A Three-Stage Multiobjective Approach Based on Decomposition for an Energy-Efficient Hybrid Flow Shop Scheduling Problem

Zhang, Biao; Pan, Quan-Ke; Meng, Leilei; Li, Xinyu; Peng, Kunkun

doi:10.1109/tsmc.2019.2916088

Cited by 118 publications

(26 citation statements)

References 43 publications

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“…For the future work, we will extend our research as the follows: (1) Consider additional characteristics such as travel time, return time, due dates and multi-objectives [47], [48].…”

Section: Discussionmentioning

confidence: 99%

An Effective Discrete Artificial Bee Colony Algorithm for Scheduling an Automatic-Guided-Vehicle in a Linear Manufacturing Workshop

2020

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This paper deals with a new automatic guided vehicle (AGV) scheduling problem from the material handling process in a linear manufacturing workshop. The problem is to determine a sequence of Cells for AGV to travel to minimize the standard deviation of the waiting time of the Cells and the total travel distance of AGV. For this purpose, we first propose an integer linear programming model based on a comprehensive investigation. Then, we present an improved nearest-neighbor-based heuristic so as to fast generate a good solution in view of the problem-specific characteristics. Next, we propose an effective discrete artificial bee colony algorithm with some novel and advanced techniques including a heuristic-based initialization, six neighborhood structures and a new evolution strategy in the onlooker bee phase. Finally, the proposed algorithms are empirically evaluated based on several typical instances from the real-world linear manufacturing workshop. A comprehensive and thorough experiment shows that the presented algorithm produces superior results which are also demonstrated to be statistically significant than the existing algorithms. INDEX TERMS Automated guided vehicle, heuristic, discrete artificial bee colony algorithm, scheduling, linear manufacturing workshop.

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“…For the future work, we will extend our research as the follows: (1) Consider additional characteristics such as travel time, return time, due dates and multi-objectives [47], [48].…”

Section: Discussionmentioning

confidence: 99%

An Effective Discrete Artificial Bee Colony Algorithm for Scheduling an Automatic-Guided-Vehicle in a Linear Manufacturing Workshop

2020

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“…To minimise makespan and maximum tardiness, Naderi et al [27] designed a novel SA with migration mechanism and giant leap. As to HFSP-SDST simultaneously minimising makespan and energy consumption, Zhang et al [28] proposed a multiobjective MILP model and a three-stage multiobjective approach based on decomposition. To minimise the total weighted tardiness of HFSP-SDST considering transportation times, Naderi et al [29] proposed an MILP model and an electromagnetism algorithm.…”

Section: Hybrid Flow Shop Scheduling Problem With Sequence-dependenmentioning

confidence: 99%

Constraint programing for solving four complex flexible shop scheduling problems

Meng

Zhang

et al. 2021

IET Collab Intel Manufact

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In recent years, with the advent of robust solvers such as Cplex and Gurobi, constraint programing (CP) has been widely applied to a variety of scheduling problems. This paper presents CP models for formulating four scheduling problems with minimal makespan and complex constraints: the no-wait hybrid flow shop scheduling problem, the hybrid flow shop scheduling problem with sequence-dependent setup times, the flexible job shop scheduling problem with worker flexibility and the semiconductor final testing problem. The advantages of CP method in solving these four complex scheduling problems are explored. Finally, a set of benchmark instances are adopted to demonstrate the effectiveness and efficiency of the CP method. Experiment results show that the proposed CP models outperform existing algorithms; in particular, several best-known solutions of benchmark instances are improved by our CP method.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…Recent years, with the shortage of energy resources and serious global warming, energy-efficient manufacturing is attracting more and more attention from manufactures from all around the world [1][2][3][4][5][6]. For reducing energy consumption, it is a straightforward way to develop energy-efficient equipments.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, energy-efficient scheduling is without additional investment and any research cycle. Up to now, energy-efficient scheduling has proved to be a good way to reduce energy consumption, and it is attracting more and more attention from researchers [2,3,[7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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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A Three-Stage Multiobjective Approach Based on Decomposition for an Energy-Efficient Hybrid Flow Shop Scheduling Problem

Cited by 118 publications

References 43 publications

An Effective Discrete Artificial Bee Colony Algorithm for Scheduling an Automatic-Guided-Vehicle in a Linear Manufacturing Workshop

An Effective Discrete Artificial Bee Colony Algorithm for Scheduling an Automatic-Guided-Vehicle in a Linear Manufacturing Workshop

Constraint programing for solving four complex flexible shop scheduling problems

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

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