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Algorithms
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Applications of Data Analytics, Simulation-Optimization, and Machine Learning in Services:...
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Applications of Data Analytics, Simulation-Optimization, and Machine Learning in Services: From Sustainable Transportation and Supply Chains to Smart Cities, Health Care and Finance

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 8794

Special Issue Editors

Prof. Dr. Angel A. Juan

E-Mail Website
Guest Editor
CIGIP, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: analytics; artificial intelligence; data science; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Markus Rabe

E-Mail Website
Guest Editor
Department of IT in Production and Logistics, TU Dortmund University, 44227 Dortmund, Germany
Interests: computer simulation; logistics assistance systems; industrial IT systems; business processes; intralogistics; supply chains; urban last-mile delivery; data farming and data analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. David Goldsman

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Javier Faulin

E-Mail Website
Guest Editor
Operations Research and Statistics at Institute of Smart Cities, Public University of Navarre, Pamplona, Spain
Interests: smart cities; transportation; vehicle routing problems; logistics; simheuristics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Many services today require the use of data analytics tools, machine learning methods, and simulation-optimization models. This is the case, for instance, of real-world practices in logistics & transportation, smart cities, health care, finance & insurance, telecommunication networks, manufacturing & production, e-commerce & e-marketing, energy & water consumption, higher education, and many other fields. In particular, we are interested in combinations of several methodologies as an efficient way to support intelligent decision-making in different services, especially those that might have a noticeable impact on citizens' quality of life. Of course, this includes relevant aspects such as sustainability, resilience, and fast adoption of emergent data-driven and environment-friendly technologies in urban and metropolitan areas.

This Special Issue aims to present a collection of high-quality papers on the aforementioned topics. Both methodological as well as practical contributions 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 several tracks of the Winter Simulation Conference (WSC). Extended versions of the best papers presented there (as well as at other conferences of similar quality) are also welcome. Still, this Special Issue is open to other submissions as well.   

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

Keywords

  • Service Science
  • Simulation
  • Heuristics and metaheuristics
  • Simheuristics
  • Sustainable Transportation & Logistics
  • Resilient Networks
  • Supply chain management
  • Smart cities
  • Intelligent transportation systems
  • Sustainable transportation and logistics
  • Simulation-based optimization
  • Machine learning
  • Learnheuristics
  • Biased-randomized algorithms
  • Industry 4.0

Published Papers (3 papers)

