Research

14 pages, 304 KiB

Open AccessArticle

Group Technology Scheduling with Due-Date Assignment and Controllable Processing Times

by Weiguo Liu and Xuyin Wang

Processes 2023, 11(4), 1271; https://doi.org/10.3390/pr11041271 - 19 Apr 2023

Cited by 6 | Viewed by 834

This paper investigates common (slack) due-date assignment single-machine scheduling with controllable processing times within a group technology environment. Under linear and convex resource allocation functions, the cost function minimizes scheduling (including the weighted sum of earliness, tardiness, and due-date assignment, where the weights [...] Read more.

This paper investigates common (slack) due-date assignment single-machine scheduling with controllable processing times within a group technology environment. Under linear and convex resource allocation functions, the cost function minimizes scheduling (including the weighted sum of earliness, tardiness, and due-date assignment, where the weights are position-dependent) and resource-allocation costs. Given some optimal properties of the problem, if the size of jobs in each group is identical, the optimal group sequence can be obtained via an assignment problem. We then illustrate that the problem is polynomially solvable in

O (℘^{3})

time, where ℘ is the number of jobs. Full article

(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing)

11 pages, 1313 KiB

Open AccessArticle

Complicated Time-Constrained Project Scheduling Problems in Water Conservancy Construction

by Song Zhang, Xiaokang Song, Liang Shen and Lichun Xu

Processes 2023, 11(4), 1110; https://doi.org/10.3390/pr11041110 - 05 Apr 2023

Cited by 1 | Viewed by 1551

Abstract

Water conservancy project scheduling is an extension to the classic resource-constrained project scheduling problem (RCPSP). It is limited by special time constraints called “forbidden time windows” during which certain activities cannot be executed. To address this issue, a specific RCPSP model is proposed, [...] Read more.

Water conservancy project scheduling is an extension to the classic resource-constrained project scheduling problem (RCPSP). It is limited by special time constraints called “forbidden time windows” during which certain activities cannot be executed. To address this issue, a specific RCPSP model is proposed, and an approach is designated for it which incorporates both a priority rule-based heuristic algorithm to obtain an acceptable solution, and a hybrid genetic algorithm to further improve the quality of the solution. In the genetic algorithm, we introduce a new crossover operator for the forbidden time window and adopt double justification and elitism strategies. Finally, we conduct simulated experiments on a project scheduling problem library to compare the proposed algorithm with other priority-rule based heuristics, and the results demonstrate the superiority of our algorithm. Full article

(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing)

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23 pages, 1249 KiB

Open AccessArticle

A Knowledge-Based Cooperative Differential Evolution Algorithm for Energy-Efficient Distributed Hybrid Flow-Shop Rescheduling Problem

by Yuanzhu Di, Libao Deng and Tong Liu

Processes 2023, 11(3), 755; https://doi.org/10.3390/pr11030755 - 03 Mar 2023

Viewed by 1178

Abstract

Due to the increasing level of customization and globalization of competition, rescheduling for distributed manufacturing is receiving more attention. In the meantime, environmentally friendly production is becoming a force to be reckoned with in intelligent manufacturing industries. In this paper, the energy-efficient distributed [...] Read more.

Due to the increasing level of customization and globalization of competition, rescheduling for distributed manufacturing is receiving more attention. In the meantime, environmentally friendly production is becoming a force to be reckoned with in intelligent manufacturing industries. In this paper, the energy-efficient distributed hybrid flow-shop rescheduling problem (EDHFRP) is addressed and a knowledge-based cooperative differential evolution (KCDE) algorithm is proposed to minimize the makespan of both original and newly arrived orders and total energy consumption (simultaneously). First, two heuristics were designed and used cooperatively for initialization. Next, a three-dimensional knowledge base was employed to record the information carried out by elite individuals. A novel DE with three different mutation strategies is proposed to generate the offspring. A local intensification strategy was used for further enhancement of the exploitation ability. The effects of major parameters were investigated and extensive experiments were carried out. The numerical results prove the effectiveness of each specially-designed strategy, while the comparisons with four existing algorithms demonstrate the efficiency of KCDE in solving EDHFRP. Full article

(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing)

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19 pages, 4167 KiB

Open AccessArticle

IoT Access Control Model Based on Blockchain and Trusted Execution Environment

by Weijin Jiang, En Li, Wenying Zhou, Ying Yang and Tiantian Luo

Processes 2023, 11(3), 723; https://doi.org/10.3390/pr11030723 - 28 Feb 2023

Cited by 8 | Viewed by 2045

Abstract

With the application and popularization of the Internet of Things (IoT), while the IoT devices bring us intelligence and convenience, the privacy protection issue has gradually attracted people’s attention. Access control technology is one of the important methods to protect privacy. However, the [...] Read more.

