Flexible job shop scheduling under condition-based maintenance: Improved version of imperialist competitive algorithm

Zandieh, M.; Khatami, Amin; Rahmati, Seyed Habib A.

doi:10.1016/j.asoc.2017.04.060

Cited by 124 publications

(38 citation statements)

References 44 publications

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“…Zandieh et al (2017) [61] proposed an improved imperialist competitive algorithm (ICA) for the FJSP scheduling problem, enhancing the performance with a hybridization of ICA with SA. In order to use manufacturing resources of job shops more effectively in satisfying customers, Güçdemir et al (2017) [62] proposed a simulated annealing based simulation optimization approach.…”

Section: Local Search Methodsmentioning

confidence: 99%

Review of job shop scheduling research and its new perspectives under Industry 4.0

et al. 2017

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Traditional job shop scheduling is concentrated on centralized scheduling or semi-distributed scheduling. Under the Industry 4.0, the scheduling should deal with a smart and distributed manufacturing system supported by novel and emerging manufacturing technologies such as mass customization, Cyber-Physics Systems (CPS), Digital Twin, and SMAC (Social, Mobile, Analytics, Cloud). The scheduling research needs to shift its focus to smart distributed scheduling modeling and optimization. In order to transferring traditional scheduling into smart distributed scheduling (SDS), we aim to answer two questions: (1) what traditional scheduling methods and techniques can be combined and reused in SDS and (2) what are new methods and techniques required for SDS. In this paper, we first review existing researches from over 120 papers and answer the first question and then we explore a future research direction in SDS and discuss the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.

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Section: Local Search Methodsmentioning

confidence: 99%

Review of job shop scheduling research and its new perspectives under Industry 4.0

et al. 2017

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“…(Mokhtari and Hasani 2017), and production scheduling with different orders (Liao and Wang 2018), or on CBM for solving various job-shop scheduling problems cf. (Shamsaei and Van Vyve 2017), (Zandieh, Khatami, and Rahmati 2017) and (Rahmati, Ahmadi, and Karimi 2018)). A few models address the link between PPC and predictive maintenance (e.g.…”

Section: Review Of Related Kbm Approachesmentioning

confidence: 99%

PriMa: a prescriptive maintenance model for cyber-physical production systems

Ansari

Grepl

Nemeth

2019

International Journal of Computer Integrated Manufacturing

142

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Cyber-physical production systems (CPPS), as an emerging Industry 4.0's technology, trigger a paradigm shift from descriptive to prescriptive maintenance. In particular, maintenance management approaches nowadays are more and more transformed to (semi-) automated knowledge-based decision support systems. This paper is intended to examine existing approaches and challenges towards rethinking maintenance in the context of Industry 4.0 and thus contributes to the literature of production management and planning, by introducing a novel prescriptive maintenance model (PriMa). PriMa is comprising of four layers (i.e. data management, predictive data analytic toolbox, recommender and decision support dashboard as well as an overarching layer for semantic-based learning and reasoning). The integrated approach of PriMa enhances two functional capabilities, namely i) efficiently processing large amount of multi-modal and heterogeneous data collected from multidimensional data sources and ii) effectively generating decision support measures and recommendations for improving and optimising forthcoming maintenance plans correlated with production planning and control (PPC) systems. An industry-oriented proof-of-concept study has been conducted to explore the feasibility of applying PriMa in real production systems by implementing a decision support solution and achieving a significant reduction of downtime. Finally, future research directions in this area are outlined. ARTICLE HISTORY

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“…Shen et al [24] designed a tabu search algorithm with a novel neighborhood structure for solving the FJSP with sequence-dependent setup times. Zandieh et al [25] presented a modified imperialist competitive algorithm for solving the FJSP with condition-based maintenance to minimize the makespan. For the FJSP, Nouiri et al [26] designed a modified particle swarm optimization (PSO) algorithm to minimize the makespan.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm

Luan

Cai

et al. 2019

Mathematics

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The flexible job shop scheduling problem (FJSP) is a difficult discrete combinatorial optimization problem, which has been widely studied due to its theoretical and practical significance. However, previous researchers mostly emphasized on the production efficiency criteria such as completion time, workload, flow time, etc. Recently, with considerations of sustainable development, low-carbon scheduling problems have received more and more attention. In this paper, a low-carbon FJSP model is proposed to minimize the sum of completion time cost and energy consumption cost in the workshop. A new bio-inspired metaheuristic algorithm called discrete whale optimization algorithm (DWOA) is developed to solve the problem efficiently. In the proposed DWOA, an innovative encoding mechanism is employed to represent two sub-problems: Machine assignment and job sequencing. Then, a hybrid variable neighborhood search method is adapted to generate a high quality and diverse population. According to the discrete characteristics of the problem, the modified updating approaches based on the crossover operator are applied to replace the original updating method in the exploration and exploitation phase. Simultaneously, in order to balance the ability of exploration and exploitation in the process of evolution, six adjustment curves of a are used to adjust the transition between exploration and exploitation of the algorithm. Finally, some well-known benchmark instances are tested to verify the effectiveness of the proposed algorithms for the low-carbon FJSP.

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Flexible job shop scheduling under condition-based maintenance: Improved version of imperialist competitive algorithm

Cited by 124 publications

References 44 publications

Review of job shop scheduling research and its new perspectives under Industry 4.0

Review of job shop scheduling research and its new perspectives under Industry 4.0

PriMa: a prescriptive maintenance model for cyber-physical production systems

Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm

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