision-making based on linguistic values. In order to use these values when selecting locations, a fuzzy set was formed using the IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) and fuzzy CRADIS (Compromise Ranking of Alternatives from Distance to the Ideal Solution) methods. The IMF SWARA method was utilized to determine the weights of the criteria, and the fuzzy CRADIS method was employed to rank the locations based on expert ratings. The location for the construction of distribution centers at Bodarište was rated the worst, while the McGowern Base location was rated the best. Based on these findings, the research question was answered, and it was demonstrated that fuzzy methods could be utilized in the selection of distribution center locations. Hence, we recommend that future research be performed on the application of fuzzy methods in the expert selection of potential sites for distribution centers. Full article
(This article belongs to the Special Issue Transport and Logistics Optimization Solution)
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32 pages, 4051 KiB  
Article
A Novel Artificial Multiple Intelligence System (AMIS) for Agricultural Product Transborder Logistics Network Design in the Greater Mekong Subregion (GMS)
by Rapeepan Pitakaso, Natthapong Nanthasamroeng, Thanatkij Srichok, Surajet Khonjun, Nantawatana Weerayuth, Thachada Kotmongkol, Peema Pornprasert and Kiatisak Pranet
Computation 2022, 10(7), 126; https://doi.org/10.3390/computation10070126 - 20 Jul 2022
Cited by 16 | Viewed by 3117
Abstract
In recent years, agriculture products have contributed to 28.75% of Thailand’s GDP. China, Vietnam, Myanmar, Cambodia, Laos and Vietnam are the main markets for agricultural products. The annual export volume exceeds 119,222 million THB. The majority of them are shipped over Thailand’s land [...] Read more.
In recent years, agriculture products have contributed to 28.75% of Thailand’s GDP. China, Vietnam, Myanmar, Cambodia, Laos and Vietnam are the main markets for agricultural products. The annual export volume exceeds 119,222 million THB. The majority of them are shipped over Thailand’s land borders to its neighbors. Small and medium-sized farmers make up more than 85% of those who produce agricultural items. Numerous scholars have studied the transportation methods used by the Greater Mekong Subregion (GMS) nations along the economic corridor, but the majority of them have concentrated on import–export operations involving sizable firms, which are not applicable to the transportation of agricultural products, particularly when attention is paid to small and medium-sized farmers. In this study, mixed-integer programming (MIP) is presented to design an agricultural product logistics network. In order to prolong the lifespan of the container used, the MIP’s primary goal is to maximize the total chain profit while maintaining the lowest container usage possible. The approach was developed to increase small and medium-sized farmers’ ability to compete. Small and medium-sized farmers bring their products to an agricultural product collecting center, also known as a container loading facility. After that, skilled logistics companies distribute the goods. In order to convey the goods to the final clients in neighboring nations, the proper locations of the containing loading centers, the correct transportation option and the borders must be decided. The issue was identified as multi-echelon location–allocation sizing (MELLS), an NP-hard problem that cannot be handled in an efficient manner. To solve a real-world problem, however, efficient techniques must be supplied. AMIS, an artificial multiple intelligence system, was created to address the suggested issue. AMIS was developed with the goal of leveraging a variety of methods for local search and development. There are several well-known heuristics techniques employed in the literature, including the genetic algorithm (GA) and the differential evolution algorithm (DE). With respect to the improved solutions obtained, the computational results show that AMIS exceeds the present heuristics, outperforming DE and GA by 9.34% and 10.95%, respectively. Additionally, the system’s farmers made a total of 15,236,832 THB in profit, with an average profit per container of 317,434 THB and an average profit per farmer of 92,344.44 THB per crop. The container loading center uses 48 containers, with a 5.33 container average per container loading center (CLC). The farmers’ annual revenues were previously less than 88,402 THB per family per year, so we can predict that the new network may increase customers’ annual income by 4.459% for each crop. Full article
(This article belongs to the Special Issue Transport and Logistics Optimization Solution)
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