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Advances in Artificial Intelligence and Operations Research in Logistics and...
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Advances in Artificial Intelligence and Operations Research in Logistics and Supply Chain Management

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 9875

Special Issue Editor

Dr. Irina Pustokhina

E-Mail Website
Guest Editor
Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, 117997 Moscow, Russia
Interests: supply chain management; logistics; logistics systems; risk management; blockchain; smart city; artificial intelligence

Special Issue Information

Dear Colleagues,

Nowadays, the increasing volume of information, as well as complexity around logistics and supply chain management, makes it imperative to jointly use optimization and artificial intelligence for devising data-driven and intelligent decision support approaches. A combination of AI with well-known OR methods offers the possibility to use synergies and advantages of both methods and lead to huge progress.

Recent trends illustrate that extensive research on different AI and OR techniques and their application in logistics and supply chain management need to be demonstrated. This Special Issue is designed to highlight recent theoretical and methodological advancements, case studies, applications, technical contributions, survey results and applications of tools and techniques to improve technical infrastructure in the application of AI and OR models to meet efficiency challenges and produce more cost-effective and sustainable solutions in logistics and supply chain management.

This Special Issue covers the following topics:

  • Unmanned ground/aerial vehicles
  • Blockchain technology supported supply chain operations
  • Advantages and disadvantages of information transparency in supply chains
  • Deep learning applications for all aspects related to logistics and supply chain management
  • Emergency logistics
  • Reverse logistics
  • Freight transportation
  • Metropolitan/city logistics
  • Smart agro-logistics
  • Internet of Things in smart logistics
  • Port smart logistics
  • Logistics 4.0
  • E-commerce logistics
  • The role of digital technologies in supporting, enabling, fostering or reshaping the logistics and supply chain management practice
  • The role of innovative actors in integrating material flow, information flow and financial flow

Dr. Irina Pustokhina
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. Mathematics 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 2600 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

  • Classical intelligent optimization algorithms
  • Population-based intelligent algorithms
  • Hybrid optimization
  • Multi-objective optimization
  • Big data
  • Internet of Things
  • Machine learning
  • Artificial intelligence
  • Operations research
  • Logistics management
  • Supply chain management

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Published Papers (3 papers)

