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Mathematics
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Advances in Scheduling Optimization and Computational Intelligence
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Advances in Scheduling Optimization and Computational Intelligence

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 1205

Special Issue Editor

Dr. Pavel Novoa-Hernández

E-Mail Website
Guest Editor
Models of Decision and Optimization Research Group, Department of Computer Science and Artificial Intelligence, University of Granada, E-18071 Granada, Spain
Interests: soft computing; computational intelligence; evolutionary dynamic optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Scheduling optimization and computational intelligence are key aspects in modern process and system engineering, aimed at optimizing the scheduling of various tasks and resources to achieve maximum efficiency and minimum cost. By utilizing various mathematical methods and modeling analysis, such as applied statistics, methodology, queuing theory, artificial intelligence, machine learning, and optimization algorithms, scheduling optimization enables decision makers to make timely and wise decisions.

In this Special Issue, we invite original research contributions including but not limited to:

  • Scheduling algorithms for manufacturing systems, logistics networks, and other industrial processes;
  • Computational intelligence techniques for decision making and optimization in dynamic and complex environments;
  • Innovative applications of artificial intelligence, machine learning, and metaheuristic algorithms in scheduling optimization;
  • Modeling and optimization of multi-objective, multi-constraint scheduling problems;
  • Evaluation and comparison of different computational intelligence approaches for scheduling optimization;
  • The effectiveness of computational intelligence techniques in real-world scheduling optimization problems.

We welcome submissions from scholars with a focus on scheduling optimization and computational intelligence, aiming to provide a platform for sharing knowledge, exchanging ideas, and creating new collaborations in this rapidly growing field.

Dr. Pavel Novoa-Hernández
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

  • mathematical optimization
  • computational intelligence
  • decision making
  • simulation modeling
  • process scheduling
  • resource allocation
  • operations research
  • production planning
  • scheduling algorithms
  • intelligent optimization techniques

Published Papers (2 papers)

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Research

15 pages, 315 KiB  
Article
Single Machine Scheduling Proportionally Deteriorating Jobs with Ready Times Subject to the Total Weighted Completion Time Minimization
by Zheng-Guo Lv, Li-Han Zhang, Xiao-Yuan Wang and Ji-Bo Wang
Mathematics 2024, 12(4), 610; https://doi.org/10.3390/math12040610 - 19 Feb 2024
Cited by 4 | Viewed by 436
Abstract
In this paper, we investigate a single machine scheduling problem with a proportional job deterioration. Under release times (dates) of jobs, the objective is to minimize the total weighted completion time. For the general condition, some dominance properties, a lower bound and an [...] Read more.
In this paper, we investigate a single machine scheduling problem with a proportional job deterioration. Under release times (dates) of jobs, the objective is to minimize the total weighted completion time. For the general condition, some dominance properties, a lower bound and an upper bound are given, then a branch-and-bound algorithm is proposed. In addition, some meta-heuristic algorithms (including the tabu search (TS), simulated annealing (SA) and heuristic (NEH) algorithms) are proposed. Finally, experimental results are provided to compare the branch-and-bound algorithm and another three algorithms, which indicate that the branch-and-bound algorithm can solve instances of 40 jobs within a reasonable time and that the NEH and SA are more accurate than the TS. Full article
(This article belongs to the Special Issue Advances in Scheduling Optimization and Computational Intelligence)
17 pages, 334 KiB  
Article
Optimal Different Due-Date Assignment Scheduling with Group Technology and Resource Allocation
by Xuyin Wang and Weiguo Liu
Mathematics 2024, 12(3), 436; https://doi.org/10.3390/math12030436 - 29 Jan 2024
Cited by 1 | Viewed by 577
Abstract
In this paper, we consider different due-date assignment scheduling with group technology and resource allocation on a single machine, where the due date of each job may be different. Under constant processing times, the objective function is to minimize the scheduling cost (i.e., [...] Read more.
In this paper, we consider different due-date assignment scheduling with group technology and resource allocation on a single machine, where the due date of each job may be different. Under constant processing times, the objective function is to minimize the scheduling cost (i.e., the weighted sum of earliness, tardiness, and due-date assignment cost, where the weights are position dependent). Under some optimal properties, we prove that this problem can be solved in O(ζlogζ) time, where ζ is the number of jobs. The problem is also extended to cases which include linear and convex functions of the quantity of resource allocation. The objective function is minimizing the sum of the scheduling cost and the resource-consumption cost. For the special case of linear and convex functions, we show that the problem is polynomially solvable in O(ζ3) time. Full article
(This article belongs to the Special Issue Advances in Scheduling Optimization and Computational Intelligence)
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