(2) Our methodology can be used to solve CVRPSTT
instances with hundreds of nodes in a reasonable
time by “reducing” a complex CVRPSTT, where no
efficient metaheuristics have been developed yet, to a
limited set ofmore tractable CVRPs where excellent,
fast, and extensively tested metaheuristics exist.
(3) Moreover, our methodology can provide decision
makers with various solutions to practical problems,
each of which considers a different percentage of
the actualmaximum vehicle working time, indicating
different levels of risk or reliability. This makes the
methodologymore flexible in order to satisfy different
decision makers.
(2) Our methodology can be used to solve CVRPSTTinstances with hundreds of nodes in a reasonabletime by “reducing” a complex CVRPSTT, where noefficient metaheuristics have been developed yet, to alimited set ofmore tractable CVRPs where excellent,fast, and extensively tested metaheuristics exist.(3) Moreover, our methodology can provide decisionmakers with various solutions to practical problems,each of which considers a different percentage ofthe actualmaximum vehicle working time, indicatingdifferent levels of risk or reliability. This makes themethodologymore flexible in order to satisfy differentdecision makers.
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