Computers & Operations Research最新文献

英文中文

Topology reconstruction in telecommunication networks: Embedding operations research within deep learning

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2024-12-26 DOI: 10.1016/j.cor.2024.106960

Tobias Engelhardt Rasmussen , Siv Sørensen , David Pisinger , Thomas Martini Jørgensen , Andreas Baum

We consider the task of reconstructing the cabling arrangements of last-mile telecommunication networks using customer modem data. In such networks, downstream data traverses from a source node down through the branches of the tree network to a set of customer leaf nodes. Each modem monitors the quality of received data using a series of continuous data metrics. The state of the data, when it reaches a modem, is contingent upon the path it traverses through the network and can be affected by, e.g., corroded cable connectors.

We train an encoder to identify irregular inherited events in modem quality data, such as network faults, and encode them as discrete data sequences for each modem. Specifically, the encoding scheme is obtained by using unsupervised contrastive learning, where a Siamese neural network is trained on a positive (true) topology, its modem data, and a set of negative (false) topologies. The weights of the Siamese network are continuously updated based on a new modified version of the Maximum Parsimony optimality criterion. This approach essentially integrates an optimization problem directly into a deep learning loss function.

We evaluate the encoder’s performance on simulated data instances with randomly added events. The performance of the encoder is tested both on its ability to extract and encode events as well as whether the encoded data sequences lead to accurate topology reconstructions under the modified version of the Maximum Parsimony optimality criterion.

Promising computational results are reported for trees with a varying number of internal nodes, up to a maximum of 20. The encoder identifies a high percentage of simulated events, leading to nearly perfect topology reconstruction. Overall, these results affirm the potential of embedding an optimization problem into a deep learning loss function, unveiling many interesting topics for further research.

{"title":"Topology reconstruction in telecommunication networks: Embedding operations research within deep learning","authors":"Tobias Engelhardt Rasmussen , Siv Sørensen , David Pisinger , Thomas Martini Jørgensen , Andreas Baum","doi":"10.1016/j.cor.2024.106960","DOIUrl":"10.1016/j.cor.2024.106960","url":null,"abstract":"<div><div>We consider the task of reconstructing the cabling arrangements of <em>last-mile</em> telecommunication networks using customer modem data. In such networks, downstream data traverses from a source node down through the branches of the tree network to a set of customer leaf nodes. Each modem monitors the quality of received data using a series of continuous data metrics. The state of the data, when it reaches a modem, is contingent upon the path it traverses through the network and can be affected by, e.g., corroded cable connectors.</div><div>We train an encoder to identify irregular inherited <em>events</em> in modem quality data, such as network faults, and encode them as discrete data sequences for each modem. Specifically, the encoding scheme is obtained by using unsupervised contrastive learning, where a Siamese neural network is trained on a positive (true) topology, its modem data, and a set of negative (false) topologies. The weights of the Siamese network are continuously updated based on a new modified version of the Maximum Parsimony optimality criterion. This approach essentially integrates an optimization problem directly into a deep learning loss function.</div><div>We evaluate the encoder’s performance on simulated data instances with randomly added events. The performance of the encoder is tested both on its ability to extract and encode events as well as whether the encoded data sequences lead to accurate topology reconstructions under the modified version of the Maximum Parsimony optimality criterion.</div><div>Promising computational results are reported for trees with a varying number of internal nodes, up to a maximum of 20. The encoder identifies a high percentage of simulated events, leading to nearly perfect topology reconstruction. Overall, these results affirm the potential of embedding an optimization problem into a deep learning loss function, unveiling many interesting topics for further research.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106960"},"PeriodicalIF":4.1,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Large Neighborhood Search-based approach to tackle the very large scale Team Orienteering Problem in industrial context

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2024-12-26 DOI: 10.1016/j.cor.2024.106954

Charly Chaigneau , Nathalie Bostel , Axel Grimault

The Team Orienteering Problem (TOP) is an optimization problem belonging to the class of Vehicle Routing Problem with Profits in which the objective is to maximize the total profit collected by visiting customers while being limited to a time limit. This paper deals with the very large scale TOP in an industrial context. In this context, computing time is decisive and classical methods may fail to provide good solutions in a reasonable computational time. To do so, we propose a Large Neighborhood Search (LNS) combined with various mechanisms in order to reduce the computational time of the method. It is applied on classical sets of instances from the literature and on a new set of very large scale instances ranging from 1001 to 5395 customers that we adapted from Kobeaga et al. (2017). On the small scale set of instances, most best-known solutions are found. On the large scale set of instances, three new best-known solutions are found while the algorithm quickly gets more than half of the other best-known solutions.

