A dynamic carsharing decision support system

This paper proposes a dynamic optimization–simulation model as a decision support system for one-way carsharing organizations. To reduce the vehicle imbalance in one-way systems, a Vehicle Relocation Optimization model is solved successively in a discrete event simulation. Each event is the arrival of a new user. The model is compared to an a priori benchmark model. Autoshare is chosen as a case study. Results show that increasing the reservation time (time between requesting and picking up a vehicle) from 0 to 30 min reduces fleet size by 86%. The model captures a tradeoff between vehicle relocation hours and fleet size.     

Robust planning and disruption management in roll-on roll-off liner shipping

This paper presents different strategies for handling disruptions in fleet deployment in roll-on roll-off liner shipping, which basically consists of assigning a fleet of vessels to predefined voyages at minimum cost. A new mathematical model of the problem is presented, including a set of robust planning strategies, such as adding slack and rewarding early arrivals. To solve real-life instances a rolling horizon heuristic is proposed. A computational study, where we also propose some recovery planning strategies, is conducted, and simulation results show that adding robustness significantly reduces the actual cost of the plan and the total delays of the voyages.     

A dynamic approach for aircraft assignment and maintenance scheduling by airlines

The problems of assigning planes to flights and of fleet maintenance operations scheduling are considered in this paper. While recent approaches make use of artificial intelligence techniques running on main frame computers to solve combinatorial optimization problems for nominal operations, a dynamic approach is proposed here to face on-line operation conditions. The proposed solution mixes a Dynamic Programming approach (to cope with the fleet assignment problem) and a heuristic technique (to solve the embedded maintenance schedule problem). When applied to a medium charter airline, this approach shows acceptability characteristics for operational staffs, while providing efficient solutions. The proposed solution scheme can be considered as the basis for the development of an on-line decision support system for fleet operations management within airlines.     

Reliability and operations: Keys to lumpy aircraft spare parts demands

This study provides unique new insights into the reasons for lumpy aircraft spare parts demands, and identifies opportunities to improve the regularity of aircraft spares demands. The study develops its unique insights into aircraft spare parts demands by considering the typical failure probability distribution (Weibull Distribution) for aircraft spare parts. The study identifies the range of Weibull model parameters that explain typical aircraft part failure rates, and uses these parameters to perform a Monte Carlo simulation of notional aircraft components in typical aircraft fleet sizes and operations. Each notional component is repeatedly used to failure and replaced, providing a simulated spare part demand rate. The data is evaluated to uncover patterns that allow a deeper understanding of how reliability and operational input factors impact the spare part demand characteristics. The study finds that the aircraft fleet size has the greatest impact on the lumpiness of aircraft spare parts demands. The study also recommends other measures that fleet managers may take to reduce the lumpiness of their spares demands.     

Optimal fleet utilization and replacement

The fleet replacement problem of a profit-maximizing manager is examined using an optimal control model that captures both utilization and replacement decisions. Conditions for optimal utilization of each vessel in the fleet and optimal vessel acquisition and retirement strategies are discussed. The results indicate that the optimal replacement schedule and fleet size are influenced by utilization schedules, and vice versa. Thus, replacement and utilization strategies should be determined jointly. We develop a numerical example to illustrate the model's potential as a practical management decision tool and the procedures to solve it.     

The dynamics between freight volatility and fleet size growth in dry bulk shipping markets

This paper studies the relationship between the time-varying volatility of dry bulk freight rates and the change of the supply of fleet trading in dry bulk markets. An abundance of research has been done to understand the time-varying characteristics of freight rate volatility, yet few have discussed the determinants of freight volatility. We therefore examine freight volatility against the changes in fleet size and other shipping market variables over January 1973–October 2010. The study employs a two-step model specification. The first step is the measurement of freight rate volatility through an AR-GARCH model; the second step is the analysis of the relationship between freight rate volatility and fleet size growth through a GMM regression. We confirm similar findings in the literature that freight rate volatility is time varying. Furthermore, the results reveal that the change in fleet size positively affects freight rate volatility, while the spot rate volatility of Capesize dry bulk exhibits a stronger reaction to the change in fleet size. The results of this study contribute in a general sense to understanding the systematic risk of shipping markets.     

