Quota travelling salesman problem with passengers, incomplete ride and collection time optimization by ant-based algorithms

Silva, Bruno C. H.; Fernandes, Islame F. C.; Goldbarg, Marco César; Goldbarg, Elizabeth Ferreira Gouvêa

doi:10.1016/j.cor.2020.104950

Cited by 19 publications

(31 citation statements)

References 29 publications

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“…The benchmark for the tests consisted of 144 QTSP-PIC instances. It was proposed by Silva et al [5]. Numerical results confirmed the effectiveness of the MS-MMAS by comparing it to other ACO variants proposed in [5].…”

Section: Introductionmentioning

confidence: 61%

“…• Experiments on a set of QTSP-PIC instances ranging: 10 to 500 cities; and 30 to 10.000 travel requests. The results showed that the proposed MMAS variant is competitive regarding the other three ACO variants presented in [5] for the QTSP-PIC.…”

Section: Introductionmentioning

confidence: 95%

“…The QTSP-PIC was introduced by Silva et al [5]. They presented a mathematical formulation and heuristics based on Ant Colony Optimization (ACO) [6].…”

Section: Introductionmentioning

confidence: 99%

“…Every ant decides which strategy to use at random with uniform distribution. The MS proposed in this study extends the original implementation proposed in [5]. MS-MMAS also incorporates RMH and MnLS and uses a memory-based technique proposed in [11] to avoid redundant work.…”

Section: Introductionmentioning

confidence: 99%

“…• The extension of the MS concept proposed in [5] with a roulette mechanism that orients the ants to choose their heuristic information based on the best quality solutions achieved;…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multi-Strategy MAX-MIN Ant System for Solving Quota Traveling Salesman Problem with Passengers, Incomplete Ride and Collection Time

Silva¹,

Fernandes²,

Goldbarg³

et al. 2021

Operations Management - Emerging Trend in the Digital Era

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This study proposes a novel adaptation of MAX-MIN Ant System algorithm for the Quota Traveling Salesman Problem with Passengers, Incomplete Ride, and Collection Time. There are different types of decisions to solve this problem: satisfaction of the minimum quota, acceptance of ride requests, and minimization of travel costs under the viewpoint of the salesman. The Algorithmic components proposed regards vehicle capacity, travel time, passenger limitations, and a penalty for delivering a passenger deliverance out of the required destination. The antbased algorithm incorporates different sources of heuristic information for the ants and memory-based principles. Computational results are reported, showing the effectiveness of this ant-based algorithm.

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Section: Introductionmentioning

confidence: 61%

Section: Introductionmentioning

confidence: 95%

“…The QTSP-PIC was introduced by Silva et al [5]. They presented a mathematical formulation and heuristics based on Ant Colony Optimization (ACO) [6].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…• The extension of the MS concept proposed in [5] with a roulette mechanism that orients the ants to choose their heuristic information based on the best quality solutions achieved;…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multi-Strategy MAX-MIN Ant System for Solving Quota Traveling Salesman Problem with Passengers, Incomplete Ride and Collection Time

Silva¹,

Fernandes²,

Goldbarg³

et al. 2021

Operations Management - Emerging Trend in the Digital Era

View full text Add to dashboard Cite

show abstract

Developing Arithmetic Optimization Algorithm for Travelling Salesman Problem

Krishna

Majhi

2022

Lecture Notes in Networks and Systems

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Performance assessment of the metaheuristic optimization algorithms: an exhaustive review

2020

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The simulation-driven metaheuristic algorithms have been successful in solving numerous problems compared to their deterministic counterparts. Despite this advantage, the stochastic nature of such algorithms resulted in a spectrum of solutions by a certain number of trials that may lead to the uncertainty of quality solutions. Therefore, it is of utmost importance to use a correct tool for measuring the performance of the diverse set of metaheuristic algorithms to derive an appropriate judgment on the superiority of the algorithms and also to validate the claims raised by researchers for their specific objectives. The performance of a randomized metaheuristic algorithm can be divided into efficiency and effectiveness measures. The efficiency relates to the algorithm's speed of finding accurate solutions, convergence, and computation. On the other hand, effectiveness relates to the algorithm's capability of finding quality solutions. Both scopes are crucial for continuous and discrete problems either in single-or multi-objectives. Each problem type has different formulation and methods of measurement within the scope of efficiency and effectiveness performance. One of the most decisive verdicts for the effectiveness measure is the statistical analysis that depends on the data distribution and appropriate tool for correct judgments.

show abstract

Quota travelling salesman problem with passengers, incomplete ride and collection time optimization by ant-based algorithms

Cited by 19 publications

References 29 publications

Multi-Strategy MAX-MIN Ant System for Solving Quota Traveling Salesman Problem with Passengers, Incomplete Ride and Collection Time

Multi-Strategy MAX-MIN Ant System for Solving Quota Traveling Salesman Problem with Passengers, Incomplete Ride and Collection Time

Developing Arithmetic Optimization Algorithm for Travelling Salesman Problem

Performance assessment of the metaheuristic optimization algorithms: an exhaustive review

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