Online vehicle routing and scheduling with dynamic travel times

The developments in mobile communication technologies are a strong motivation for the study of dynamic vehicle routing and scheduling problems. In particular, the planned routes can be quickly modified to account for the occurrence of new customer requests, which might imply diverting a vehicle away from its current destination. In this paper, a previously developed problem-solving approach for a vehicle routing problem with dynamic requests and dynamic travel times is extended to account for more sophisticated communication means between the drivers and the central dispatch office. Computational results are reported to empirically demonstrate the benefits of this extension.     

Two-stage hybrid flow shop scheduling with dynamic job arrivals

Motivated by applications in semiconductor manufacturing industry, we consider a two-stage hybrid flow shop where a discrete machine is followed by a batching machine. In this paper, we analyze the computational complexity of a class of two-machine problems with dynamic job arrivals. For the problems belonging to P we present polynomial algorithms. For the NP-complete problems we propose the heuristics, and then establish the upper bounds on the worst case performance ratios of the heuristics. In addition, we give the improved heuristics that can achieve better performances.     

Adaptive granular local search heuristic for a dynamic vehicle routing problem

The advance of communication and information technologies based on satellite and wireless networks have allowed transportation companies to benefit from real-time information for dynamic vehicle routing with time windows. During daily operations, we consider the case in which customers can place requests such that their demand and location are stochastic variables. The time windows at customer locations can be violated although lateness costs are incurred. The objective is to define a set of vehicle routes which are dynamically updated to accommodate new customers in order to maximize the expected profit. This is the difference between the total revenue and the sum of lateness costs and costs associated with the total distance traveled. The solution approach makes use of a new constructive heuristic that scatters vehicles in the service area and an adaptive granular local search procedure. The strategies of letting a vehicle wait, positioning a vehicle in a region where customers are likely to appear, and diverting a vehicle away from its current destination are integrated within a granular local search heuristic. The performance of the proposed approach is assessed in test problems based on real-life Brazilian transportation companies.     

Vehicle routing problem with stochastic travel times including soft time windows and service costs

This paper studies a vehicle routing problem with soft time windows and stochastic travel times. A model is developed that considers both transportation costs (total distance traveled, number of vehicles used and drivers' total expected overtime) and service costs (early and late arrivals). We propose a Tabu Search method to solve this model. An initialization algorithm is developed to construct feasible routes by taking into account the travel time stochasticity. Solutions provided by the Tabu Search algorithm are further improved by a post-optimization method. We conduct our computational experiments for well-known problem instances. Results show that our Tabu Search method performs well by obtaining very good final solutions in a reasonable amount of time.     

Vehicle routing under time-dependent travel times: The impact of congestion avoidance

Daily traffic congestion forms a major problem for businesses such as logistic service providers and distribution firms. It causes late arrivals at customers and additional costs for hiring the truck drivers. Such costs caused by traffic congestion can be reduced by taking into account and avoiding predictable traffic congestion within vehicle route plans. In the literature, various strategies are proposed to avoid traffic congestion, such as selecting alternative routes, changing the customer visit sequences, and changing the vehicle-customer assignments. We investigate the impact of these and other strategies in off-line vehicle routing on the performance of vehicle route plans in reality. For this purpose, we develop a set of vehicle routing problem instances on real road networks, and a speed model that reflects the key elements of peak hour traffic congestion. The instances are solved for different levels of congestion avoidance using a modified Dijkstra algorithm and a restricted dynamic programming heuristic. Computational experiments show that 99% of late arrivals at customers can be eliminated if traffic congestion is accounted for off-line. On top of that, about 87% of the extra duty time caused by traffic congestion can be eliminated by clever congestion avoidance strategies.     

Vehicle routing problem with a heterogeneous fleet and time windows

In this paper, a problem variant of the vehicle routing problem with time windows is introduced to consider vehicle routing with a heterogeneous fleet, a limited number of vehicles and time windows. A method that extends an existing tabu search procedure to solve the problem is then proposed. To evaluate the performance of the proposed method, experiments are conducted on a large set of test cases, which comprises several benchmark problems from numerous problem variants of the vehicle routing problem with a heterogeneous fleet. It is observed that the proposed method can be used to give reasonably good results for these problem variants. In addition, some ideas are presented to advance the research in heuristics, such as fair reporting standards, publication of benchmark problems and executable routines developed for algorithmic comparison.     

