Optimizing road network daily maintenance operations with stochastic service and travel times

This paper studies optimization methods for a routing problem encountered in daily maintenance operations of a road network. Stochastic service and travel times on road segments are considered. The problem is formulated as a variation of the capacitated arc routing problem (CARP). A chance-constrained programming model is firstly developed and solved by a branch-and-cut algorithm. A stochastic programming model with recourse is also proposed to take into account the recourse costs in case of route failure. The problem is solved by an adaptive large neighborhood search algorithm. The computational experiments demonstrate the effectiveness of the algorithm.     

A Linear Programming approach for robust network revenue management in the airline industry

The classical revenue management problem consists of allocating a fixed network capacity to different customer classes, so as to maximize revenue. This area has been widely applied in service industries that are characterized by a fixed perishable capacity, such as airlines, cruises, hotels, etc.It is traditionally assumed that demand is uncertain, but can be characterized as a stochastic process (See Talluri and van Ryzin (2005) for a review of the revenue management models). In practice, however, airlines have limited demand information and are unable to fully characterize demand stochastic processes. Robust optimization methods have been proposed to overcome this modeling challenge. Under robust optimization framework, demand is only assumed to lie within a polyhedral uncertainty set (Lan et al. (2008); Perakis and Roels (2010)).In this paper, we consider the multi-fare, network revenue management problem for the case demand information is limited (i.e. the only information available is lower/upper bounds on demand). Under this interval uncertainty, we characterize the robust optimal booking limit policy by use of minimax regret criterion. We present an LP (Linear Programming) solvable mathematical program for the maximum regret so our model is able to solve large-scale problems for practical use. A genetic algorithm is proposed to find the booking limit control to minimize the maximum regret. We provide computational experiments and compare our methods to existing ones. The results demonstrate the effectiveness of our robust approach.     

Study on China-EU container shipping network in the context of Northern Sea Route

The emerging Northern Sea Route (NSR) represents change to the existing liner network for China-EU container shipping. It is necessary to re-examine the container network in this context and assist liner companies in decision-making. This paper assesses the potential of the NSR based on designing a multi-port multi-trip liner service by establishing a two-stage optimization model. Based on the estimated data of NSR shipping, ship routing schemes on both the NSR and conventional routes are proposed. It is determined that container service along the NSR is largely influenced by ice-breaking charge, seasonality, and cargo volume, which makes NSR more likely to act as a supplementary line of the liner network in the short or medium term. The results also indicate that use of NSR may drive the redeployment of shipping network and hub ports in the long term. This study's conclusions may prove useful for strategic planning by liner companies, port authorities, and governments to assess the operation of liner service via the NSR.     

Robust optimization for United States Department of Agriculture food aid bid allocations

The U.S. Department of Agriculture (USDA) currently uses a bidding system to determine carriers and suppliers that would partner in providing food aid annually in response to global emergencies and famine. We mimic the USDA approach via a robust optimization model featuring box and ellipsoid uncertainty frameworks to account for uncertainties in demand, supplier and carrier bid prices. Through a case study utilizing historical invoice data, we demonstrate our model applicability in improving ocean carrier and food supplier bid pricing strategy and similar supply chain network optimization problems. Through a validation algorithm we demonstrate the value of our robust models.     

Vehicle routing optimization with soft time windows in a fuzzy random environment

This paper is concerned with a vehicle routing problem with soft time windows (VRPSTW) in a fuzzy random environment. Two objectives are considered: (1) minimize the total travel cost and (2) maximize the average satisfaction level of all customers. After setting up the model for the VRPSTW in a fuzzy random environment, the fuzzy random expected value concept is used to deal with the constraints and its equivalent crisp model is derived. The global–local–neighbor particle swarm optimization with exchangeable particles (GLNPSO-ep) is employed to solve the equivalent crisp model. A case study is also presented to illustrate the effectiveness of the proposed approach.     

Combining service frequency and vehicle routing for managing supplier shipments

This paper proposes a new approach to designing inbound material collection routes that considers pick-up frequency and spatial design as joint decisions to minimize total logistics (transportation plus inventory) cost. The clustering-based optimization uses an approximation to the actual cost of a routing solution without actual route construction. We show that the problem is analogous to a single-source fixed-charge facility location problem, and near-optimal solutions can be found using an efficient heuristic algorithm. Tests show the effectiveness of how this model is formulated and a case study demonstrates that substantial total cost savings can be achieved in realistic applications.     

Robust grain supply chain design considering post-harvest loss and harvest timing equilibrium

This paper presents a bi-level robust optimization model, where a food company maximizes its profit and minimizes post-harvest loss by optimally deploying grain processing/storage facilities and determining grain purchase price, while a group of spatially distributed non-cooperative farmers determine harvest time, shipment, storage, and market decisions under yield uncertainty and market equilibrium. The non-cooperative behavior of the food company and the farmers is represented by a bi-level Stackelberg leader follower’s game model with mixed-integer decision variables. The proposed model and solution approach are applied to case studies for Illinois and Brazil.     

