A dual encoding-based meta-heuristic algorithm for solving a constrained hybrid flow shop scheduling problem

Costa, Antonio; Cappadonna, Fulvio Antonio; Fichera, Sergio

doi:10.1016/j.cie.2013.01.004

Cited by 22 publications

(8 citation statements)

References 27 publications

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“…To validate the performance of the proposed algorithm, ANOVA (Analysis of Variance) is used as the statistical analysis tool to demonstrate whether one or more solutions significantly vary [ 40 ]. The authors used one-way ANOVA test [ 41 ] to show significance in proposed algorithm.…”

Section: Experimental Design and Analysismentioning

confidence: 99%

Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem

Rajeswari

Amudhavel

Sujatha

et al. 2017

Computational Intelligence and Neuroscience

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The Nurse Rostering Problem is an NP-hard combinatorial optimization, scheduling problem for assigning a set of nurses to shifts per day by considering both hard and soft constraints. A novel metaheuristic technique is required for solving Nurse Rostering Problem (NRP). This work proposes a metaheuristic technique called Directed Bee Colony Optimization Algorithm using the Modified Nelder-Mead Method for solving the NRP. To solve the NRP, the authors used a multiobjective mathematical programming model and proposed a methodology for the adaptation of a Multiobjective Directed Bee Colony Optimization (MODBCO). MODBCO is used successfully for solving the multiobjective problem of optimizing the scheduling problems. This MODBCO is an integration of deterministic local search, multiagent particle system environment, and honey bee decision-making process. The performance of the algorithm is assessed using the standard dataset INRC2010, and it reflects many real-world cases which vary in size and complexity. The experimental analysis uses statistical tools to show the uniqueness of the algorithm on assessment criteria.

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Section: Experimental Design and Analysismentioning

confidence: 99%

Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem

Rajeswari

Amudhavel

Sujatha

et al. 2017

Computational Intelligence and Neuroscience

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“…Dugardin et al focused on the multi-objective resolution of a re-entrant HFS [8]. More recently, Costa et al presented a dual encoding-based meta-heuristic algorithm for solving a constrained hybrid flow shop scheduling problem [9]. Figielska proposed a heuristic for scheduling in a two-stage HFS with renewable resources shared among the two stages [10].…”

Section: Introductionmentioning

confidence: 98%

Solving the steelmaking casting problem using an effective fruit fly optimisation algorithm

Pan

Mao

et al. 2014

Knowledge-Based Systems

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“…In manufacturing shops, production scheduling plays an important role on achieving one or more manufacturing optimal objectives, such as minimizing the maximum completion time (makespan), mean flow time, earliness and tardiness. Flow-shop scheduling problem(FSP) is one of classical production scheduling problems and widely exists in chemical [1], metallurgy [2], micro-electronics [3], and so on. Hybrid flow shop with unrelated parallel machines (HFS) is an extension of FSP, but it is more complex than FSP, because at least in one stage there are multiple parallel…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Multi-Objective Teaching-Learning Based Optimization for Scheduling Problem of Hybrid Flow Shop With Unrelated Parallel Machine

Song

2021

IEEE Access

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Efficient scheduling benefits productivity promotion, energy savings and the customer's satisfaction. In recent years, with a growing concern about the energy saving and environmental impact, energy oriented scheduling is going to be a hot issue for sustainable manufacturing. In this study, we investigate an energy-oriented scheduling problem deriving from the hybrid flow shop with unrelated parallel machine. First, we formulate the scheduling problem with a mixed integer linear programming (MILP) model, which considers two objectives including minimizing the completion time and energy consumption. Second, a hybrid multi-objective teaching-learning based optimization (HMOTLBO) algorithm based on decomposition is proposed. In the proposed HMOTLBO, a new solution presentation and five decoding rules are designed for mining the optimal solution. To reduce the standby energy consumption and turning on/off energy consumption, a greedy shifting algorithm is developed without changing the completion time of a scheduling. To improve the converge speed of the algorithm, a weight matching strategy is designed to avoid randomly matching weight vectors with students. To enhance the exploration and exploitation capacities of the algorithm, A teaching operator based on crossover and a self-learning operator based on a variable neighborhood search(VNS) are proposed. Finally, fourth different experiments are performed on 15 cases, the comparison result verified the effectiveness and the superiority of the proposed algorithm.INDEX TERMS Hybrid flow shop scheduling, teaching and learning based optimization, multi-objective, makespan, energy consumption.

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A dual encoding-based meta-heuristic algorithm for solving a constrained hybrid flow shop scheduling problem

Cited by 22 publications

References 27 publications

Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem

Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem

Solving the steelmaking casting problem using an effective fruit fly optimisation algorithm

A Hybrid Multi-Objective Teaching-Learning Based Optimization for Scheduling Problem of Hybrid Flow Shop With Unrelated Parallel Machine

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