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Advances in Artificial Intelligence and Metaheuristics Methods for Planning and...
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Advances in Artificial Intelligence and Metaheuristics Methods for Planning and Scheduling

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

Deadline for manuscript submissions: 30 August 2024 | Viewed by 12150

Special Issue Editors

Prof. Dr. Ana M. Madureira

E-Mail Website
Guest Editor
School of Engineering, Polytechnic of Porto (ISEP/IPP), 4200-072 Porto, Portugal
Interests: artificial intelligence; metaheuristics; user modeling; dynamic scheduling; data science
Special Issues, Collections and Topics in MDPI journals
Dr. Joao Ferreira

E-Mail Website
Guest Editor
Department of Information Sciences, Technologies and Architecture, University Institute of Lisbon, 1649-026 Lisbon, Portugal
Interests: electric vehicle; data science; IoT; blockchain; artificial intelligence; smart grids
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. André Santos

E-Mail Website
Guest Editor
School of Engineering, Polytechnic of Porto (ISEP/IPP), 4200-072 Porto, Portugal
Interests: metaheuristics; scheduling; industrial management

Special Issue Information

Dear Colleagues,

Planning and scheduling problems are a family of problems that includes several examples which appear daily in all administrative and productive sectors. In general, these are problems of high computational complexity, so exact resolution strategies are impracticable. Thus, they constitute a promised field of application of many artificial intelligence techniques, such as metaheuristics, swarm intelligence, and other nature-inspired optimization techniques.

We are interested in academic problems since they allow us to evaluate the optimization approaches through experimentation on benchmark problems from research community repositories. We are also very interested in developing solutions to current real-world problems, including dynamic and pervasive scenarios.

This Special Issue will focus on recent theoretical and computational studies of the application of metaheuristics and nature-inspired optimization techniques to planning and scheduling in real-world problems. Topics include but are not limited to:

  1. Intelligent optimization for production planning and scheduling.
  2. Evolutionary computation and metaheuristics: theory and foundations.
  3. Hybrid optimization techniques (swarm intelligence, evolutionary algorithms).
  4. Computation using neural networks and fuzzy systems.
  5. Dynamic production scheduling.
  6. Metaheuristic parametrization and self-parametrization techniques.
  7. Machine learning for planning and scheduling optimization.
  8. AI for planning and scheduling.
  9. Multi-criteria approach in planning and scheduling.
  10. Applications in industry, transportation, services, and others.
  11. Use case applications in Industry 4.0, transportation, health systems, and telecommunication (5G).

Advances in artificial intelligence and metaheuristics methods and their hybrids for planning and scheduling are particularly welcome in this Special Issue.

Prof. Dr. Ana Maria Madureira
Prof. Dr. Joao Ferreira
Prof. Dr. André Santos
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

  • Artificial intelligence
  • Metaheuristics
  • Nature-inspired optimization techniques
  • Swarm intelligence
  • Evolutionary computation
  • Hybrid approaches
  • Self-parametrization
  • Machine learning
  • Intelligent systems for planning and scheduling
  • Dynamic scheduling

Published Papers (6 papers)

