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Special Issue "Optimization Algorithms in Logistics, Transportation, and SCM"

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

Deadline for manuscript submissions: 31 October 2023 | Viewed by 2820

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Special Issue Editors

Prof. Dr. Javier Faulin

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Operations Research and Statistics at Institute of Smart Cities, Public University of Navarre, Pamplona, Spain
Interests: smart cities; transportation; vehicle routing problems; logistics; simheuristics
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Prof. Dr. Angel A. Juan

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Department of Applied Statistics and Operations Research, Universitat Politècnica de València, 03801 Alcoy, Spain
Interests: metaheuristics; simulation; machine learning; smart cities
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Prof. Dr. David Goldsman

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Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, GA 30332, USA
Interests: simulation output analysis, statistical ranking and selection methods, and medical and humanitarian applications of operations research
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Prof. Dr. Markus Rabe

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Department of IT in Production and Logistics, TU Dortmund University, 44227 Dortmund, Germany
Interests: logistics; information systems; simulation
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Special Issue Information

Dear Colleagues,

Transportation, logistics, and supply chain systems and networks constitute one of the pillars of modern economies and societies. From sustainable traffic management in smart cities or air transportation to green and socially responsible logistics practices, many enterprises and governments around the world have to make decisions that affect the efficiency of these complex systems. Typically, optimization algorithms are employed to deal with these challenges, and simulation approaches are utilized when considering scenarios under uncertainty. However, better results might be achieved by hybridizing both optimization algorithms with simulation techniques to deal with real-life transportation, logistics, and SCM challenges, which often are large-scale and NP-hard problems under uncertainty conditions. Hence, simheuristic algorithms (combining metaheuristics with simulation) as well as other simulation-optimization approaches constitute an effective way to support decision makers in such complex scenarios.

This Special Issue aims to present a collection of high-quality papers on simulation and optimization in transportation, logistics, and supply chain management. Simulation-optimization algorithms, including simheuristics and simulation-based optimization approaches, and their practical applications in the solving of rich and realistic scenarios under uncertainty are welcome. The Special Issue is open to well-known researchers in these topics. In particular, this Special Issue is strongly connected to the topics covered in the Winter Simulation Conference (WSC) track on logistics, transportation, and SCM, which includes a track on simheuristic algorithms as well. Extended versions of the best papers presented at WSC’22 and WSC’23 (as well as at other conferences of similar quality) are also invited.

Prof. Dr. Javier Faulin
Prof. Dr. Angel A. Juan
Prof. Dr. David Goldsman
Prof. Dr. Markus Rabe
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. 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.

Published Papers (4 papers)

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Research

Open AccessArticle

A Framework for Determining Collision Likelihood Using Continuous Friction Values in a Connected Vehicle Environment

Qian Xie

and

Tae J. Kwon

Algorithms 2023, 16(9), 426; https://doi.org/10.3390/a16090426 - 06 Sep 2023

Viewed by 289

Abstract

Jurisdictions currently provide information on winter road conditions through qualitative descriptors like bare and fully snow-covered. Ideally, these descriptors are meant to warn drivers beforehand about hazardous roads. In practice, however, discerning between safe and unsafe roads is sometimes unclear due to intermediate RSC classes covering too wide a range of conditions. This study aims at solving this safety ambiguity issue by proposing a framework for predicting collision likelihood within a road segment. The proposed framework converts road surface images into friction coefficients, which are then converted into continuous measurements through an interpolator. To find the best-performing interpolator, we evaluated geostatistical, machine learning, and hybrid interpolators. It was found that ordinary kriging had the lowest estimation error and was the least sensitive to changes in distance between measurements. After developing an interpolator, collision likelihood models were developed for segment lengths ranging from 0.5 km to 20 km. We chose the 6.5 km model based on its accuracy and intuitiveness. This model had 76.9% accuracy and included friction and AADT as predictors. It was also estimated that if the proposed framework were implemented in an environment with connected vehicles and intelligent transportation systems, it would offer significant safety improvements. Full article

