Robust schedule design for liner shipping services

This paper examines the design of liner ship route schedules that can hedge against the uncertainties in port operations, which include the uncertain wait time due to port congestion and uncertain container handling time. The designed schedule is robust in that uncertainties in port operations and schedule recovery by fast steaming are captured endogenously. This problem is formulated as a mixed-integer nonlinear stochastic programming model. A solution algorithm which incorporates a sample average approximation method, linearization techniques, and a decomposition scheme, is proposed. Extensive numerical experiments demonstrate that the algorithm obtains near-optimal solutions with the stochastic optimality gap less 1.5% within reasonable time.     

Airline ground operations: Optimal schedule recovery with uncertain arrival times

Airline operations are subject to a number of stochastic influences which result into variable ground and block times for same flights on different days. Our research explores how airline operations control centers may benefit from an integrated decision support system for schedule recovery during aircraft ground operations. In this paper, we study the sensitivity of an optimal set of schedule recovery options towards uncertain arrival times. The calculation of recovery options is based upon an integrated and iterative scheduling and optimization algorithm, which incorporates uncertainties for arrival flights as a function of a given look-ahead time. Potential recovery options include stand re-allocation, quick-turnaround, quick passenger transfer, waiting for transfer passengers, cancellation of passenger or crew connections, and arrival prioritization. Within a case study setting at Frankfurt Airport, 20 aircraft turnarounds are analysed during a morning peak with their respective estimated arrival times (including potential arrival delays). The analysis of simulation results reveals an almost identical set of selected recovery options under high uncertainty circumstances and from post-operational point-of-view, which indicates high solution stability.     

Minimizing fuel emissions by optimizing vessel schedules in liner shipping with uncertain port times

We consider the problem of designing an optimal vessel schedule in the liner shipping route to minimize the total expected fuel consumption (and emissions) considering uncertain port times and frequency requirements on the liner schedule. The general optimal scheduling problem is formulated and tackled by simulation-based stochastic approximation methods. For special cases subject to the constraint of 100% service level, we prove the convexity and continuous differentiability of the objective function. Structural properties of the optimal schedule under certain conditions are obtained with useful managerial insights regarding the impact of port uncertainties. Case studies are given to illustrate the results.     

Multi-objective optimization for planning liner shipping service with uncertain port times

This paper considers a joint tactical planning problem for the number of ships, the planned maximum sailing speed, and the liner service schedule in order to simultaneously optimize the expected cost, the service reliability and the shipping emission in the presence of port time uncertainty. The problem is formulated into a stochastic multi-objective optimization problem at the operational level. The relationships between the objectives and the decision variables are established. A simulation-based non-dominated sorting genetic algorithm is then presented to solve this problem. A case study is provided to illustrate the results and the application of the model.     

A model for taxi pooling with stochastic vehicle travel times

Taxi pooling means the sharing of a taxi by more than one passenger with at least a semi-common route. Currently, a trial-and-error process is adopted, taking into account expected travel times, for taxi pooling, a method which is neither effective nor efficient. This is because stochastic disturbances arising from variations in the taxi travel times that occur in actual traveling are neglected. In the worst case scenario, where vehicle travel times fluctuate wildly during operations, the planned schedule can be disturbed enough to lose its optimality. Therefore, in this study, a network flow technique is used to construct a stochastic taxi pooling model incorporating stochastic vehicle travel times. A solution algorithm is also proposed to efficiently solve the problem. The performance of the proposed models and the use of the solution method in practice are evaluated by executing numerical tests using real data with suitable assumptions. The test results show that the stochastic model and the solution method could be useful references for practice.     

Aircraft operational costs and turnaround efficiency at airports

The aim of this paper is to investigate the relationship between flight schedule punctuality and aircraft turnaround efficiency at airports, in order to minimise system operational costs and meanwhile to maintain a required level of schedule punctuality. Investigations of aircraft operational costs, passenger delay costs and airline schedule time-opportunity costs are carried out in this paper. A mathematical model is applied to simulate aircraft turnaround operations by considering the stochastic effects of schedule punctuality as well as aircraft turnaround performance. Two numerical examples demonstrate the performance of the aircraft turnaround model. Results show the significance of a proper use of schedule buffer time in maintaining schedule punctuality performance.     

