Determination of Order Delivery Time in Event Organizer Industry Using a Non-Delay Scheduling Approach

2020 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Nur Indrianti ◽  
Vina Islamia Vervly Suandevin
Keyword(s):  
Production Process ◽  
Processing Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Service Industries ◽  
Delivery Time ◽  
Priority Rules ◽  
Balanced Distribution ◽  
Spt Rule

This study discusses the determination of order delivery time in the event organizer (EO) industry. With regard to the characteristics of the EO production process that is identical to the job shop production process in the manufacturing industry, a non-delay scheduling approach is applied. The non-delay schedule is compiled using the non-delay algorithm with the criteria for makespan minimization. Job assignment is done using the shortest processing time (SPT), longest processing time (LPT), and first comes first served (FCFS) priority rules. We consider the situation where all orders arrive simultaneously (offline) and at different time (online). As a case study, the modified non-delay algorithm is examined to solve the problem of an EO in Indonesia. The results of the study show that the nondelay algorithm using SPT rule provides the best schedule performance which results in the shortest makespan and the lowest resource idle time. In addition to determining the delivery time, the resulting non-delay schedule can be used to control the execution of each order. The method of determining order delivery time in this study can be applied to other service industries. Further study can be developed for situations where order arrival and processing time are probabilistic. Furthermore, it is also necessary to consider the balanced distribution of the workload among operators. Determination of Order Delivery Time in Event

scholarly journals USULAN PENJADWALAN PRODUKSI PRODUK ST 37777 PT EBAKO NUSANTARA PADA DEPARTEMEN SMOOTHMILLING UNTUK MEMINIMASI MAKESPAN

2018 ◽  
Vol 13 (1) ◽  
pp. 61
Author(s):  
Mellysa Asmawar
Keyword(s):  
Production Process ◽  
Processing Time ◽  
Manufacturing Industry ◽  
Job Scheduling ◽  
Makespan Minimization ◽  
Central Java ◽  
Short Processing Time ◽  
Jobshop Scheduling ◽  
Machine Work

AbstrakProses produksi ST 37777 di PT Ebako Nusantara menggunakan jadwal yang didasarkan oleh proses-proses yang dilakukan dengan menggunakan data historis yang telah ada dari proses produksi yang telah dilakukan. PT Ebako Nusantara merupakan industri manufaktur yang bergerak di bidang furnitur yang berlokasi di Terboyo, Semarang, Jawa Tengah. Dalam proses produksi ST 37777, terdapat 11 mesin dan 16 job dimana setiap job memiliki urutan mesin yang berbeda. Penjadwalan yang ada untuk produk tipe ST 37777 dengan tipe jobshop belum menerapkan suatu ketetapan dalam penentuan waktu dan urutan pengerjaan mesin yang efektif sehingga masih banyak job yang selesai terlambat. Untuk itu diperlukan suatu penjadwalan mesin yang efektif sehingga dapat memenuhi waktu produksi pesanan sesuai dengan yang telah disepakati. Penjadwalan jobshop diperlukan untuk memaksimumkan efisiensi dan utilitas sumber daya di lantai produksi. Penentuan jadwal mesin ini bertujuan meminimasi makespan dengan menggunakan Software WINQSB modul job schedulling. Metode yang digunakan adalah metode Short Processing Time. Hasil penjadwalan menggunakan Software WINQSB diperoleh makespan menjadi 15 jam dengan hasil penjadwalan tersebut tidak ada job yang terlambat dan semua job dikerjakan berurutan. AbstractThe production process of ST 37777 in PT Ebako Nusantara uses a schedule based on the processes performed using existing historical data from the production process that has been done. PT Ebako Nusantara is a manufacturing industry engaged in furnitur located in Terboyo, Semarang, Central Java. In the production process ST 37777, there are 11 machines and 16 jobs where each job has a different sequence of machines. The existing scheduling for ST 37777 type product with jobshop type has not been applied a determination in the timing and sequence of effective machine work so that many jobs are finished too late. For that required an effective engine scheduling so that it can meet the production time of orders in accordance with the agreed. Jobshop scheduling is needed to maximize efficiency and resource utilities on the production floor. Determination of this machine schedule aims to minimize the makespan using WINQSB Software job scheduling module. The method used is the method of Short Processing Time. The scheduling result using WINQSB software obtained makespan to 15 hours with scheduling result no job is late and all job done in sequence. Keywords: Jobshop Scheduling; Short Processing Time; Makespan Minimization

scholarly journals MANUFACTURING INDUSTRY JOB SHOP FLOOR DECISIONS THROUGH HYBRID GENETIC ALGORITHM

2021 ◽  
Vol 9 (2) ◽  
pp. 62-76
Author(s):  
Dr. Nageswara Rao. M, Et. al.
Keyword(s):  
Genetic Algorithm ◽  
Completion Time ◽  
Processing Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Hybrid Genetic Algorithm ◽  
Shop Floor ◽  
Scheduling Problems ◽  
Priority Rules ◽  
Simultaneous Scheduling

