Pay Today for a Rainy Day: Improved Approximation Algorithms for Demand-Robust Min-Cut and Shortest Path Problems

The time-dependent vehicle routing problems have lately received great attention for logistics companies due to their crucial roles in reducing the time and economic costs, as well as fuel consumption and carbon emissions. However, the dynamic routing environment and traffic congestions have made it challenging to make the actual travelling trajectory optimal during the delivery process. To overcome this challenge, this study proposed an unconventional path optimization approach, fissile ripple spreading algorithm (FRSA), which is based on the advanced structure of coevolutionary path optimization (CEPO). The objective of the proposed model is to minimize the travelling time and path length of the vehicle, which are the popular indicators in path optimization. Some significant factors usually ignored in other research are considered in this study, such as congestion evolution, routing environment dynamics, signal control, and the complicated correlation between delivery sequence and the shortest path. The effectiveness of the proposed approach was demonstrated well in two sets of simulated experiments. The results prove that the proposed FRSA can scientifically find out the optimal delivery trajectory in a single run via global research, effectively avoid traffic congestion, and decrease the total delivery costs. This finding paves a new way to explore a promising methodology for addressing the delivery sequence and the shortest path problems at the same time. This study can provide theoretical support for the practical application in logistics delivery.

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Solving RFID mobile reader path problem with optimization algorithms

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v13.i3.pp1110-1116 ◽

2019 ◽

Vol 13 (3) ◽

pp. 1110

Author(s):

M. Zaki Zakaria ◽

Sofianita Mutalib ◽

Shuzlina Abdul Rahman ◽

Shamsul J Elias ◽

A Zambri Shahuddin

Keyword(s):

Shortest Path ◽

Radio Frequency Identification ◽

Signal Strength ◽

Search Space ◽

Rfid Tags ◽

Object A ◽

Shortest Path Problems ◽

Search Area ◽

Frequency Identification ◽

Data Grouping

Radio Frequency Identification (RFID) is a one of the fastest growing and most beneficial technologies being adopted by businesses today. One of the important issues is localization of items in a warehouse or business premise and to keep track of the said items, it requires devices which are costly to deploy. This is because many readers need to be placed in a search space. In detecting an object, a reader will only report the signal strength of the tag detected. Once the signal strength report is obtained, the system will compute the coordinates of the RFID tags based on each data grouping. In this paper, algorithms using genetic algorithm, particle swarm, ant colony optimization are proposed to achieve the shortest path for an RFID mobile reader, while covering full search area. In comparison, for path optimization, the mobile reader traverses from one node to the next, moving around encountered obstacles in its path. The tag reading process is iterative, in which the reader arrives at its start point at the end of each round. Based on the shortest path, an algorithm that computes the location of items in the search area is used. The simulation results show that the ACO method works more effectively and efficiently compare to others when solving shortest path problems.

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