Multi-population genetic algorithm with ER network for solving flexible job shop scheduling problems

Shi, Xiaohu; Long, Wei; Li, Yanyan; Deng, Dayong

doi:10.1371/journal.pone.0233759

Cited by 23 publications

(21 citation statements)

References 39 publications

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“…Other hybrid methods for optimizing the FJSP are described in Bharti & Jain (2020) , where 28 benchmark instances are taken from three different data sets to demonstrate the performance of these methods. In Shi et al (2020) , a multi-population genetic algorithm with ER network is proposed and tested with 18 benchmark problems. MO-FJSP instances are solved with a hybrid non-dominated sorting biogeography-based optimization algorithm ( An et al, 2021 ).…”

Section: State Of the Art Of Fjspmentioning

confidence: 99%

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

Serna¹,

Seck-Tuoh-Mora²,

Medina-Marín³

et al. 2021

PeerJ Computer Science

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The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called smart-cells generates and shares information that helps to optimize instances of the FJSP. The GLNSA algorithm is accompanied by a tabu search that implements a simplified version of the Nopt1 neighborhood defined in Mastrolilli & Gambardella (2000) to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms, using four benchmark sets and 101 test problems.

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Section: State Of the Art Of Fjspmentioning

confidence: 99%

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

Serna¹,

Seck-Tuoh-Mora²,

Medina-Marín³

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

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“…For simplicity without losing generality, we choose to minimize makespan as the objective. e mathematical model is as follows [25]:…”

Section: 2mentioning

confidence: 99%

“…However, for MPGAs, the focus of interaction structures among subpopulations is mainly rings, tori, hypercubes, and so on, and complex networks have rarely been utilized to represent subpopulations and their interaction purposefully. erefore, in our previous studies [24,25], we addressed how seven different network structures, including the ringshaped network and the small-world network, influence the propagation rate of advantageous genes and thus affect the performance of MPGA for solving the FJSP. However, only the scale of networks is discussed, and the influence of other structural parameters is not researched.…”

Section: Introductionmentioning

confidence: 99%

Multipopulation Genetic Algorithms with Different Interaction Structures to Solve Flexible Job-Shop Scheduling Problems: A Network Science Perspective

Deng

Long

et al. 2020

Mathematical Problems in Engineering

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Populations of multipopulation genetic algorithms (MPGAs) parallely evolve with some interaction mechanisms. Previous studies have shown that the interaction structures can impact on the performance of MPGAs to some extent. This paper introduces the concept of complex networks such as ring-shaped networks and small-world networks to study how interaction structures and their parameters influence the MPGAs, where subpopulations are regarded as nodes and their interaction or migration of elites between subpopulations as edges. After solving the flexible job-shop scheduling problem (FJSP) by MPGAs with different parameters of interaction structures, simulation results were measured by criteria, such as success rate and average optimal value. The analysis reveals that (1) the smaller the average path length (APL) of the network is, the higher the propagation rate will be; (2) the performance of MPGAs increased first and then decreased along with the decrease of APL, indicating that, for better performance, the networks should have a proper APL, which can be adjusted by changing the structural parameters of networks; and (3) because the edge number of small-world networks remains unchanged with different rewiring possibilities of edges, the change in performance indicates that the MPGA can be improved by a more proper interaction structure of subpopulations as other conditions remain unchanged.

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“…The multi-population strategy is universally utilized in EAs to maintain diversity and prevent premature convergence [46]. How the interaction structure of these subpopulations affects the performance of an EA has been discussed from the perspective of complex networks [47][48][49]. In this paper, we utilize the ring-shaped network as the topology of the communication structures of subpopulations.…”

Section: Migrationmentioning

confidence: 99%

Building Robust Closed-Loop Supply Networks against Malicious Attacks

Deng

Long

et al. 2020

Processes

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With recent industrial upgrades, it is essential to transform the current forward supply networks (FSNs) into closed-loop supply networks (CLSNs), which are formed by the integration of forward and reverse logistics. The method chosen in this paper for building reverse logistics is to add additional functions to the existing forward logistics. This process can be regarded as adding reverse edges to the original directed edges in an FSN. Due to the limitation of funds and the demand for reverse flow, we suppose that a limited number of reverse edges can be built in a CLSN. To determine the transformation schemes with excellent robustness against malicious attacks, this paper proposes a multi-population evolutionary algorithm with novel operators to optimize the robustness of the CLSN, and this algorithm is abbreviated as MPEA-RSN. Then, both the generated and realistic SNs are taken as examples to validate the effectiveness of MPEA-RSN. The simulation results show that the index R, introduced to evaluate the robustness of CLSNs, can be improved by more than 95%, and this indicates that (1) the different schemes for adding reverse routes to an FSN can lead to different robustness values, and (2) the robustness of the transformed CLSN to malicious attacks can be significantly improved after optimization by MPEA-RSN. When an FSN is to be transformed into a CLSN, this paper can provide a frame of reference for building a CLSN that is robust to malicious attacks from a network structural perspective.

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Multi-population genetic algorithm with ER network for solving flexible job shop scheduling problems

Cited by 23 publications

References 39 publications

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

Multipopulation Genetic Algorithms with Different Interaction Structures to Solve Flexible Job-Shop Scheduling Problems: A Network Science Perspective

Building Robust Closed-Loop Supply Networks against Malicious Attacks

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