This article addresses flexible manufacturing system (FMS) Performance is likely to improve with employment of various resources efficiently. Initially simultaneous scheduling problems are solved by means of priority rules like first come first serve (FCFS), shortest processing time (SPT) and longest processing time (LPT) to find out the operational completion time for 120 problems. Later gene rearrangement genetic algorithm (HGA) is implemented for same set of problems with makespan as objective and the results are compared with the results of priority rules. The results are performed well by using HGA.  The same HGA is used to find the finest optimal sequence that minimize the operational completion time.  

scholarly journals Flexible Job Shop Scheduling using Software

2020 ◽  
Vol 9 (3) ◽  
pp. 67-70 ◽  
Keyword(s):  
Processing Time ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flow Time ◽  
Critical Ratio ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Priority Rules ◽  
Shop Scheduling ◽  
Programming Package

This present paper speaks about Flexible Job Shop scheduling Problem using a software. Flexible Job shop scheduling is most typical and complex manufacturing environments in production planning issues. A case study was conducted on a manufacturing business set in Vijayawada. Information collected from the producing company to get the time taken by the conventional scheduling method and gathered data for 5 jobs requiring 8 machines that arrived during the analysis period. The Project additionally speaks about the study of varied programming strategies, learning of LEKIN programming package is a software tool has a flexibility to develop new heuristic models to produce effective schedules be applied practically. Input data, taken from the manufacturing company, converted to tables and routing sequences. The LEKIN programming package [3], develops the different schedules for given data with reference to priority rules, such as First Come First Serve (FCFS), Longest Processing Time (LPT), Shortest Processing Time (SPT), Earliest Due Date (EDD), Critical Ratio (CR) [6]. The schedules obtained from priority rules analysed through performance measures like Make Span, No of late jobs, Total Flow Time, Total Tardiness, Maximum Tardiness, Total Weighted Tardiness, Total Weighted Flow Time. Our goal is to come up with a optimized schedule with in the process of flexible job shop scheduling by using LEKIN scheduling software, using various priority rules as mentioned above and to minimize the make span i.e. the time length of the schedule, during which all the operations for all jobs is completed in an engineering company

Rescheduling Activities in Face of Disruption in House Hold Goods Manufacturing Supply Chain

2016 ◽  
Vol 3 (2) ◽  
pp. 47-65
Author(s):  
K. V.N.V.N. Rao ◽  
G. Ranga Janardhana
Keyword(s):  
Performance Measures ◽  
Processing Time ◽  
Manufacturing Industry ◽  
Parallel Machines ◽  
Raw Material ◽  
Priority Rules ◽  
Average Delay ◽  
Delivery Performance ◽  
Manufacturing Supply Chain ◽  
Remaining Time

A case study from the household goods manufacturing industry is presented in this paper. The manufacturing system consists of parallel machines to make three varieties of products with different processing time. Order cancel, raw material delay and entry of the returned goods from the after sales service network are considered as disruptive events and delivery performance, average delay and waiting time of the products are selected as performance measures. Hybrid right shift-left shift rescheduling method was applied to evaluate the performance measures. Longest processing time, shortest remaining time and longest remaining time priority rules were followed for sequencing the jobs. The objective of the work is to study the influence of the priority rules and disruption on performance measures. Contribution of this paper is to make the rescheduling activity in the multi-product parallel machine environment under multiple disruptions. These findings will be useful to the industry personnel to handle the disruptions without spoiling the delivery promise.