Container fleet sizing and empty repositioning in liner shipping systems

This paper considers the joint container fleet sizing and empty container repositioning problem in multi-vessel, multi-port and multi-voyage shipping systems with dynamic, uncertain and imbalanced customer demands. The objective is to minimize the expected total costs including inventory-holding costs, lifting-on/lifting-off costs, transportation costs, repositioning costs, and lost-sale penalty costs. A simulation-based optimization tool is developed to optimize the container fleet size and the parameterized empty repositioning policy simultaneously. The optimization procedure is based on Genetic Algorithms and Evolutionary Strategy combined with an adjustment mechanism. Case studies are given to demonstrate the results.     

The benefit of advance load information for truckload carriers

This paper models and measures the profit improvement trucking companies can achieve by collaborating with their clients to obtain advance load information (ALI). The main approach is to formulate a comprehensive and flexible mixed integer mathematical model and implement it in a dynamic rolling horizon context. The findings illustrate that access to the second and the third day ALI can improve the profit by averages of 22% and 6%, respectively. We also found that the impact of ALI depends on radius of service and trip length but is statistically independent of load density and fleet size.     

Three-stage airline fleet planning model

One of the main factors affecting airline success is bringing supply and demand as closely together as possible. In order to achieve this goal, an airline needs to adopt an appropriate methodological approach for the fleet planning process. Selection of an aircraft for operating a defined route network is a key element which has a direct impact on the increase of an airline's profitability and on the reduction of an airline's costs. The objective of this paper is to develop a robust model for fleet planning that deals with both fleet size and fleet composition problems for airlines operating on short haul and medium haul routes. The three-stage model for fleet planning involves approximate fleet composition, fleet sizing and aircraft type selection based on fuzzy logic, heuristic and analytic approaches, and multi-criteria decision making, respectively. This model is exemplified with a hypothetical airline based at Belgrade Airport.     

Containership routing with time deadlines and simultaneous deliveries and pick-ups

In this paper we seek to determine optimal routes for a containership fleet performing pick-ups and deliveries between a hub and several spoke ports. A capacitated vehicle routing problem with pick-ups, deliveries and time deadlines is formulated and solved using a hybrid genetic algorithm for establishing routes for a dedicated containership fleet. Results on the performance of the algorithm and the feasibility of the approach show that a relatively small fleet of containerships could provide efficient services within deadlines. Moreover, through sensitivity analysis we discuss performance robustness and consistency of the developed algorithm under a variety of problem settings and parameters values.     

Optimizing the service area and trip selection of an electric automated taxi system used for the last mile of train trips

We propose two integer programming models for optimizing an automated taxi (AT) system for last mile of train trips. Model S1: trip reservations are accepted or rejected by the operator according to the profit maximization; model S2: any reservation on a selected zone by the model must be satisfied. Models were applied to a case-study. Results indicate that fleet size influences the profitability of the taxi system: a fleet of 40 ATs is optimal in S1 and 60 ATs in S2. Having electric ATs constrains the system for small fleets because ATs will not have time for charging.     

Optimizing station location and fleet composition for a high-speed rail line

This paper proposes a new strategic planning model for high-speed rail ventures. It is a mixed-integer optimization model that applies to a given line and focuses on two key strategic decisions: station location and fleet composition. Our purpose is to improve on previous station location models by including fleet composition decisions. In the new model, we additionally take into account in an approximate fashion the interrelationships between strategic and subsequent tactical decisions, regarding line planning, train scheduling and fleet assignment issues. The usefulness of the model is demonstrated for a case study involving a planned Lisbon-Oporto high-speed rail line.     

An effective genetic algorithm for the fleet size and mix vehicle routing problems

This paper studies the fleet size and mix vehicle routing problem (FSMVRP), in which the fleet is heterogeneous and its composition to be determined. We design and implement a genetic algorithm (GA) based heuristic. On a set of twenty benchmark problems it reaches the best-known solution 14 times and finds one new best solution. It also provides a competitive performance in terms of average solution.     