The value of information on supply risk under random yields

For companies facing challenge of managing unreliable supply sources, one of the operational goals is to increase the overall profit by improving performance of uncertain supply. We here develop stochastic decision frameworks which evaluate necessary information about uncertainties and help newsvendor and suppliers to enhance supply reliability. We first analyze the newsvendor’s purchasing decision under supply uncertainty and the derived decision framework is used to identify stochastic dominance conditions. We found that partial supply risk information is sufficient to determine regular ordering quantity, but to improve the overall profit, it is important to gather more information about stochastic dominance conditions.     

Dynamic network design for reverse logistics operations under uncertainty

The design of reverse logistics network has attracted growing attention with the stringent pressures from environmental and social requirements. In general, decisions about reverse logistics network configurations are made on a long-term basis and factors influencing such reverse logistics network design may also vary over time. This paper proposes dynamic location and allocation models to cope with such issues. A two-stage stochastic programming model is further developed by which a deterministic model for multiperiod reverse logistics network design can be extended to account for the uncertainties. A solution approach integrating a recently proposed sampling method with a heuristic algorithm is also proposed in this research. A numerical experiment is presented to demonstrate the significance of the developed stochastic model as well as the efficiency of the proposed solution method.     

The Northern Sea Route versus the Suez Canal: cases from bulk shipping

The navigation distance via the Northern Sea Route (NSR) from a Northwest-European port to the Far East is approximately 40% shorter compared to the route via the Suez Canal. The shorter distance may facilitate more than a doubling of vessels’ operational energy efficiency performance. There is at present substantial uncertainty in schedule reliability via the NSR. Unless the schedule reliability is improved, the NSR should primarily be explored for bulk rather than for liner shipping. A major disadvantage with the NSR is its seasonality. Shipping operations in the summer time via the NSR may already today be profitable for minor bulk trades. Additional shipping routes may give more flexibility, and the NSR route choice option may facilitate supply chain agility and adaptability.     

The multistage stochastic integer load planning problem

Time-definite freight delivery common carriers, who pickup, consolidate and deliver small shipments are a key third-party logistics service provider in the supply chains. Under uncertain demands, carrier’s multistage stochastic integer load planning in the pure hub-and-spoke line-haul operations network is to determine multistage freight paths and distribute trailers over time while meeting operational restrictions, service requirements, and balancing trailer inventory at the termination of planning horizon. We developed a heuristic approach, scenario aggregation with embedded branching on the binary variables. The numerical experiments showed a proactive operations strategy with a lower operating cost than the conventionally deterministic approach.     

Time efficiency assessment of ship movements in maritime ports: A case study of two ports based on AIS data

Operational efficiency of a maritime port is an important issue for shipping lines and port authorities. It is desirable to move ships in and out of a maritime port as efficiently as possible. Automatic identification system (AIS) data recording the trajectory of ship movements allow us to assess the operational efficiency as ships move in and out of a port. This study proposes a time efficiency assessment framework that evaluates the amount of time each ship spends in the different areas within a port (i.e., berth, anchorage, and fairway) based on the space-time trajectories of ship movements derived from AIS data. According to the statistical distributions of time spent by different types of ship in each port area, the proposed framework can compare time efficiency across different zones within a port and between different ports. This study uses AIS data of four types of ships (i.e., container, cargo, tanker, and passenger ships) at two selected ports in China, Shanghai Yangshan Port and Xiamen Port, to demonstrate how the proposed approach can effectively assess and compare time efficiency levels of ship movements between the times entering and leaving the vessel traffic service (VTS) lines of a port. This study demonstrates the value of deriving space-time trajectories from AIS data based on the concepts of time geography to assess time efficiency levels of maritime ports and monitor their performance over time, which offer useful information to both shipping lines and port authorities for operations such as efficient scheduling and logistic support.     

Container yard template planning under uncertain maritime market

A yard template determines the assignment of spaces in a yard for arriving vessels. Fluctuation of demand for freight transportation brings new challenges for making a robust yard template when facing uncertain maritime market. A model is proposed for yard template planning considering random numbers of containers that will be loaded onto vessels that visit the port periodically. Traffic congestions and multiple schedule cycle times for vessel arrival patterns are also considered. Moreover, a meta-heuristic method is developed for solving the model in large-scale cases. Numerical experiments are conducted to validate effectiveness and efficiency of the model.     