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14 pages, 295 KiB  
Article
A Biased-Randomized Discrete-Event Algorithm for the Hybrid Flow Shop Problem with Time Dependencies and Priority Constraints
by Christoph Laroque, Madlene Leißau, Pedro Copado, Christin Schumacher, Javier Panadero and Angel A. Juan
Algorithms 2022, 15(2), 54; https://doi.org/10.3390/a15020054 - 02 Feb 2022
Cited by 2 | Viewed by 1834
Abstract
Based on a real-world application in the semiconductor industry, this article models and discusses a hybrid flow shop problem with time dependencies and priority constraints. The analyzed problem considers a production where a large number of heterogeneous jobs are processed by a number [...] Read more.
Based on a real-world application in the semiconductor industry, this article models and discusses a hybrid flow shop problem with time dependencies and priority constraints. The analyzed problem considers a production where a large number of heterogeneous jobs are processed by a number of machines. The route that each job has to follow depends upon its type, and, in addition, some machines require that a number of jobs are combined in batches before starting their processing. The hybrid flow model is also subject to a global priority rule and a “same setup” rule. The primary goal of this study was to find a solution set (permutation of jobs) that minimizes the production makespan. While simulation models are frequently employed to model these time-dependent flow shop systems, an optimization component is needed in order to generate high-quality solution sets. In this study, a novel algorithm is proposed to deal with the complexity of the underlying system. Our algorithm combines biased-randomization techniques with a discrete-event heuristic, which allows us to model dependencies caused by batching and different paths of jobs efficiently in a near-natural way. As shown in a series of numerical experiments, the proposed simulation-optimization algorithm can find solutions that significantly outperform those provided by employing state-of-the-art simulation software. Full article
(This article belongs to the Special Issue Applications of Data Analytics, Simulation-Optimization, and Machine Learning in Services: From Sustainable Transportation and Supply Chains to Smart Cities, Health Care and Finance)
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18 pages, 367 KiB  
Article
Applying Simheuristics to Minimize Overall Costs of an MRP Planned Production System
by Wolfgang Seiringer, Juliana Castaneda, Klaus Altendorfer, Javier Panadero and Angel A. Juan
Algorithms 2022, 15(2), 40; https://doi.org/10.3390/a15020040 - 27 Jan 2022
Cited by 4 | Viewed by 3236
Abstract
Looking at current enterprise resource planning systems shows that material requirements planning (MRP) is one of the main production planning approaches implemented there. The MRP planning parameters lot size, safety stock, and planned lead time, have to be identified for each MRP planned [...] Read more.
Looking at current enterprise resource planning systems shows that material requirements planning (MRP) is one of the main production planning approaches implemented there. The MRP planning parameters lot size, safety stock, and planned lead time, have to be identified for each MRP planned material. With increasing production system complexity, more planning parameters have to be defined. Simulation-based optimization is known as a valuable tool for optimizing these MRP planning parameters for the underlying production system. In this article, a fast and easy-to-apply simheuristic was developed with the objective to minimize overall costs. The simheuristic sets the planning parameters lot size, safety stock, and planned lead time for the simulated stochastic production systems. The developed simheuristic applies aspects of simulation annealing (SA) for an efficient metaheuristic-based solution parameter sampling. Additionally, an intelligent simulation budget management (SBM) concept is introduced, which skips replications of not promising iterations. A comprehensive simulation study for a multi-item and multi-staged production system structure is conducted to evaluate its performance. Different simheuristic combinations and parameters are tested, with the result that the combination of SA and SBM led to the lowest overall costs. The contributions of this article are an easy implementable simheuristic for MRP parameter optimization and a promising concept to intelligently manage simulation budget. Full article
(This article belongs to the Special Issue Applications of Data Analytics, Simulation-Optimization, and Machine Learning in Services: From Sustainable Transportation and Supply Chains to Smart Cities, Health Care and Finance)
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19 pages, 5353 KiB  
Article
Analysis and Prediction of Carsharing Demand Based on Data Mining Methods
by Chunxia Wang, Jun Bi, Qiuyue Sai and Zun Yuan
Algorithms 2021, 14(6), 179; https://doi.org/10.3390/a14060179 - 05 Jun 2021
Cited by 9 | Viewed by 2783
Abstract
With the development of the sharing economy, carsharing is a major achievement in the current mode of transportation in sharing economies. Carsharing can effectively alleviate traffic congestion and reduce the travel cost of residents. However, due to the randomness of users’ travel demand, [...] Read more.
With the development of the sharing economy, carsharing is a major achievement in the current mode of transportation in sharing economies. Carsharing can effectively alleviate traffic congestion and reduce the travel cost of residents. However, due to the randomness of users’ travel demand, carsharing operators are faced with problems, such as imbalance in vehicle demand at stations. Therefore, scientific prediction of users’ travel demand is important to ensure the efficient operation of carsharing. The main purpose of this study is to use gradient boosting decision tree to predict the travel demand of station-based carsharing users. The case study is conducted in Lanzhou City, Gansu Province, China. To improve the accuracy, gradient boosting decision tree is designed to predict the demands of users at different stations at various times based on the actual operating data of carsharing. The prediction results are compared with results of the autoregressive integrated moving average. The conclusion shows that gradient boosting decision tree has higher prediction accuracy. This study can provide a reference value for user demand prediction in practical application. Full article
(This article belongs to the Special Issue Applications of Data Analytics, Simulation-Optimization, and Machine Learning in Services: From Sustainable Transportation and Supply Chains to Smart Cities, Health Care and Finance)
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