Fleet composition of commercial jet aircraft 1952–2005: Developments in uniformity and scale

The fleet composition of an airline is important in determining its costs and operational performance. This composition can be measured using numerical values. An index for measuring fleet uniformity is available, and a structured way of measuring fleet scale is introduced here. The history of all jet aircraft operated by commercial passenger or cargo airlines world-wide is analyzed both in general terms and using these measures. The analysis shows that uniformity in airline fleets has been steadily decreasing, while their scale has been steadily increasing.     

An adaptive memory programming metaheuristic for the heterogeneous fixed fleet vehicle routing problem

This paper studies the heterogeneous fixed fleet vehicle routing problem (HFFVRP), in which the fleet is composed of a fixed number of vehicles with different capacities, fixed costs, and variable costs. Given the fleet composition, the HFFVRP is to determine a vehicle scheduling strategy with the objective of minimizing the total transportation cost. We propose a multistart adaptive memory programming (MAMP) and path relinking algorithm to solve this problem. Through the search memory, MAMP at each iteration constructs multiple provisional solutions, which are further improved by a modified tabu search. As an intensification strategy, path relinking is integrated to enhance the performance of MAMP. We conduct a series of experiments to evaluate and demonstrate the effectiveness of the proposed algorithm.     

Revenue management policies for the truck rental industry

In this paper, we consider the problem of managing a fleet of trucks with different capacity to serve the requests of different customers that arise randomly over time. The problem is formulated via dynamic programming. Linear programming approximations of the problem are presented and their solutions are exploited to develop partitioned booking limits and bid prices policies. The numerical experiments show that the proposed policies can be profitably used in supporting the decision maker.     

Scenario tree airline fleet planning for demand uncertainty

This paper proposes an innovative multi-period modeling approach to solve the airline fleet planning problem under demand uncertainty. The problem is modeled using a scenario tree approach. The tree is composed of nodes, which represent points of decision in multiple time stages of the planning horizon, and branches, representing demand variation scenarios. The branches link the decision nodes in consequent time stages and compose scenario paths. Fleet decisions are modeled according to these scenario paths, resembling the real-life process in which fleet plans are not defined in a single moment but instead are adjusted according to the demand development. Given that some scenario paths share common decision nodes, decisions among scenarios need to be synchronized. A mixed-integer linear programming model is proposed to determine the ideal fleet composition for each scenario in the tree and to describe this interdependency between scenarios. Considering the probability of a scenario, fleet composition probabilities for each time-period can be determined. Two real-world based case studies are performed to show the validity of the model. Results show that the proposed scenario tree approach can provide flexible multi-period airline fleet plans, which are more robust to future demand scenarios than fleet solutions obtained using the traditional approach of considering a single deterministic demand evolution scenario.     

The impact of control strategies on the management of returnable transport items

This research examines the impact of different control strategies on the management of returnable transport items (RTIs) in closed-loop supply chains. A simulation model is developed based on an empirical case and is used to explore different scenarios. The results suggest that the choice of control strategy has a significant impact on investments and operating costs, and that RTI shrinkage can be controlled either through the use of tracking systems, or choice of control strategy. Moreover, a simulation-based method for estimating the fleet size required to operate the system for the different strategies is presented.     

Robust optimization for fleet planning under uncertainty

We create a formulation and a solution procedure for fleet sizing under uncertainty in future demands and operating conditions. The formulation focuses on robust optimization, using a partial moment measure of risk. This risk measure is incorporated into the expected recourse function of a two-stage stochastic programming formulation, and stochastic decomposition is used as a solution procedure. A numerical example illustrates the importance of including uncertainty in the fleet sizing problem formulation, and the nature of the fundamental tradeoff between acquiring more vehicles and accepting the risk of potentially high costs if insufficient resources are available.     

Coordinated inventory replenishment and outsourced transportation operations

We consider a one-warehouse N retailers supply chain with stochastic demand. Inventory is managed in-house whereas transportation is outsourced to a 3PL provider. We develop analytical expressions for the operating characteristics under both periodic and continuous joint replenishment policies. We identify the settings where a periodic review policy is comparable to a continuous review one. In our numerical test-bed, the periodic policy performed best in larger supply chains operating with larger trucks. We also observed that if the excess utilization charge is less than 25%, outsourcing becomes beneficial even if outsourcing cost is 25% more than the in-house fleet costs.