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Research

29 pages, 432 KiB  
Article
Analysis of Stochastic State-Dependent Arrivals in a Queueing-Inventory System with Multiple Server Vacation and Retrial Facility
by M. Nithya, Gyanendra Prasad Joshi, C. Sugapriya, S. Selvakumar, N. Anbazhagan, Eunmok Yang and Ill Chul Doo
Mathematics 2022, 10(17), 3041; https://doi.org/10.3390/math10173041 - 23 Aug 2022
Cited by 6 | Viewed by 2357
Abstract
This article analyses a four-dimensional stochastic queueing-inventory system with multiple server vacations and a state-dependent arrival process. The server can start multiple vacations at a random time only when there is no customer in the waiting hall and the inventory level is zero. [...] Read more.
This article analyses a four-dimensional stochastic queueing-inventory system with multiple server vacations and a state-dependent arrival process. The server can start multiple vacations at a random time only when there is no customer in the waiting hall and the inventory level is zero. The arrival flow of customers in the system is state-dependent. Whenever the arriving customer finds that the waiting hall is full, they enter into the infinite orbit and they retry to enter the waiting hall. If there is at least one space in the waiting hall, the orbital customer enters the waiting hall. When the server is on vacation, the primary (retrial) customer enters the system with a rate of λ1(θ1). If the server is not on vacation, the primary (retrial) arrival occurs with a rate of λ2(θ2). Each arrival rate follows an independent Poisson distribution. The service is provided to customers one by one in a positive time with the rate of μ, which follows exponential distribution. When the inventory level drops to a fixed s, reorder of Q items is triggered immediately under (s,Q) ordering policy. The stability of the system has been analysed, and using the Neuts matrix geometric approach, the stationary probability vectors have been obtained. Moreover, various system performance measures are derived. The expected total cost analysis explores and verifies the characteristics of the assumed parameters of this model. The average waiting time of a customer in the waiting hall and orbit are investigated using all the parameters. The monotonicity of the parameters is verified with its characteristics by the numerical simulation. The discussion about the fraction time server being on vacation suggests that as the server’s vacation duration reduces, its fraction time also reduces. The mean number of customers in the waiting hall and orbit is reduced whenever the average service time per customer and average replenishment time are reduced. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence and Operations Research in Logistics and Supply Chain Management)
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18 pages, 1014 KiB  
Article
A Correlative Study of Modern Logistics Industry in Developing Economy and Carbon Emission Using ARDL: A Case of Pakistan
by Dong Mu, Salman Hanif, Khalid Mehmood Alam and Omer Hanif
Mathematics 2022, 10(4), 629; https://doi.org/10.3390/math10040629 - 18 Feb 2022
Cited by 11 | Viewed by 2921
Abstract
The modern logistics industry in relation to economic growth and carbon emission has opened new strategic perspectives. Recent research work have analyzed such complex interference from a broad perspective. However, analyzing this overlap needs comprehensive insight into the logistics industry while simultaneously estimating [...] Read more.
The modern logistics industry in relation to economic growth and carbon emission has opened new strategic perspectives. Recent research work have analyzed such complex interference from a broad perspective. However, analyzing this overlap needs comprehensive insight into the logistics industry while simultaneously estimating its short-run and long-run effects from regional aspects due to continue-evolving factors and their impact on it. This paper competently analyzes logistics industry components in connection with economic prosperity, energy consumption, trade development, and carbon emission from a more specific regional perspective of a developing country. Methodologically, an autoregressive distributive lag model (ARDL) is employed using correlative evaluation of the dynamic factors and their interactive impact in short and long run on this relation, based on time-series data of Pakistan from 1990 to 2019. The study results endorse the previous studies’ outcomes by recognizing that an increase in carbon emission depends on trade development, energy usage, economic development, and the logistics industry’s various components except for air logistics. However, study results show a unidirectional long-run causality directing from economic development, logistics industry, energy utilization, and trade development to carbon emission. Moreover, these results reveal that this emission is the leading factor to introduce stringent emission standards that further overlap with regional demographics trends, i.e., carbon emission implications. These findings imply that economic development applies a substantial demand-pull impact on national logistics, i.e., regional economic development directs to the growth of the logistics industry in the corresponding region. Consequently, high-income geographical regions have higher long-run risk concerning contemporary developmental activities of the logistics industry when adhering to carbon emission standards. Particularly, the influence of upcoming emission standards must be prioritized when planning the future returns of contemporary research and development activities of the logistics industry in a given geographic area, such as CPEC. Given Pakistan’s perspective, the proposed empirical analysis can be exampled to other developing countries. This analysis may facilitate the design and development of strategies for upcoming financial funding in the modern logistics industry to seek its sustainable development-goals in developing economies. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence and Operations Research in Logistics and Supply Chain Management)
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31 pages, 7275 KiB  
Article
Optimization of the Collaborative Hub Location Problem with Metaheuristics
by Mohamed Amine Gargouri, Nadia Hamani, Nassim Mrabti and Lyes Kermad
Mathematics 2021, 9(21), 2759; https://doi.org/10.3390/math9212759 - 30 Oct 2021
Cited by 9 | Viewed by 2897
Abstract
By creating new job opportunities and developing the regional economy, the transport of goods generates significant costs, environmental and sanitary nuisances, and high greenhouse gas (GHG) emissions. In this context, collaboration is an interesting solution that can be used to enable companies to [...] Read more.
By creating new job opportunities and developing the regional economy, the transport of goods generates significant costs, environmental and sanitary nuisances, and high greenhouse gas (GHG) emissions. In this context, collaboration is an interesting solution that can be used to enable companies to overcome some problems such as globalization, economic crisis, health crisis, issues related to sustainability, etc. This study deals with the design of a multiperiod multiproduct three-echelon collaborative distribution network with a heterogeneous fleet. By applying the mixed integer linear problem (MILP) formulations, it was possible to study the three dimensions of sustainability (economic, environmental, and social/societal). Since the examined problem was NP-hard, it was solved using four metaheuristic approaches to minimize the different logistics costs or CO2 emissions. The social/societal aspect evaluated the accident rate and the noise level generated by the freight transport. Four algorithms were developed to achieve our objectives: a genetic algorithm, a simulated annealing, a particle swarm algorithm, and a vibration damping optimization algorithm. Considering a French distribution network, these algorithms overcame the limits of the exact solution method by obtaining optimal solutions with reasonable execution time. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence and Operations Research in Logistics and Supply Chain Management)
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