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1 |
A hybrid route planning approach for logistics with pickup and delivery
یک رویکرد برنامه ریزی مسیر ترکیبی برای تدارکات با پیکاپ و تحویل-2019 With the busy life of modern people, more and more consumers are preferring to shop online. This change on shopping behavior results in large volumes of packages must be transported, and thus re- search on logistics planning considering real constraints has increased. To solve this problem, several heuristics or evolutionary methods with expert knowledge were proposed previously, but they are usu- ally inefficient or need a large amount of memory. In this paper, we propose a hybrid approach called Iterative Logistics Solution Planner ( ILSP ) for not only quickly finding a nice logistics solution but also itera- tively improving the solution quality while meeting the real logistics constraints. ILSP contains two main phases including initial logistics solution generation and iterative logistics solution improvement based on the intelligence and knowledge from domain experts. Several algorithms and strategies are designed in ILSP for package partitioning, route planning and quality improvement. From the view of expert sys- tems, the significance and impact of ILSP are simultaneously taking both computational efficiency and iterative quality improvement based on the expert knowledge into account on logistics planning problem with pickup and delivery. Through the rigorous experimental evaluations of real logistics data, the results demonstrated the excellent performance of ILSP . Keywords: Hybrid approach | Logistics planning | Smart city| Expert system |
مقاله انگلیسی |
2 |
A big data approach for logistics trajectory discovery from RFID-enabled production data
رویکرد داده های بزرگ برای کشف تدارکات مسیر از داده های تولید RFID فعال شده-2015 Radio frequency identification (RFID) has been widely used in supporting the logistics management on
manufacturing shopfloors where production resources attached with RFID facilities are converted into smart
manufacturing objects (SMOs) which are able to sense, interact, and reason to create a ubiquitous
environment. Within such environment, enormous data could be collected and used for supporting further
decision-makings such as logistics planning and scheduling. This paper proposes a holistic Big Data approach
to excavate frequent trajectory from massive RFID-enabled shopfloor logistics data with several innovations
highlighted. Firstly, RFID-Cuboids are creatively introduced to establish a data warehouse so that the RFIDenabled logistics data could be highly integrated in terms of tuples, logic, and operations. Secondly, a Map
Table is used for linking various cuboids so that information granularity could be enhanced and dataset
volume could be reduced. Thirdly, spatio-temporal sequential logistics trajectory is defined and excavated so
that the logistics operators and machines could be evaluated quantitatively. Finally, key findings from the
experimental results and insights from the observations are summarized as managerial implications, which
are able to guide end-users to carry out associated decisions.
Keywords:
RFID
Big data
Logistics control
Trajectory pattern
Shopfloor manufacturing |
مقاله انگلیسی |