A genetic algorithm for permutation flowshop scheduling under practical make-to-order production system

2016 ◽  
Vol 31 (1) ◽  
pp. 87-103 ◽  
Author(s):  
Humyun Fuad Rahman ◽  
Ruhul Sarker ◽  
Daryl Essam
Keyword(s):  
Real Time ◽  
Production System ◽  
Production Scheduling ◽  
Real Life ◽  
Real Data ◽  
Production Environment ◽  
Permutation Flowshop ◽  
Make To Order ◽  
Wide Range ◽  
Range Of Variation

AbstractThe aim of this work is to bridge the gap between the theory and actual practice of production scheduling by studying a problem from a real-life production environment. This paper considers a practical Sanitaryware production system as a number of make-to-order permutation flowshop problems. Due to the wide range of variation in its products, real-time arrival of customer orders, dynamic batch adjustments, and time for machine setup, Sanitaryware production system is complex and also time sensitive. In practice, many such companies run with suboptimal solutions. To tackle this problem, in this paper, a memetic algorithm based real-time approach has been proposed. Numerical experiments based on real data are also been presented in this paper.

scholarly journals ANALISA PERBANDINGAN PENJADWALAN PRODUKSI DENGAN METODE CDS (CAMPBELL DUDEK SMITH), EDD (EARLIEST DUE DATE) DAN FCFS (FIRST COME FIRST SERVE) PADA PT. SARI WARNA ASLI UNIT V

2021 ◽  
Vol 1 (2) ◽  
pp. 46-51
Author(s):  
Dwi Ayu Lestari, Vikha Indira Asri
Keyword(s):  
Production Process ◽  
Production System ◽  
Production Scheduling ◽  
Job Shop ◽  
Allocation Of Resources ◽  
Due Date ◽  
Scheduling System ◽  
Make To Order ◽  
First Come First Serve ◽  
Scheduling Method

Scheduling is defined as the process of sequencing the manufacture of a product as a whole on several machines. All industries need proper scheduling to manage the allocation of resources so that the production system can run quickly and precisely as of it can produce optimal product. PT. Sari Warna Asli Unit V is one of the companies that implements a make to order production system with the FCFS system. Thus, scheduling the production process at this company is also known as job shop production scheduling. The methods used in this research are the CDS method, the EDD method and the FCFS method. The purpose of this research is to minimize the production time and determine the best method that can be applied to the company. The results of this research showed that the makespan obtained in the company's scheduling system with FCFS rules was 458 minutes, and the results of scheduling using the CDS method obtained a makespan value of 329 minutes, then the best production scheduling method that had the smallest makespan value was the CDS method.

Multi-armed bandits for bid shading in first-price real-time bidding auctions

2021 ◽  
pp. 1-15
Author(s):  
Tuomo Tilli ◽  
Leonardo Espinosa-Leal
Keyword(s):  
Real Time ◽  
Economic Benefit ◽  
Real Data ◽  
Demand Side ◽  
Production Environment ◽  
Online Advertisements ◽  
Ad Auctions ◽  
Real Scenario ◽  
Second Price Auctions ◽  
Naive Approach

Online advertisements are bought through a mechanism called real-time bidding (RTB). In RTB, the ads are auctioned in real-time on every webpage load. The ad auctions can be of two types: second-price or first-price auctions. In second-price auctions, the bidder with the highest bid wins the auction, but they only pay the second-highest bid. This paper focuses on first-price auctions, where the buyer pays the amount that they bid. This research evaluates how multi-armed bandit strategies optimize the bid size in a commercial demand-side platform (DSP) that buys inventory through ad exchanges. First, we analyze seven multi-armed bandit algorithms on two different offline real datasets gathered from real second-price auctions. Then, we test and compare the performance of three algorithms in a production environment. Our results show that real data from second-price auctions can be used successfully to model first-price auctions. Moreover, we found that the trained multi-armed bandit algorithms reduce the bidding costs considerably compared to the baseline (naïve approach) on average 29%and optimize the whole budget by slightly reducing the win rate (on average 7.7%). Our findings, tested in a real scenario, show a clear and substantial economic benefit for ad buyers using DSPs.

scholarly journals An Agent-Based System for Automated Configuration and Coordination of Robotic Operations in Real Time—A Case Study on a Car Floor Welding Process