考虑时间窗与随机航行时间的船舶航线规划

良好的航线设计是船运公司降低运营成本,提高服务质量的关键。船舶运输中存在航行时间不确定,码头资源稀缺等特点,基于此将其航线问题抽象为考虑时间窗与随机旅行时间的多重流动旅行商问题。针对航行时间的随机性,设计了线性近似方法,提出了虚拟时间窗的概念,构建了初始模型与修正模型;给出了该问题的一个算例,验证了模型与算法的有效性和合理性。     

A parametric fuzzy logic approach to dynamic part routing under full routing flexibility

Manufacturing flexibility is a competitive weapon for surviving today’s highly variable and volatile markets. It is critical therefore, to select the appropriate type of flexibility for a given manufacturing system, and to design effective strategies for using this flexibility in a way to improve the system performance. This study focuses on full routing flexibility which includes not only alternative machines for operations but also alternative sequences of operations for producing the same work piece. Upon completion of an operation, an on-line dispatching decision called part routing is required to choose one of the alternatives as the next step. This study introduces three new approaches, including a fuzzy logic approach, for dynamic part routing. The fuzzy part routing system adapts itself to the characteristics of a given flexible manufacturing system (FMS) installation by setting the key parameters of the membership functions as well as its Takagi-Sugeno type rule base, in such a way to capture the bottlenecks in the environment. Thus, the model does not require a search or training for the parameter set. The proposed approaches are tested against several crisp and fuzzy routing algorithms taken from the literature, by means of extensive simulation experiments in hypothetical FMS environments under variable system configurations. The results show that the proposed fuzzy approach remains robust across different system configurations and flexibility levels, and performs favourably compared to the other algorithms. The results also reveal important characteristic behaviour regarding routing flexibility.     

Anticipatory routing of police helicopters

We have developed a decision support application for the Dutch Aviation Police and Air Support unit for routing their helicopters in anticipation of unknown future incidents. These incidents are not known in advance, yet do require a swift response. A response might include the dispatch of a police helicopter to support the police on the ground. If a helicopter takes too long to arrive at the crime scene, it might be too late to assist. Hence, helicopters have to be proximate when an incident happens to increase the likelihood of being able to support the police on the ground in apprehending suspects. We propose the use of a forecasting technique, followed by a routing heuristic to maximize the number of incidents where a helicopter provides a successful assist. We have implemented these techniques in a decision support application in collaboration with the Dutch Aviation Police and Air Support. Using numerical experiments, we show that our application has the potential to improve the success rate with a factor nine. The Dutch Air Support and Aviation Police are now using the application.     

A large neighbourhood search heuristic for ship routing and scheduling with split loads

The purpose of this paper is to present and solve a new, important planning problem faced by many shipping companies dealing with the transport of bulk products. These shipping companies are committed to carrying some contract cargoes and will try to derive additional revenue from optional spot cargoes. In most of the literature on ship routing and scheduling problems a cargo cannot be transported by more than one ship. By introducing split loads this restriction is removed and each cargo can be transported by several ships. In this paper we propose a large neighbourhood search heuristic for the ship routing and scheduling problem with split loads. Computational results show that the heuristic provides good solutions to real-life instances within reasonable time. It is also shown that introducing split loads can yield significant improvements.     

A record-to-record travel algorithm for solving the heterogeneous fleet vehicle routing problem

In the heterogeneous fleet vehicle routing problem (HVRP), several different types of vehicles can be used to service the customers. The types of vehicles differ with respect to capacity, fixed cost, and variable cost. We assume that the number of vehicles of each type is fixed and equal to a constant. We must decide how to make the best use of the fixed fleet of heterogeneous vehicles.     

Solving the ship inventory routing and scheduling problem with undedicated compartments

This paper presents a ship inventory routing and scheduling problem with undedicated compartments (sIRPSP-UC). The objective of the problem is to find a minimum cost solution while satisfying a number of technical and physical constraints within a given planning horizon. In this problem, we identify four sub-problems that need to be decided simultaneously: route selections, ship selection, loading, and unloading activity procedures. To solve this problem, first, we developed a mixed integer linear programming model. We then developed a one-step greedy heuristic, and then based on this heuristic, we propose a set of heuristics. Each heuristic has a combination of rules for each sub-problem. A number of problem instances are used to compare the solutions of the two approaches. We applied selected good combinations of rules to solve each problem using the heuristic approach. The results show that 8 out 12 of the considered problem instances have no gap with MILP solution solved using LINGO. We also find that the average gap is 1.96%. In contrast when we consistently use the same combination for all iterations, there are no dominant combinations of heuristics that can find good solutions for all the problem instances.     