With the application and popularization of the Internet of Things (IoT), while the IoT devices bring us intelligence and convenience, the privacy protection issue has gradually attracted people’s attention. Access control technology is one of the important methods to protect privacy. However, the existing IoT access control technologies have extensive problems such as coarse-grainedness, weak auditability, lack of access process control, and excessive privileges, which make the security and privacy of our IoT devices face great threats. Based on this, a blockchain-based and encrypted currency-based access control model CcBAC supported by Trusted Execution Environment (TEE) technology is proposed, which can provide fine-graininess, strong auditability, and access procedure control for the Internet of Things. In this study, the technical principle, characteristics, and research status of the control model are introduced, and the framework of the CcBAC model is expounded in detail and formally defined. Moreover, the functions in the model are described in detail, and a specific access control process in general scenarios is presented for the model. Finally, the practicability of this model is verified through theoretical analysis and experimental evaluation, which proves that this model not only enables resource owners to fully control the access to their resources, but also takes into account the fine-graininess and auditable access control. Full article

(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing)

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14 pages, 960 KiB

Open AccessArticle

Solving the Two-Crane Scheduling Problem in the Pre-Steelmaking Process

by Xie Xie, Yongyue Zheng, Tianwei Mu, Fucai Wan and Hai Dong

Processes 2023, 11(2), 549; https://doi.org/10.3390/pr11020549 - 10 Feb 2023

Cited by 1 | Viewed by 1318

Abstract

This research is motivated by the practical pre-steelmaking stage in large iron and steel companies, which have steady and heavy demands for the steelmaking production process. Our problem studied the pre-steelmaking stage, which consists of two steps that are needed in each convertor [...] Read more.

This research is motivated by the practical pre-steelmaking stage in large iron and steel companies, which have steady and heavy demands for the steelmaking production process. Our problem studied the pre-steelmaking stage, which consists of two steps that are needed in each convertor before the steelmaking process. During each step, a necessary transportation must be operated by a crane. In contrast to the classical two-machine flowshop problem during which both machines are fixed, these transporting operations are performed by two mounted, removeable cranes. Our problem is scheduling two-crane operations for the sake of minimizing the last convertors’ completion time (makespan); that is, the last finish time among the total operation of the two cranes is minimized. This study was concerned with resolving the interference between two cranes by determining the sequence of loading operations and how each crane avoids the other in order to let it complete its next operation first. A mixed integer linear programming (MILP) model was developed to represent the problem, and we further present the computational complexity of the problem. The result implies that our problem is very difficult to solve, and it is computationally challenging to solve the model. A special case is provided, which can be optimally solved in polynomial time. Furthermore, an evolutionary algorithm cuckoo search (CS) algorithm was attempted to obtain near-optimal solutions for medium- and large-scale problems. Finally, the efficiency and effectiveness of our methods were validated by numerical results in both simulated instances as well as real data from a practical production process. Full article

(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing)

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16 pages, 1997 KiB

Open AccessArticle

The Integrated Rescheduling Problem of Berth Allocation and Quay Crane Assignment with Uncertainty

by Hongxing Zheng, Zhaoyang Wang and Hong Liu

Processes 2023, 11(2), 522; https://doi.org/10.3390/pr11020522 - 08 Feb 2023

Cited by 3 | Viewed by 1362

Abstract

The baseline plan of terminals will be impacted to a certain extent after being affected by uncertain events, such as vessel delay and unscheduled vessel arrival, resulting in disorderly terminal operations, wasted resources, and reduced loading and unloading efficiency, which further aggravates terminal [...] Read more.

