{"title":"预拌混凝土车多厂自动调度","authors":"B. Hettiarachchi, H. Bandara, N. A. Samarasekera","doi":"10.1109/SOLI.2018.8476729","DOIUrl":null,"url":null,"abstract":"Ready-Mixed Concrete (RMC) is a perishable product; hence, specifications such as ASTM C94 recommend the delivery of RMC under 1.5 hours to ensure the quality. It is known that certain scheduling practices and driving behaviors lead to operational inefficiencies and poor-quality RMC. We propose a model to schedule RMC trucks while maximizing both the job coverage and profit, as well as meeting constraints such as ASTM C94 and continuous casting. The proposed solution consists of a rule checker and a scheduler. Rule checker enforces constraints such as deadlines, working hours, travel time. The scheduler uses simulated annealing to assign as many jobs as possible while maximizing the overall profit. We consider scenarios where trucks are attached to a given RMC plant, as well as allowed to move across plants as per the job requirements Using a workload derived from an actual RMC delivery company; we demonstrate that the proposed solution has good coverage of jobs while maximizing the overall profit. For example, compared to the manual job allocation, proposed solution increases the average job coverage and profit by 13% and 9%, respectively. When trucks are allowed to move across plants, job coverage and profit increase to 16% and 14%, respectively. By automatically adjusting the first unload time by a few 10s of minutes to reduce job conflicts we further enhanced above numbers by 21% and 13%, respectively.","PeriodicalId":424115,"journal":{"name":"2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Automated Multi-Plant Scheduling of Ready-Mixed Concrete Trucks\",\"authors\":\"B. Hettiarachchi, H. Bandara, N. A. Samarasekera\",\"doi\":\"10.1109/SOLI.2018.8476729\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ready-Mixed Concrete (RMC) is a perishable product; hence, specifications such as ASTM C94 recommend the delivery of RMC under 1.5 hours to ensure the quality. It is known that certain scheduling practices and driving behaviors lead to operational inefficiencies and poor-quality RMC. We propose a model to schedule RMC trucks while maximizing both the job coverage and profit, as well as meeting constraints such as ASTM C94 and continuous casting. The proposed solution consists of a rule checker and a scheduler. Rule checker enforces constraints such as deadlines, working hours, travel time. The scheduler uses simulated annealing to assign as many jobs as possible while maximizing the overall profit. We consider scenarios where trucks are attached to a given RMC plant, as well as allowed to move across plants as per the job requirements Using a workload derived from an actual RMC delivery company; we demonstrate that the proposed solution has good coverage of jobs while maximizing the overall profit. For example, compared to the manual job allocation, proposed solution increases the average job coverage and profit by 13% and 9%, respectively. When trucks are allowed to move across plants, job coverage and profit increase to 16% and 14%, respectively. By automatically adjusting the first unload time by a few 10s of minutes to reduce job conflicts we further enhanced above numbers by 21% and 13%, respectively.\",\"PeriodicalId\":424115,\"journal\":{\"name\":\"2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SOLI.2018.8476729\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SOLI.2018.8476729","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automated Multi-Plant Scheduling of Ready-Mixed Concrete Trucks
Ready-Mixed Concrete (RMC) is a perishable product; hence, specifications such as ASTM C94 recommend the delivery of RMC under 1.5 hours to ensure the quality. It is known that certain scheduling practices and driving behaviors lead to operational inefficiencies and poor-quality RMC. We propose a model to schedule RMC trucks while maximizing both the job coverage and profit, as well as meeting constraints such as ASTM C94 and continuous casting. The proposed solution consists of a rule checker and a scheduler. Rule checker enforces constraints such as deadlines, working hours, travel time. The scheduler uses simulated annealing to assign as many jobs as possible while maximizing the overall profit. We consider scenarios where trucks are attached to a given RMC plant, as well as allowed to move across plants as per the job requirements Using a workload derived from an actual RMC delivery company; we demonstrate that the proposed solution has good coverage of jobs while maximizing the overall profit. For example, compared to the manual job allocation, proposed solution increases the average job coverage and profit by 13% and 9%, respectively. When trucks are allowed to move across plants, job coverage and profit increase to 16% and 14%, respectively. By automatically adjusting the first unload time by a few 10s of minutes to reduce job conflicts we further enhanced above numbers by 21% and 13%, respectively.