A GRASP approach to transporter scheduling and routing at a shipyard

We address the transporter scheduling and routing problem at a shipyard, which can be transformed into parallel machine scheduling with sequence-dependent setup times and precedence constraints. The objective is to maximize the workload balance among transporters under the time constraint that all assembly blocks should be transported in the predetermined period. We develop the GRASP algorithm for transporter scheduling and routing. Through simulation experiments we analyze some aspects of the developed GRASP algorithm and verify the performance of the developed GRASP algorithm. The comparison experiments show that the developed GRASP algorithm is a promising heuristic for transporter scheduling and routing.     

Heuristics for scheduling problems with an unavailability constraint and position-dependent processing times

We study a single machine scheduling problem, where the machine is unavailable for processing for a pre-specified time period. We assume that job processing times are position-dependent. The objective functions considered are minimum makespan, minimum total completion time and minimum number of tardy jobs. All these problems are known to be NP-hard even without position-dependent processing times. For all three cases we introduce simple heuristics which are based on solving the classical assignment problem. Lower bounds, worst case analysis and asymptotic optimality are discussed. All heuristics are shown numerically to perform extremely well.     

The dynamic multiperiod vehicle routing problem with probabilistic information

This paper introduces the Dynamic Multiperiod Vehicle Routing Problem with Probabilistic Information, an extension of the Dynamic Multiperiod Vehicle Routing Problem in which, at each time period, the set of customers requiring a service in later time periods is unknown, but its probability distribution is available. Requests for service must be satisfied within a given time window that comprises several time periods of the planning horizon. We propose an adaptive service policy that aims at estimating the best time period to serve each request within its associated time window in order to reduce distribution costs. The effectiveness of this policy is compared with that of two alternative basic policies through a series of computational experiments.     

REUSE: A combined routing and link scheduling mechanism for wireless mesh networks

Increasing the capacity of wireless mesh networks has motivated numerous studies. In this context, the cross-layer optimization techniques involving joint use of routing and link scheduling are able to provide better capacity improvements. Most works in the literature propose linear programming models to combine both mechanisms. However, this approach has high computational complexity and cannot be extended to large-scale networks. Alternatively, algorithmic solutions are less complex and can obtain capacity values close to the optimal. Thus, we propose the REUSE algorithm, which combines routing and link scheduling and aims to increase throughput capacity in wireless mesh networks. Through simulations, the performance of the proposal is compared to a developed linear programming model, which provides optimal results, and to other proposed mechanisms in the literature that also deal with the problem algorithmically. We observed higher values of capacity in favor of our proposal when compared to the benchmark algorithms.     

Multiperiod integrated routing and scheduling of World Food Programme cargo planes in Angola

This paper deals with the weekly planning of WFP emergency deliveries of food aid by air in Angola, where planes are required to travel back and forth between depots and clients, deliver entire loads to the client reached every time and, at night, park at a depot, subject to parking space availability. An ILP model has been formulated, called the VRVDFL model, tailored for the real problem, and has been solved. The computational experience is reported.     

The impact of particular components of the PSO-based algorithm solving the Dynamic Vehicle Routing Problem

This paper presents and analyzes a Two-Phase Multi-Swarm Particle Swarm Optimizer (2MPSO) solving the Dynamic Vehicle Routing Problem (DVRP). The research presented in this paper focuses on finding a configuration of several optimization improvement techniques, dedicated to solving dynamic optimization problems, within the 2MPSO framework. Techniques, whose impact on results achieved for DVRP is analyzed, include: solving the current state of a problem with a capacitated clustering and routing heuristic algorithms, solving requests-to-vehicles assignment by the PSO algorithm, route optimization by a separate instance of the PSO algorithm, and knowledge transfer between subsequent states of the problem. The results obtained by the best chosen configuration of the 2MPSO are compared with the state-of-the-art literature results on a popular set of benchmark instances.Our study shows that strong results achieved by 2MPSO should be attributed to three factors: generating initial solutions with a clustering heuristic, optimizing the requests-to-vehicle assignment with a metaheuristic approach, direct passing of solutions obtained in the previous stage (times step) of the problem solving procedure to the next stage. Additionally, 2MPSO outperforms the average results obtained by other algorithms presented in the literature, both in the time limited experiments, as well as those restricted by the number of fitness function evaluations.