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22 pages, 4857 KiB  
Article
Optimizing PV Sources and Shunt Capacitors for Energy Efficiency Improvement in Distribution Systems Using Subtraction-Average Algorithm
by Idris H. Smaili, Dhaifallah R. Almalawi, Abdullah M. Shaheen and Hany S. E. Mansour
Mathematics 2024, 12(5), 625; https://doi.org/10.3390/math12050625 - 20 Feb 2024
Viewed by 613
Abstract
This work presents an optimal methodology based on an augmented, improved, subtraction-average-based technique (ASABT) which is developed to minimize the energy-dissipated losses that occur during electrical power supply. It includes a way of collaborative learning that utilizes the most effective response with the [...] Read more.
This work presents an optimal methodology based on an augmented, improved, subtraction-average-based technique (ASABT) which is developed to minimize the energy-dissipated losses that occur during electrical power supply. It includes a way of collaborative learning that utilizes the most effective response with the goal of improving the ability to search. Two different scenarios are investigated. First, the suggested ASABT is used considering the shunt capacitors only to minimize the power losses. Second, simultaneous placement and sizing of both PV units and capacitors are handled. Applications of the suggested ASAB methodology are performed on two distribution systems. First, a practical Egyptian distribution system is considered. The results of the simulation show that the suggested ASABT has a significant 56.4% decrease in power losses over the original scenario using the capacitors only. By incorporating PV units in addition to the capacitors, the energy losses are reduced from 26,227.31 to 10,554 kW/day with a high reduction of 59.75% and 4.26% compared to the initial case and the SABT alone, respectively. Also, the emissions produced from the substation are greatly reduced from 110,823.88 kgCO2 to 79,189 kgCO2, with a reduction of 28.54% compared to the initial case. Second, the standard IEEE 69-node system is added to the application. Comparable results indicate that ASABT significantly reduces power losses (5.61%) as compared to SABT and enhances the minimum voltage (2.38%) with a substantial reduction in energy losses (64.07%) compared to the initial case. For both investigated systems, the proposed ASABT outcomes are compared with the Coati optimization algorithm, the Osprey optimization algorithm (OOA), the dragonfly algorithm (DA), and SABT methods; the proposed ASABT shows superior outcomes, especially in the standard deviation of the obtained losses. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence and Metaheuristics Methods for Planning and Scheduling)
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25 pages, 2534 KiB  
Article
A Variant Iterated Greedy Algorithm Integrating Multiple Decoding Rules for Hybrid Blocking Flow Shop Scheduling Problem
by Yong Wang, Yuting Wang and Yuyan Han
Mathematics 2023, 11(11), 2453; https://doi.org/10.3390/math11112453 - 25 May 2023
Cited by 3 | Viewed by 1274
Abstract
This paper studies the hybrid flow shop scheduling problem with blocking constraints (BHFSP). To better understand the BHFSP, we will construct its mixed integer linear programming (MILP) model and use the Gurobi solver to demonstrate its correctness. Since the BHFSP exists parallel machines [...] Read more.
This paper studies the hybrid flow shop scheduling problem with blocking constraints (BHFSP). To better understand the BHFSP, we will construct its mixed integer linear programming (MILP) model and use the Gurobi solver to demonstrate its correctness. Since the BHFSP exists parallel machines in some processing stages, different decoding strategies can obtain different makespan values for a given job sequence and multiple decoding strategies can assist the algorithm to find the optimal value. In view of this, we propose a hybrid decoding strategy that combines both forward decoding and backward decoding to select the minimal objective function value. In addition, a hybrid decoding-assisted variant iterated greedy (VIG) algorithm to solve the above MILP model. The main novelties of VIG are a new reconstruction mechanism based on the hybrid decoding strategy and a swap-based local reinforcement strategy, which can enrich the diversity of solutions and explore local neighborhoods more deeply. This evaluation is conducted against the VIG and six state-of-the-art algorithms from the literature. The 100 tests showed that the average makespan and the relative percentage increase (RPI) values of VIG are 1.00% and 89.6% better than the six comparison algorithms on average, respectively. Therefore, VIG is more suitable to solve the studied BHFSP. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence and Metaheuristics Methods for Planning and Scheduling)
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30 pages, 5504 KiB  
Article
Simulation-Based Optimization Approaches for Dealing with Dual-Command Crane Scheduling Problem in Unit-Load Double-Deep AS/RS Considering Energy Consumption
by Hsien-Pin Hsu, Chia-Nan Wang and Thanh-Tuan Dang
Mathematics 2022, 10(21), 4018; https://doi.org/10.3390/math10214018 - 29 Oct 2022
Cited by 4 | Viewed by 1426
Abstract
Automated storage/retrieval systems (AS/RS) have been increasingly used to support operations in manufacturing firms, warehouses, and distribution centers. Usually, AS/RSs are expensive. To achieve a good return on investment (ROI), an AS/RS must operate optimally. This research focuses on solving the crane scheduling [...] Read more.
Automated storage/retrieval systems (AS/RS) have been increasingly used to support operations in manufacturing firms, warehouses, and distribution centers. Usually, AS/RSs are expensive. To achieve a good return on investment (ROI), an AS/RS must operate optimally. This research focuses on solving the crane scheduling problem, which has a great and immediate impact on the performance of an AS/RS. To optimize the design and operations of an AS/RS, many past studies have applied the simulation approach. However, the simulation and optimization have been often loosely coupled, resulting in a rigorous and labor-intensive optimization procedure. Using population- and evolution-based metaheuristics to deal with the crane scheduling problem of an AS/RS is one of the research trends. However, the whale optimization algorithm (WOA) and its variants have not been used for this purpose. To address the said gaps, this research first proposes a framework for coupling the simulation and optimization closely, in which various heuristics/metaheuristics, including first-come first-serve (FCFS), RANDOM, WOA, genetic algorithms (GAs), particle swarm optimization (PSO), and especially an improved WOA (IWOA), together with dynamic programming (DP), have been used as alternative sequencing methods. Based on this framework, different simulation-based optimization approaches have been developed for solving the dual-command crane scheduling problem in a unit-load double-deep AS/RS. The experimental results show that IWOA+DP outperforms the others in terms of energy consumption. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence and Metaheuristics Methods for Planning and Scheduling)
