Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria 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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A MODIFIED GENETIC ALGORITHM FOR FINDING FUZZY SHORTEST PATHS IN UNCERTAIN NETWORKS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b2-299-2016 ◽

2016 ◽

Vol XLI-B2 ◽

pp. 299-304 ◽

Cited By ~ 4

Author(s):

A. A. Heidari ◽

M. R. Delavar

Keyword(s):

Genetic Algorithm ◽

Shortest Path ◽

Shortest Paths ◽

The Body ◽

Shortest Path Problems ◽

Conventional Procedure ◽

Path Lengths ◽

The Cost ◽

Uncertain Networks ◽

Exploration Exploitation

In realistic network analysis, there are several uncertainties in the measurements and computation of the arcs and vertices. These uncertainties should also be considered in realizing the shortest path problem (SPP) due to the inherent fuzziness in the body of expert's knowledge. In this paper, we investigated the SPP under uncertainty to evaluate our modified genetic strategy. We improved the performance of genetic algorithm (GA) to investigate a class of shortest path problems on networks with vague arc weights. The solutions of the uncertain SPP with considering fuzzy path lengths are examined and compared in detail. As a robust metaheuristic, GA algorithm is modified and evaluated to tackle the fuzzy SPP (FSPP) with uncertain arcs. For this purpose, first, a dynamic operation is implemented to enrich the exploration/exploitation patterns of the conventional procedure and mitigate the premature convergence of GA technique. Then, the modified GA (MGA) strategy is used to resolve the FSPP. The attained results of the proposed strategy are compared to those of GA with regard to the cost, quality of paths and CPU times. Numerical instances are provided to demonstrate the success of the proposed MGA-FSPP strategy in comparison with GA. The simulations affirm that not only the proposed technique can outperform GA, but also the qualities of the paths are effectively improved. The results clarify that the competence of the proposed GA is preferred in view of quality quantities. The results also demonstrate that the proposed method can efficiently be utilized to handle FSPP in uncertain networks.

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Random assignment and shortest path problems

Discrete Mathematics & Theoretical Computer Science ◽

10.46298/dmtcs.3504 ◽

2006 ◽

Vol DMTCS Proceedings vol. AG,... (Proceedings) ◽

Author(s):

Johan Wästlund

Keyword(s):

Limit Theorem ◽

Shortest Path ◽

Complete Graph ◽

Assignment Problem ◽

Shortest Path Problem ◽

Random Assignment ◽

Shortest Path Problems ◽

Parisi Formula ◽

International Audience ◽

Random Assignment Problem

International audience We explore a similarity between the $n$ by $n$ random assignment problem and the random shortest path problem on the complete graph on $n+1$ vertices. This similarity is a consequence of the proof of the Parisi formula for the assignment problem given by C. Nair, B. Prabhakar and M. Sharma in 2003. We give direct proofs of the analogs for the shortest path problem of some results established by D. Aldous in connection with his $\zeta (2)$ limit theorem for the assignment problem.

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