引用次数: 0

Minimum cost consensus model considering dual behavior preference

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2024-12-26 DOI: 10.1016/j.cor.2024.106961

Yingying Liang , Jindong Qin , Witold Pedrycz

In actual consensus-reaching problems, decision makers (DMs) may exhibit non-unique behaviors originating from comparisons between themselves and expectations and reality, such as fairness concern and overconfidence behaviors, which may result in solution recommendation deviation when using the existing minimum cost consensus models (MCCMs). In order to handle consensus issues when DMs show fairness concern behavior, a behavior between DMs, the MCCM considering fairness concern (MCCM-FC) is established. Moreover, DMs may exhibit overconfidence regarding their own opinions, which is managed by the MCCM considering overconfidence (MCCM-O) to offset the actual difference between expectations and reality. To cope with the scenario that incorporates both behaviors simultaneously, the integrated fairness concern and overconfidence MCCM (MCCM-FC-O) is constructed and the relationships of the three MCCMs are discussed. The proposed models are justified through an illustrated application, and further sensitivity and comparative analyses are conducted to illustrate their practicability.

引用次数: 0

Optimizing the Transport of Organs for Transplantation

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2024-12-23 DOI: 10.1016/j.cor.2024.106934

Isaac Balster , Joyce Azevedo Caetano , Glaydston Mattos Ribeiro , Laura Bahiense

As an organ becomes available for transplantation, a recipient must be selected. Usually, donor and recipient are geographically apart. Therefore, the transport of the organ must be planned and executed within the time window imposed by the maximum preservation time of the organ, which can impact recipient selection. The Cold Ischemia Time - CIT, that is the time elapsed between the surgical removal of the organ and its transplantation, must be the minimum possible to improve the transplantation success. In this sense, the air transport becomes the best option and, sometimes, it is the only way to deliver the organ before perishing. The planning of an organ transportation means choosing, among thousands of possible sequences of flights, the option that delivers the organ faster to its destination. This problem can be modeled as a resource constrained shortest path. Given the urgency and importance of this task, which is solved manually in Brazil, we present a labeling algorithm to find the optimal sequence of flights. Computational tests performed on 25 Brazilian real cases showed a reduction, on average, of 37,46% for the CITs and 44,17% for the transport times.

{"title":"Optimizing the Transport of Organs for Transplantation","authors":"Isaac Balster , Joyce Azevedo Caetano , Glaydston Mattos Ribeiro , Laura Bahiense","doi":"10.1016/j.cor.2024.106934","DOIUrl":"10.1016/j.cor.2024.106934","url":null,"abstract":"<div><div>As an organ becomes available for transplantation, a recipient must be selected. Usually, donor and recipient are geographically apart. Therefore, the transport of the organ must be planned and executed within the time window imposed by the maximum preservation time of the organ, which can impact recipient selection. The Cold Ischemia Time - CIT, that is the time elapsed between the surgical removal of the organ and its transplantation, must be the minimum possible to improve the transplantation success. In this sense, the air transport becomes the best option and, sometimes, it is the only way to deliver the organ before perishing. The planning of an organ transportation means choosing, among thousands of possible sequences of flights, the option that delivers the organ faster to its destination. This problem can be modeled as a resource constrained shortest path. Given the urgency and importance of this task, which is solved manually in Brazil, we present a labeling algorithm to find the optimal sequence of flights. Computational tests performed on 25 Brazilian real cases showed a reduction, on average, of 37,46% for the CITs and 44,17% for the transport times.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106934"},"PeriodicalIF":4.1,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

New models for close enough facility location problems

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2024-12-21 DOI: 10.1016/j.cor.2024.106957

Alejandro Moya-Martínez , Mercedes Landete , Juan F. Monge , Sergio García

Two integer programming problems are introduced and formulated in this paper, both based on the concepts of close enough and facility location. Location problems using the notion of close enough allow customers to pick up their demand at pickup points different from the facilities but that are still not too far from the latter.

