A preference-inspired multi-objective soft scheduling algorithm for the practical steelmaking-continuous casting production

Jiang, Sheng-Long; Zheng, Zhong; Liu, Min

doi:10.1016/j.cie.2017.10.028

Cited by 25 publications

(17 citation statements)

References 23 publications

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“…Regarding the meta-heuristics, Atighehchian,et al [27] combined ant colony optimization and iterative algorithm. Considering the controllable processing times at the last stage, Jiang, et al [5] presented an improved differential evolution algorithm. To solve the SCC scheduling problem with dynamic operation and skipping features, Li and Pan [28] proposed an improved discrete artificial bee colony (DABC) algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For the remaining constraints, constraints (4) and (5) mean that a machine at any stage can operate at most one charge and a charge must be processed exactly once at any stage at each event point. Constraint (6) implies that each batch must be allocated to exactly one caster at the last stage and all charges belonging to a batch must be processed sequentially on the specified machine based on the predetermined order without interruption.…”

Section: S Umentioning

confidence: 99%

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An Improved Migrating Birds Optimization Algorithm for a Hybrid Flow Shop Scheduling within Steel Plants

et al. 2020

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Steelmaking and the continuous-casting (SCC) scheduling problem is a realistic hybrid flow shop scheduling problem with continuous-casting production at the last stage. This study considers the SCC scheduling problem with diverse products, which is a vital and difficult problem in steel plants. To tackle this problem, this study first presents the mixed-integer linear programming (MILP) model to minimize the objective of makespan. Then, an improved migrating birds optimization algorithm (IMBO) is proposed to tackle this considered NP-hard problem. In the proposed IMBO, several improvements are employed to achieve the proper balance between exploration and exploitation. Specifically, a two-level decoding procedure is designed to achieve feasible solutions; the simulated annealing-based acceptance criterion is employed to ensure the diversity of the population and help the algorithm to escape from being trapped in local optima; a competitive mechanism is developed to emphasize exploitation capacity by searching around the most promising solution space. The computational experiments demonstrate that the proposed IMBO obtains competing performance and it outperforms seven other implemented algorithms in the comparative study.

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Section: Literature Reviewmentioning

confidence: 99%

Section: S Umentioning

confidence: 99%

An Improved Migrating Birds Optimization Algorithm for a Hybrid Flow Shop Scheduling within Steel Plants

et al. 2020

View full text Add to dashboard Cite

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“…It has been proved that manufacturing scheduling problems, essentially belonging to Job shop scheduling problems (JSP), are a class of NP-hard problems [ 59 ]. Many models and algorithms have been proposed to obtain a suboptimal solution, such as PICRO (preference-inspired chemical reaction optimization algorithm) [ 60 ], HPGA (hybrid PSO-GA algorithm) [ 61 ], PRGA-Sched (priority rule-based genetic algorithm scheduling) [ 62 ], LCAFS (league championship algorithm with free search) [ 63 ], MOGA-TIG (multi-objective genetic algorithm with tabu-enhanced iterated greedy local search strategy) [ 64 ], and so on. Though an approximate optimal production plan can be effectively solved by these methods, the availability and reliability of sensor nodes is seldom followed closely.…”

Section: Related Workmentioning

confidence: 99%

“…Since the computation complexity of J-EPMS grows exponentially, we proposed the double chains quantum genetic algorithm with Taboo search (DCQGA-TS) to obtain a suboptimal solution. Some studies have proved that genetic algorithms can effectively tackle these kinds of NP-hard problems [ 60 ]. Other studies have shown that the quantum genetic algorithm can more effectively and rapidly find a suboptimal solution, compared with the traditional genetic algorithm [ 72 ].…”

Section: Joint Energy Provisioning and Manufacturing Scheduling (Jmentioning

confidence: 99%

A Framework of Joint Energy Provisioning and Manufacturing Scheduling in Smart Industrial Wireless Rechargeable Sensor Networks

Feng

Wang

Zheng

et al. 2018

Sensors

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Energy provisioning is always a crucial problem restricting the further development and application of smart industrial wireless sensor networks in smart factories. In this paper, we present that it is necessary to develop smart industrial wireless rechargeable sensor networks (SIWRSNs) in a smart factory environment. Based on the complexity and time-effectiveness of factory operations, we establish a joint optimization framework named J-EPMS to effectively coordinate the charging strategies of wireless sensors and the scheduling plans of machines running. Then, we propose a novel double chains quantum genetic algorithm with Taboo search (DCQGA-TS) for J-EPMS to obtain a suboptimal solution. The simulation results demonstrate that the DCQGA-TS algorithm can maximally ensure the continuous manufacturing and markedly shorten the total completion time of all production tasks.

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“…Some of the studies have some special restrictions, such as that the assignment of the machine for a certain heat is unique. 70)…”

mentioning

confidence: 99%

Scheduling in Continuous Steelmaking Casting: A Systematic Review

et al. 2020

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The converter furnace and the continuous casting are the most important stages in steel production. Modern integrated processes directly connect the converter furnaces and the continuous casting machines with a flow of molten metal, and the steel is manufactured synchronously between the various machines. Starting from the traditional handmade programs for the management of these processes, during the last two decades there has been a notable increase in studies and publications in this field, trying to formulate scheduling in a rational and automatic way. The process has been approached for years through the development of both physical and operational research models, many of them theoretically. The main purpose of this study is to present a critical and in-depth evaluation of the previous studies, so that the state of the art of scheduling in steelworks can be evaluated. An approach based on a Systematic Literature Review (SLR) has been used, trying to search, evaluate, synthesize and analyze all the relevant studies for this specific field. As a result, the conclusions of the various analyses are presented and a study route is proposed for the design of the optimal planning methodology in the steelworks.

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A preference-inspired multi-objective soft scheduling algorithm for the practical steelmaking-continuous casting production

Cited by 25 publications

References 23 publications

An Improved Migrating Birds Optimization Algorithm for a Hybrid Flow Shop Scheduling within Steel Plants

An Improved Migrating Birds Optimization Algorithm for a Hybrid Flow Shop Scheduling within Steel Plants

A Framework of Joint Energy Provisioning and Manufacturing Scheduling in Smart Industrial Wireless Rechargeable Sensor Networks

Scheduling in Continuous Steelmaking Casting: A Systematic Review

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