Mathematical Methods and Operation Research in Planning, Scheduling and Supply Chain Operations Management

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 965

Special Issue Editors


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Guest Editor
Engineering Department, Universidad Nacional del Sur INMABB-CONICET, Bahia Blanca 8000, Argentina
Interests: industrial engineering; optimization; scheduling; operations research
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Guest Editor
Faculty of Mathematics, Otto-von-Guericke-University, P.O. Box 4120, D-39016 Magdeburg, Germany
Interests: scheduling; development of exact and approximate algorithms; stability investigations; discrete optimization; scheduling with interval processing times; complex investigations for scheduling problems; train scheduling; graph theory; logistics; supply chains; packing; simulation; applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent times, there have been vertiginous changes made to industrial systems, giving rise to profound transformations in the way decision-making processes are approached in operation environments. These modifications are associated with the growing level of digitization of processes and operations, which increases and improves the decision-making capacity due to a higher level of information being available. Likewise, this digitization allows for the integration into the same management system of tools capable of processing data and solving situations in an autonomous way and in an optimal manner. Therefore, these transformations allow for the integration of different operational research tools that are needed at each operations management level.

On the other hand, the growing demands to reduce the environmental impact of industrial operations have generated a specific need for the design of tools that allow the optimization of processes in order to ensure sustainability. In this Special Issue, a sustainable approach aligned with the UN SDG agenda is considered, and it is expected to encourage the development of OR methods and tools capable of addressing these needs.

For this Special Issue, we encourage authors to make contributions that highlight the most advanced and innovative ideas addressing operations planning problems. Operations planning can address short-term problems, as well as tactical or strategic decisions. Contributions that manage to add theoretical contributions to the existing body of knowledge are welcome, as are works on applications and study cases where the application of operations research is innovative. 

Potential topics of OR applications include, but are not limited to:

  • Intelligent manufacturing systems;
  • Intelligent operations management;
  • Data-driven production planning;
  • OR methods applied to green manufacturing;
  • Machine learning approaches in manufacturing systems operations;
  • Operations management;
  • Sustainable management;
  • Cyber-physical production systems;
  • Analytics models and inventory control;
  • Production networks;
  • Assembly line and production planning;
  • Scheduling problems;
  • Decision making in supply chain management;
  • Digital twin implementations in production environments;
  • Closed-loop supply chains.

Dr. Daniel A. Rossit
Prof. Dr. Frank Werner
Guest Editors

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Keywords

  • operations research
  • production planning
  • supply chain, optimization
  • operations Management
  • decision making

Published Papers (1 paper)

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Research

16 pages, 323 KiB  
Article
Developing New Bounds for the Performance Guarantee of the Jump Neighborhood for Scheduling Jobs on Uniformly Related Machines
by Felipe T. Muñoz, Guillermo Latorre-Núñez and Mario Ramos-Maldonado
Mathematics 2024, 12(1), 6; https://doi.org/10.3390/math12010006 - 19 Dec 2023
Viewed by 577
Abstract
This study investigates the worst-case performance guarantee of locally optimal solutions to minimize the total weighted completion time on uniformly related parallel machines. The investigated neighborhood structure is Jump, also called insertion or move. This research focused on establishing the local optimality condition [...] Read more.
This study investigates the worst-case performance guarantee of locally optimal solutions to minimize the total weighted completion time on uniformly related parallel machines. The investigated neighborhood structure is Jump, also called insertion or move. This research focused on establishing the local optimality condition expressed as an inequality and mapping that maps a schedule into an inner product space so that the norm of the mapping is closely related to the total weighted completion time of the schedule. We determine two new upper bounds for the performance guarantee, which take the form of an expression based on parameters that describe the family of instances: the speed of the fastest machine, the speed of the slowest machine, and the number of machines. These new bounds outperform the parametric upper bound previously established in the existing literature and enable a better understanding of the performance of the solutions obtained for the Jump neighborhood in this scheduling problem, according to parameters that describe the family of instances. Full article
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