3.3. Collection patterns
Collection decisions mainly involve the choice of the collection
days when the different operations must be performed. In particular,
important decisions concern the maximum number of
accumulation days (that is, the maximum number of days between
two consecutive removals), and the service frequency
(for instance, once or twice a week). In fact, if such decisions are
not taken properly (for instance, there are too many accumulation
days, or the service frequency is not adequate) the risk is that the
refuse removal is not uniform during the week and the trucks are
fully exploited only in some peak days, while they are only
partially filled in the other days. This would lead to increased
costs for the subsequent operational collection phase. Thus, a
minimization of the service costs can be obtained through a
minimization of the maximum quantity removed in a day (peak
quantity). This is the objective pursued by Mansini and Speranza
[54]. In this paper, the problem of the efficient management of
municipal household refuse collection at a tactical level is tackled,
aiming at deciding the collection days for a single city district. In
particular, municipal refuse generation is assumed to occur at a
constant rate and collection is supposed to be periodic. At each
collection site, the number of consecutive accumulation days must
be contained into a user-defined set. Mansini and Speranza [54]
consider the problem of minimizing the maximum amount (peak)
of refuse collected in a day. This choice is roughly equivalent to
minimizing the number of vehicles and crews required to collect
waste. By solving their model on data from the city of Brescia, the
authors are able to obtain a peak reduction ranging between 10%
and 16% (depending on the collection frequency), compared to the
peak values without using the proposed model
3.3. Collection patternsCollection decisions mainly involve the choice of the collectiondays when the different operations must be performed. In particular,important decisions concern the maximum number ofaccumulation days (that is, the maximum number of days betweentwo consecutive removals), and the service frequency(for instance, once or twice a week). In fact, if such decisions arenot taken properly (for instance, there are too many accumulationdays, or the service frequency is not adequate) the risk is that therefuse removal is not uniform during the week and the trucks arefully exploited only in some peak days, while they are onlypartially filled in the other days. This would lead to increasedcosts for the subsequent operational collection phase. Thus, aminimization of the service costs can be obtained through aminimization of the maximum quantity removed in a day (peakquantity). This is the objective pursued by Mansini and Speranza[54]. In this paper, the problem of the efficient management ofmunicipal household refuse collection at a tactical level is tackled,aiming at deciding the collection days for a single city district. Inparticular, municipal refuse generation is assumed to occur at aconstant rate and collection is supposed to be periodic. At eachcollection site, the number of consecutive accumulation days mustbe contained into a user-defined set. Mansini and Speranza [54]consider the problem of minimizing the maximum amount (peak)of refuse collected in a day. This choice is roughly equivalent tominimizing the number of vehicles and crews required to collectwaste. By solving their model on data from the city of Brescia, theauthors are able to obtain a peak reduction ranging between 10%and 16% (depending on the collection frequency), compared to thepeak values without using the proposed model
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