1. T. Raviv and M. Kaspi, The locomotive fleet fueling problemOperations Research Letters, 40(1), 39-45, 2012.

  2. M. Kaspi and T. Raviv, Service Oriented Line Planning and Timetabling for Passenger Trains. Transportation Science, 47(3), 295-311, 2013. (Israel railways instance)

  3. M. Kaspi, T. Raviv and M. Tzur, Parking reservation policies in one-way vehicle sharing systems. Transportation Research Part B: Methodological, 62, 35-50, 2014.

  4. M. Kaspi, T. Raviv, M. Tzur and H. Galili, Regulating vehicle sharing systems through parking reservation policies: Analysis and performance bounds. European Journal of Operational Research, 251(3), 969-987, 2016.

  5. M. Kaspi, T. Raviv and M. Tzur, Detection of Unusable Bicycles in Bike-Sharing Systems. Omega, 65, 10-16, 2016.

  6. M. Kaspi, T. Raviv and M. Tzur, Bike Sharing Systems: User Dissatisfaction in the Presence of Unusable Bicycles. IISE Transactions, 49(2), 144-158, 2017.

  7. C. Bongiovanni, M. Kaspi and N. Geroliminis, The Electric Autonomous Dial-a-Ride Problem, Transportation Research Part B: Methodological, 122, 436-456, 2019.

  8. M. Repoux, M. Kaspi, B. Boyaci and N. Geroliminis, Dynamic prediction-based relocation policies in one-way station-based car-sharing systems with complete journey reservations. Transportation Research Part B: Methodological, 130, 83-104, 2019.

Under Review

  1. Y. Molenbruch, K. Braekers, O. Eisenhandler, M. Kaspi,  The Electric Dial-a-Ride Problem on a Fixed Circuit.

The locomotive fleet fueling problem (2012)

This paper considers the problem of how to determine an optimal fueling schedule and contracting policy with fuel suppliers so as to minimize the total cost of the fueling operation. The problem is formulated as a mixed integer program and the formulation is enhanced by valid inequalities and domination rules. The enhanced model allows us to obtain near optimal solutions for large scale instances.

Service Oriented Line Planning and Timetabling for Passenger Trains (2013)

An integrated line planning and timetabling model is formulated with the objective of minimizing both user inconvenience and operational costs. User inconvenience is modeled as the total time passengers spend in a railway system, including waiting at origin and transfer stations. The model is solved using a cross-entropy metaheuristic. The line plan and timetable of Israel Railways is used as a benchmark. Using the same amount of resources, the average journey time of passengers is reduced by 20%.

Parking reservation policies in one-way vehicle sharing systems (2014)

In this study, we propose improving the performance of one-way vehicle sharing systems by incorporating parking reservation policies. In particular, we study a parking space reservation policy in which, upon rental, the users are required to state their destination and the system then reserves a parking space for them until they arrive at their destinations. We measure the performance of the vehicle sharing system by the total excess time users spend in the system. The excess time is defined as the difference between the actual journey time and the shortest possible travel time from the desired origin to the desired destination. A Markovian model of the system is formulated. Using this model, we prove that under realistic demand rates, this policy improves the performance of the system. This result is confirmed via a simulation study of a large real system, Tel-O-Fun, the bike-sharing system in Tel-Aviv. For all the tested demand scenarios, the parking reservation policy reduces the total excess time users spend in the system, with a relative reduction varying between 14% and 34%. Through the simulation we examine additional service-oriented performance measures and demonstrate that they all improve under the parking reservation policy.

Regulating vehicle sharing systems through parking reservation policies:

Analysis and performance bounds (2016)

We study the regulation of one-way station-based vehicle sharing systems through parking reservation policies. We measure the performance of these systems in terms of the total excess travel time of all users caused as a result of vehicle or parking space shortages. We devise mathematical programming based bounds on the total excess travel time of vehicle sharing systems under any passive regulation (i.e., policies that do not involve active vehicle relocation) and, in particular, under any parking space reservation policy. These bounds are compared to the performance of several partial parking reservation policies, a parking space overbooking policy and to the complete parking reservation (CPR) and no-reservation (NR) policies introduced in a previous paper. A detailed user behavior model for each policy is presented, and a discrete event simulation is used to evaluate the performance of the system under various settings. The analysis of two case studies of real-world systems shows the following: (1) a significant improvement of what can theoretically be achieved is obtained via the CPR policy; (2) the performances of the proposed partial reservation policies monotonically improve as more reservations are required; and (3) parking space overbooking is not likely to be beneficial. In conclusion, our results reinforce the effectiveness of the CPR policy and suggest that parking space reservations should be used in practice, even if only a small share of users are required to place reservations.

