| Title |
A Big-Data-Analytics Framework for Supporting Logistics Problems in Smart-City Environments |
| ID_Doc |
41400 |
| Authors |
Cuzzocrea, A; Nolich, M; Ukovich, W |
| Title |
A Big-Data-Analytics Framework for Supporting Logistics Problems in Smart-City Environments |
| Year |
2019 |
| Published |
|
| DOI |
10.1016/j.procs.2019.09.257 |
| Abstract |
Containers delivery management is a problem widely studied. Typically, it concerns the container movement on a truck from ships to factories or wholesalers and vice-versa. As there is an increasing interest in shipping goods by container, and that delivery points can be far from railways in various areas of interest, it is important to evaluate techniques for managing container transport that involves several days. The time horizon considered is a whole working week, rather than a single day as in classical drayage problems. Truck fleet management companies are typically interested in such optimization, as they plan how to match their truck to the incoming transportation order. This planning is a relevant both for strategical consideration and operational ones, as prices of transportation orders strictly depends on how they are fulfilled. It is worth noting that, from a mathematical point of view, this is an NP-Hard problem. In this paper, a Decision Support System for managing the tasks to be assigned to each truck of a fleet is presented, in order to optimize the number of transportation order fulfilled in a week. The proposed system implements a hybrid optimization algorithm capable of improving the performances typically presented in literature. The proposed heuristic implements an hybrid genetic algorithm that generate chains of consecutive orders that can be executed by a truck. Moreover, it uses an assignment algorithm based to evaluate the optimal solution on the selected order chains. (C) 2019 The Authors. Published by Elsevier B.V. |
| Author Keywords |
Decision Support Systems; Hybrid Algorithms; Logistics |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000571151500269 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Manufacturing; Engineering, Electrical & Electronic; Operations Research & Management Science |
| Research Area |
Computer Science; Engineering; Operations Research & Management Science |
| PDF |
https://doi.org/10.1016/j.procs.2019.09.257
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