The vehicle routing problem with hard time windows and stochastic travel and service time

Miranda, Douglas Moura; Conceição, Samuel Vieira

doi:10.1016/j.eswa.2016.07.022

Cited by 96 publications

(47 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kenyon and Morton (2003) (Neuts, 1981). Miranda and ao (2016) investigated the CVRP with hard time windows and stochastic travel and service time. The service time of each customer has to start within the range time and if the vehicle arrives early then it must wait.…”

Section: Overview Of the Stochastic Routing Problemmentioning

confidence: 99%

Simulation-based optimisation for stochastic maintenance routing in an offshore wind farm

Irawan

Eskandarpour

Ouelhadj

et al. 2021

European Journal of Operational Research

View full text Add to dashboard Cite

Scheduling maintenance routing for an offshore wind farm is a challenging and complex task. The problem is to find the best routes for the Crew Transfer Vessels to maintain the turbines in order to minimise the total cost. This paper primarily proposes an efficient solution method to solve the deterministic maintenance routing problem in an offshore wind farm. The proposed solution method is based on the Large Neighbourhood Search metaheuristic. The efficiency of the proposed metaheuristic is validated against state of the art algorithms. The results obtained from the computational experiments validate the effectiveness of the proposed method. In addition, as the maintenance activities are affected by uncertain conditions, a simulation-based optimisation algorithm is developed to tackle these uncertainties. This algorithm benefits from the fast computational time and solution quality of the proposed metaheuristic, combined with Monte Carlo simulation. The uncertain factors considered include the travel time for a vessel to visit turbines, the required time to maintain a turbine, and the transfer time for technicians and equipment to a turbine. Moreover, the proposed simulation-based optimisation algorithm is devised to tackle unpredictable broken-down turbines. The performance of this algorithm is evaluated using a case study based on a reference wind farm scenario developed in the EU FP7 LEANWIND project.

show abstract

Section: Overview Of the Stochastic Routing Problemmentioning

confidence: 99%

Simulation-based optimisation for stochastic maintenance routing in an offshore wind farm

Irawan

Eskandarpour

Ouelhadj

et al. 2021

European Journal of Operational Research

View full text Add to dashboard Cite

show abstract

“…Constraint (10) defines z ijk and n jk as 0-1 variables. Constraint (11) indicates that the order quantity of each customer is equal to the sum of the orders placed by each merchant. Constraint (12) constrains the order quantity placed by the customer at the merchant not to exceed the upper limit of the total order quantity of the merchant.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Qianwen, et al [10] proposed an improved fuzzy comprehensive evaluation method to measure customer satisfaction of takeaway distribution. Unlike most previous studies in literatures, Miranda, et al [11] introduced a hard time window, random travel, and service time into the multi-objective vehicle routing problem, taking the minimum operating cost and maximizing the service level as optimization objectives. Daya, et al [12] used the column generation heuristic algorithm to solve the vehicle routing problem.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Routing Optimization of Instant Distribution Routing Based on Customer Satisfaction

Zhang

Yuan

2020

Information

View full text Add to dashboard Cite

Since the actual factors in the instant distribution service scenario are not considered enough in the existing distribution route optimization, a route optimization model of the instant distribution system based on customer time satisfaction is proposed. The actual factors in instant distribution, such as the soft time window, the pay-to-order mechanism, the time for the merchant to prepare goods before delivery, and the deliveryman’s order combining, were incorporated in the model. A multi-objective optimization framework based on the total cost function and time satisfaction of the customer was established. Dual-layer chromosome coding based on the deliveryman-to-node mapping and the access order was conducted, and the nondominated sorting genetic algorithm version II (NSGA-II) was used to solve the problem. According to the numerical results, when time satisfaction of the customer was considered in the instant distribution routing problem, the customer satisfaction increased effectively and the balance between customer satisfaction and delivery cost in the means of Pareto optimization were obtained, with a minor increase in the delivery cost, while the number of deliverymen slightly increased to meet the on-time delivery needs of customers.

show abstract

“…Constraints (2) and (3) make sure that each technician starts from and finishes to a depot and he goes along a tour. Constraints (4) indicate that each customer is served by one technician. Constraints (5) guarantee each technician serves no more than customers.…”

Section: A Problem Statementmentioning

confidence: 99%

Service scheduling to minimise the risk of missing appointments

Liret²,

Owusu

et al. 2017

2017 Computing Conference

View full text Add to dashboard Cite

This paper introduces the risk minimisation objective in the Stochastic Vehicle Routing Problem (SVRP). In the studied variant of SVRP, technicians drive to customer sites to provide service. The service times and travel times are stochastic, and a time window is required for the start of the service for each customer. Most previous research uses a chance-constrained approach to the problem. Some consider the probability of journey duration exceeding the threshold of the driver's workload while others set restrictions on the probability of individual time window constraints being violated. Their objectives are related to traditional routing costs whilst a different approach was taken in this paper. The risk of missing a task is defined as the probability that the technician assigned to the task arrives at the customer site later than the time window. The problem studied in this paper is to generate a schedule that minimises the maximum risk and sum of risks of the tasks. Each task duration may be considered as following a known normal distribution. However the distribution of the start time of the service at a customer site will not be normally distributed due to time window constraints. Therefore a multiple integral expression of the risk was derived, and this expression works whether task distribution is normal or not. Additionally a deterministic heuristic searching method was applied to solve the problem. Experiments are carried out to test the method. Results of this work have been applied to an industrial case of SVRP where field engineering individuals drive to customer sites to provide time-constrained services. This original approach allows organisations to pay more attention to increasing customer satisfaction and become more competitive in the market. Keywords-vehicle routing with time windows; stochastic service time; risk minimisation I.

show abstract

The vehicle routing problem with hard time windows and stochastic travel and service time

Cited by 96 publications

References 20 publications

Simulation-based optimisation for stochastic maintenance routing in an offshore wind farm

Simulation-based optimisation for stochastic maintenance routing in an offshore wind farm

Vehicle Routing Optimization of Instant Distribution Routing Based on Customer Satisfaction

Service scheduling to minimise the risk of missing appointments

Contact Info

Product

Resources

About