32,408 research outputs found
Flexibility and Complexity in Periodic Distribution Problems
In this paper, we explore trade-offs between operational flexibility and operational complexity in periodic distribution
problems. We consider the gains from operational flexibility in terms of vehicle routing costs and customer service benefits,
and the costs of operational complexity in terms of implementation difficulty. Periodic distribution problems arise in a
number of industries, including food distribution, waste management and mail services. The period vehicle routing problem
(PVRP) is a variation of the classic vehicle routing problem in which driver routes are constructed for a period of time; the
PVRP with service choice (PVRP-SC) extends the PVRP to allow service (visit) frequency to become a decision of the
model. While introducing operational flexibility in periodic distribution systems can increase efficiency, it poses three
challenges: the difficulty of modeling this flexibility accurately; the computational effort required to solve the problem as
modeled with such flexibility; and the complexity of operationally implementing the resulting solution. This paper considers
these trade-offs between the system performance improvements due to operational flexibility and the resulting increases in
operational and computational complexity as they relate to periodic vehicle routing problems. In particular, increasing the
operational complexity of driver routes can be problematic in industries where some level of system regularity is required. As
discussed in the paper, recent work in the literature suggests that dispatching drivers consistently to the same geographic
areas results in driver familiarity and improved driver performance. Additionally, having the same driver visit a customer on
a continual basis can foster critical relationships. According to UPS, such driver-customer relationships are a key competitive
advantage in its package delivery operations, attributing 60 million packages a year to sales leads generated by drivers. In this
paper, we develop a set of quantitative measures to evaluate the trade-offs between flexibility and complexity
Workload Equity in Vehicle Routing Problems: A Survey and Analysis
Over the past two decades, equity aspects have been considered in a growing
number of models and methods for vehicle routing problems (VRPs). Equity
concerns most often relate to fairly allocating workloads and to balancing the
utilization of resources, and many practical applications have been reported in
the literature. However, there has been only limited discussion about how
workload equity should be modeled in VRPs, and various measures for optimizing
such objectives have been proposed and implemented without a critical
evaluation of their respective merits and consequences.
This article addresses this gap with an analysis of classical and alternative
equity functions for biobjective VRP models. In our survey, we review and
categorize the existing literature on equitable VRPs. In the analysis, we
identify a set of axiomatic properties that an ideal equity measure should
satisfy, collect six common measures, and point out important connections
between their properties and those of the resulting Pareto-optimal solutions.
To gauge the extent of these implications, we also conduct a numerical study on
small biobjective VRP instances solvable to optimality. Our study reveals two
undesirable consequences when optimizing equity with nonmonotonic functions:
Pareto-optimal solutions can consist of non-TSP-optimal tours, and even if all
tours are TSP optimal, Pareto-optimal solutions can be workload inconsistent,
i.e. composed of tours whose workloads are all equal to or longer than those of
other Pareto-optimal solutions. We show that the extent of these phenomena
should not be underestimated. The results of our biobjective analysis are valid
also for weighted sum, constraint-based, or single-objective models. Based on
this analysis, we conclude that monotonic equity functions are more appropriate
for certain types of VRP models, and suggest promising avenues for further
research.Comment: Accepted Manuscrip
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
Optimizing departure times in vehicle routes
Most solution methods for the vehicle routing problem with time\ud
windows (VRPTW) develop routes from the earliest feasible departure time. However, in practice, temporal traffic congestions make\ud
that such solutions are not optimal with respect to minimizing the\ud
total duty time. Furthermore, VRPTW solutions do not account for\ud
complex driving hours regulations, which severely restrict the daily\ud
travel time available for a truck driver. To deal with these problems,\ud
we consider the vehicle departure time optimization (VDO) problem\ud
as a post-processing step of solving a VRPTW. We propose an ILP-formulation that minimizes the total duty time. The obtained solutions are feasible with respect to driving hours regulations and they\ud
account for temporal traffic congestions by modeling time-dependent\ud
travel times. For the latter, we assume a piecewise constant speed\ud
function. Computational experiments show that problem instances\ud
of realistic sizes can be solved to optimality within practical computation times. Furthermore, duty time reductions of 8 percent can\ud
be achieved. Finally, the results show that ignoring time-dependent\ud
travel times and driving hours regulations during the development of\ud
vehicle routes leads to many infeasible vehicle routes. Therefore, vehicle routing methods should account for these real-life restrictions
Routing design for less-than-truckload motor carriers using ant colony techniques
One of the most important challenges for Less-Than-Truck-Load carriers consists of determining how to consolidate flows of small shipments to minimize costs while maintaining a certain level of service. For any origin-destination pair, there are several strategies to consolidate flows, but the most usual ones are: peddling/collecting routes and shipping through one or more break-bulk terminals. Therefore, the target is determining a route for each origin-destination pair that minimizes the total transportation and handling cost guaranteeing a certain level of service. Exact resolution is not viable for real size problems due to the excessive computational time required. This research studies different aspects of the problem and provides a metaheuristic algorithm (based on Ant Colonies Optimization techniques) capable of solving real problems in a reasonable computational time. The viability of the approach has been proved by means of the application of the algorithm to a real Spanish case, obtaining encouraging results
Planning and Scheduling Transportation Vehicle Fleet in a Congested Traffic Environment
Transportation is a main component of supply chain competitiveness since it plays a major role in the inbound, inter-facility, and outbound logistics. In this context, assigning and scheduling vehicle routing is a crucial management problem. Despite numerous publications dealing with efficient scheduling methods for vehicle routing, very few addressed the inherent stochastic nature of travel times in this problem. In this paper, a vehicle routing problem with time windows and stochastic travel times due to potential traffic congestion is considered. The approach developed introduces mainly the traffic congestion component based on queueing theory. This is an innovative modeling scheme to capture the stochastic behavior of travel times. A case study is used both to illustrate the appropriateness of the approach as well as to show that time-independent solutions are often unrealistic within a congested traffic environment which is often the case on the european road networkstransportation; vehicle fleet; planning; scheduling; congested traffic
Time Slot Management in Attended Home Delivery
Many e-tailers providing attended home delivery, especially e-grocers, offer narrow delivery time slots to ensure satisfactory customer service. The choice of delivery time slots has to balance marketing and operational considerations, which results in a complex planning problem. We study the problem of selecting the set of time slots to offer in each of the zip codes in a service region. The selection needs to facilitate cost-effective delivery routes, but also needs to ensure an acceptable level of service to the customer. We present two fully-automated approaches that are capable of producing high-quality delivery time slot offerings in a reasonable amount of time. Computational experiments reveal the value of these approaches and the impact of the environment on the underlying trade-offs.integer programming;vehicle routing;continuous approximation;e-grocery;home delivery;time slots
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