Robust scheduling of a two-stage hybrid flow shop with uncertain interval processing times

Feng, Xin; Zheng, Feifeng; Xu, Yinfeng

doi:10.1080/00207543.2016.1162341

Cited by 51 publications

(15 citation statements)

References 25 publications

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“…Other typical models for two-stage hybrid flow shop problems include the following: Vairaktarakis and Lee [1] discussed the problem that two tasks both can be processed on any machine, and gave an approximate algorithm with the worst-case ratio of 1.618; Tan et al [7] considered a flexible flowshop scheduling problem with batch processing machines at each stage, and gave an iterative stage-based decomposition approach to solve this problem; Feng et al [8] studied a two-stage hybrid flowshop with uncertain processing time and gave a heuristic algorithm for their problem; Ahonen and Alvarenga [9] proposed a new two-stage hybrid flow shop problem where the job's processing time is related to the starting time of the job, and used annealing algorithm and tabu search method to solve their problem; Hidri et al [10] addressed a two machines hybrid flow shop scheduling problem with transportation times between two machines, and presented a heuristic based on the optimal solution of the parallel machine scheduling problem with release date and delivery time; Zhang et al [11] considered a hybrid flowshop problem with four batching machines, and used the clustering and genetic algorithm to calculate the good solution for this problem. For multi-stage hybrid flow shop problems, recently, Jiang and Zhang [12] investigated an energy-oriented scheduling problem deriving from the hybrid flow shop with limited buffers.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Programming Algorithms for Two-Machine Hybrid Flow-Shop Scheduling With a Given Job Sequence and Deadline

Wei

2020

IEEE Access

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In ''Shared Manufacturing'' environment, orders are processed in a given job sequence which is based on the time of receipt of orders. This paper studies a problem of scheduling two-task jobs in a two-machine hybrid flow-shop subject to a given job sequence which is used in production of electronic circuits under shared manufacturing. Each job has two tasks: the first one is a flexible task, which can be processed on either of the two machines, and the second one is a preassigned task, which can only be processed on the second machine after the first task is finished. Each job has a processing deadline. Three objective functions related to deadlines are considered. The computational complexity of the problem for any of three objective functions is showed to be ordinary NP-hard, a dynamic programming algorithm (DPA) is presented for each case and the time complexity of each algorithm is given. The results of computational experiments show the relationship between the running time of DPA and the parameters, and also show the advantages of DPA in dealing with this problem compared with branch-and-bound algorithm and iterated greedy algorithm. INDEX TERMS Shared manufacturing, hybrid flow shop, dynamic programming algorithm, computational complexity.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamic Programming Algorithms for Two-Machine Hybrid Flow-Shop Scheduling With a Given Job Sequence and Deadline

Wei

2020

IEEE Access

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“…The task processing times are assumed to be uncertain with unavailable probability distribution and membership functions due to some stochastic variability (Feng, Zheng, and Xu 2016). The only information is that processing time of task i by robot k lies in an interval [t ik…”

Section: Approaches To Deal With Uncertain Task Processing Timesmentioning

confidence: 99%

Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time

Fang

Ming

et al. 2019

International Journal of Production Research

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Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

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“…For the single machine scheduling problems with uncertain processing time, Allahverdi et al [42] addressed some heuristics based on the polynomial time using the values of the upper and lower bounds of processing time. Feng et al [43] designed a min-max-regret scheduling model to reflect the uncertain processing time, in which a worstcase scenario and heuristic methods are proposed to solve the above problem. One common idea in the above literature is that the upper and lower bounds of the interval processing time are taken advantage of so as to convert the interval values into deterministic values.…”

Section: Literature Review a Flow Shop And Job Shop Scheduling Pmentioning

confidence: 99%

Multi-Objective Migrating Birds Optimization Algorithm for Stochastic Lot-Streaming Flow Shop Scheduling With Blocking

Han

Gong

et al. 2019

IEEE Access

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Blocking lot-streaming flow shop scheduling problem with the stochastic processing time has a wide range of applications in various industrial systems. However, this problem has not yet been well studied. In this paper, the above-mentioned problem is transformed into a determinate multi-objective optimization one using the Monte Carlo sampling method. A Multi-Objective Migrating Birds Optimization (MOMBO) algorithm is then proposed to solve the above-mentioned re-formulated multi-objective scheduling problem, in which the multiple-based PFE is proposed to yield the initial solutions with high quality, the information of the non-dominated solutions is learned and sampled to improve the global searching ability of MOMBO, and a reference-point-assisted local search method for multi-objective optimization is applied to further enhance the exploitation capability of MOMBO. To evaluate the performance of the MOMBO, several comparative experiments are executed on 180 test scheduling instances. The experimental results demonstrate that the MOMBO outperforms the compared algorithms in convergence and distributivity and has capacities to tackle the uncertainties. INDEX TERMS Scheduling, multi-objective, blocking lot-streaming flow shop, stochastic processing time, migrating birds optimization.

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Robust scheduling of a two-stage hybrid flow shop with uncertain interval processing times

Cited by 51 publications

References 25 publications

Dynamic Programming Algorithms for Two-Machine Hybrid Flow-Shop Scheduling With a Given Job Sequence and Deadline

Dynamic Programming Algorithms for Two-Machine Hybrid Flow-Shop Scheduling With a Given Job Sequence and Deadline

Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time

Multi-Objective Migrating Birds Optimization Algorithm for Stochastic Lot-Streaming Flow Shop Scheduling With Blocking

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