The baseline plan of terminals will be impacted to a certain extent after being affected by uncertain events, such as vessel delay and unscheduled vessel arrival, resulting in disorderly terminal operations, wasted resources, and reduced loading and unloading efficiency, which further aggravates terminal congestion. To effectively cope with the disturbance of terminal operations by the above uncertain events and improve the operational efficiency of container terminals, this paper investigates the integrated rescheduling problem of berth allocation and quay crane assignment with vessel delay and unscheduled vessel arrival. Two steps are designed to deal with uncertainty shocks. The first step is to determine the rescheduling moment by using a rolling time-domain approach. The second step is to establish a rescheduling model and design an improved genetic algorithm(IGA) to obtain a rescheduling solution using various rescheduling strategies at the rescheduling moment. Moreover, through scenario experiments, comparisons with commercial solvers and other algorithms, it can be seen that the solution speed of IGA is better than that of commercial solvers and the average gap does not exceed 6%, which verifies the effectiveness and superiority of this algorithm. Full article

(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing)

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26 pages, 1943 KiB

Open AccessArticle

A Novel Parallel Simulated Annealing Methodology to Solve the No-Wait Flow Shop Scheduling Problem with Earliness and Tardiness Objectives

by Ismet Karacan, Ozlem Senvar and Serol Bulkan

Processes 2023, 11(2), 454; https://doi.org/10.3390/pr11020454 - 02 Feb 2023

Cited by 3 | Viewed by 1536

Abstract

In this paper, the no-wait flow shop problem with earliness and tardiness objectives is considered. The problem is proven to be NP-hard. Recent no-wait flow shop problem studies focused on familiar objectives, such as makespan, total flow time, and total completion time. However, [...] Read more.

In this paper, the no-wait flow shop problem with earliness and tardiness objectives is considered. The problem is proven to be NP-hard. Recent no-wait flow shop problem studies focused on familiar objectives, such as makespan, total flow time, and total completion time. However, the problem has limited studies with solution approaches covering the concomitant use of earliness and tardiness objectives. A novel methodology for the parallel simulated annealing algorithm is proposed to solve this problem in order to overcome the runtime drawback of classical simulated annealing and enhance its robustness. The well-known flow shop problem datasets in the literature are utilized for benchmarking the proposed algorithm, along with the classical simulated annealing, variants of tabu search, and particle swarm optimization algorithms. Statistical analyses were performed to compare the runtime and robustness of the algorithms. The results revealed the enhancement of the classical simulated annealing algorithm in terms of time consumption and solution robustness via parallelization. It is also concluded that the proposed algorithm could outperform the benchmark metaheuristics even when run in parallel. The proposed algorithm has a generic structure that can be easily adapted to many combinatorial optimization problems. Full article

(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing)

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18 pages, 1961 KiB

Open AccessArticle

Joint Optimization of Pre-Marshalling and Yard Cranes Deployment in the Export Block

by Shuang Duan, Hongxing Zheng and Xiaomin Gan

Processes 2023, 11(2), 311; https://doi.org/10.3390/pr11020311 - 17 Jan 2023

Cited by 1 | Viewed by 1153

Abstract

To improve the efficiency of loading operation by researching the optimization of the pre-marshalling operation scheme in the export container block between the time when the ship stowage chart was published and the beginning time of loading, a two-stage mixed integer programming model [...] Read more.

To improve the efficiency of loading operation by researching the optimization of the pre-marshalling operation scheme in the export container block between the time when the ship stowage chart was published and the beginning time of loading, a two-stage mixed integer programming model was established. The first stage established an optimization model of the container reshuffling location, based on the objective function of the least time-consuming operation of a single-bay-yard crane, and designed an improved artificial bee colony algorithm to solve it. Based on the first stage, an optimization model of yard crane configuration and scheduling was built to minimize the maximum completion time of the yard crane in the export block, and an improved genetic algorithm was designed to solve the built model. Through comparative analysis, the performance of our algorithm was better than CPLEX and traditional heuristic algorithms. It could still solve the 30 bays quickly, and the solving quality was 8.53% and 11.95% higher than GA and TS on average, which verified the effectiveness of the model and the science of the algorithm and could provide a reference for improving the efficiency of port operation. Full article

(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing)

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28 pages, 5779 KiB

Open AccessArticle

Multi-Task Multi-Agent Reinforcement Learning for Real-Time Scheduling of a Dual-Resource Flexible Job Shop with Robots

by Xiaofei Zhu, Jiazhong Xu, Jianghua Ge, Yaping Wang and Zhiqiang Xie

Processes 2023, 11(1), 267; https://doi.org/10.3390/pr11010267 - 13 Jan 2023

Cited by 6 | Viewed by 4191

Abstract

In this paper, a real-time scheduling problem of a dual-resource flexible job shop with robots is studied. Multiple independent robots and their supervised machine sets form their own work cells. First, a mixed integer programming model is established, which considers the scheduling problems [...] Read more.