ANALISIS PERHITUNGAN HARGA POKOK PRODUKSI DAN PENENTUAN HARGA JUAL ATAS PRODUK (Studi Kasus Pada PT Dasa Windu Agung)

2016 ◽  
Vol 1 (2) ◽  
pp. 183-190
Author(s):  
Dwi Urip Wardoyo
Keyword(s):  
Manufacturing Industry ◽  
Sampling Method ◽  
Manufacturing Companies ◽  
Test Analysis ◽  
Automotive Components ◽  
Convenience Sampling ◽  
Cost Of Production ◽  
The Cost ◽  
Keywords Cost

This study aims to determine the determination of the cost of production for products produced by PT. DWA. The Company is engaged in the manufacturing industry specialized in automotive components. Its activity is carried out through a series of production processes, so that expenses spent in the production will be calculated into the cost of the production sold. The population in this study were all manufacturing companies in Jakarta. Convenience sampling method selected one of the companies that get the confidence to assemble three national car project in Indonesia, namely Timor, Bakrie and Maleo. Test analysis used in this study is to test the calculation of full costing with job order costing. This study shows that (a) determination of the cost elements associated with the cost of production and (b) determining the cost of production on a product-based job costing with full costing approach. Keywords: cost of production, full costing

scholarly journals Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4836
Author(s):  
Liping Zhang ◽  
Yifan Hu ◽  
Qiuhua Tang ◽  
Jie Li ◽  
Zhixiong Li
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Real Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Data Driven ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Production Data ◽  
Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

scholarly journals The real-time big data processing method based on LSTM or GRU for the smart job shop production process

2020 ◽  
Vol 14 ◽  
pp. 174830262096239 ◽  
Author(s):  
Chuang Wang ◽  
Wenbo Du ◽  
Zhixiang Zhu ◽  
Zhifeng Yue
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Production Process ◽  
Real Time ◽  
Job Shop ◽  
Production Data ◽  
Time Data ◽  
Big Data Processing ◽  
The Real ◽  
Data Processing Method

With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.

Determination of lethality and processing time in a continuous sterilization system containing particulates

1990 ◽  
Vol 11 (1) ◽  
pp. 67-92 ◽  
Author(s):  
Jun Ho Lee ◽  
Rakesh K. Singh ◽  
John W. Larkin
Keyword(s):  
Processing Time

The Demographics of Trade-Affected Services and Manufacturing Workers (1987–1990): A Comparative Analysis

1995 ◽  
Vol 23 (4) ◽  
pp. 43-67
Author(s):  
Bartholomew Armah
Keyword(s):  
Manufacturing Industry ◽  
Service Sector ◽  
Manufacturing Sector ◽  
College Education ◽  
Service Industries ◽  
Highly Educated ◽  
Black And White ◽  
Industry Group ◽  
White Males ◽  
Group A

Using input-output data for 1987 and 1990, this study identifies the demographic characteristics of trade-affected workers in U.S. manufacturing and service industries. Trade-affected workers are defined as employees in industries that experienced a change (positive or negative) in net total (direct and indirect) trade-related employment between 1987 and 1990. For the period 1987–1990, three industry categories were examined: (a) industries that experienced an increase in positive net trade-related employment; (b) industries that experienced a decline in positive net trade-related employment; and (c) industries that suffered net trade-related employment losses in both years yet experienced an improvement over the period. The study finds that, while manufacturing industry workers in the most favorably affected industry group (i.e., group “a”) were more likely to be highly skilled (i.e., scientists & engineers), highly educated (i.e., over four years of college education), unionized, married and white males, corresponding service sector workers were predominantly unskilled (laborers), less educated, non-unionized, young (i.e., aged 16–24) and male (black and white). Furthermore, the service sector was associated with greater mean trade-related employment and output gains and lower mean employment and output losses than was the manufacturing sector.

Similarity-Based Decision Support for Internet Enabled Supply-Web Interactions

1998 ◽  
Author(s):  
Raj Veeramani ◽  
Narayanan Viswanathan ◽  
Shailesh M. Joshi
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Manufacturing Industry ◽  
Job Shop ◽  
The Internet ◽  
Agent Based ◽  
New Approaches ◽  
Effective Decision ◽  
Use Of The Internet ◽  
Effective Decision Making

Abstract New approaches for decision making are emerging to support the use of the Internet for supply-web interactions in the manufacturing industry. In this paper, we discuss one such paradigm, namely similarity-based decision support. It recognizes that knowledge of similar experiences can support rapid and effective decision making in various forms of supply-web interactions. We illustrate this approach using two prototype systems, WebScout (an agent-based system for customer–supplier matchmaking in the job-shop machining industry context) and TOME (Treasury of Manufacturing Experiences — an Intranet application to aid manufacturability assessment in foundries).

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