2020 ◽  
Vol 4 (3) ◽  
pp. 95
Author(s):  
Sotiris Makris ◽  
Kosmas Alexopoulos ◽  
George Michalos ◽  
Andreas Sardelis
Keyword(s):  
Real Time ◽  
Welding Process ◽  
Real Data ◽  
Robotic Assembly ◽  
Production Environment ◽  
Agent Based ◽  
Manufacturing Operations ◽  
Assembly Cell ◽  
Production Automation

This paper investigates the feasibility of using an agent-based framework to configure, control and coordinate dynamic, real-time robotic operations with the use of ontology manufacturing principles. Production automation agents use ontology models that represent the knowledge in a manufacturing environment for control and configuration purposes. The ontological representation of the production environment is discussed. Using this framework, the manufacturing resources are capable of autonomously embedding themselves into the existing manufacturing enterprise with minimal human intervention, while, at the same time, the coordination of manufacturing operations is achieved without extensive human involvement. The specific framework was implemented, tested and validated in a feasibility study upon a laboratory robotic assembly cell with typical industrial components, using real data derived from a car-floor welding process.

Robust Production Scheduling Using Autonomous Distributed Systems

2012 ◽  
Vol 516 ◽  
pp. 166-169
Author(s):  
Shunsuke Oike ◽  
Tomohisa Tanaka ◽  
Jiang Zhu ◽  
Yoshio Saito
Keyword(s):  
Distributed Systems ◽  
Real Time ◽  
Production System ◽  
Production Scheduling ◽  
Parallel Scheduling ◽  
Real Time Scheduling ◽  
Time Scheduling ◽  
Method Of Production ◽  
Structure Of Production

This research proposes a method of production scheduling using autonomous distributed systems. A concrete message protocol is proposed to realize the production scheduling which includes not only Machine but also Human and AGV scheduling. Moreover this method realizes real time scheduling and parallel scheduling. Therefore, a new structure of production scheduling is proposed, which can realize a change of the type of production scheduler to correspond with a type of production system.

scholarly journals Description and clinical validation of a real-time AI diagnostic companion for fetal ultrasound examination

2021 ◽  
Author(s):  
Julien Stirnemann ◽  
Remi Besson ◽  
Emmanuel Spaggiari ◽  
Sandra Rojo ◽  
Frederic Loge ◽  
...  
Keyword(s):  
Success Rate ◽  
Real Time ◽  
Ultrasound Examination ◽  
Correct Diagnosis ◽  
Real Life ◽  
Real Data ◽  
Fetal Ultrasound ◽  
Postmortem Diagnosis ◽  
Knowledge Based ◽  
Phenotype Model

Objective: To describe a real-time decision support system (DSS), named SONIO, to assist ultrasound-based prenatal diagnosis and to assess its performance using a clinical database of precisely phenotyped postmortem examinations. Population and Methods: This DSS is knowledge-based and comprises a dedicated thesaurus of 294 syndromes and diseases. It operates by suggesting, at each step of the ultrasound examination, the best next symptom to check for in order to optimize the diagnostic pathway to the smallest number of possible diagnoses. This assistant was tested on a single-center database of 251 cases of postmortem phenotypes with a definite diagnosis. Adjudication of discordant diagnoses was made by a panel of external experts. The primary outcome was a target concordance rate >90% between the postmortem diagnosis and the top-7 diagnoses given by SONIO when providing the full phenotype as input. Secondary outcomes included concordance for the top-5 and top-3 diagnoses; We also assessed a '1-by-1' model, providing only the anomalies sequentially prompted by the system, mimicking the use of the software in a real-life clinical setting. Results: The validation database covered 96 of the 294 (32.65%) syndromes and 79% of their overall prevalence in the SONIO thesaurus. The adjudicators discarded 42/251 cases as they were not amenable to ultrasound based diagnosis. SONIO failed to make the diagnosis on 7/209 cases. On average, each case displayed 6 anomalies, 3 of which were considered atypical for the condition. Using the 'full-phenotype' model, the success rate of the top-7 output of Sonio was 96.7% (202/209). This was 91.9% and 87.1% for the top-5 and top-3 outputs respectively. Using the '1-by-1' model, the correct diagnosis was within the top-7, top-5 and top-3 of SONIO's output in 72.4%, 69.3% and 63.1%. Conclusion: Sonio is a robust DSS with a success-rate >95% for top-7 ranking diagnoses when the full phenotype is provided, using a large database of noisy real data. The success rate over 70% using the '1-by-1' model was understandably lower, given that SONIO's sequential queries may not systematically cover the full phenotype.

