Insights on Car Relocation Operations in One-Way Carsharing Systems

Recently, carsharing has emerged as an alternative to vehicle ownership in many countries worldwide. This study examines the system parameters of one-way carsharing systems to determine a trade-off between the lost sales rate and system parameters. Four system parameters are considered in this study, namely average number of vehicles per station, rate of one-way trip, average number of staffs per station, and relocation policy. The performance of round-trip carsharing systems is also compared to that of one-way carsharing systems. A regression model is developed to predict the lost sales rate based on the various operational conditions for a one-way carsharing system. The findings of the study provide tools to understand the impact of carsharing system parameters on the efficiency of carsharing operations and predict the performance of such systems when any modifications occur.

The dominant challenge in one-way carsharing systems is the vehicle stock imbalance. Previous studies have proposed relocation approaches to handle it using optimization and simulation models. However, these models do not consider the interdependence between supply and demand in carsharing systems. In this paper, we develop an integrated optimization model to link supply and demand together. A discrete choice model that includes vehicle availability as a parameter directly affecting user’s mode choice is introduced and incorporated within the optimization formulation. In this framework, carsharing travel demand is influenced by vehicle supply. The reaction of the demand further changes vehicle availability in the system. The incorporation of a discrete choice model with the Integer Linear Programming formulation leads to a nonlinear model. We propose a linearization scheme to reformulate it. We test the model in realistic case studies representative of an Australian carsharing operator. A sensitivity analysis on total travel demand, system capacity, one-way trip price, and vehicle availability coefficient is undertaken to evaluate their impacts on system profit. The results reveal that the pattern of profit over trip price varies across scenarios with different vehicle availability coefficients and travel demand. The profit-efficiency of enlarging carsharing network is also dependent on travel demand. We conclude that the interdependence between demand and supply should be considered when setting network development plans and pricing strategies in one-way carsharing systems. If there is a strong interaction between demand and supply, the supply of carsharing vehicles has a critical impact on system profit.

The concept of carsharing involves sharing a small number of reserved cars to be used individually by a larger number of people as required. This study examines the operating parameters of one-way carsharing systems in order to determine the appropriate operating conditions that minimizes the lost sales rate. Five operating parameters are tested in this study: the number of stations, the average number of vehicles per station, the rate of one-way trip, the average number of staffs per station, and the relocation policy. The performance of round-trip carsharing systems is also compared to that of one-way carsharing systems. A simulation model is developed and simulations are performed to determine the appropriate combination of operating parameter and levels. The simulation results show that the average number of vehicles per station is the most critical parameter. Other key findings obtained from this research are as follows. First, applying the appropriate relocation policy to one-way carsharing systems can allow more customers to rent vehicles than the traditional round-trip carsharing systems. Second, the appropriate relocation policy should be selected based on the average number of vehicles per station in order to minimize the lost sales rate. Third, the number of stations does not affect the lost sales rate. This study findings will provide tools to understand impact of the carsharing system parameters on the efficiency of the carsharing operations.

The discounted knapsack problem (DKP) is an NP-hard combinatorial optimization problem that has gained much attention recently. Due to its high complexity, the usual solution combines a global search algorithm with a greedy local search algorithm to repair candidate solutions. The current greedy algorithms use a heuristic that ignores the items already in a candidate solution. This paper presents a new greedy algorithm for DKP that uses an expanded set of operators and better heuristics that are more effective at considering the selected items. Experimental results show that the proposed greedy algorithm has superior performance to three well-known greedy algorithms for DKP, both when operating independently and when combined with global search algorithms.KeywordsCombinatorial optimizationDiscounted knapsack problemGreedy algorithmLocal search

The paper raises the issue of evaluation and selection of suppliers in a large manufacturing company. Till now, the decision-making process in the company was based on the human factor. The presented work was aimed at developing new suppliers selection system. That would make it possible to minimize costs of ordered materials. Solving the problem of optimization is offered by Tabu Search and greedy algorithms. The tuning process of algorithms and the verification of algorithm performance are reported in the paper.

Car-sharing is emerging as an effective and flexible alternative to car ownership in many large cities in the world. However, car-sharing is still dominated by single-occupancy vehicles, and its convenience may attract still more passengers from public transport, thus aggravating congestion and pollution. Ride sharing can mitigate this problem by pooling together passengers doing similar trips, thus decreasing the number of cars on the road. In this work we propose a strategy for offering ride-sharing in one-way car-sharing systems. To this end, we design a graph-based approach to model the trip-matching problem in a car-sharing system using passenger pooling. Then, we formulate both online and offline trip-matching algorithms, and we investigate their impact on the performance of the system using a large data set from the city of New York. We consider both the point of view of the operator and the users, in terms of net revenues and utility, respectively. We show that with the right fare structure a ride-sharing option can lead to a solution that increases both net revenues for the operator and the utility of passengers, while decreasing vehicles-km travelled.

