Prediksi Keandalan Sistem Pendingin Berdasarkan Kerusakan Sistem Dengan Menggunakan Distribusi Probabilitas Poisson

Operasi suatu unit/sistem yang tidak disertai dengan pemeliharaan yang baik akan berakibat pada turunnya reliability unit/sistem dimaksud. Jika failure (kegagalan fungsi) unit/sistem dimaksud dapat diprediksi dengan baik maka pemeliharaan akan dapat direncanakan dengan baik pula. Prediksi failure sangat berguna bagi diterapkannya Preventive Maintenance (PM) atau Conditional Based Maintenance (CBM). Prediksi failure didasarkan pada ketersediaan data historis failure suatu unit/sistem yang cukup. Metode yang digunakan untuk memprediksi failure adalah Random Poisson. Rata-rata hasil simulasi pembangkitan pola failure menggunakan random Poisson digunakan sebagai output prediksi kejadian failure di periode berikutnya. Hasil prediksi yang diperoleh akan menjadi dasar untuk memperkirakan reliability sistem di periode dimaksud

Constraint Programming and Local Search Heuristic: A Matheuristic Approach for Routing and Scheduling Feeder Vessels in Multi-Terminal Ports

International audienceIn the liner shipping business, shipping ports represent the main nodes in the maritime transportation network. These ports have a collection of terminals where container vessels can load and discharge containers. However, the logistics and planning of operations differ depending on the vessel size. Large container vessels visit a single terminal, whereas smaller container vessels, or feeder vessels, visit several terminals to transport all containers within the multiple terminals of the port. In this paper, we study the Port Scheduling Problem, the problem of scheduling the operations of feeder vessels in multi-terminal ports. The resulting problem can be identified as a version of the General Shop Scheduling Problem. We consider a Constraint Programming formulation of the problem, and we propose a math-heuristic solution approach for solving large instances. The proposed math-heuristic is a hybrid solution method that combines Constraint Programming with a local search heuristic. The solution approach benefits from the fast search capabilities of local search heuristics to explore the solution space using an Adaptive Large Neighbourhood Search heuristic. During the search, we further use the Constraint Programming model as an intensification technique, every time a new best-known solution is found. We conduct detailed computational experiments on the PortLib instances, showing that the incorporation of Constraint Programming within the heuristic search can result in significant benefits. The high instability in solution quality obtained by local search heuristics can be lowered by a simple combination of both methods

Variant-oriented Planning Models for Parts/Products Grouping, Sequencing and Operations

This research aims at developing novel methods for utilizing the commonality between part/product variants to make modern manufacturing systems more flexible, adaptable, and agile for dealing with less volume per variant and minimizing total changes in the setup between variants. Four models are developed for use in four important domains of manufacturing systems: production sequencing, product family formation, production flow, and products operations sequences retrieval. In all these domains, capitalizing on commonality between the part/product variants has a pivotal role. For production sequencing; a new policy based on setup similarity between product variants is proposed and its results are compared with a developed mathematical model in a permutation flow shop. The results show the proposed algorithm is capable of finding solutions in less than 0.02 seconds with an average error of 1.2%. For product family formation; a novel operation flow based similarity coefficient is developed for variants having networked structures and integrated with two other similarity coefficients, operation and volume similarity, to provide a more comprehensive similarity coefficient. Grouping variants based on the proposed integrated similarity coefficient improves changeover time and utilization of the system. A sequencing method, as a secondary application of this approach, is also developed. For production flow; a new mixed integer programing (MIP) model is developed to assign operations of a family of product variants to candidate machines and also to select the best place for each machine among the candidate locations. The final sequence of performing operations for each variant having networked structures is also determined. The objective is to minimize the total backtracking distance leading to an improvement in total throughput of the system (7.79% in the case study of three engine blocks). For operations sequences retrieval; two mathematical models and an algorithm are developed to construct a master operation sequence from the information of the existing variants belonging to a family of parts/products. This master operation sequence is used to develop the operation sequences for new variants which are sufficiently similar to existing variants. Using the proposed algorithm decreases time of developing the operations sequences of new variants to the seconds

Makespan Minimization in Re-entrant Permutation Flow Shops

Re-entrant permutation flow shop problems occur in practical applications such as wafer manufacturing, paint shops, mold and die processes and textile industry. A re-entrant material flow means that the production jobs need to visit at least one working station multiple times. A comprehensive review gives an overview of the literature on re-entrant scheduling. The influence of missing operations received just little attention so far and splitting the jobs into sublots was not examined in re-entrant permutation flow shops before. The computational complexity of makespan minimization in re-entrant permutation flow shop problems requires heuristic solution approaches for large problem sizes. The problem provides promising structural properties for the application of a variable neighborhood search because of the repeated processing of jobs on several machines. Furthermore the different characteristics of lot streaming and their impact on the makespan of a schedule are examined in this thesis and the heuristic solution methods are adjusted to manage the problem’s extension