scholarly journals An Internet of Things based framework to enhance just-in-time manufacturing

2017 ◽  
Vol 232 (13) ◽  
pp. 2353-2363 ◽  
Author(s):  
Yuchun Xu ◽  
Mu Chen
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Production Scheduling ◽  
Just In Time ◽  
Manufacturing Strategy ◽  
Production Environment ◽  
Planning And Scheduling ◽  
Parts Manufacturing ◽  
Real Time Information ◽  
Internet Of Things Technology

Just-in-time manufacturing is a main manufacturing strategy used to enhance manufacturers’ competitiveness through inventory and lead time reduction. Implementing just-in-time manufacturing has a number of challenges, for example, effective, frequent and real-time information sharing and communication between different functional departments, responsive action for adjusting the production plan against the continually changing manufacturing situation. Internet of Things technology has the potential to be used for capturing desired data and information from production environment in real time, and the collected data and information can be used for adjusting production schedules corresponding to the changing production environment. This article presents an Internet of Things based framework to support responsive production planning and scheduling in just-in-time manufacturing. The challenges of implementing just-in-time manufacturing are identified first and then an Internet of Things based solution is proposed to address these challenges. A framework to realise the proposed Internet of Things solution is developed and its implementation plan is suggested based on a case study on automotive harness parts manufacturing. This research contributes knowledge to the field of just-in-time manufacturing by incorporating the Internet-of-Things technology to improve the connectivity of production chains and responsive production scheduling capability.

Multi-Channel Source Separation

2010 ◽  
pp. 207-245
Author(s):  
Nilesh Madhu ◽  
André Gückel
Keyword(s):  
Real Time ◽  
Source Separation ◽  
Real Life ◽  
Wide Scope ◽  
Computational Power ◽  
Challenging Problem ◽  
Signal Model ◽  
Related Field ◽  
Wide Range ◽  
Target Speaker

Machine-based multi-channel source separation in real life situations is a challenging problem, and has a wide range of applications, from medical to military. With the increase in computational power available to everyday devices, source separation in real-time has become more feasible, contributing to the boost in the research in this field in the recent past. Algorithms for source separation are based on specific assumptions regarding the source and signal model – which depends upon the application. In this chapter, the specific application considered is that of a target speaker enhancement in the presence of competing speakers and background noise. It is the aim of this contribution to present not only an exhaustive overview of state-of-the-art separation algorithms and the specific models they are based upon, but also to highlight the relations between these algorithms, where possible. Given this wide scope of the chapter, we expect it will benefit both, the student beginning his studies in the field of machine audition, and those already working in a related field and wishing to obtain an overview or insights into the field of multi-channel source separation.

scholarly journals Methods for the Identification of Microclimates for Olive Fruit Fly

Agronomy ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 337
Author(s):  
Romanos Kalamatianos ◽  
Ioannis Karydis ◽  
Markos Avlonitis
Keyword(s):  
Life Cycle ◽  
Real Life ◽  
Qualitative Evaluation ◽  
Real Data ◽  
Olive Tree ◽  
Fruit Fly ◽  
Olive Fruit Fly ◽  
Climate Conditions ◽  
Olive Fruit ◽  
Wide Range

The support and development of the primary agri-food sector is receiving increasing attention. The complexity of modern farming issues has lead to the widespread penetration of Integrated Pest Management (IPM) Decision Support Systems (DSS). IPM DSSs are heavily dependent on numerous conditions of the agro-ecological environment used for cultivation. To test and validate IPM DSSs, permanent crops, such as olive cultivation, are very important, thus this work focuses on the pest that is most potentially harmful to the olive tree and fruit: the olive fruit fly. Existing research has indicated a strong dependency on both temperature and relative humidity of the olive fruit fly’s population dynamics but has not focused on the localised environmental/climate conditions (microclimates) related to the pest’s life-cycle. Accordingly, herein we utilise a collection of a wide-range of integrated sensory and manually tagged datasets of environmental, climate and pest information. We then propose an effective and efficient two-stage assignment of sensory records into clusters representing microclimates related to the pest’s life-cycle, based on statistical data analysis and neural networks. Extensive experimentation using the two methods was applied and the results were very promising for both parts of the proposed methodology. The identified microclimates in the experimentation were shown to be consistent with intuitive and real data collected in the field, while their qualitative evaluation also indicates the applicability of the proposed method to real-life uses.

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