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Mathematical Methods of Operational Research and Data Analytics in Operations...
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Mathematical Methods of Operational Research and Data Analytics in Operations Planning and Scheduling

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 4534

Special Issue Editors

Prof. Dr. Fazle Baki

E-Mail Website
Guest Editor
Odette School of Business, University of Windsor, Windsor, ON N9B3P4, Canada
Interests: scheduling; manufacturing; healthcare; supply chain; combinatorial optimization; computational complexity; metaheuristics
Prof. Dr. Ahmed Azab

E-Mail Website
Guest Editor
Production & Operations Management Research Lab, University of Windsor, Windsor, ON N9B 3P4, Canada
Interests: computer aided process planning (CAPP); facility layout problem; scheduling; power systems planning; manufacturing resources planning

Special Issue Information

Dear Colleagues,

We are pleased to present this Special Issue, which aims to explore the latest advances, methodologies, and applications of operational research and data analytics in the fields of planning and scheduling. The articles presented here underscore the importance of operational research and data analytics in addressing complex planning and scheduling challenges across various industries such as manufacturing, transportation, logistics, and healthcare, to name a few. The key topics covered in this Special Issue include, but are not limited to, the following:

  • Innovative mathematical algorithms: These papers feature research on cutting-edge mathematical algorithms and optimization techniques, devised to tackle specific planning and scheduling challenges. This Special Issue also highlights hybrid approaches that combine various methods to enhance performance.
  • Machine learning and artificial intelligence: These papers examine the integration of machine learning and artificial intelligence methods within operational research, showcasing their potential in augmenting decision-making processes and predictive capabilities in planning and scheduling tasks.
  • Real-world applications and case studies: These papers delve into the practical implementation of mathematical operational research techniques across diverse sectors, emphasizing their impact on efficiency, cost reduction, and overall performance improvement.
  • Theoretical advancements in production and operations management: These papers concentrate on the development of new mathematical models and frameworks, contributing to a deeper understanding of the principles of production and operations management.

This Special Issue offers a comprehensive perspective on the current state of operational research and data analytics applied to various problems in production and operations management. By showcasing groundbreaking research and real-world applications, we aim to further the development and refinement of operational research and data analytics techniques and strategies to address complex decision-making challenges in various industries.

Prof. Dr. Fazle Baki
Prof. Dr. Ahmed Azab
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. 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

  • planning
  • scheduling, models
  • frameworks
  • manufacturing
  • healthcare
  • transportation
  • logistics
  • supply chain
  • combinatorial optimization
  • computational complexity
  • design of algorithms
  • special cases
  • polynomial algorithms
  • exact methods
  • heuristic methods
  • metaheuristics
  • machine learning
  • artificial intelligence

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

Published Papers (3 papers)