Detection of Unusable Bicycles in Bike-Sharing Systems (2016)

In bike-sharing systems, a small percentage of the bicycles become unusable every day. Currently, there is no reliable on-line information that indicates the usability of bicycles. We present a model that estimates the probability that a specific bicycle is unusable as well as the number of unusable bicycles in a station, based on available trip transaction data. Further on, we present some information based enhancements of the model and discuss an equivalent model for detecting locker failures.

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Bike Sharing Systems: User Dissatisfaction in the Presence of Unusable Bicycles (2017)

In bike-sharing systems, at any given moment, a certain share of the bicycle fleet is unusable. This phenomenon may significantly affect the quality of service provided to the users. However, to date this matter has not received any attention in the literature. In this article, the users' quality of service is modeled in terms of their satisfaction from the system. We measure user dissatisfaction using a weighted sum of the expected shortages of bicycles and lockers at a single station. The shortages are evaluated as a function of the initial inventory of usable and unusable bicycles at the station. We analyze the convexity of the resulting bivariate function and propose an accurate method for fitting a convex polyhedral function to it. The fitted polyhedral function can later be used in linear optimization models for operational and strategic decision making in bike-sharing systems. Our numerical results demonstrate the significant effect of the presence of unusable bicycles on the level of user dissatisfaction. This emphasizes the need to have accurate real-time information regarding bicycle usability.

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The Electric Autonomous Dial-a-Ride Problem (2019)

In the Dial-a-Ride-Problem (DARP) a fleet of vehicles provides shared-ride services to users specifying their origin, destination, and preferred arrival time. Typically, the problem consists of finding minimum cost routes, satisfying operational constraints such as time-windows, origin-destination precedences, user maximum ride-times, and vehicle maximum route-durations. This paper presents a problem variant for the DARP which considers the use of electric autonomous vehicles (e-ADARP). The problem covers battery management, detours to charging stations, recharge times, and selection of destination depots, along with classic DARP features. The goal of the problem is to minimize a weighted objective function consisting of the total travel time of all vehicles and excess ride-time of the users. We formulate the problem as a 3-index and a 2-index mixed-integer-linear program and devise a branch-and-cut algorithm with new valid inequalities derived from e-ADARP properties. Computational experiments are performed on adapted benchmark instances from DARP literature and on instances based on real data from Uber Technologies Inc. Instances with up to 5 vehicles and 40 requests are solved to optimality.

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Dynamic prediction-based relocation policies

in one-way station-based car-sharing systems with complete journey reservations (2019)

In this paper, we study the operations of a one-way station-based carsharing system implementing a complete journey reservation policy. We consider the percentage of served demand as a primary performance measure and analyze the effect of several dynamic staff-based relocation policies. Specifically, we introduce a new proactive relocation policy based on Markov chain dynamics that utilizes reservation information to better predict the future states of the stations. This policy is compared to a state-of-the art staff-based relocation policy and a centralistic relocation model assuming full knowledge of the demand. Numerical results from a real-world implementation and a simulation analysis demonstrate the positive impact of dynamic relocations and highlight the improvement in performance obtained with the proposed proactive relocation policy.

The Electric Dial-a-Ride Problem on a Fixed Circuit (Under Review)

Shared mobility services involving electric autonomous shuttles have increasingly been implemented in recent years. Due to various restrictions, these services are currently offered on fixed circuits and operated with fixed schedules. This study introduces a service variant with flexible stopping patterns and schedules. Specifically, in the Electric Dial-a-Ride Problem on a Fixed Circuit (eDARP-FC), a fleet of capacitated electric shuttles operates on a given circuit, consisting of a recharging depot and a sequence of stations where users can be picked-up/dropped-off. The shuttles may perform multiple laps between which they may need to recharge. The goal of the problem is to determine the vehicles’ stopping sequences and schedules, including recharging plans, so as to minimize a weighted sum of the total user excess time and the total number of laps. The eDARP-FC is formulated as a non-standard lap-based MILP and is shown to be NP-Hard. Efficient polynomial time algorithms are devised for two special scheduling sub-problems. These algorithms and several heuristics are then applied as sub-routines within a Large Neighborhood Search metaheuristic. Experiments on instances derived from a real-life service demonstrate that the flexible service results in a 60%-85% decrease in the excess time at the same operational costs.

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