LUNDRY MANAGEMENT SYSTEM PROJECT REPORT
원문 초록
Laundry firms currently use a manual system for the management and maintenance of critical information. The current system requires numerous paper forms, with data stores spread throughout the laundry management infrastructure. Often information is incomplete or does not follow management standards. Records are often lost in transit during computation requiring a comprehensive auditing process to ensure that no vital information is lost. Multiple copies of the same information exist in the laundry firm data and may lead to inconsistencies in data in various data stores. A significant part of the operation of any laundry firm involves the acquisition, management and timely retrieval of great volumes of information. This information typically involves; customer personal information and clothing records history, user information, price of delivery and received date, users scheduling as regards customers details and dealings in service rendered, also our products package waiting list. All of this information must be managed in an efficient and cost wise fashion so that the organization resources may be effectively utilized. We present the design and implementation of a laundry database management system (LBMS) used in a laundry establishment. Laundry firms are usually faced with difficulties in keeping detailed records of customers clothing; this little problem as seen to most laundry firms is highly discouraging as customers are filled with disappointments, arising from issues such as customer clothes mix-ups and untimely retrieval of clothes. The aim of this application is to determine the number of clothes collected, in relation to their owners, as this also helps the users fix a date for the collection of their clothes. Also customer’s information is secured, as a specific id is allocated per registration to avoid contrasting information.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Autonomous Resource Management in Microservice Systems via Reinforcement Learning
Intent-aware Radio Resource Scheduling in a RAN Slicing Scenario using Reinforcement Learning
Average Peak Age of Information in Underwater Information Collection With Sleep-Scheduling
AKG: Automatic Kernel Generation for Neural Processing Units using Polyhedral Transformations
Elastic Deep Learning in Multi-Tenant GPU Clusters
From Roots to Rewards: Dynamic Tree Reasoning with RL
토론
이 논문과 연결된 포럼 글이 아직 없습니다.