Stochastic resource allocation for containerized cargo transportation networks when capacities are uncertain

We consider the stochastic resource allocation problem for containerized cargo transportation with uncertain capacities and network effects, in which a freight operator needs to allocate a certain amount of capacity to each product to maximize the expected profit. We formulate the problem as a constrained stochastic programming model and provide theoretical results that completely characterize the optimal solution to the model under a special case. Under a general case, we build an approximation model of the problem and propose a sampling based algorithm to solve the approximation model. A number of numerical experiments are offered to test the algorithm.     

Storage pricing strategies for import container terminals under stochastic conditions

This paper presents a model for determining the optimal storage pricing schedule for import containers. A generic schedule which is characterized by a flat rate and a storage time charge is adopted. The model considers analytically the stochastic behavior of the storage yard, as input/output flows are random variables, and includes the migration to an off-dock warehouse. Two objective functions are proposed: maximizing terminal operator profits and minimizing total integrated cost of the system. Some numerical experiments are provided and a sensitivity analysis is performed to investigate the effect of main variables and approaches on the optimal solution.     

A multi-objective optimization with a delay-aware component for airport stand allocation

Airport management is regularly challenged by the task of assigning flights to existing parking positions in the most efficient way while complying with existing policies, restrictions and capacity limitations. However, such process is frequently disrupted by various events, affecting punctuality of airline operations. This paper describes an innovative approach for obtaining an efficient stand assignment considering the stochastic nature of airport environment. Furthermore, the presented methodology combines benefits of Bayesian modelling and metaheuristics for generating solutions that are more robust to airport flight schedule perturbations. In addition, this paper illustrates that the application of the presented methodology combined with simulation provides a valuable tool for assessing the robustness of the developed stand assignment to flight delays.     

Bioethanol supply chain system planning under supply and demand uncertainties

A mixed integer stochastic programming model is established to support strategic planning of bioenergy supply chain systems and optimal feedstock resource allocation in an uncertain decision environment. The two-stage stochastic programming model, together with a Lagrange relaxation based decomposition solution algorithm, was implemented in a real-world case study in California to explore the potential of waste-based bioethanol production. The model results show that biowaste-based ethanol can be a viable part of sustainable energy solution for the future.     

Development of seaport–dry port dyads: two cases from Northern Europe

An increase in sea freight flows generates an almost proportional increase in inland freight flows, and what takes place inland will influence the ability of intermodal transport systems to further accommodate the growth of international trade. This could be facilitated by dry ports, which have been developed to support seaport operations as well as the overall operations of intermodal transport systems.The aim of this paper is to develop the Wilmsmeier et al. (2011) framework of directional development by taking into account development over time. The purpose is to contribute to the understanding of the development of seaport–dry port dyads by the analysis of two in-depth case studies from Northern Europe. A qualitative research strategy is applied to increase understanding of the development. The results contribute to earlier studies of spatial development of seaport–dry port development by taking into account development over time and by increasing the understanding of the actors, the types of dry ports and the services influencing the development of the dyads.     

Sustainable hub location under mixed uncertainty

This paper addresses a novel sustainable hub location problem (SHLP) in which two new environmental-based cost functions accounting for air and noise pollution of vehicles are incorporated. To cope with uncertain data incorporated in the model, a mixed possibilistic–stochastic programming approach is proposed to construct the crisp counterpart. A simulated annealing (SA) and an imperialist competitive algorithm (ICA) with a new solution representation are developed to solve real-sized instances whose performances are compared with a proposed lower bound. Finally, some computational experiments are provided to demonstrate the effectiveness of the proposed model and solution approaches.     

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 bilevel storage pricing model for outbound containers in a dry port system

Serving as a potential solution for seaport congestion and capacity limitation, dry port development is increasingly popular in the freight transport industry. This paper pioneers the research on dry port operations by modelling the storage pricing problem for outbound containers. The interaction between a dry port and multiple shippers is modelled as a bilevel program. The optimal properties of the proposed model under certain conditions are derived analytically, from which a closed-form solution is obtained. Contrary to intuition, the increase of container delivery frequency from shippers may lead to the reduction of dry port’s profit according to model outcomes.