Long-Term Optimal Delivery Planning for Replacing the Liquefied Petroleum Gas Cylinder

Abstract

This study proposes a method for efficient delivery of liquefied petroleum gas cylinders based on demand forecasts of gas usage. To maintain a liquefied petroleum gas service, gas providers visit each customer to check the gas meter and replace the gas cylinder depending on the remaining amount of gas. These visits can be categorized into three patterns: non-replacement visit, replacement before the customer runs out of gas, and replacement after the customer runs out of gas. The last pattern is a crucial problem in sustaining a liquefied petroleum gas service, and it must be reduced. By contrast, frequent non-replacement visits are required to prevent the customer from running out of gas, but it requires considerable effort. One of the most severe difficulties of this problem is acquiring the gas usages of each customer only by visiting. However, with the recent spread of smart sensors, the daily gas consumption of each house can be monitored without having to visit customers. Such sensors can be utilized to solve the above problems. Our main idea is to categorize all customers into three groups: high-risk, moderate-risk, and lowrisk by focusing on an urgent need for cylinder replacement based on the demand forecast. Based on this idea, we construct an algorithm to maximize the delivery for moderate-risk customers while ensuring delivery to high-risk customers. Long-term optimal delivery planning is realized by achieving workload balancing per day. The verification experiment in Chiba prefecture in Japan showed the effectiveness of our algorithm in reducing the number of out-of-gas cylinders. Moreover, the proposed model is a new generic framework for building an optimal vehicle routing plan, consisting of a complementary algorithm, demand forecast, delivery list optimization, and delivery route optimization for realizing a long-term optimal delivery plan by setting the priority. The delivery optimization of other areas such as the grocery and oil industries incurs similar problems. Therefore, our model has significant potential for solving real-world delivery problems.