Given a discrete set of customers, a discrete set of potential facility locations, and a maximum distance that each customer is willing to travel free of charge to pick up their order, the Close Enough Facility Location Problem consists in determining which facilities to open among the candidates, on which points on the plane to install pickup points, and how to assign customers to both facilities and pickup points, in an optimal way taking into account different costs. In this work we propose two generalizations of this problem. The first is to consider that the pickup points have capacities. The second is to consider that the communications network is restricted to a graph, and that therefore the pickup points cannot be installed on any point on the plane but only on the network. These problems are named the Capacitated Close-Enough Facility Location Problem and the Network Capacitated Close-Enough Facility Location Problem, respectively. We propose a column generation algorithm for the two introduced problems that allows us to obtain better results for large-scale problems than the CPLEX solver.

{"title":"New models for close enough facility location problems","authors":"Alejandro Moya-Martínez , Mercedes Landete , Juan F. Monge , Sergio García","doi":"10.1016/j.cor.2024.106957","DOIUrl":"10.1016/j.cor.2024.106957","url":null,"abstract":"<div><div>Two integer programming problems are introduced and formulated in this paper, both based on the concepts of <em>close enough</em> and facility location. Location problems using the notion of <em>close enough</em> allow customers to pick up their demand at pickup points different from the facilities but that are still not too far from the latter.</div><div>Given a discrete set of customers, a discrete set of potential facility locations, and a maximum distance that each customer is willing to travel free of charge to pick up their order, the Close Enough Facility Location Problem consists in determining which facilities to open among the candidates, on which points on the plane to install pickup points, and how to assign customers to both facilities and pickup points, in an optimal way taking into account different costs. In this work we propose two generalizations of this problem. The first is to consider that the pickup points have capacities. The second is to consider that the communications network is restricted to a graph, and that therefore the pickup points cannot be installed on any point on the plane but only on the network. These problems are named the Capacitated Close-Enough Facility Location Problem and the Network Capacitated Close-Enough Facility Location Problem, respectively. We propose a column generation algorithm for the two introduced problems that allows us to obtain better results for large-scale problems than the CPLEX solver.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106957"},"PeriodicalIF":4.1,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Genetic algorithm-based selection of optimal Monte Carlo simulations

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2024-12-20 DOI: 10.1016/j.cor.2024.106958

Francesco Strati , Luca G. Trussoni

The aim of this work is to propose the use of a genetic algorithm to solve the problem of the optimal subsampling of Monte Carlo simulations to obtain desired statistical properties. It is designed to optimally select the best

m

Monte Carlo simulations from a larger pool of

N > m

simulations. The concept of an “optimal selection” is defined through a target metric, in this work the first and second moments of the distribution, from the set of

N

simulations, to which the subset of

m

simulations should closely converge. The implementation employs an objective function, allowing the algorithm to balance computational efficiency and optimization performance, achieving fast and precise selection of the

m

simulations.

引用次数: 0

Optimal investment planning for production networks with fixed production profiles

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2024-12-20 DOI: 10.1016/j.cor.2024.106955

Donghao Liu , Benjamin D. Leibowicz , Jonathan F. Bard , Yuzixuan Zhu , Yuanyuan Guo , Yufen Shao

In this paper, we consider an oilfield planning problem with decisions about where and when to invest in wells and facilities to maximize profit. The model, in the form of a mixed-integer linear program, includes an option to expand capacity for existing facilities, annual budget constraints, well closing decisions, and fixed production profiles once wells are opened. While fixed profiles are a novel and important feature, they add another set of time-indexed binary variables that makes the problem difficult to solve. To find solutions, we develop a three-phase sequential algorithm that includes (1) ranking, (2) branching, and (3) refinement. Phases 1 and 2 determine which facilities and wells to open, along with well-facility assignments. Phase 3 ensures feasibility with respect to budget constraints and adjusts construction times and facility capacities to increase profit. We first demonstrate how our algorithm navigates the problem’s complex features by applying it to a case study parameterized with realistic production profiles. Then, we perform computational experiments on small instances and show that our algorithm generally achieves the same objective function values as CPLEX but in much less time. Lastly, we solve larger instances using our three-phase algorithm and several variations to demonstrate its scalability and to highlight the roles of specific algorithmic components.

