Nesting is a common problem in industries such as shipbuilding, auto-maker, clothing, shoe-making, and furniture, in which various parts are cut off from a stock or stocks while minimizing the wastes or maximizing the...Nesting is a common problem in industries such as shipbuilding, auto-maker, clothing, shoe-making, and furniture, in which various parts are cut off from a stock or stocks while minimizing the wastes or maximizing the utilization of the stock. Berth allocation at seaside is also considered one form of two dimensional nesting problems, in which a ship is assigned a location for service during a certain time slot. This paper presents an expert system using a heuristic search method for nesting problems. The parts and stocks are represented by pixels with which utility function is used to evaluate current state in search tree. The system is developed in CLIPS, an expert system shell and applied to various example problems with different constraints and to a berth allocation example to illustrate its applicability under different conditions.展开更多
With the rapid growth of e-commerce, customers increasingly write online reviews of the product they purchase. These customer reviews are one of the most valuable sources of information affecting selection of products...With the rapid growth of e-commerce, customers increasingly write online reviews of the product they purchase. These customer reviews are one of the most valuable sources of information affecting selection of products or services. Summarizing these customer reviews is becoming an interesting area of research, inspiring researchers to develop a more condensed, concise summarization for users. However, most of the current efforts at summarization are based on general product features without feature＇s relationship. As a result, these summaries either ignore feedback from customers or do a poor job of reflecting the opinions expressed in customer reviews. To remedy this summarization shortcoming, we propose a feature network-driven quadrant mapping that captures and incorporates opinions from customer reviews. Our focus is on construction of a feature network, which is based on co-occurrence and sematic similarities, and a quadrant display showing the opinions polarity of feature groups. Moreover, the proposed approach involves clustering similar product features, and thus, it is different from standard text summarization based on abstraction and extraction. The summarized results can help customers better understand the overall opinions about a product.展开更多
文摘Nesting is a common problem in industries such as shipbuilding, auto-maker, clothing, shoe-making, and furniture, in which various parts are cut off from a stock or stocks while minimizing the wastes or maximizing the utilization of the stock. Berth allocation at seaside is also considered one form of two dimensional nesting problems, in which a ship is assigned a location for service during a certain time slot. This paper presents an expert system using a heuristic search method for nesting problems. The parts and stocks are represented by pixels with which utility function is used to evaluate current state in search tree. The system is developed in CLIPS, an expert system shell and applied to various example problems with different constraints and to a berth allocation example to illustrate its applicability under different conditions.
文摘With the rapid growth of e-commerce, customers increasingly write online reviews of the product they purchase. These customer reviews are one of the most valuable sources of information affecting selection of products or services. Summarizing these customer reviews is becoming an interesting area of research, inspiring researchers to develop a more condensed, concise summarization for users. However, most of the current efforts at summarization are based on general product features without feature＇s relationship. As a result, these summaries either ignore feedback from customers or do a poor job of reflecting the opinions expressed in customer reviews. To remedy this summarization shortcoming, we propose a feature network-driven quadrant mapping that captures and incorporates opinions from customer reviews. Our focus is on construction of a feature network, which is based on co-occurrence and sematic similarities, and a quadrant display showing the opinions polarity of feature groups. Moreover, the proposed approach involves clustering similar product features, and thus, it is different from standard text summarization based on abstraction and extraction. The summarized results can help customers better understand the overall opinions about a product.