Reliable design of a forward/reverse logistics network under uncertainty: A robust-M/M/c queuing model

This paper presents a novel model for designing a reliable network of facilities in closed-loop supply chain under uncertainty. For this purpose, a bi-objective mathematical programming formulation is developed which minimizes the total costs and the expected transportation costs after failures of facilities of a logistics network. To solve the model, a new hybrid solution methodology is introduced by combining robust optimization approach, queuing theory and fuzzy multi-objective programming. Computational experiments are provided for a number of test problems using a realistic network instance.     

Reliable design of a forward/reverse logistics network under uncertainty: A robust-M/M/c queuing model

This paper presents a novel model for designing a reliable network of facilities in closed-loop supply chain under uncertainty. For this purpose, a bi-objective mathematical programming formulation is developed which minimizes the total costs and the expected transportation costs after failures of facilities of a logistics network. To solve the model, a new hybrid solution methodology is introduced by combining robust optimization approach, queuing theory and fuzzy multi-objective programming. Computational experiments are provided for a number of test problems using a realistic network instance.     

A strategic model of port-hinterland freight distribution networks

This paper presents a strategic model for port-hinterland freight distribution networks. The approach utilizes a combination of a multi-objective optimization model to estimate locations and networks of distribution centers and an assignment model that recognizes distributed service level preferences. Our example application concerns the European continent and is transferable to other regions. The model calibration is able to explain the European port-hinterland distribution structures satisfactorily. We compute novel performance measures that take into account port-hinterland distribution structures. The measures include port-hinterland transport cost, port-hinterland transport time, and distribution center-hinterland transport time. These measures can provide inputs for port-connectivity studies.     

Using time-varying tolls to optimize truck arrivals at ports

An analytical point-wise stationary approximation model is proposed to analyze time-dependent truck queuing processes with stochastic service time distributions at gates and yards of a port terminal. A convex nonlinear programming model is developed which minimizes the total truck turn time and discomfort due to shifted arrival times. A two-phase optimization approach is used to first compute a system-optimal truck arrival pattern, and then find a desirable pattern of time-varying tolls that leads to the optimal arrival pattern. Numerical experiments are conducted to test the computational efficiency and accuracy of the proposed optimization models.     

Multi-period hub location problems in transportation

Many transport service providers operate on hub-and-spoke network structures. Major operators may have several dedicated hub facilities that are leased for a time horizon rather than being owned or constructed. For a given discrete planning horizon, service providers must decide on the location of the hub ports (i.e. terminals), the period when the lease contract starts, the period when the existing contracts must be terminated and the flow routing over the entire planning horizon so as to minimize the total operational cost. Thus, we propose a mathematical model for a Multi-period Uncapacitated Multiple Allocation Hub Location Problem with Budget Constraint. The proposed model incorporates several features of practice, particularly from maritime and land transport practices. We also propose a meta-heuristic solution algorithm that produces high-quality solutions in a reasonable amount of time. By exploiting the decomposable structure of the model, we extended a Benders decomposition approach by proposing several improvements. Extensive computational experiments confirm the efficiency of the proposed methods and also show its limitations.     

Pareto efficient allocation of an in-motion wireless charging infrastructure for electric vehicles in a multipath network

Electric vehicles (EV) use an eco-friendly technology that limits the greenhouse gas emissions of the transport sector, but the limited battery capacity and the density of the battery are the major barriers to the widespread adoption of EV. To mitigate this, a good method seems to be the innovative wireless charging technology called ‘On-Line EV (OLEV)’, which is a contactless electric power transfer technology. This EV technology has the potential to charge the vehicle’s battery dynamically while the vehicle is in motion. This system helps to reduce not only the size of the battery but also its cost, and it also contributes to extending the driving range before the EV has to stop. The high cost of this technology requires an optimal location of the infrastructure along the route. For this reason, the objective of this paper is to study the problem of the location of the wireless charging infrastructure in a transport network composed of multiple routes between the origin and the destination. To find a strategic solution to this problem, we first and foremost propose a nonlinear integer programming solution to reach a compromise between the cost of the battery, which is related to its capacity, and the cost of installing the power transmitters, while maintaining the quality of the vehicle’s routing. Second, we adapt the multi-objective particle swarm optimization (MPSO) approach to our problem, as the particles were robust in solving nonlinear optimization problems. Since we have a multi-objective problem with two binary variables, we combine the binary and discrete versions of the particle swarm optimization approach with the multi-objective one. The port of Le Havre is presented as a case study to illustrate the proposed methodology. The results are analyzed and discussed in order to point out the efficiency of our resolution method.     

