Dynamic rescheduling problem is an important issue in modern manufacturing system with the feature of combinatorial computation complexity. A dynamic rescheduling model, which is based on Multi-Agent System (MAS), INTRODUCTIONToday's manufacturing businesses are facing immense pressures to react rapidly and robustly to dynamic fluctuations in demand distributions across products and changing product mix. Traditional manufacturing systems and approaches to production, involving sequential stages from manufacturing systems design, construct, and setup in the preparation phase to production planning, scheduling, and control in the operational phase, can be challenging in satisfying the requirement of the variation. Efficient and practical methods for scheduling and optimization technology are the key to improve the productivity and efficiency of a manufacturing plant [1]. The traditional scheduling and optimization process, which always deals with a clear schedule and a fixed processing time, while for the actual processing problem, there are many uncertain factors, for example, changes in processing time, product demand, delivery, equipment failure, resources and production variations. The dynamic interference of these factors causes that the original dynamic scheduling can not be implemented successfully. Therefore, the rescheduling model and its solution method are of significant importance for the dynamic scheduling problem [2].Job shop scheduling is to schedule a set of jobs on a set of machines, which is subject to the constraint that each machine can process one job at most at a given time and the fact that each job has a specified processing order through the machines. It is not only a NP-hard problems, it also has the well-earned reputation of being one of the strong combinatorial problems in manufacturing systems. Recently, two single-machine rescheduling problems with linear deteriorating jobs under disruption was studied by Zhao and Tang [3]. Job shop rescheduling problem was being considered as minimizing the total completion time under a limit of the disruption from the original scheduling. However, little information has been given about the autonomic decision mechanism. A reactive scheduling framework based on domain knowledge and constraint programming was proposed by Novasand Henning [4]. An explicit object-oriented domain representation and a constraint programming (CP) approach to the model were utilized to the modeling and realizing method when unforeseen event occurs. A reactive scheduling methodology for job shop, make-to-order industries, by inserting new orders in a predetermined schedule, was introduced to iteratively update the schedules [5]. By applying local rescheduling in response to schedule disruptions was presented to reduce the size of the regarded problems by applying methods of partial rescheduling in literature [6]. Mehta [7] processed the way to absorb the random failure of the disturbance proposed by the appropriate method of inserting new orders in idle time. Kim [8] propos...
The most popular inventory model to determine production lot size is Economic Production Quantity (EPQ). It shows enterprise how to minimize total production cost by reducing inventory cost. But, three main parameters in EPQ which are demand, machine set up cost, and holding cost, are not suitable to solve issues nowadays. When an enterprise has two types of demand, continue and discrete demand, the basic EPQ would be no longer useful. Demand continues comes from a customer who wants their needs to be fulfilled every time per unit time, while the fulfillment of demand discrete is at a fixed interval of time. A literature review is done by writers to observe other formulation of EPQ model. As there is no other research can be found which adopt this topic, this study tries to develop EPQ model considering two types of demand simultaneously.
In the face of the Revolution industry 4.0, global connection, artificial intelligence, and automation have disrupted technology. This made the industrial world's development in work competition, not linear and even created new jobs. Digital talent and innovation are needed to face the world of work. This study discusses the construct effect between digital talent, individual innovation behavior, and Skills Revolution Industry 4.0, and the effect of Skills Revolution Industry 4.0) as a mediator to digital talent constructs' relationship. Data collection is obtained directly (face to face). Samples were previously clustered by sampling technique. Questionnaires use the Likert Scale. Then, the data gotten were processed by SEM-PLS with Software 3.8.2. The result showed that digital talent has a positive effect on individual innovation behavior. This meant that skill of revolution industry 4.0 as a construct mediator was successful. The stronger digital talent influences, the stronger individual innovation behavior influences, and it is accelerated with revolution industry skills 4. This study proposes a model to build mastery of digital talent and individual innovation behavior of Universitas Andalas students through the mastery of skills of revolution 4.0 as a mediator. This research can pave the way to improve students' readiness in facing the world of revolution 4.0, one of which is in the field of digital innovation.
Fakultas Teknik Universitas Ibnu Sina bergerak dibidang pendidikan yang menyediakan berupa layanan mahasiswa, namun berdasarkan hasil penelitian pendahuluan yang telah dilakukan mahasiswa masih merasa belum puas terhadap layanan yang disediakan oleh Fakultas Teknik Univeristas Ibnu Sina seperti fasilitas layanan belum lengkap, lambat respon terhadap keluahan, toilet kurang bersih, ruangan perkuliahan belum rapi, wifi kurang lancar, oleh karena itu diperlukan strategi perbaikan layanan yang dapat memenuhi keinginan mahasiswa. Untuk mencapai semua itu diperlukan metode strategi perbaikan layanan ini yaitu dengan menggunakan metode SWOT dan QSPM, berdasarkan dari hasil perumusan strategi dengan menggunakan matriks IFAS EFAS maka terdapat 10 usulan Strategy perbaikan layanan sedangkan hasil perhitungan matriks QSPM peningkatan akreditasi prodi dan institusi dengan nilai Atractive Score (AS) 124 Total Atractive Score (TAS) 6,29 sedangkan prioritas yang kedua adalah melakukan pelatihan rutin kepada SDM layanan dengan total nilai Atractive Score (AS) 114 Total Atractive Score (TAS) 5,994
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