Efficient routing optimization with discrete penguins search algorithm for MTSP

Mzili, Ilyass; Mzili, Toufik; Riffi, Mohammed Essaid

doi:10.31181/dmame04092023m

Cited by 12 publications

(5 citation statements)

References 24 publications

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“…For large-scale problems, traditional algorithms, such as the dynamic programming method and branch and bound method, can not achieve the speed and accuracy of small-scale scheduling optimization problems. This leads researchers to study evolutionary algorithms (EA), like penguin search optimization algorithm (PeSOA) [ 20 ], GA [ 21 ], cuckoo search (CS) [ 22 ] and particle swarm optimization (PSO) [ 23 ]. The current widely used evolutionary algorithms (EA) [ 24 ] are shown in Table 1 , which comprehensively considers the global search ability, Rate of convergence, simplicity and ease of implementation, applicability of complex constraint.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective optimization of dual resource integrated scheduling problem of production equipment and RGVs considering conflict-free routing

Zhang,

Hu,

Liu

et al. 2024

PLoS ONE

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In flexible job shop scheduling problem (FJSP), the collision of bidirectional rail guided vehicles (RGVs) directly affects RGVs scheduling, and it is closely coupled with the allocation of production equipment, which directly affects the production efficiency. In this problem, taking minimizing the maximum completion time of RGVs and minimizing the maximum completion time of products as multi-objectives a dual-resource integrated scheduling model of production equipment and RGVs considering conflict-free routing problem (CFRP) is proposed. To solve the model, a multi-objective improved discrete grey wolf optimizer (MOID-GWO) is designed. Further, the performance of popular multi-objective evolutionary algorithms (MOEAs) such as NSGA-Ⅱ, SPEA2 and MOPSO are selected for comparative test. The results show that, among 42 instances of different scales designed, 37, 34 and 28 instances in MOID-GWO are superior to the comparison algorithms in metrics of generational distance (GD), inverted GD (IGD) and Spread, respectively. Moreover, in metric of Convergence and Diversity (CD), the Pareto frontier (PF) obtained by MOID-GWO is closer to the optimal solution. Finally, taking the production process of a construction machinery equipment component as an example, the validity and feasibility of the model and algorithm are verified.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-objective optimization of dual resource integrated scheduling problem of production equipment and RGVs considering conflict-free routing

Zhang,

Hu,

Liu

et al. 2024

PLoS ONE

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show abstract

“…The authors in [9] employ genetic algorithms, a metaheuristic technique, to solve the dynamic routing of shipping containers in a fuzzy environment, which brings a more realistic approach to the problem. Similarly, the work in [10] addresses the multiple traveling salesman problem, arguably a routing problem, through a novel metaheuristic optimization algorithm referred to as penguin search. Finally, the problem modeling employed in the research in [11], which is also interesting, deals with a multi-depot vehicle routing problem.…”

Section: Artificial Intelligence For Transfer Point Allocationmentioning

confidence: 99%

Optimization of Rural Demand-Responsive Transportation through Transfer Point Allocation

Martí,

Jordán,

De la Prieta

et al. 2023

Electronics

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Rural mobility has a lack of innovative proposals in contrast with its urban counterpart. This research aims to bring solutions that ease the implementation of reliable and flexible rural transportation. Demand-responsive transportation is chosen to develop a public transportation service providing interurban trips among several rural settlements. Given the characteristics of rural displacement demand, a novel approach is introduced to optimize the service’s economic costs: the dynamic transfer point allocation. The problem is fully formulated and an architecture is introduced describing the workflow of the whole system. Data from an interurban bus transportation service are used to build a case study of a rural area of Valencia, Spain, and develop several examples illustrating the benefits of the proposed approach. The results reveal that the dynamic creation of transfer points can simplify the transportation fleet’s itineraries and boost the amount of served travel requests. Finally, a discussion of the benefits and dangers of flexible features in rural transportation is developed, underscoring the need to achieve a balance between dynamic operation and service quality.

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“…They combined elite selection operators, 2-opt mutation, and Order crossover (OX crossover) methods to further optimize solutions under the guidance of reinforcement learning. Meanwhile, Ilyass Mzili et al [ 10 ] introduced the Discrete Penguin Search Optimization Algorithm (PeSOA), which successfully addressed the Multiple Traveling Salesman Problem (MTSP). Through experiments, they demonstrated the efficiency of PeSOA.…”

Section: Introductionmentioning

confidence: 99%

Optimization of offshore wind farm inspection paths based on K-means-GA

Peng,

Sun,

Tong

et al. 2024

PLoS ONE

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As global demand for offshore wind energy continues to rise, the imperative to enhance the profitability of wind power projects and reduce their operational costs becomes increasingly urgent. This study proposes an innovative approach to optimize the inspection routes of offshore wind farms, which integrates the K-means clustering algorithm and genetic algorithm (GA). In this paper, the inspection route planning problem is formulated as a multiple traveling salesman problem (mTSP), and the advantages of the K-means clustering algorithm in distance similarity are utilized to effectively group the positions of wind turbines, thereby optimizing the inspection schedule for vessels. Subsequently, by harnessing the powerful optimization capability and robustness of genetic algorithms, further refinement is conducted to search for the optimal inspection routes, aiming to achieve cost reduction objectives. The results of simulation experiments demonstrate the effectiveness of this integrated approach. Compared to traditional genetic algorithms, the inspection route length has been significantly reduced, from 93 kilometers to 79.36 kilometers. Simultaneously, operational costs have also experienced a notable decrease, dropping from 141,500 Chinese Yuan to 125,600 Chinese Yuan.

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Efficient routing optimization with discrete penguins search algorithm for MTSP

Cited by 12 publications

References 24 publications

Multi-objective optimization of dual resource integrated scheduling problem of production equipment and RGVs considering conflict-free routing

Multi-objective optimization of dual resource integrated scheduling problem of production equipment and RGVs considering conflict-free routing

Optimization of Rural Demand-Responsive Transportation through Transfer Point Allocation

Optimization of offshore wind farm inspection paths based on K-means-GA

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