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Use commands in English to control Blender with OpenAI's GPT-4 - GitHub - gd3kr/BlenderGPT: Use commands in English to control Blender with OpenAI's GPT-4
Examples of queue optimization and goods positioning in supermarkets are: Queue Optimization: - Queue counting systems: People counters enable supermarkets to capture and track consumer and employee movements around the retail space. They generate reports on the average wait time, count the number of consumers in every queue, and the average number of people waiting in every line. This reporting can help predict the amount of cashiers that are needed to reduce consumer’s wait times[1]. - Queue flow: Sometimes there’s not much a retailer can do to prevent queues. In such situations, retailers focus on speed[2]. - Queue management systems: They help supermarkets reduce the queue and improve employee performance, which attracts more customers. The system can accurately track the idle time of each individual cashier/service agent, the wait time of each customer, and the service delivery/transaction time of each customer. This data can help managers monitor and improve employee performance, which could have a significant impact on service quality and customer satisfaction[3]. Goods Positioning: - Pricing: Pricing plays a vital role in optimization. Retailers can test for pricing strategies that work best for their customers[2]. - Performance metrics: Evaluating how staff impacts store operations and sales is a critical feature of in-store optimization. Retailers use performance metrics to build the schedule and evaluate the stores and employees. For example, service intensity, service level, sales per hour, and service productivity[2]. - Game theory of queuing: Customers choose which queue to join based on their strategy. Retailers can use game-theoretic models for queuing in a supermarket to optimize the queue[6]. Overall, queue optimization and goods positioning are essential for supermarkets to improve customer satisfaction, increase sales, and reduce wait times.
S. Long, A. Piché, V. Zantedeschi, T. Schuster, и A. Drouin. (2023)cite arxiv:2307.02390Comment: Peer reviewed and accepted for presentation at the Structured Probabilistic Inference & Generative Modeling (SPIGM) workshop at ICML 2023, Hawaii, USA.