2015
DOI: 10.1007/s10852-015-9274-3
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A New Bi-objective Location-routing Problem for Distribution of Perishable Products: Evolutionary Computation Approach

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Cited by 46 publications
(29 citation statements)
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“…The solution process is divided in two steps: first, generation of the efficient solutions. By following the same demarche as Khalili-Damghani et al (2015), in epsilon constraint method, the range of minor objective function has been divided into 10 equal intervals after finishing the calculation of the payoff table. The right hand side of minor objective function, which has been a constraint, is set equal to one of the break points in each run (See Eq.…”
Section: Figmentioning
confidence: 99%
“…The solution process is divided in two steps: first, generation of the efficient solutions. By following the same demarche as Khalili-Damghani et al (2015), in epsilon constraint method, the range of minor objective function has been divided into 10 equal intervals after finishing the calculation of the payoff table. The right hand side of minor objective function, which has been a constraint, is set equal to one of the break points in each run (See Eq.…”
Section: Figmentioning
confidence: 99%
“…By adding the maximum possible time, where each route could last to a single stage problem, which decides about the location of distribution centers among the potential places and then the vehicle routing problem among customers and opened depots, Khalili-Damghani et al (2015) customized the LRP for perishable products such as fish, dairy products, etc. They used heterogeneous vehicles and solved a bi-objective problem.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Rezaei-Malek et al [15] propose a multiobjective model to design a disaster relief logistics hub location considering the lifetime of perishable products by adjusting certain time windows and consider the inherent uncertainty of input data. Khalili-Damghani et al [16] propose a biobjective model to reduce the total cost of a company and consider the location of warehouses and the routing of vehicles for the distribution of perishable products.…”
Section: Scientific Programmingmentioning
confidence: 99%
“…Constraint (15) ensures that each customer has only one candidate distribution center to deliver to him or her. Constraint (16) indicates that the volume of fresh foods delivered to the customer cannot be less than the customer demand, due to possible cargo damage. Constraint (17) ensures that the selected distribution center can provide delivery.…”
Section: Location Mathematical Model Under Determinatementioning
confidence: 99%