{"title":"Optimal investment planning for production networks with fixed production profiles","authors":"Donghao Liu , Benjamin D. Leibowicz , Jonathan F. Bard , Yuzixuan Zhu , Yuanyuan Guo , Yufen Shao","doi":"10.1016/j.cor.2024.106955","DOIUrl":"10.1016/j.cor.2024.106955","url":null,"abstract":"<div><div>In this paper, we consider an oilfield planning problem with decisions about where and when to invest in wells and facilities to maximize profit. The model, in the form of a mixed-integer linear program, includes an option to expand capacity for existing facilities, annual budget constraints, well closing decisions, and fixed production profiles once wells are opened. While fixed profiles are a novel and important feature, they add another set of time-indexed binary variables that makes the problem difficult to solve. To find solutions, we develop a three-phase sequential algorithm that includes (1) ranking, (2) branching, and (3) refinement. Phases 1 and 2 determine which facilities and wells to open, along with well-facility assignments. Phase 3 ensures feasibility with respect to budget constraints and adjusts construction times and facility capacities to increase profit. We first demonstrate how our algorithm navigates the problem’s complex features by applying it to a case study parameterized with realistic production profiles. Then, we perform computational experiments on small instances and show that our algorithm generally achieves the same objective function values as CPLEX but in much less time. Lastly, we solve larger instances using our three-phase algorithm and several variations to demonstrate its scalability and to highlight the roles of specific algorithmic components.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106955"},"PeriodicalIF":4.1,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An efficient MILP-based algorithm for the qualitative flexible multi-criteria method under incomplete or conflicting weights

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2024-12-19 DOI: 10.1016/j.cor.2024.106951

Saeed Alaei , Seyed Hossein Razavi Hajiagha , Mahnaz Hosseinzadeh

This study first proposes a mixed-integer linear programming model for the qualitative flexible multi-criteria method (QUALIFLEX) within an interval type-2 fuzzy environment. This extends an efficient QUALIFLEX method that already exists in the literature. The computational complexity of QUALIFLEX grows exponentially with an increase in the number of alternatives, and the extended model efficiently solves a multi-criteria decision problem and determines the best permutation regardless of the number of alternatives. A new QUALIFLEX algorithm is also developed to handle imprecise and conflicting preference structures for criteria weights. This algorithm includes both a single-objective and a bi-objective model to address incomplete and conflicting weight information, respectively, and these models are subsequently linearized. The newly developed algorithm solves the models only once to produce the best permutation and the corresponding weights, rather than requiring the solution of

m!

nonlinear models as in previous studies. The implications of the proposed extended and developed algorithms are illustrated using numerical examples, and their performance is analyzed against existing methods across a set of 30 problems with varying numbers of alternatives. The formulated model achieves similar results to the previous version with a limited number of alternatives using only one model-solving attempt and demonstrates superior performance in terms of computation time for problems with a larger number of alternatives.

{"title":"An efficient MILP-based algorithm for the qualitative flexible multi-criteria method under incomplete or conflicting weights","authors":"Saeed Alaei , Seyed Hossein Razavi Hajiagha , Mahnaz Hosseinzadeh","doi":"10.1016/j.cor.2024.106951","DOIUrl":"10.1016/j.cor.2024.106951","url":null,"abstract":"<div><div>This study first proposes a mixed-integer linear programming model for the qualitative flexible multi-criteria method (QUALIFLEX) within an interval type-2 fuzzy environment. This extends an efficient QUALIFLEX method that already exists in the literature. The computational complexity of QUALIFLEX grows exponentially with an increase in the number of alternatives, and the extended model efficiently solves a multi-criteria decision problem and determines the best permutation regardless of the number of alternatives. A new QUALIFLEX algorithm is also developed to handle imprecise and conflicting preference structures for criteria weights. This algorithm includes both a single-objective and a bi-objective model to address incomplete and conflicting weight information, respectively, and these models are subsequently linearized. The newly developed algorithm solves the models only once to produce the best permutation and the corresponding weights, rather than requiring the solution of<span><math><mrow><mspace></mspace><mi>m</mi><mo>!</mo><mspace></mspace></mrow></math></span>nonlinear models as in previous studies. The implications of the proposed extended and developed algorithms are illustrated using numerical examples, and their performance is analyzed against existing methods across a set of 30 problems with varying numbers of alternatives. The formulated model achieves similar results to the previous version with a limited number of alternatives using only one model-solving attempt and demonstrates superior performance in terms of computation time for problems with a larger number of alternatives.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106951"},"PeriodicalIF":4.1,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Perishable inventory control with backlogging penalties: A mixed-integer linear programming model via two-step approximation