In this paper, a real-time scheduling problem of a dual-resource flexible job shop with robots is studied. Multiple independent robots and their supervised machine sets form their own work cells. First, a mixed integer programming model is established, which considers the scheduling problems of jobs and machines in the work cells, and of jobs between work cells, based on the process plan flexibility. Second, in order to make real-time scheduling decisions, a framework of multi-task multi-agent reinforcement learning based on centralized training and decentralized execution is proposed. Each agent interacts with the environment and completes three decision-making tasks: job sequencing, machine selection, and process planning. In the process of centralized training, the value network is used to evaluate and optimize the policy network to achieve multi-agent cooperation, and the attention mechanism is introduced into the policy network to realize information sharing among multiple tasks. In the process of decentralized execution, each agent performs multiple task decisions through local observations according to the trained policy network. Then, observation, action, and reward are designed. Rewards include global and local rewards, which are decomposed into sub-rewards corresponding to tasks. The reinforcement learning training algorithm is designed based on a double-deep Q-network. Finally, the scheduling simulation environment is derived from benchmarks, and the experimental results show the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing)

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18 pages, 1597 KiB

Open AccessArticle

Vehicle Rescheduling with Delivery Delay Considering Perceived Waiting Cost of Heterogeneous Customers

by Lirong Wu and Hang Zhang

Processes 2022, 10(12), 2643; https://doi.org/10.3390/pr10122643 - 08 Dec 2022

Viewed by 956

Abstract

The original schedule may not be optimal or feasible due to delivery delay caused by disruption. To solve the vehicle rescheduling problem with delivery delay based on loss aversion in prospect theory and customer heterogeneity, a mathematical model is established to minimize the [...] Read more.

The original schedule may not be optimal or feasible due to delivery delay caused by disruption. To solve the vehicle rescheduling problem with delivery delay based on loss aversion in prospect theory and customer heterogeneity, a mathematical model is established to minimize the sum of distance cost and penalty cost. Next, an improved compressed annealing algorithm with heterogeneous pressure is proposed to solve the model. Finally, numerical experiments are executed on the basis of 30 classic Solomon benchmarks to test the performance of the proposed solution approach. Sensitivity tests are carried out for the customer waiting sensitivity parameter, the length of delay time, and the time when the delivery delay occurs. The computational results show that, compared to the traditional rescheduling method, the higher the degree of customer heterogeneity, the longer the length of delay time, and, the earlier the distribution delay occurs, the stronger the validity and practicability of the model and algorithm proposed in this paper. Full article

(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing)

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29 pages, 1068 KiB

Open AccessArticle

A Truthful Mechanism for Multibase Station Resource Allocation in Metaverse Digital Twin Framework

by Jixian Zhang, Mingyi Zong and Weidong Li

Processes 2022, 10(12), 2601; https://doi.org/10.3390/pr10122601 - 05 Dec 2022

Cited by 6 | Viewed by 1593

Abstract

The concept of the metaverse has gained increasing attention in recent years, and the development of various new technologies, including digital twin technology, has made it possible to see the metaverse coming to pass. Many academics have begun to investigate various problems after [...] Read more.

The concept of the metaverse has gained increasing attention in recent years, and the development of various new technologies, including digital twin technology, has made it possible to see the metaverse coming to pass. Many academics have begun to investigate various problems after realizing the importance of digital twin technology in building the metaverse. However, when utilizing digital twin technology to construct a metaverse, there remains limited research on how to allocate multibase station resources. This research translates a multibase station wireless resource allocation problem into an integer linear programming constraint model when virtual service providers construct a metaverse. In addition, the optimal VCG reverse auction (OPT-VCGRA) mechanism is designed to maximize social welfare and solve the problem of IoT devices competing for base station wireless resources. Specifically, the problem of the optimal allocation of wireless channel resources and payment rule based on the Vickrey–Clarke–Groves mechanism is solved to achieve optimal allocation and calculation of payment prices. Since the optimal allocation problem is NP-hard, this paper also designs a metaverse digital twin resource allocation and pricing (MDTRAP) mechanism based on monotonic allocation and key value theory. The mechanism sends the resource allocation results of multiple base stations to IoT devices and calculates the price payment when building a metaverse in the real world. This paper shows that both auction mechanisms have incentive compatibility and individual rationality properties. Through experiments, this paper compares the two mechanisms in terms of social welfare, the number of winners, and the overall payment. The MDTRAP mechanism performs similarly to the OPT-VCGRA mechanism in terms of social welfare, the number of winners, and channel utilization but is far superior to the OPT-VCGRA mechanism in terms of execution time and total payment. The trustful experiment also verified the truthfulness of the MDTRAP mechanism. The experimental results show that the MDTRAP mechanism can be used to solve the resource allocation problem of multiple base stations to IoT devices when building a metaverse in the real world and can effectively maximize social welfare. Full article

