An adapted multi-objective genetic algorithm for solving the cash in transit vehicle routing problem with vulnerability estimation for risk quantification

Hazardous material (Hazmat) transportation is an inseparable section of the industry, despite its major financial and health risks. In order to optimize Hazmat transportation, a multi-objective Hazmat routing model with time windows is employed where the risk and distance are minimized. Due to the uncertainty of Hazmat transportation risk, a Z-number fuzzy approach is used to estimate the risk, in which the probability of occurrence and the severity is considered in the context of Z-information. The severity of the event includes the affected population and depends on the amount of transported Hazmat and the number of individuals affected by the explosion. To tackle the proposed model, the present paper utilizes a multi-objective hybrid genetic algorithm, the validity of which is tested by Solomon's problems. Furthermore, the optimization of a case study concerning the Hazmat distribution in Iran is analyzed using the suggested approach to assess the efficiency of the proposed fuzzy problem in real-world applications.

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“…To transform the Z-number risk, initially, the second component of the Z-number (reliability) is transformed to a crisp number by Eq. (19).…”

Section: Table 3 Transformation Rules Of Linguistic Variables Of Reli...mentioning

confidence: 99%

“…Reverse algorithm: In this algorithm, two points are randomly selected from a route and the point between them is reversed [19].…”

Section: Mutationmentioning

confidence: 99%

Z-trapezoidal risk assessment for multi-objective Hazmat routing model with time windows

Zandieh

Ghannadpour

2022

RAIRO-Oper. Res.

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“…e comparison between the Im-NSGA-II and the other two famous multiobjective optimization algorithms is conducted to evaluate the performance of the designed algorithm. In this section, the NSGA-II and the multiobjective genetic algorithm (MOGA) are employed since they are of interest to researchers in multiobjective optimization fields [71,72]. e logistics networks in the modified "Solomon datasets" [73] and the "MDVRPTW instances 2 " [69] are selected as benchmarks.…”

Section: Algorithm Comparison and Analysismentioning

confidence: 99%

Two-Echelon Multidepot Logistics Network Design with Resource Sharing

Luo

Wang

Tang

et al. 2021

Journal of Advanced Transportation

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Resource sharing within a logistics network offers an effective way to solve problems resulting from inefficient and costly operations of individual logistics facilities. However, the existing analysis of resource sharing and profit allocation is still limited. Therefore, this study aims to model resource sharing in two-echelon delivery and pickup logistics networks to improve the overall efficiency and decrease the total network operating cost. A bi-objective integer programming model is first proposed for two-echelon collaborative multidepot pickup and delivery problems with time windows (2E-CMDPDTW) to seek the minimization of operating costs and number of vehicles. Integrating a customer clustering algorithm, a greedy algorithm, and an improved nondominated sorting genetic algorithm-II (Im-NSGA-II), a hybrid method is then designed to handle the 2E-CMDPDTW model. The customer clustering and the greedy algorithms are employed to generate locally optimized initial solutions to accelerate the calculating velocity and guarantee the diversity of feasible solutions. The Im-NSGA-II combines the order crossover operation and the polynomial mutation process to find the optimal solution of the 2E-CMDPDTW. The comparative results show that the proposed hybrid method outperforms the NSGA-II and the multiobjective genetic algorithm. Furthermore, a Shapley value method is used for allocating total profits of established alliances and finding an optimal coalition sequence of the logistics facilities joining alliances based on the strictly monotonic path strategy. Finally, a case study of 2E-CMDPDTW in Chongqing China is conducted to validate the feasibility. Results indicate that this study contributes to long-term partnerships between logistics facilities within multi-echelon logistics networks in practice and contributes to the long-term sustainability of urban logistics pickup and delivery networks’ development.