Sessenta anos de Shop Scheduling : uma revisão sistemática da literatura

Orientador : Prof. Dr. Cassius Tadeu ScarpinDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Produção. Defesa: Curitiba, 09/02/2017Inclui referências : f. 449-492Resumo: Desde o seminal artigo de Johnson em 1954, a Programação da Produção em Shop Scheduling tem se tornado uma área relevante dentro da Pesquisa Operacional e, atualmente, duzentos trabalhos tangentes à temática são publicados anualmente. Dentre os artigos aqui citados tem-se aqueles que se dedicam à apresentação e síntese do estado da arte desse assunto, intitulados artigos de revisão. Quando tais artigos são elaborados a partir de um conjunto objetivo de critérios, relativos à categorização dos artigos selecionados, tem-se a Revisão Sistemática da Literatura (RSL). O presente trabalho realiza uma RSL em Shop Scheduling, a partir da análise de cada ambiente fabril que o compõe. Fez-se o escrutínio de 560 artigos, à luz de um conjunto de métricas, que constitui a estrutura basilar da proposta de nova taxonomia do Shop Scheduling, complementar à notação de Graham, objetivo fulcral do presente trabalho. Além disso, utilizou-se uma representação em redes dos resultados obtidos em algumas das métricas empregadas, como a característica dos itens, algo outrora inaudito em estudos de revisão desse assunto. Ademais, outro ponto relevante desse estudo repousa na identificação de campos pouco explorados, de modo a colaborar com a pesquisa futura neste tomo. Palavras-chave: Shop Scheduling. Revisão Sistemática da Literatura. Taxonomia. Representação em Redes.Abstract: Since Johnson's seminal article in 1954, Shop Scheduling in Production Scheduling has become a relevant area within Operational Research, and currently hundreds of tangential works on the subject are published annually. Among the articles cited here are those dedicated to the presentation and synthesis of the state of the art of this subject, which are entitled review articles. When these articles are elaborated from an objective set of criteria, regarding the categorization of the selected articles, we have the Systematic Review of Literature (SLR). The present work performs a SLR in Shop Scheduling, based on the analysis of each manufacturing environment that composes it. There were 560 articles scrutinized based on a set of metrics, which is the basic structure of the proposed new Taxonomy of Shop Scheduling, complementary to Graham's notation, the main objective of this work. In addition to that a network representation of the results was obtained in some of the metrics used, such as the job characteristics, something previously unheard of in review studies of this subject. Moreover, another relevant point of this study lies in the identification of less explored fields in order to collaborate with future research in this matter. Keywords: Shop Scheduling. Systematic Literature Review. Taxonomy. Network Representation

Multi-Objective Hybrid Metaheuristic Search Algorithm for Distributed Reentrant Permutation Flow Shop Scheduling Via Considering Preventive Maintenance under Uncertainty