(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing)

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16 pages, 2892 KiB

Open AccessArticle

Research on Green Reentrant Hybrid Flow Shop Scheduling Problem Based on Improved Moth-Flame Optimization Algorithm

by Feng Xu, Hongtao Tang, Qining Xun, Hongyi Lan, Xia Liu, Wenfang Xing, Tianyi Zhu, Lei Wang and Shibao Pang

Processes 2022, 10(12), 2475; https://doi.org/10.3390/pr10122475 - 22 Nov 2022

Cited by 4 | Viewed by 1360

Abstract

To address the green reentrant hybrid flow shop-scheduling problem (GRHFSP), we performed lifecycle assessments for evaluating the comprehensive impact of resources and the environment. An optimization model was established to minimize the maximum completion time and reduce the comprehensive impact of resources and [...] Read more.

To address the green reentrant hybrid flow shop-scheduling problem (GRHFSP), we performed lifecycle assessments for evaluating the comprehensive impact of resources and the environment. An optimization model was established to minimize the maximum completion time and reduce the comprehensive impact of resources and the environment, and an improved moth-flame optimization algorithm was developed. A coding scheme based on the number of reentry layers, stations, and machines was designed, and a hybrid population initialization strategy was developed, according to a situation wherein the same types of nonequivalent parallel machines were used. Two different update strategies were designed for updating the coding methods of processes and machines. The population evolution strategy was adopted to improve the local search ability of the proposed algorithm and the quality of the solution. Through simulation experiments based on different datasets, the effectiveness of the proposed algorithm was verified, and comparative evaluations revealed that the proposed algorithm could solve the GRHFSP more effectively than other well-known algorithms. Full article

(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing)

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18 pages, 2500 KiB

Open AccessArticle

An Improved Arc Flow Model with Enhanced Bounds for Minimizing the Makespan in Identical Parallel Machine Scheduling

by Anis Gharbi and Khaled Bamatraf

Processes 2022, 10(11), 2293; https://doi.org/10.3390/pr10112293 - 04 Nov 2022

Viewed by 1214

Abstract

In this paper, an identical parallel machine problem was considered with the objective of minimizing the makespan. This problem is NP-hard in the strong sense. A mathematical formulation based on an improved arc flow model with enhanced bounds was proposed. A variable neighborhood [...] Read more.

In this paper, an identical parallel machine problem was considered with the objective of minimizing the makespan. This problem is NP-hard in the strong sense. A mathematical formulation based on an improved arc flow model with enhanced bounds was proposed. A variable neighborhood search algorithm was proposed to obtain an upper bound. Three lower bounds from the literature were utilized in the improved arc flow model to improve the efficiency of the mathematical formulation. In addition, a graph compression technique was proposed to reduce the size of the graph. As a consequence, the improved arc flow model was compared with an arc flow model from the literature. The computational results on benchmark instances showed that the improved arc flow model outperformed the literature arc flow model at finding optimal solutions for 99.97% of the benchmark instances, with the overall percentage of the reduction in time reaching 87%. Full article

(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing)

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18 pages, 1573 KiB

Open AccessArticle

Research on the Siting Model of Emergency Centers in a Chemical Industry Park to Prevent the Domino Effect

by Kerang Cao, Linqi Liang, Yaru Liu, Liwei Wang, Kwang-Nam Choi and Jingyu Gao

Processes 2022, 10(11), 2287; https://doi.org/10.3390/pr10112287 - 04 Nov 2022

Viewed by 1218

Abstract

A chemical industry park (CIP) has a wide variety of hazardous chemicals, and once an accident occurs, the level of danger increases geometrically, while the domino effect may bring devastating consequences. To improve the emergency rescue capability of a chemical park and prevent [...] Read more.

