A shuffled frog-leaping algorithm for hybrid flow shop scheduling with two agents

Lei, Deming; Guo, Xiuping

doi:10.1016/j.eswa.2015.08.025

Cited by 52 publications

(18 citation statements)

References 26 publications

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“…The evolution of each memeplex and the shuffling process continue until the defined convergence criteria are satisfied. More detailed steps of SFLA refer to papers [29][30][31][33][34][35][36].…”

Section: The Proposed Algorithm Hsfla For Dfjspmentioning

confidence: 99%

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MILP Modeling and Optimization of Energy- Efficient Distributed Flexible Job Shop Scheduling Problem

et al. 2020

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With the global warming problem and increasing energy cost, manufacturing firms are paying more and more attention to reducing energy consumption. This paper addresses the distributed flexible job shop scheduling problem (DFJSP) with minimizing energy consumption. To solve the problem, firstly, a novel mixed integer linear programming (MILP) model is developed to solve small-scaled problems to optimality. Due to the NP-hardness of DFJSP, we then propose an efficient hybrid shuffled frog-leaping algorithm (HSFLA) for solving DFJSP, particularly for large-sized problems. HSFLA combines the shuffled frog-leaping algorithm (SFLA) with powerful global search ability and variable neighborhood search (VNS) with good local search ability. Moreover, in HSFLA, the encoding method, the decoding method, the initialization method and the memetic evolution process are specifically designed. Finally, numerical experiments are conducted to evaluate the performance of the proposed MILP model and HFSLA. INDEX TERMS Distributed flexible job shop scheduling problem, mixed integer linear programming, shuffled frog-leaping algorithm, variable neighborhood search, energy consumption.

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Section: The Proposed Algorithm Hsfla For Dfjspmentioning

confidence: 99%

“…With regard to memeplex construction, binary tournament selection is used to divide the population into several memeplexes. The detailed steps of binary tournament selection can refer to paper [35]. By using the binary tournament selection, not all solutions are allocated into memeplexes.…”

Section: ) Memeplex Constructionmentioning

confidence: 99%

MILP Modeling and Optimization of Energy- Efficient Distributed Flexible Job Shop Scheduling Problem

et al. 2020

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“…This algorithm has been successfully used in various problem domains such as water distribution network design. 14 parameter identi¯cation, 15 unit commitment, 16 classi¯cation, 17 robot optimal controller design, 18 job shop scheduling, 19 hybrid°ow shop scheduling, 20 and so on.…”

Section: Introductionmentioning

confidence: 99%

Application of the Shuffled Frog Leaping Algorithm (SFLA) in Constructing Fuzzy Classification Systems

Mirhosseini

Haghighi

2019

Int. J. Comp. Intel. Appl.

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A Fuzzy Inference System (FIS) is a way of mapping an input space to an output space using the fuzzy logic. FISs are widely used to solve classification problems. The Shuffled Frog Leaping Algorithm (SFLA) is a metaheuristic inspired by the natural evolution of frogs in searching for the largest source of food. By using local and global searches simultaneously, SFLA is effective in solving various optimization problems. This paper first proposes a new method to create zero-order Sugeno Fuzzy Inference Systems using SFLA. Then, the paper introduces an approach to use resulting SFLA-based Fuzzy Inference Systems to build test oracles. Test oracle is a mechanism for determining whether a test on a software program has passed or failed. The experimental results show that SFLA creates fuzzy systems more efficiently than three other evolutionary algorithms, including Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). Moreover, with respect to the accuracy and convergence speed criteria, SFLA and PSO outperform other evolutionary algorithms, while their performances are comparable to each other. At last, the experimental results indicate that SFLA-based FISs can be used to create test oracles with acceptable accuracy.

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“…This algorithm combines the advantages of particle swarm optimization (PSO) and shuffled complex evolution (SCE) algorithm, and it has been proved that the algorithm has good performance in convergence speed and solution precision [25,26]. It was used to solve many real-word problems such as job shop scheduling and cloud computing resource allocation [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

An Improved Shuffled Frog Leaping Algorithm and Its Application in Dynamic Emergency Vehicle Dispatching

Duan

Niu

Huang

2018

Mathematical Problems in Engineering

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The traditional method for solving the dynamic emergency vehicle dispatching problem can only get a local optimal strategy in each horizon. In order to obtain the dispatching strategy that can better respond to changes in road conditions during the whole dispatching process, the real-time and time-dependent link travel speeds are fused, and a time-dependent polygonal-shaped link travel speed function is set up to simulate the predictable changes in road conditions. Response times, accident severity, and accident time windows are taken as key factors to build an emergency vehicle dispatching model integrating dynamic emergency vehicle routing and selection. For the unpredictable changes in road conditions caused by accidents, the dispatching strategy is adjusted based on the real-time link travel speed. In order to solve the dynamic emergency vehicle dispatching model, an improved shuffled frog leaping algorithm (ISFLA) is proposed. The global search of the improved algorithm uses the probability model of estimation of distribution algorithm to avoid the partial optimal solution. Based on the Beijing expressway network, the efficacy of the model and the improved algorithm were tested from three aspects. The results have shown the following: (1) Compared with SFLA, the optimization performance of ISFLA is getting better and better with the increase of the number of decision variables. When the possible emergency vehicle selection strategies are 815, the objective function value of optimal selection strategies obtained by the base algorithm is 210.10% larger than that of ISFLA. (2) The prediction error of the travel speed affects the accuracy of the initial emergency vehicle dispatching. The prediction error of ±10 can basically meet the requirements of the initial dispatching. (3) The adjustment of emergency vehicle dispatching strategy can successfully bypassed road sections affected by accidents and shorten the response time.

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A shuffled frog-leaping algorithm for hybrid flow shop scheduling with two agents

Cited by 52 publications

References 26 publications

MILP Modeling and Optimization of Energy- Efficient Distributed Flexible Job Shop Scheduling Problem

MILP Modeling and Optimization of Energy- Efficient Distributed Flexible Job Shop Scheduling Problem

Application of the Shuffled Frog Leaping Algorithm (SFLA) in Constructing Fuzzy Classification Systems

An Improved Shuffled Frog Leaping Algorithm and Its Application in Dynamic Emergency Vehicle Dispatching

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