2021
DOI: 10.1016/j.ejor.2019.07.049
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Multi-objective optimization of a two-echelon vehicle routing problem with vehicle synchronization and ‘grey zone’ customers arising in urban logistics

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Cited by 76 publications
(36 citation statements)
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“…Moreover, a real-world case study was analyzed in Sari of Iran to assess the performance of the model, and eventually, numerous sensitivity analyses were accomplished to analyze the behavior of the objective functions. Anderluh et al (2019) attempted to simulate different scenarios in the duration of the trip and carried out a solution in this context. The computational conclusions indicate that the rate of synchronization is straightly associated with the possible advancements with the reuse of the program.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Moreover, a real-world case study was analyzed in Sari of Iran to assess the performance of the model, and eventually, numerous sensitivity analyses were accomplished to analyze the behavior of the objective functions. Anderluh et al (2019) attempted to simulate different scenarios in the duration of the trip and carried out a solution in this context. The computational conclusions indicate that the rate of synchronization is straightly associated with the possible advancements with the reuse of the program.…”
Section: Literature Reviewmentioning
confidence: 99%
“…(1) Objective function (1) aims to minimize the distribution cost, including the effective transport cost and penalty; Constraint (2) means the mean customer satisfaction must be greater than or equal to the given value of β; Constraint (3) means the number of vehicles leaving the distribution center should not surpass k; Constraint (4) means all vehicles leaving the distribution center must return to the center; Constraints (5) and (6) mean that each vehicle can only serve each customer once; Constraint (7) means the quantity of products loaded onto a vehicle should not surpass the loading capacity of the vehicle; Constraint (8) means the time window required by each customer; Constraint (9) means the value range of xijk; Constraint (10) means the travel time from customer i to customer j; Constraint (11) means the travel time from the distribution center to customer i; Constraint (12) means the penalty for the delivery beyond the time window; Constraint (13) means the degree of satisfaction of customer i with different arrival times ti.…”
Section: Model Constructionmentioning
confidence: 99%
“…Therefore, these algorithms have been combined into hybrid algorithms to fully exert their complementary advantages. The hybrid algorithms include SA-GA, GA-PSO, ACA-PSO, etc [5][6][7]. This sub-section selects the SA-GA to solve the established VRPSTW model, in view of the features of the problem and the strengths of this hybrid algorithm.…”
Section: Model Optimization By Sa-gamentioning
confidence: 99%
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“…The paper [15] deals with the routing issue to decrease the environmental and noise pollution in the first echelon through the Flow simulation product to generate a rational vehicle path. In the research [16], the routing problem consist in a vehicle assignment to serve the consumers in different echelons. М. Soysal et al [17] in routing problems.…”
Section: Literature Reviewmentioning
confidence: 99%