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2024-12-17 DOI: 10.1016/j.cor.2024.106953

Yulun Wu , Shunji Tanaka

This study proposes a novel approximate mixed-integer linear programming (MILP) model for the perishable inventory control problem considering non-stationary demands and backlogging penalties. Because of the existence of the waste costs incurred by outdated products in the cost function, it is difficult to apply the linearization technique employed for the non-perishable inventory control problem directly to our problem. To address this difficulty, we develop a two-step approximation method. In the first step, we approximate each expected cost to simplify the cost function, making it easy to handle. In the second step, we apply an existing linearization technique to linearize this function and then obtain the MILP model. We evaluate the proposed model in computer simulations by comparing it with other existing methods. The results show that our model closely matches a benchmark method capable of obtaining near-optimal solutions in solution quality, and it achieves a better trade-off between solution quality and computational efficiency than existing heuristics.

引用次数: 0

Mathematical modeling and hybrid evolutionary algorithm to schedule flexible job shop with discrete operation sequence flexibility

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2024-12-17 DOI: 10.1016/j.cor.2024.106952

Shuai Yuan , Xiaomin Zhu , Wei Cai , Jinsheng Gao , Runtong Zhang

In actual industrial production, several operations of a job may not have precedence relationships and can be placed at any point in the process route. However, traditional flexible job shop scheduling problems (FJSP) often assume that all operations of each job must be processed in strict linear order. Therefore, this research addresses the FJSP with discrete operation sequence flexibility (FJSPDS) with the objective of minimizing the makespan. Based on existing models, two novel mixed-integer linear programming (MILP) models are formulated by improving the description methods of variables and constraints, significantly enhancing the models’ performance. Additionally, a hybrid evolutionary algorithm (HEA) is proposed to solve large-scale instances through the following three aspects. An improved encoding method is proposed, which makes the search space of the HEA and solution space of the problem more compatible and reduces the possibility of optimal solutions being missed. A special neighborhood structure is designed according to the characters of sequence-free operations, and an iterative local search method is introduced to improve the quality of the solution. A knowledge-driven reinitialization operator is developed, which generates new individuals based on the features of the historical elite population, guiding the evolution of populations, avoiding premature convergence while also avoiding falling into local optima. Finally, a total of 110 benchmark problem instances are utilized to verify the superior effectiveness of the MILP models and the HEA in solving FJSPDS.

{"title":"Mathematical modeling and hybrid evolutionary algorithm to schedule flexible job shop with discrete operation sequence flexibility","authors":"Shuai Yuan , Xiaomin Zhu , Wei Cai , Jinsheng Gao , Runtong Zhang","doi":"10.1016/j.cor.2024.106952","DOIUrl":"10.1016/j.cor.2024.106952","url":null,"abstract":"<div><div>In actual industrial production, several operations of a job may not have precedence relationships and can be placed at any point in the process route. However, traditional flexible job shop scheduling problems (FJSP) often assume that all operations of each job must be processed in strict linear order. Therefore, this research addresses the FJSP with discrete operation sequence flexibility (FJSPDS) with the objective of minimizing the makespan. Based on existing models, two novel mixed-integer linear programming (MILP) models are formulated by improving the description methods of variables and constraints, significantly enhancing the models’ performance. Additionally, a hybrid evolutionary algorithm (HEA) is proposed to solve large-scale instances through the following three aspects. An improved encoding method is proposed, which makes the search space of the HEA and solution space of the problem more compatible and reduces the possibility of optimal solutions being missed. A special neighborhood structure is designed according to the characters of sequence-free operations, and an iterative local search method is introduced to improve the quality of the solution. A knowledge-driven reinitialization operator is developed, which generates new individuals based on the features of the historical elite population, guiding the evolution of populations, avoiding premature convergence while also avoiding falling into local optima. Finally, a total of 110 benchmark problem instances are utilized to verify the superior effectiveness of the MILP models and the HEA in solving FJSPDS.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106952"},"PeriodicalIF":4.1,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Computers & Operations Research

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