Abstract: Distributing the production activities among the supply chain facilities with regard to the considered criteria can have a significant impact on the productive management. In this paper, a comprehensive mathematical model for reentrant permutation flow shop scheduling via considering a preventive maintenance and distributed jobs on different facilities is proposed. The uncertainty of the time of preventive maintenance operation is handled using robust optimization technique based on the uncertainty budget approach. Job assignment to production facilities and job scheduling are determined in the proposed model by considering multiple objectives include Cmax minimization, production cost minimization, and average tardiness. Due to the NP-hard nature of the proposed flow shop scheduling problem, a new hybrid meta-heuristic based on the novel adaptive large neighborhood search and the simulated annealing is adopted. The obtained results from an extensive numerical experimentation indicate the efficiency of the proposed model and solution algorithm to tackle the proposed problem. Introduction: In certain manufacturing industries, it has been observed that the classical assumption of flow shop scheduling, stating that each job visits each machine exactly once, is occasionally violated. The prime example can be noticed in the high-tech industries, i.e. semiconductor wafer fabrication in which the operation processes of the jobs are performed by re-visiting some workstations (Gupta & Sivakumar, 2006). The scheduling problem of this nature of processing is categorized as a distinct flow shop with reentrant line configuration, called reentrant flow shop scheduling (RFS) (Katragjini et. al., 2015). The significance of RFS is the processing layers l. Each layer begins from the  first workstation and completes on the last workstation. It means that once a job finished a layer of a set of operations, it will repeat its process to the next layer starting on the first workstation until all operations are completed. The RFS scheduling has been an active research area and attracted a considerable attention since the past decade due to the development and improvement of high-tech industry. The complexity of RFS cannot be circumvented since it involves more operations than the classical flow shop. Moreover, the cyclic operations where the jobs with higher layers may overlap other jobs in the same work station are essential to be considered. As a result, these complexities have triggered the development of the efficient scheduling approaches to improve the system performance. Various researchers surveyed the scheduling techniques in semiconductor manufacturing and providing the global view on reentrant scheduling problems. Another form of RFS is reentrant permutation flow shop (RPFS) where at each level no passing is permitted, that is, not only the machine sequence the same for all jobs, but also the job sequence is the same for each machine (Rifai et al., 2016).    Despite the enormous literature on the RFS, most studies -if not all- base their research on the assumption that the process only involves a single production line. Some studies exploredthe problem on hybrid RFS where the production stages have more than one machines available to process the jobs. Nevertheless, hybrid RFS is based on the single production line. Nowadays, single factory firms are less common, with multi-plant companies and supply chains taking a more important role in practice. Several literatures mentioned that multiple production lines with more than one production center, named as distributed manufacturing system, enables companies to achieve higher product quality, lower production costs and lower management risks. However, existing studies focused more on the economic field anddistributed finite capacity scheduling is seldom tackled. Materials and Methods:In this section, a novel hybrid meta-heuristic via considering the specific assumptions of the flow shop problem as a NP-hard problem is proposed. The proposed solution algorithm incorporates adaptive large neighborhood search and the simulated annealing algorithms. Various new construction and deconstruction neighborhood structures are applied in the proposed adaptive large neighborhood search algorithm. Details of the proposed algorithm is presented in Fig.1. Results and Discussion: The results of the proposed solution algorithm assessment are presented based on the two common performance assessment criteria which are proposed in the literature after 10 times runs of the applied solution algorithms. These criteria are the average number of obtained Pareto solution at each iteration of  the algorithm and average number of Pareto solutions which are not dominated by solutions from  other compared algorithms. In addition, computational time is considered as a third criteria for performance assessment of the proposed solution algorithm (See Table 1). Obtained results indicate the superiority of the  proposed solution algorithm.       Fig. 1- Peseudo code of the proposed solution algorithm   Table 1- Performance assessment of the proposed solution algorithm Compared Alg. Proposed Alg. Pro. Compared Alg. Proposed Alg. Pro. 1 2 3 1 2 3 1 2 3 1 2 3 0.7270 39.30 72.01 0.4129 25.95 57.12 15 0.6120 18.71 3392.26 0.5114 13.80 3019.18 75 0.6991 34.43 180.23 0.5972 28.80 152.11 30 0.6523 18.01 5900.74 0.5304 11.20 5631.07 90 0.9009 48.23 479.19 0.6543 38.10 398.06 45 0.6914 15.23 6801.69 0.5713 11.83 6594.29 105 0.9207 47.3 1308.21 0.7810 36.30 1200.47 60                 Conclusion: In this study, a comprehensive optimization model for an extended reentrant permutation flow shop scheduling via considering a preventive maintenance and distributed jobs on different facilities is proposed. To enhance the applicability of the proposed model, uncertainty of the time of preventive maintenance operation is handled using robust optimization technique based on the uncertainty budget approach. In the proposed mathematical model, multiple objectives include Cmax minimization, production cost minimization, and average tardiness are considered. The aim of the proposed model is to determine the job assignment to production facilities and job scheduling. A new hybrid meta-heuristic based on the novel adaptive large neighborhood search and the simulated annealing is applied as a consequence of the NP-hard nature of the proposed flow shop scheduling problem,. The obtained results from an extensive numerical experimentation indicate the efficiency of the proposed model and solution algorithm to tackle the proposed problem.   References Gupta, A. K., & Sivakumar, A. I. (2006). “Job Shop Scheduling Techniques In Semiconductor Manufacturing”. Int. J. Adv. Manuf. Technol, 27(11), 1163-1169. Katragjini, K., Vallada, E., & Ruiz, R. (2015). “Rescheduling Flowshops Under Simultaneous Disruptions”. Paper presented at the 6th IESM Conference, Seville, Spain. Rifai, A. P., Nguyen, H. T., & Dawal, S. Z. M. (2016). “Multi-Objective Adaptive Large Neighborhood Search For Distributed Reentrant Permutation Flow Shop Scheduling”. Applied Soft Computing, 40(1), 42–57