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25 pages, 550 KiB  
Article
Multitask Emergency Logistics Planning under Multimodal Transportation
by Hongbin Liu, Guopeng Song, Tianyu Liu and Bo Guo
Mathematics 2022, 10(19), 3624; https://doi.org/10.3390/math10193624 - 03 Oct 2022
Cited by 4 | Viewed by 1715
Abstract
Multitask emergency logistics planning is a complex optimization problem in practice. When a disaster occurs, relief materials or rescue teams should be dispatched to destinations as soon as possible. In a nutshell, the problem can be described as an optimization of multipoint-to-multipoint transportation [...] Read more.
Multitask emergency logistics planning is a complex optimization problem in practice. When a disaster occurs, relief materials or rescue teams should be dispatched to destinations as soon as possible. In a nutshell, the problem can be described as an optimization of multipoint-to-multipoint transportation delivery problem in a given multimodal traffic network. In this study, a multimodal traffic network is considered for emergency logistics transportation planning, and a mixed-integer programming (MIP) formulation is proposed to model the problem. In order to solve this model, we propose a two-layer solution method. The inner layer is to manage the single-task route recommendation, for which we develop a shortest-path algorithm with the multimodal traffic network. Here, the optimal substructure of the algorithm and its time complexity are presented. With the route of each task calculated by the single-task solver, a general optimization algorithm based on improved particle swarm optimization (PSO) is proposed at the outer layer to coordinate the execution of each task constrained by the limited transportation capacity, so as to derive solutions for multi-commodity emergency logistics planning. Extensive computational results show that the proposed method can find solutions of good quality in reasonable time. Meanwhile, through the sensitivity analysis of the algorithm, we find the appropriate parameters for general optimization algorithm to solve the problem proposed in this paper. The proposed approach is effective and practical for solving multitask emergency logistics planning problem under multimodal transportation, which can find a satisfactory solution in an acceptable time. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence and Metaheuristics Methods for Planning and Scheduling)
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23 pages, 4300 KiB  
Article
A Self-Parametrization Framework for Meta-Heuristics
by André S. Santos, Ana M. Madureira and Leonilde R. Varela
Mathematics 2022, 10(3), 475; https://doi.org/10.3390/math10030475 - 01 Feb 2022
Cited by 2 | Viewed by 2356
Abstract
Even while the scientific community has shown great interest in the analysis of meta-heuristics, the analysis of their parameterization has received little attention. It is the parameterization that will adapt a meta-heuristic to a problem, but it is still performed, mostly, empirically. There [...] Read more.
Even while the scientific community has shown great interest in the analysis of meta-heuristics, the analysis of their parameterization has received little attention. It is the parameterization that will adapt a meta-heuristic to a problem, but it is still performed, mostly, empirically. There are multiple parameterization techniques; however, they are time-consuming, requiring considerable computational effort and they do not take advantage of the meta-heuristics that they parameterize. In order to approach the parameterization of meta-heuristics, in this paper, a self-parameterization framework is proposed. It will automatize the parameterization as an optimization problem, precluding the user from spending too much time on parameterization. The model will automate the parameterization through two meta-heuristics: A meta-heuristic of the solution space and one of the parameter space. To analyze the performance of the framework, a self-parameterization prototype was implemented. The prototype was compared and analyzed in a SP (scheduling problem) and in the TSP (traveling salesman problem). In the SP, the prototype found better solutions than those of the manually parameterized meta-heuristics, although the differences were not statistically significant. In the TSP, the self-parameterization prototype was more effective than the manually parameterized meta-heuristics, this time with statistically significant differences. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence and Metaheuristics Methods for Planning and Scheduling)
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24 pages, 664 KiB  
Article
One-Machine Scheduling with Time-Dependent Capacity via Efficient Memetic Algorithms
by Raúl Mencía and Carlos Mencía
Mathematics 2021, 9(23), 3030; https://doi.org/10.3390/math9233030 - 26 Nov 2021
Cited by 1 | Viewed by 1992
Abstract
This paper addresses the problem of scheduling a set of jobs on a machine with time-varying capacity, with the goal of minimizing the total tardiness objective function. This problem arose in the context scheduling the charging times of a fleet of electric vehicles [...] Read more.
This paper addresses the problem of scheduling a set of jobs on a machine with time-varying capacity, with the goal of minimizing the total tardiness objective function. This problem arose in the context scheduling the charging times of a fleet of electric vehicles and it is NP-hard. Recent work proposed an efficient memetic algorithm for solving the problem, combining a genetic algorithm and a local search method. The local search procedure is based on swapping consecutive jobs on a C-path, defined as a sequence of consecutive jobs in a schedule. Building on it, this paper develops new memetic algorithms that stem from new local search procedures also proposed in this paper. The local search methods integrate several mechanisms to make them more effective, including a new condition for swapping pairs of jobs, a hill climbing approach, a procedure that operates on several C-paths and a method that interchanges jobs between different C-paths. As a result, the new local search methods enable the memetic algorithms to reach higher-quality solutions. Experimental results show significant improvements over existing approaches. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence and Metaheuristics Methods for Planning and Scheduling)
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