Scheduling Dual-Objective Stochastic Hybrid Flow Shop With Deteriorating Jobs via Bi-Population Evolutionary Algorithm

Fu, Yaping; Zhou, MengChu; Guo, Xiwang; Qi, Liang

doi:10.1109/tsmc.2019.2907575

Cited by 145 publications

(44 citation statements)

References 59 publications

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“…Meta-heuristic algorithm has been well known as one of best optimizers for the complex optimization problems. [39][40][41] NP is a new meta-heuristic algorithm introduced by Shi and Olafsson. 42 It has been successfully Index sets: N job index set, N = 1, 2, .…”

Section: Solution Methodsmentioning

confidence: 99%

A meta-heuristic algorithm for integrated optimization of dynamic resource allocation planning and production scheduling in parallel machine system

Wang

2019

Advances in Mechanical Engineering

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With the wide application of advanced information technology and intelligent equipment in the manufacturing system, the decisions of design and operation have become more interdependent and their integration optimization has gained great concerns from the community of operational research recently. This article investigates an optimization problem of integrating dynamic resource allocation and production schedule in a parallel machine environment. A meta-heuristic algorithm, in which heuristic-based partition, genetic-based sampling, promising index calculation, and backtracking strategies are employed, is proposed for solving the investigated integration problem in order to minimize the makespan of the manufacturing system. The experimental results on a set of random-generated test instances indicate that the presented model is effective and the proposed algorithm exhibits the satisfactory performance that outperforms two stateof-the-art algorithms from literature.

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Section: Solution Methodsmentioning

confidence: 99%

A meta-heuristic algorithm for integrated optimization of dynamic resource allocation planning and production scheduling in parallel machine system

Wang

2019

Advances in Mechanical Engineering

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“…The rider individual with the best fitness value is the leader. The equation of success rate is equation (20).…”

Section: B Parameter Initializationmentioning

confidence: 99%

“…In literature [19], a multi-objective evolutionary algorithm was introduced to search the optimal solutions of hot-rolling scheduling problem in the compact strip production. Reference [20] employed a dual-objective evolutionary algorithm to solve stochastic hybrid flowshop deteriorating scheduling problem considering makespan and total tardiness.…”

Section: Introductionmentioning

confidence: 99%

A Discrete Multi-Objective Rider Optimization Algorithm for Hybrid Flowshop Scheduling Problem Considering Makespan, Noise and Dust Pollution

Chen

et al. 2020

IEEE Access

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Many optimization algorithms have been proposed to solve hybrid flowshop scheduling problem (HFSP). However, with the development of industry and society, labor right and labor safety have become important problem to consider in production scheduling. So the green HFSP considering makespan, noise and dust pollution becomes an urgent problem to be solved. In this paper, the rider optimization algorithm (ROA) is modified into the multi-objective rider optimization algorithm (MOROA) using Pareto archive and neighborhood sorting techniques. The Pareto archive and neighborhood sorting technology make the Pareto optimal solution set of MOROA have higher coverage and more solutions. Then MOROA is discretized into discrete MOROA (DMOROA) to solve the HFSP considering makespan, noise and dust pollution. DMOROA is tested on 10, 30 and 50 jobs HFSP considering makespan, noise and dust pollution. The test results are compared with two multi-objective algorithms to verify the performance of DMOROA. And the test results verify that the DMOROA is superior to the comparison algorithms in search accuracy, number of non-dominated solutions, diversity of solution set and stability. Therefore, DMOROA is effective in solving multi-objective HFSP considering makespan, noise and dust pollution. INDEX TERMS Hybrid flowshop scheduling problem, multi-objective meta-heuristic algorithm, rider optimization algorithm, labor right.

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“…Flow shop scheduling optimization can help manufacturers manage production process, so as to achieve various objectives. The scheduling goal is to minimize makespan and total tardiness in [26,27]. Besides, in [28], idle time for total machine, total device availability, total machine setup times and total job blocking time are also evaluation indexes of the scheduling result.…”

Section: Flow Shop Scheduling Optimizationmentioning

confidence: 99%

“…Analysis and discussion are presented below. Give that most research [26][27][28][29][30][31][32][33][34] defines both optimizations to be nonlinear programming and addresses them by heuristic algorithms, we also adopted genetic algorithm to solve them.…”

Section: Regulation Effect Of Flow Shopsmentioning

confidence: 99%

Flow Shop Providing Frequency Regulation Service in Electricity Market

Wang

Pei

et al. 2020

Energies

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Electricity cost is one of main production costs for flow shops. Providing frequency regulation services can help electric loads reduce their electricity costs. Previous studies mostly focus on automatic generation control (AGC) strategies for other types of electric loads, such as air conditioners, EVs or battery storage. In this paper, we find flow shops competent to follow regulation signals and avoid interrupts of processing with the help of scheduling optimization. This finding may be an aid for flow shops by availing regulation services to the market and making a profit. Hence, we propose an AGC strategy for optimizing flow shop scheduling, without affecting the operation. To formulate the bidding strategy for flow shops in regulation market, we considered as many relevant factors as possible, including the regulation performance and yield of flow shops, constraints on load power, regulation reserve capacity and machines operation, inventory of each semi-finished product, AGC strategy-as well as the coupling between the bids in both energy market and regulation market. Our case study shows the potential of the methodology proposed in this paper to cut down the electric cost of flow shops and supplies of performance-qualified frequency regulation service.

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Scheduling Dual-Objective Stochastic Hybrid Flow Shop With Deteriorating Jobs via Bi-Population Evolutionary Algorithm

Cited by 145 publications

References 59 publications

A meta-heuristic algorithm for integrated optimization of dynamic resource allocation planning and production scheduling in parallel machine system

A meta-heuristic algorithm for integrated optimization of dynamic resource allocation planning and production scheduling in parallel machine system

A Discrete Multi-Objective Rider Optimization Algorithm for Hybrid Flowshop Scheduling Problem Considering Makespan, Noise and Dust Pollution

Flow Shop Providing Frequency Regulation Service in Electricity Market

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