2,013 research outputs found
Distribution planning in a weather-dependent scenario with stochastic travel times: a simheuristics approach
In real-life logistics, distribution plans might be affected by weather conditions (rain, snow, and fog), since they might have a significant effect on traveling times and, therefore, on total distribution costs. In this paper, the distribution problem is modeled as a multi-depot vehicle routing problem with stochastic traveling times. These traveling times are not only stochastic in nature but the specific probability distribution used to model them depends on the particular weather conditions on the delivery day. In order to solve the aforementioned problem, a simheuristic approach combining simulation within a biased-randomized heuristic framework is proposed. As the computational experiments will show, our simulation-optimization algorithm is able to provide high-quality solutions to this NP-hard problem in short computing times even for large-scale instances. From a managerial perspective, such a tool can be very useful in practical applications since it helps to increase the efficiency of the logistics and transportation operations.Peer ReviewedPostprint (published version
Distribution planning in a weather-dependent scenario with stochastic travel times: a simheuristics approach
In real-life logistics, distribution plans might be affected by weather conditions (rain, snow, and fog), since they might have a significant effect on traveling times and, therefore, on total distribution costs. In this paper, the distribution problem is modeled as a multi-depot vehicle routing problem with stochastic traveling times. These traveling times are not only stochastic in nature but the specific probability distribution used to model them depends on the particular weather conditions on the delivery day. In order to solve the aforementioned problem, a simheuristic approach combining simulation within a biased-randomized heuristic framework is proposed. As the computational experiments will show, our simulation-optimization algorithm is able to provide high-quality solutions to this NP-hard problem in short computing times even for large-scale instances. From a managerial perspective, such a tool can be very useful in practical applications since it helps to increase the efficiency of the logistics and transportation operations.Peer ReviewedPostprint (published version
On green routing and scheduling problem
The vehicle routing and scheduling problem has been studied with much
interest within the last four decades. In this paper, some of the existing
literature dealing with routing and scheduling problems with environmental
issues is reviewed, and a description is provided of the problems that have
been investigated and how they are treated using combinatorial optimization
tools
Job Selection in a Network of Autonomous UAVs for Delivery of Goods
This article analyzes two classes of job selection policies that control how
a network of autonomous aerial vehicles delivers goods from depots to
customers. Customer requests (jobs) occur according to a spatio-temporal
stochastic process not known by the system. If job selection uses a policy in
which the first job (FJ) is served first, the system may collapse to
instability by removing just one vehicle. Policies that serve the nearest job
(NJ) first show such threshold behavior only in some settings and can be
implemented in a distributed manner. The timing of job selection has
significant impact on delivery time and stability for NJ while it has no impact
for FJ. Based on these findings we introduce a methodological approach for
decision-making support to set up and operate such a system, taking into
account the trade-off between monetary cost and service quality. In particular,
we compute a lower bound for the infrastructure expenditure required to achieve
a certain expected delivery time. The approach includes three time horizons:
long-term decisions on the number of depots to deploy in the service area,
mid-term decisions on the number of vehicles to use, and short-term decisions
on the policy to operate the vehicles
A review of the Tabu Search Literature on Traveling Salesman Problems
The Traveling Salesman Problem (TSP) is one of the most widely studied problems inrncombinatorial optimization. It has long been known to be NP-hard and hence research onrndeveloping algorithms for the TSP has focused on approximate methods in addition to exactrnmethods. Tabu search is one of the most widely applied metaheuristic for solving the TSP. Inrnthis paper, we review the tabu search literature on the TSP, point out trends in it, and bringrnout some interesting research gaps in this literature.
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