单车场大规模车辆路径优化问题研究

针对传统优化技术在解决大规模车辆路径问题中存在的缺陷,提出了一种解决单车场大规模车辆路径优化问题的综合启发式算法。首先,采用Sweep技术将区域分解成几个子区。其次,设计了分区的禁忌搜索算法,并采用相邻区域综合优化技术,提高了算法的全局搜索能力。仿真试验表明,该算法能够有效解决大规模车辆路径优化问题。     

A robust mathematical model and heuristic algorithms for integrated aircraft routing and scheduling,with consideration of fleet assignment problem

One of the most important airline's products is to determine the aircraft routing and scheduling and fleet assignment. The key input data of this problem is the traffic forecasting and allocation that forecasts traffic on each flight leg. The complexity of this problem is to define the connecting flights when passengers should change the aircraft to reach the final destination. Moreover, as there exists various types of uncertainties during the flights, finding a solution which is able to absorb these uncertainties is invaluable. In this paper, a new robust mixed integer mathematical model for the integrated aircraft routing and scheduling, with consideration of fleet assignment problem is proposed. Then to find good solutions for large-scale problems in a rational amount of time, a heuristic algorithm based on the Simulated Annealing (SA) is introduced. In addition, some examples are randomly generated and the proposed heuristic algorithm is validated by comparing the results with the optimum solutions. The effects of robust vs non-robust solutions are examined, and finally, a hybrid algorithm is generated which results in more effective solution in comparison with SA, and Particle Swarm Optimization (PSO).     

Robust accessibility: Measuring accessibility based on travelers' heterogeneous strategies for managing travel time uncertainty

Uncertainties in travel times due to traffic congestion and delay are risks for drivers and public transit users. To avoid undesired consequences such as losing jobs or missing medical appointments, people can manage the risks of missing on-time arrivals to destinations using different strategies, including leaving earlier to create a safety margin and choosing routes that have more reliable rather than fastest travel times. This research develops a general analytical framework for measuring accessibility considering automobile or public transit travelers' heterogeneous strategies for dealing with travel time uncertainty. To represent different safety margin plans, we use effective travel time (expected time + safety margin), given specified on-time arrival probabilities. Heterogeneity in routing strategy is addressed using different Pareto-optimal routes with two main criteria: faster travel time vs. higher reliability. Based on various safety margin and routing strategy combinations, we examine how accessibility changes under varying safety margin plans and routing strategies. Also, we define and measure robust accessibility: geographic regions that are accessible regardless of the safety margin planning and routing strategy. Robust accessibility can provide a conservative and reasonable view of accessibility under travel time uncertainty. To demonstrate the applicability of the methods, we carry out an empirical study on measuring the impacts of new transit service on healthcare accessibility in a deprived neighborhood in Columbus, Ohio, USA.     

A model for development of optimized feeder routes and coordinated schedules—A genetic algorithms approach

Many attempts have been made to solve bus route network design problems by splitting it in two stages, one for routing and the other for scheduling. Some researchers have made attempts to solve network design problems using non-traditional optimization techniques also, but not much has been done on modelling coordinated operations involving transfers from one mode to another. In this research, feeder routes and frequencies leading to schedule coordination of feeder buses with main transit are developed simultaneously using genetic algorithms. The coordinated schedules of feeder buses are determined for the existing given schedules of main transit. Thus the developed feeder routes and schedules are complementary to each other. As a case study the Dun Laoghaire Dublin Area Rapid Transit (DART) (heavy rail suburban service) station of Dublin in Ireland is selected. Finally the outcome of the research is a generated feeder route network for feeder buses and coordinated schedules of feeder buses for the existing schedules of DART at the selected station. The results of the proposed model indicate improved load factors on developed routes and also the overall load factor is also improved considerably as compared to the authors’ earlier model.     

城市轨道交通大小交路结合快慢车开行方案优化

城市轨道交通线路可以实行大小交路的开行模式,以克服断面客流不均衡导致的能力浪费,而这种模式使长距离乘客的出行时间增加,可以在列车开行大小交路的基础上考虑开行快慢车。以乘客出行成本与企业运营成本最小为目标,考虑列车停站时间与客流需求的关系,将停站时间纳入乘客出行成本,建立双目标非线性整数优化模型,求解乘客与运营企业的成本博弈均衡点。通过算例验证模型的有效性,结果表明,相对于大小交路嵌套的运营模式,实行“大小交路+快慢车”的运营模式可以有效降低乘客出行成本与企业运营成本。     

The ship routing and freight assignment problem for daily frequency operation of maritime liner shipping

To cope with excess capacity and improve service quality, maritime international liner carriers have recently adopted a new operational model known as daily frequency. In this new model, carriers provide daily pickup and delivery service to customers at major ports along the Pacific Rim. We investigate the ship routing and freight assignment problem for daily frequency operation of liner shipping. A solution procedure that incorporates a Lagrangian relaxation technique and local search was proposed. The numerical results show that Shanghai, Hong Kong and Singapore are ports that are ideal for carriers in establishing daily frequency operations along the Pacific Rim.     

Robust hub network design problem

This paper presents a robust formulation for the uncapacitated single and multiple allocation hub location problem where demand is uncertain and its distribution is not fully specified. The proposed robust model is formulated as a mixed integer nonlinear program and then transformed into a mixed integer conic quadratic program. An efficient linear relaxation strategy is proposed which is found to deliver the optimal solutions for all the cases considered in this paper. Numerical experiments suggest location of more number of hubs when accounting for demand uncertainty using robust optimization compared to the deterministic setting.