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Research

21 pages, 1329 KiB  
Article
Solving Logistical Challenges in Raw Material Reception: An Optimization and Heuristic Approach Combining Revenue Management Principles with Scheduling Techniques
by Reinaldo Gomes, Ruxanda Godina Silva and Pedro Amorim
Mathematics 2025, 13(6), 919; https://doi.org/10.3390/math13060919 - 10 Mar 2025
Viewed by 428
Abstract
The cost of transportation of raw materials is a significant part of the procurement costs in the forestry industry. As a result, routing and scheduling techniques were introduced to the transportation of raw materials from extraction sites to transformation mills. However, little to [...] Read more.
The cost of transportation of raw materials is a significant part of the procurement costs in the forestry industry. As a result, routing and scheduling techniques were introduced to the transportation of raw materials from extraction sites to transformation mills. However, little to no attention has been given to date to the material reception process at the mill. Another factor that motivated this study was the formation of large waiting queues at the mill gates and docks. Queues increase the reception time and associated costs. This work presents the development of a scheduling and reception system for deliveries at a mill. The scheduling system is based on Trucking Appointment Systems (TAS), commonly used at maritime ports, and on revenue management concepts. The developed system allocates each delivery to a timeslot and to an unloading dock using revenue management concepts. Each delivery is segmented according to its priority. Higher-segment deliveries have priority when there are multiple candidates to be allocated for one timeslot. The developed scheduling system was tested on a set of 120 daily deliveries at a Portuguese paper pulp mill and led to a reduction of 66% in the daily reception cost when compared to a first-in, first-out (FIFO) allocation approach. The average waiting time was also significantly reduced, especially in the case of high-priority trucks. Full article
(This article belongs to the Special Issue Mathematical Methods of Operational Research and Data Analytics in Operations Planning and Scheduling)
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11 pages, 284 KiB  
Article
Single-Machine Rescheduling with Rejection and an Operator No-Availability Period
by Guanghua Wu and Hongli Zhu
Mathematics 2024, 12(23), 3678; https://doi.org/10.3390/math12233678 - 24 Nov 2024
Viewed by 561
Abstract
In this paper, we investigate a rescheduling problem with rejection and an operator non-availability period on a single machine. An optimal original schedule with the objective of minimizing the total weighted completion time has been made in a deterministic production scheduling system without [...] Read more.
In this paper, we investigate a rescheduling problem with rejection and an operator non-availability period on a single machine. An optimal original schedule with the objective of minimizing the total weighted completion time has been made in a deterministic production scheduling system without an unavailable interval. However, prior to the start of formal job processing, a time interval becomes unavailable due to the operator. No jobs can start or complete in the interval; nonetheless, a job that begins prior to this interval and finishes afterward is possible (if there is such a job, we call it a crossover job). In order to deal with the operator non-availability period, job rejection is allowed. Each job is either accepted for processing or rejected by paying a rejection cost. The planned original schedule is required to be rescheduled. The objective is to minimize the total weighted completion time of the accepted jobs plus the total penalty of the rejected jobs plus the weighted maximum tardiness penalty between the original schedule and the new reschedule. We present a pseudo-polynomial time dynamic programming exact algorithm and subsequently develop it into a fully polynomial time approximation scheme. Full article
(This article belongs to the Special Issue Mathematical Methods of Operational Research and Data Analytics in Operations Planning and Scheduling)
31 pages, 9958 KiB  
Article
Optimization of Truck–Cargo Online Matching for the Less-Than-Truck-Load Logistics Hub under Real-Time Demand
by Weilin Tang, Xinghan Chen, Maoxiang Lang, Shiqi Li, Yuying Liu and Wenyu Li
Mathematics 2024, 12(5), 755; https://doi.org/10.3390/math12050755 - 2 Mar 2024
Cited by 6 | Viewed by 2787
Abstract
Reasonable matching of capacity resources and transported cargoes is the key to realizing intelligent scheduling of less-than-truck-load (LTL) logistics. In practice, there are many types and numbers of participating objects involved in LTL logistics, such as customers, orders, trucks, unitized implements, etc. This [...] Read more.
Reasonable matching of capacity resources and transported cargoes is the key to realizing intelligent scheduling of less-than-truck-load (LTL) logistics. In practice, there are many types and numbers of participating objects involved in LTL logistics, such as customers, orders, trucks, unitized implements, etc. This results in a complex and large number of matching schemes where truck assignments interact with customer order service sequencing. For the truck–cargo online matching problem under real-time demand, it is necessary to comprehensively consider the online matching process of multi-node orders and the scheduling of multi-types of trucks. Combined with the actual operation scenario, a mixed-integer nonlinear programming model is introduced, and an online matching algorithm with a double-layer nested time window is designed to solve it. By solving the model in a small numerical case using Gurobi and the online matching algorithm, the validity of the model and the effectiveness of the algorithm are verified. The results indicate that the online matching algorithm can obtain optimization results with a lower gap while outperforming in terms of computation time. Relying on the realistic large-scale case for empirical analysis, the optimization results in a significant reduction in the number of trips for smaller types of trucks, and the average truck loading efficiency has reached close to 95%. The experimental results demonstrate the general applicability and effectiveness of the algorithm. Thus, it helps to realize the on-demand allocation of capacity resources and the timely response of transportation scheduling of LTL logistics hubs. Full article
(This article belongs to the Special Issue Mathematical Methods of Operational Research and Data Analytics in Operations Planning and Scheduling)
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