A chemical industry park (CIP) has a wide variety of hazardous chemicals, and once an accident occurs, the level of danger increases geometrically, while the domino effect may bring devastating consequences. To improve the emergency rescue capability of a chemical park and prevent the domino effect, a certain number of emergency centers are built at sites near the park for the purpose of rapid emergency rescue and deployment of emergency supplies. Based on this, in our study, a siting model of the emergency center of the chemical park, which aims to prevent the domino effect, was constructed by considering the timeliness and safety, while adopting the prevention of the domino effect as a constraint. The NSGA-II algorithm is used to solve the siting model, and the CPLEX method is used for the comparison. This study combines the prevention of the domino effect with multi-objective optimization theory, which has a good and simple applicability for solving the considered problem and can obtain solutions in line with science and reality. It also adds the risk radius of the demand point based on the traditional siting model and proposes a model that combines the risk and distance to reduce the risk of accidents across the whole region. Finally, the model is applied to a chemical park in China for an arithmetic analysis to provide decision makers with a targeted reference base for the siting of an emergency center. The experimental results show that the NSGA-II algorithm can effectively solve the model of the emergency center in the chemical park and outperforms the results obtained from the CPLEX solution in terms of its cost and safety. Full article

(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing)

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17 pages, 1850 KiB

Open AccessArticle

Scheduling Large-Size Identical Parallel Machines with Single Server Using a Novel Heuristic-Guided Genetic Algorithm (DAS/GA) Approach

by Mohammad Abu-Shams, Saleem Ramadan, Sameer Al-Dahidi and Abdallah Abdallah

Processes 2022, 10(10), 2071; https://doi.org/10.3390/pr10102071 - 13 Oct 2022

Cited by 1 | Viewed by 1828

Abstract

Parallel Machine Scheduling (PMS) is a well-known problem in modern manufacturing. It is an optimization problem aiming to schedule n jobs using m machines while fulfilling certain practical requirements, such as total tardiness. Traditional approaches, e.g., mix integer programming and Genetic Algorithm (GA), [...] Read more.

Parallel Machine Scheduling (PMS) is a well-known problem in modern manufacturing. It is an optimization problem aiming to schedule n jobs using m machines while fulfilling certain practical requirements, such as total tardiness. Traditional approaches, e.g., mix integer programming and Genetic Algorithm (GA), usually fail, particularly in large-size PMS problems, due to computational time and/or memory burden and the large searching space required, respectively. This work aims to overcome such challenges by proposing a heuristic-based GA (DAS/GA). Specifically, a large-scale PMS problem with n independent jobs and m identical machines with a single server is studied. Individual heuristic algorithms (DAS) and GA are used as benchmarks to verify the performance of the proposed combined DAS/GA on 18 benchmark problems established to cover small, medium, and large PMS problems concerning standard performance metrics from the literature and a new metric proposed in this work (standardized overall total tardiness). Computational experiments showed that the heuristic part (DAS-h) of the proposed algorithm significantly enhanced the performance of the GA for large-size problems. The results indicated that the proposed algorithm should only be used for large-scale PMS problems because DAS-h trapped GA in a region of local optima, limiting its capabilities in small- and mainly medium-sized problems. Full article

(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing)

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14 pages, 860 KiB

Open AccessArticle

An Efficient Ant Colony Algorithm Based on Rank 2 Matrix Approximation Method for Aircraft Arrival/Departure Scheduling Problem

by Bo Xu, Weimin Ma, Hua Ke, Wenjuan Yang and Hao Zhang

Processes 2022, 10(9), 1825; https://doi.org/10.3390/pr10091825 - 10 Sep 2022

Cited by 4 | Viewed by 1192

Abstract

The Aircraft Arrival/Departure Problem (AADSP) is the core problem in current runway system, even has become the bottleneck to prevent the improvement of the airport efficiency. This paper studies the single runway AADSP. A Mixed Integer Programming (MIP) model is constructed and an [...] Read more.

The Aircraft Arrival/Departure Problem (AADSP) is the core problem in current runway system, even has become the bottleneck to prevent the improvement of the airport efficiency. This paper studies the single runway AADSP. A Mixed Integer Programming (MIP) model is constructed and an algorithm named Ant Colony based on Rank 2 Matrix Approximation (RMA-AC) method is proposed. Numerical results validate that the new algorithm, as well as the new model, exhibits better performance than CPLEX and the traditional two-phase algorithm. The runway efficiency enhanced by RMA-AC, within 20 s computation, is about 2–5% even for the 800 aircraft sequences. It is a promising method to improve the efficiency of the future aircraft scheduling system. Full article

(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing)

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