A Hybrid Cooperative Coevolution Algorithm for Fuzzy Flexible Job Shop Scheduling

Sun, Lu; Lin, Lin; Gen, Mitsuo; Li, Haojie

doi:10.1109/tfuzz.2019.2895562

Cited by 94 publications

(30 citation statements)

References 54 publications

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“…Considering the real situation, uncertainty (e.g. fuzzy processing time and fuzzy due date time) has also been introduced and made the FJSS more difficult in which the objectives are fuzzy makespan (Sun et al, 2019;, fuzzy tardiness (Niroomand et al, 2016), etc. There are two main kinds of models to describe the uncertain number (Sun et al, 2019): fuzzy interval and fuzzy number.…”

Section: Different Objectives In Fjssmentioning

confidence: 99%

“…fuzzy processing time and fuzzy due date time) has also been introduced and made the FJSS more difficult in which the objectives are fuzzy makespan (Sun et al, 2019;, fuzzy tardiness (Niroomand et al, 2016), etc. There are two main kinds of models to describe the uncertain number (Sun et al, 2019): fuzzy interval and fuzzy number. This uncertainty has extended the traditional FJSS to fuzzy FJSS in which the operators of optimisation algorithms should be improved (Lin, 2019).…”

Section: Different Objectives In Fjssmentioning

confidence: 99%

See 1 more Smart Citation

Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling

Mei

Wang

et al. 2021

Evolutionary Computation

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Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and can have multiple conflicting objectives. Genetic Programming Hyper-Heuristic (GPHH) is a promising approach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithm evolves dispatching rules (DRs) that are used to make decisions during the scheduling process (i.e. the so-called heuristic template). In DFJSS, there are two kinds of scheduling decisions: the routing decision that allocates each operation to a machine to process it, and the sequencing decision that selects the next job to be processed by each idle machine. The traditional heuristic template makes both routing and sequencing decisions in a non-delay manner, which may have limitations in handling the dynamic environment. In this paper, we propose a novel heuristic template that delays the routing decisions rather than making them immediately. This way, all the decisions can be made under the latest and more accurate information. We propose three different delayed routing strategies, and automatically evolve the rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with Delayed Routing (GPHH-DR) on a multi-objective DFJSS that optimises the energy efficiency and mean tardiness. The experimental results show that GPHH-DR significantly outperformed the state-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristic template with delayed routing, which suggests the importance of delaying the routing decisions.

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Section: Different Objectives In Fjssmentioning

confidence: 99%

Section: Different Objectives In Fjssmentioning

confidence: 99%

Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling

Mei

Wang

et al. 2021

Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…(iii) Fuzzification and defuzzification operations. On the one hand, the fuzzification operation, according to the literature [26], is defined as…”

Section: Definition 3 Fuzzy Data Transmission Timementioning

confidence: 99%

“…In response to the uncertainty, the fuzzy theory has emerged as an effective tool [25]. Sun et al [26] and Lei [27] fuzzified the processing time, completion time, and deadline, and then, the job scheduling method was studied under specific constraints. Based on the analytic hierarchy process (AHP) model, a data placement strategy was proposed in [28] to select the most suitable storage sites, which applied the fuzzy comprehensive evaluation to candidate data centers for different users.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Theory-Based Data Placement for Scientific Workflows in Hybrid Cloud Environments

Chen

Zhao

Lin

2020

Discrete Dynamics in Nature and Society

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In hybrid cloud environments, reasonable data placement strategies are critical to the efficient execution of scientific workflows. Due to various loads, bandwidth fluctuations, and network congestions between different data centers as well as the dynamics of hybrid cloud environments, the data transmission time is uncertain. Thus, it poses huge challenges to the efficient data placement for scientific workflows. However, most of the traditional solutions for data placement focus on deterministic cloud environments, which lead to the excessive data transmission time of scientific workflows. To address this problem, we propose an adaptive discrete particle swarm optimization algorithm based on the fuzzy theory and genetic algorithm operators (DPSO-FGA) to minimize the fuzzy data transmission time of scientific workflows. The DPSO-FGA can rationally place the scientific workflow data while meeting the requirements of data privacy and the capacity limitations of data centers. Simulation results show that the DPSO-FGA can effectively reduce the fuzzy data transmission time of scientific workflows in hybrid cloud environments.

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“…In remanufacturing environment, rescheduling is necessary after machine recovery to reduce makespan. Instability is a key metric to measure rescheduling solutions' quality [8], [12]. To solve the concerned problem, Jaya is improved by using our proposed local search strategies.…”

Section: Introductionmentioning

confidence: 99%

Improved Jaya Algorithm for Flexible Job Shop Rescheduling Problem

Gao

Yang

et al. 2020

IEEE Access

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Machine recovery is met from time to time in real-life production. Rescheduling is often a necessary procedure to cope with it. Its instability gauges the number of changes to the existing scheduling solutions. It is a key criterion to measure a rescheduling solution's quality. This work aims at solving a flexible job shop problem with machine recovery, which arises from the scheduling and rescheduling of pump remanufacturing systems. In their scheduling phase, the objective is to minimize makespan. In their rescheduling phase, two objectives are to minimize both instability and makespan. By introducing two novel local search operators into the original Jaya algorithm, this work proposes an improved Jaya algorithm to solve it. It performs experiments on ten different-scale cases of real-life remanufacturing environment. The results show that the improved Jaya is effective and efficient for solving a flexible job shop scheduling and rescheduling problems. It can effectively balance instability and makespan in a rescheduling phase. INDEX TERMS Jaya algorithm, flexible job shop scheduling, machine recovery, remanufacturing, scheduling and rescheduling.

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A Hybrid Cooperative Coevolution Algorithm for Fuzzy Flexible Job Shop Scheduling

Cited by 94 publications

References 54 publications

Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling

Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling

Fuzzy Theory-Based Data Placement for Scientific Workflows in Hybrid Cloud Environments

Improved Jaya Algorithm for Flexible Job Shop Rescheduling Problem

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