Optimization of personalized price discounting scheme for one-way station-based carsharing systems

This paper explores the Minimum Weighted Independent Dominating Set Problem and proposes novel approaches to tackle it. Namely, two integer linear programming formulations and a fast greedy heuristic as an alternative approach are proposed. Extensive computational experiments are conducted to evaluate the performance of these approaches on the established set of benchmark instances for the problem. The obtained results demonstrate that the introduced integer linear programming models are able to achieve optimal solutions on all instances with 100 nodes and significantly outperform existing exact methods on numerous other instances. Additionally, the greedy heuristic exhibits superior performance compared to competing greedy heuristics, particularly on random graphs. These findings suggest promising directions for future research, including the integration of these methods into hybrid algorithms or metaheuristic frameworks.

Equal allocation of bandwidth and/or power may not be efficient for wireless multi-user networks with limited bandwidth and power resources. Optimal joint bandwidth and power allocation strategies for wireless multi-user networks with and without relaying are proposed in this paper for (1) the maximization of the sum capacity of all users; (2) the maximization of the worst user capacity; and (3) the minimization of the total power consumption of all users. It is shown that the proposed allocation problems are convex and, therefore, can be solved efficiently. Moreover, joint bandwidth and power allocation for admission control is considered. A suboptimal greedy search algorithm is developed to solve the admission control problem efficiently. Instructive analysis of the greedy search shows that it can achieve good performance, and the condition under which the greedy search is optimal is derived. The formal and in-depth analysis of the greedy search algorithm presented in this paper can serve as a benchmark for analyzing similar algorithms in other applications. The performance improvements offered by the proposed optimal joint bandwidth and power allocation are demonstrated by simulations. The advantages of the suboptimal greedy search algorithm for admission control are also shown in numerical results.

The weighted independent domination problem: Integer linear programming models and metaheuristic approaches

A method for space mission trajectory design is presented in the form of a greedy global search algorithm. It uses invariant manifolds of unstable periodic orbits and its main advantage is that it performs a global search for the suitable legs of the invariant manifolds to be connected for a preliminary transfer design, as well as the appropriate points of the legs for maneuver application. The designed indirect algorithm bases the greedy choice on the optimality conditions that are assumed for the theoretical minimum transfer cost of a spacecraft when using invariant manifolds. The method is applied to a test case space mission design project in the Earth–Moon system and is found to compare favorably with previous techniques applied to the same project.

In the paper, rationalization of production order execution in a large manufacturing company is suggested. Till now, the decision-making process was based on the human factor, which resulted in irregular utilization of manufacturing resources. The presented work was aimed at developing new order selection system. That would make it possible to utilize the admitted resources possibly best and thus to meet the deadlines and to adapt to specific production requirements. In the work, the greedy and Tabu Search algorithms were used. As a basis for the research employing, historical data were accepted. Each order were given a priority, production time, profit and penalty for failure. Additionally, the machine failure risk was calculated based on empirical measurements. Simulations of several subsequent working weeks were performed in order to analyse the results obtained thanks to the suggested methods and to compare them with the results presently reached by the company.

In large organizations, the allocation of buildings and office space to departments and employees is a challenging task. Optimal office space allocation has the potential to maximize synergies between employees within an organization. In this paper, we study the performance of a greedy search algorithm and a tabu search algorithm for generating high quality solutions to the office space allocation problem. The objectives are to maximize synergies in the organization, minimize the overusage of limited office space and maximize the number of buildings and rooms that can be completely closed. Computational experiments show that a tabu search algorithm generates higher quality solutions than a greedy local search algorithm with the same computational budget.

When faced with a new machine learning problem, selecting which classifier is the best to perform the task at hand is a very hard problem. Most solutions proposed in the literature are based on meta-learning, and use meta-data about the problem to recommend an effective algorithm to solve the task. This paper proposes a new approach to this problem: to build an algorithm tailored to the application problem at hand. More specifically, we propose an evolutionary algorithm (EA) to automatically evolve Bayesian Network Classifiers (BNCs). The method receives as input a list of the main components of BNC algorithms, and uses an EA to encode these components. Given an input dataset, the method tests different combinations of components to that specific application domain. The method was tested in 10 UCI datasets, and compared to three classical BNCs and a greedy search algorithm. Results show that the current algorithms can indeed be improved, but that the EA is currently outperformed by the greedy search.

When learning Bayesian networks from data, in many real circumstances, experts of domains could give the relationships between the classes of variables. In this paper, the problem of `learning Bayesian networks based on ordering of sets' is formulated. To solve this problem, we propose a partitioned greedy search algorithm for learning structures of Bayesian networks based on the ordering of sets. The results of experiments show that, with the ordering of sets, compared with traditional greedy DAG search algorithm, the score of the structure obtained by our algorithm is improved, and the search time is greatly reduced