2020
DOI: 10.1007/s12351-020-00593-3
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A column generation approach for an inventory routing problem with fuzzy time windows

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Cited by 4 publications
(3 citation statements)
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References 39 publications
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“…VRPFTW has uncertain service time to customer points. Qamsari et al [18] propose a novel approach to the inventory routing problem for fuzzy time windows considering customer satisfaction and present a multi-priority structure for vehicles to visit customers. Li [19] considers the characteristics of fresh food delivery to develop a multi-objective vehicle routing optimization model with fuzzy time window for fresh food delivery, and designs an improved ACA based on rolling time-domain delay distribution to solve the problem according to the idea of the single-order distribution to get the optimal combination of the distribution cost and the customer satisfaction.…”
Section: Fuzzy Time Window With Vrp (Vrpftw)mentioning
confidence: 99%
“…VRPFTW has uncertain service time to customer points. Qamsari et al [18] propose a novel approach to the inventory routing problem for fuzzy time windows considering customer satisfaction and present a multi-priority structure for vehicles to visit customers. Li [19] considers the characteristics of fresh food delivery to develop a multi-objective vehicle routing optimization model with fuzzy time window for fresh food delivery, and designs an improved ACA based on rolling time-domain delay distribution to solve the problem according to the idea of the single-order distribution to get the optimal combination of the distribution cost and the customer satisfaction.…”
Section: Fuzzy Time Window With Vrp (Vrpftw)mentioning
confidence: 99%
“…This interval value can be a tolerance value in the penalty time window, if a delay in delivery occurs. More specifically, the level of customer satisfaction is directly related to the degree of fuzzy membership of each, when the customer is visited by one of the vehicles from the warehouse (Qamsari et al, 2022). In this diagram the parameters e and l are the limits of the crisp time windows (0 or 1) that provide the highest utility for the customer.…”
Section: Fuzzy Logicmentioning
confidence: 99%
“…There are 36 articles that benefitted from different meta-heuristic methods. Most of these studies use several meta-heuristics [127,151,[160][161][162]. The most encountered metaheuristics for BSC are genetic algorithm, simulation annealing, particle swarm optimization, and tabu search algorithm, with nine, six, five, and four implementations, respectively.…”
Section: Meta-heuristicsmentioning
confidence: 99%