2010
DOI: 10.1016/j.cor.2009.06.022
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A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows

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Cited by 214 publications
(135 citation statements)
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References 30 publications
(75 reference statements)
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“…An effective way to relax the time window constraint and efficiently calculate the time window violation was presented by Nagata et al (2010) and was enhanced by Schneider et al (2013) in the context of VRPTW. A time window penalty (TW) counting the cumulative time needed to travel back in time in order to satisfy the time window constraints is introduced.…”
Section: Concatenation Operators From the Literaturementioning
confidence: 99%
“…An effective way to relax the time window constraint and efficiently calculate the time window violation was presented by Nagata et al (2010) and was enhanced by Schneider et al (2013) in the context of VRPTW. A time window penalty (TW) counting the cumulative time needed to travel back in time in order to satisfy the time window constraints is introduced.…”
Section: Concatenation Operators From the Literaturementioning
confidence: 99%
“…The same rule applies to the parameter β with respect to w(s). The initial values of α and β are both set to 1, as suggested by a number of studies that have similar cost functions and weight parameters (Cordeau et al , 1997Ibaraki et al 2008;Nagata et al 2010).…”
Section: Local Search Proceduresmentioning
confidence: 99%
“…However, it takes O(n) to compute the penalties d(s) and w(s) in (13). Nagata et al (2010) propose an evaluation technique to compute the violation of time window constraints in amortized O(1) time for most traditional neighbourhood operators including 2-opt, inter-route swaps, and inter-route inserts. Vidal et al (2013) extend this technique to allow the evaluation of both duration and time windows violations not only for inter-route but also for intra-route operators.…”
Section: Move Evaluationmentioning
confidence: 99%
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“…For the routing in this phase, we implement a simple insertion heuristic to quickly generate solutions for the sample days. We adopt the objective function and the time window handling introduced by Nagata et al (2010), which allows the temporary violation of time window and capacity constraints.…”
Section: Driver Learningmentioning
confidence: 99%