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“…Zhang et al (2020) studied the stochastic VRP, considering probability constraints with distribution uncertainty in deadlines. Ghannadpour and Zandiyeh (2020) proposed a multiobjective GA to solve transit vehicle routing problem with vulnerability estimation for risk quantification to optimize the safety of cash/valuable commodities transportation. Finally, Vincent et al (2021) introduced the problem of heterogeneous fleet vehicle routing problem with multiple cross-docks using an adaptive neighborhood simulated annealing algorithm to solve the problem.…”

Section: Vehicle Routing Problemmentioning

confidence: 99%

“…The problem has different operational constraints to consider real characteristics of distribution systems (TOTH; VIGO, 2002). Several recent articles address optimization problems involving VRP (ZHANG et al, 2020;ALI;CÔTÉ;COELHO, 2020;CHEN et al, 2020;GHANNADPOUR;ZANDIYEH, 2020;MOLINA et al, 2020). OSSP is directly related to VRP because, as the OSSP is a production scheduling problem, it is necessary to schedule the delivery of products to the customers after the production phase in the open shop environment.…”

Section: Introductionmentioning

confidence: 99%

Contributions to new variants of the open shop scheduling problem: modeling and solution methods

Abreu¹

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Este trabalho é dedicado à minha mãe Meire Rose, cujo empenho em me educar sempre veio em primeiro lugar. Aqui estão os resultados dos seus esforços. Perdão pelos momentos de ausência. Com muito amor e gratidão, do seu filho, Levi. AGRADECIMENTOSA Deus, que me deu o privilégio e a oportunidade de chegar até aqui.Agradeço à Universidade Federal do Ceará (UFC) pela minha graduação como Engenheiro de Produção Mecânica.Ao meu orientador Nagano, que muito me apoiou durante minha trajetória no doutorado e que, apesar da distância e do período de pandemia, sempre prestou ajuda e orientação em todos os momentos em que estive escrevendo minha tese. Sua criatividade, sua disponibilidade e sua paciência foram essenciais para a conclusão desse trabalho, agradeço também por todas as conversas e orientações sobre os diversos aspectos da vida.Agradeço aos professores do departamento de Engenharia de Produção da UFC, pelo empenho diário na transmissão dos conhecimentos do curso e, em especial, aos professores Albertin, Anselmo, Bruno, Heráclito, Moccellin (in memoriam) e Sergio (in memoriam) pelas orientações concedidas durante todo o período da graduação, pelo incentivo e pela motivação para eu seguir a carreia acadêmica.Aos meus amigos do curso de Engenharia de Produção da UFC, Calixto, Ingrid, Joab, Jônatas e Roniel, e do Programa de Educação Tutorial (PET), meu muito obrigado por tudo. Certamente, com suas divertidas conversas e seus incentivos, me desenvolvi como ser humano, aluno e profissional de Engenharia de Produção.Um agradecimento especial aos membros do laboratório de Otimização em Produção e Logística (OPL) da UFC, Kennedy e Vitor Hugo, pelo auxílio no desenvolvimento de modelos e algoritmos de fundamental importância para o progresso da tese, e aos professores Bruno, Framinan e Tavares por todas as orientações no desenvolvimento dos artigos provenientes desse trabalho.Agradeço a minha mãe, Meire Rose, por moldar o meu caráter de maneira singular e por me apoiar com tamanho afinco ao longo dos meus anos de vida. Sem dúvida alguma, todos os postos que galguei até hoje foram alcançados pelas condições e pela dedicação incondicional propiciadas por ela.Por fim, quero agradecer ao Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq -831621/1999-0), à Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES -001) e à Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP -2021/11586-2) por todo o apoio concedido para a realização desse trabalho.

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An adapted multi-objective genetic algorithm for solving the cash in transit vehicle routing problem with vulnerability estimation for risk quantification

Cited by 34 publications

References 56 publications

Z-trapezoidal risk assessment for multi-objective Hazmat routing model with time windows

Z-trapezoidal risk assessment for multi-objective Hazmat routing model with time windows

Two-Echelon Multidepot Logistics Network Design with Resource Sharing

Contributions to new variants of the open shop scheduling problem: modeling and solution methods

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