(This article belongs to the Special Issue Optimization Algorithms in Logistics, Transportation, and SCM)

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Open AccessArticle

The Electric Vehicle Traveling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics

and

Algorithms 2023, 16(9), 402; https://doi.org/10.3390/a16090402 - 23 Aug 2023

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Abstract

With the increasing demand for online shopping and home delivery services, optimizing the routing of electric delivery vehicles in urban areas is crucial to reduce environmental pollution and improve operational efficiency. To address this opportunity, we optimize the Steiner Traveling Salesman Problem (STSP) for electric vehicles (EVs) in urban areas by combining city graphs with topographic and traffic information. The STSP is a variant of the traditional Traveling Salesman Problem (TSP) where it is not mandatory to visit all the nodes present in the graph. We train an artificial neural network (ANN) model to estimate electric consumption between nodes in the route using synthetic data generated with historical traffic simulation and topographical data. This allows us to generate smaller-weighted graphs that transform the problem from an STSP to a normal TSP where the 2-opt optimization algorithm is used to solve it with a Nearest Neighbor (NN) initialization. Compared to the approach of optimizing routes based on distance, our proposed algorithm offers a fast solution to the STSP for EVs (EV-STSP) with routes that consume 17.34% less energy for the test instances generated. Full article

(This article belongs to the Special Issue Optimization Algorithms in Logistics, Transportation, and SCM)

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Open AccessArticle

Decision Making under Conditions of Uncertainty and Risk in the Formation of Warehouse Stock of an Automotive Service Enterprise

and

Algorithms 2023, 16(8), 388; https://doi.org/10.3390/a16080388 - 13 Aug 2023

Viewed by 482

Abstract

This article is devoted to the problem of determining the rational amount of spare parts in the warehouse of a service center of an automobile manufacturer’s branded network used for maintenance and current repairs. This problem was solved on the basis of the accumulated statistical data of failures that occurred during the warranty period of vehicle operation. In the calculation, game methods were used. This took into account the stochastic need for spare parts and the consequences of their presence or absence in stock, which are expressed in the form of a profit and an additional possible payment of a fine in case of a discrepancy between the current level of demand for spare parts and the available spare parts. Two cases of decision making are considered: under conditions of risk and uncertainty, the occurrence of which depends on the amount of information about the input flow of enters to the service center. If such statistics are accumulated, then the decision is made taking into account the possible risk associated with the uncertainty of a specific need for spare parts. Otherwise, the probability of a particular need is calculated on the basis of special criteria. To optimize the collection of information about the state of warehouse stocks, the transfer of information, and the assessment and forecasting of stocks, well-organized feedback is needed, which is shown in the form of an algorithm. Full article

(This article belongs to the Special Issue Optimization Algorithms in Logistics, Transportation, and SCM)

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Open AccessArticle

Developing Prediction Model of Travel Times of the Logistics Fleets of Large Convenience Store Chains Using Machine Learning

Yang-Kuei Lin

Chien-Fu Chen

and

Tien-Yin Chou

Algorithms 2023, 16(6), 286; https://doi.org/10.3390/a16060286 - 01 Jun 2023

Viewed by 638

Abstract

Convenience store chains are many people’s top choice for dining and leisure and have logistics procedures that involve each store receiving multiple deliveries because of the varying delivery periods and suitable temperatures for different goods. The estimated arrival time for each delivery has a huge impact on the route arrangement and convenience store preparation for dispatchers to schedule future deliveries. This study collected global positioning system travel data from a fleet of one of the top convenience store chains in Taiwan between April 2021 and March 2022 and proposed machine learning to establish a model to predict travel times. For unavailable data, we proposed the nonlinear regression equation to fill in the missing GPS data. Moreover, the study used the data between April 2022 and September 2022 with mean absolute percentage error to validate the prediction effects exceeding 97%. Therefore, the proposed model based on historical data and the machine learning algorithm in this study can help logistics fleets estimate accurate travel times for their scheduling of future delivery tasks and arranging routes. Full article

(This article belongs to the Special Issue Optimization Algorithms in Logistics, Transportation, and SCM)

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