2,978 research outputs found
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
Hybrid Meta-Heuristics for Robust Scheduling
The production and delivery of rapidly perishable goods in distributed supply networks involves a number of tightly coupled decision and optimization problems regarding the just-in-time production scheduling and the routing of the delivery vehicles in order to satisfy strict customer specified time-windows. Besides dealing with the typical combinatorial complexity related to activity assignment and synchronization, effective methods must also provide robust schedules, coping with the stochastic perturbations (typically transportation delays) affecting the distribution process. In this paper, we propose a novel metaheuristic approach for robust scheduling. Our approach integrates mathematical programming, multi-objective evolutionary computation, and problem-specific constructive heuristics. The optimization algorithm returns a set of solutions with different cost and risk tradeoffs, allowing the analyst to adapt the planning depending on the attitude to risk. The effectiveness of the approach is demonstrated by a real-world case concerning the production and distribution of ready-mixed concrete.Meta-Heuristics;Multi-Objective Genetic Optimization;Robust Scheduling;Supply Networks
The stochastic vehicle routing problem : a literature review, part II : solution methods
Building on the work of Gendreau et al. (Oper Res 44(3):469–477, 1996), and complementing the first part of this survey, we review the solution methods used for the past 20 years in the scientific literature on stochastic vehicle routing problems (SVRP). We describe the methods and indicate how they are used when dealing with stochastic vehicle routing problems. Keywords: vehicle routing (VRP), stochastic programmingm, SVRPpublishedVersio
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Resource constrained routing and scheduling: Review and research prospects
In the service industry, it is crucial to efficiently allocate scarce resources to perform tasks and meet particular service requirements. What considerably complicates matters is when these resources, for example skilled technicians, nurses, and home carers have to visit different customer locations. This paper provides a comprehensive survey on resource constrained routing and scheduling that unveils the problem characteristics with respect to resource qualifications, service requirements and problem objectives. It also identifies the most effective exact and heuristic algorithms for this class of problems. The paper closes with several research prospects
Two-Echelon Vehicle and UAV Routing for Post-Disaster Humanitarian Operations with Uncertain Demand
Humanitarian logistics service providers have two major responsibilities
immediately after a disaster: locating trapped people and routing aid to them.
These difficult operations are further hindered by failures in the
transportation and telecommunications networks, which are often rendered
unusable by the disaster at hand. In this work, we propose two-echelon vehicle
routing frameworks for performing these operations using aerial uncrewed
autonomous vehicles (UAVs or drones) to address the issues associated with
these failures. In our proposed frameworks, we assume that ground vehicles
cannot reach the trapped population directly, but they can only transport
drones from a depot to some intermediate locations. The drones launched from
these locations serve to both identify demands for medical and other aids
(e.g., epi-pens, medical supplies, dry food, water) and make deliveries to
satisfy them. Specifically, we present two decision frameworks, in which the
resulting optimization problem is formulated as a two-echelon vehicle routing
problem. The first framework addresses the problem in two stages: providing
telecommunications capabilities in the first stage and satisfying the resulting
demands in the second. To that end, two types of drones are considered. Hotspot
drones have the capability of providing cell phone and internet reception, and
hence are used to capture demands. Delivery drones are subsequently employed to
satisfy the observed demand. The second framework, on the other hand, addresses
the problem as a stochastic emergency aid delivery problem, which uses a
two-stage robust optimization model to handle demand uncertainty. To solve the
resulting models, we propose efficient and novel solution approaches
Algorithms for the multi-objective vehicle routing problem with hard time windows and stochastic travel time and service time
This paper introduces a multi-objective vehicle routing problem with hard time windows and stochastic travel and service times. This problem has two practical objectives: minimizing the operational costs, and maximizing the service level. These objectives are usually conflicting. Thus, we follow a multi-objective approach, aiming to compute a set of Pareto-optimal alternatives with different trade-offs for a decision maker to choose from. We propose two algorithms (a Multi-Objective Memetic Algorithm and a Multi-Objective Iterated Local Search) and compare them to an evolutionary multi-objective optimizer from the literature. We also propose a modified statistical method for the service level calculation. Experiments based on an adapted version of the 56 Solomon instances demonstrate the effectiveness of the proposed algorithms
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