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This paper presents a study on the location problem of single-server battery swap stations, identifying instances of excessively long waiting times at certain stations during their operation in a real-world company scenario. This study innovatively transforms the problem into an extended version of the classic maximal covering location problem, incorporating technology selection and three sets of additional constraints: budget, closest-assignment, and average waiting time constraints. In particular, the closest-assignment constraint is specifically designed to mimic human behavioral patterns. A non-convex integer programming model is proposed and then reformulated thus enabling it to be tackled by some general-purpose solvers. The model is validated considering a test bed of randomly generated instances. The model comprehensiveness added-value is assessed. Finally, real-world data is used which allows discussing the extraction of information in the studied context. That information is used to build an instance that is solved and analyzed to provide valuable managerial insights, emphasizing the importance of technological improvement to the management. [ABSTRACT FROM AUTHOR]

• Two new repositioning rules are proposed for repositioning operations of free-floating bike sharing system. • Particular features of bike distribution are considered when adopting the repositioning rule. • A Hausdorff distance based indicator is proposed to measure the degree of unbalancing in free-floating bike sharing system. • A mathematical model is proposed for each repositioning rule based on MTVRP and MTPDVRP. • An adaptive neighborhood search based heuristic is devised for solving large-scale problems. In this work different repositioning rules are investigated and compared in the context of free-floating bike-sharing systems. A static complete reposition setting is adopted, i.e., the system is operated (re-balanced) when the number of users riding bicycles can be neglected as it happens in many cities, for instance during the night. The popular 'healthy or broken' repositioning rule is revisited and examined along with two newly proposed rules, i.e., 'pickup or delivery' and 'pickup and delivery' rules. A discussion is also provided in terms of measuring the degree of an unbalancing in such a system. A mathematical model is proposed for each repositioning rule. Depending on the rule one will be facing a multi-trip vehicle routing or a multi-trip pickup and delivery vehicle routing problem, which is a problem not much investigated in the literature. An approximate algorithm is also devised for the problem, which is adapted to the multi-trip vehicle routing, and also suitable for multi-trip pickup and delivery vehicle routing problem with some corresponding adjustment. Computational tests are reported on to assess the methodological contributions of this work. These tests consider both randomly generated instances and two instances using real data. The results shown that 'pickup and delivery' rule is better than others when distribution degree of repositioning scenario is less than 0.5, while if the distribution degree exceeds 0.5, the 'pickup or delivery' rule is the most cost-effective one. [ABSTRACT FROM AUTHOR]

• The rebalancing operations of bike sharing system are taken under the perspective of entire city. • The characteristics of rebalancing demand are found by using the realistic cycling trip data. • Multiple rebalancing vehicles and time-varying rental costs are considered in the proposed integer programming models. • A chance constraint programming model is proposed to solve the realistic problem. • Different evolutionary mechanisms are considered in our proposed genetic algorithms. Bike-sharing networks have become a carbon-emission and environmentally friendly form of transportation in recent years. However, the asymmetric demand patterns of user behaviour, both temporally and spatially, inevitably lead to an imbalance in the distribution of shared bikes in cities, thereby becoming the greatest obstacle to the networks' development. Based on the real-world data of cycling trips, we analyse the challenging problem of imbalanced bike distribution from the entire-city perspective, establishing that the static rebalancing demand for the whole city is a stochastic variable with multi-scenario characteristics. On this basis, we develop an integer programming model to consider multiple rebalancing vehicles with time-varying rental costs, to alleviate the imbalanced bike distribution, while also analysing the intrinsic properties of such a model. We further propose a chance constraint programming model, optimising a bike-sharing network through the implementation of various genetic algorithms that employ block crossover and variable mutation operators. We reveal the inability of deterministic models in addressing the real-world problem of rebalancing demands for operational bike-sharing. In the meantime, supported with stochastic simulation, we demonstrate that the proposed approach can resolve this problem both effectively and efficiently, ensuring the delivery of a high-level bike-sharing service across an entire metropolitan city. [ABSTRACT FROM AUTHOR]

• ATO data is used to show the inefficiency of EEO strategy. • Data-driven method is proposed to simulate the regenerative energy absorption. • A uniformity measure is defined to quantify the impact of headway adjustment. • GA with adaptive operator selection and reduction technique is proposed. As an emerging energy-efficient management approach, the essential idea of subway regenerative energy optimization is to maximize the absorption of energy generated from train deceleration by adjusting train schedules. In the extant literature, performance evaluation and optimization rely on synthetic data (e.g., computer simulations) and train energy-efficient operation (EEO) strategy. However, when compared to real automatic train operation (ATO) data, the above-mentioned method exhibits significant errors. In this work, we develop a new optimization method driven by real ATO data for maximizing subway regenerative energy based on the following three steps: first, we provide a high-frequency ATO data-driven method for simulating the amount of regenerative energy absorption; second, we propose a concept of uniformity to measure the homogeneous degree of headway distribution; third, we formulate a headway optimization model to maximize the regenerative energy absorption under uniformity constraint. To handle the high complexity of the ATO data-driven objective function (e.g., non-monotonicity, non-convexity, multi-modality), we propose an improved genetic algorithm with multiple crossover and mutation operators to search for near-optimal solutions, in which an adaptive operator selection mechanism with reduction process on ATO data is considered for speeding up the regenerative energy simulation and optimization. The effectiveness of the proposed method is confirmed by using the real ATO data of Beijing Subway Changping line. Our numerical study reveals that, benchmarked with the uniform headway distribution (the policy that is presently in use), our proposed approach achieves a relative improvement of 7.75% at off-peak hours and 42.44% at peak hours for the regenerative energy absorption; and we show that such a significant performance improvement is obtained by allowing a small level of scheduling flexibility (less than 6% relaxation on uniformity level). [ABSTRACT FROM AUTHOR]