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REVIEW 5 major objections 6 minor 62 references

Smart Ride and Delivery Services with Electric Vehicles: Leveraging Bidirectional Charging for Profit Optimisation

T0 review · 5 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that Vehicle-to-Grid capability roughly doubles a ride-hailing or delivery driver's profit when order selection and charge/discharge timing are optimised together, with energy arbitrage contributing about a fifth of the…

desk verdict Genuinely new V2G orienteering variant and plausible heuristics, but the MIP anchoring the near-optimal claim is dimensionally inconsistent and the baseline is too weak to support the headline profit gain. read the letter →

arxiv 2506.20401 v2 pith:ALV7EUN7 submitted 2025-06-25 cs.AI

Jinchun Du , Bojie Shen , Muhammad Aamir Cheema , Adel N. Toosi This is my paper · ORCID
classification cs.AI MSC 90C1190C2790B06
keywords ElectricVehicleOrienteeringProblemVehicle-to-GridV2Gprofitmaximisationmixedintegerprogramminglargeneighbourhoodsearchevolutionaryalgorithmdynamicelectricitypricing
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces EVOP-V2G, a profit-maximisation problem in which a single electric-vehicle driver working in ride-hailing or delivery chooses which customer orders to accept, in which order to serve them, and when and where to charge or discharge the battery, under time-varying electricity prices and station-dependent charging rates. The paper's central argument is that Vehicle-to-Grid (V2G) capability, letting the EV sell stored energy back to the grid during high-price periods, adds a second revenue stream that earlier orienteering formulations ignore, so order selection and energy trading must be optimised together. The authors formulate the problem as a Mixed Integer Programming model and design two fast heuristics, an evolutionary algorithm and an adaptive large-neighbourhood search, then test them on real trip and price data. Their empirical claim is that both heuristics produce schedules with more than twice the profit of a greedy baseline, stay near the MIP optimum on small instances, and scale to 900 orders with 70 charging stations, with V2G contributing roughly 20% of profit under default settings. A reader should care because, if correct, the result means an EV driver's route and the grid's flexibility needs can be priced and scheduled as one optimisation rather than managed separately.

What carries the argument

The carrying object is the EVOP-V2G objective, the sum of accepted-order fares plus discharge revenue minus charging costs: $$\sum_{v_i \in V_o}\sum_{v_j} x_{ij}p_i + \sum_{v_i \in V_c}\sum_{t_k \in T}\left($dc^{{t_k}}$_i $P^{{D t_k}}$_i - $rc^{{t_k}}$_i $P^{{C t_k}}$_i\right),$$ evaluated over a planning horizon discretised into time slots of length $\delta$. The MIP encodes three constraint families, node visiting, travel time with order time windows, and battery-level tracking with a capacity bound, plus the rule that charging and discharging cannot occupy the same time slot at the same station. The scalable machinery is the large-neighbourhood search: destroy operators (random, worst-profit, closeness, and Shaw) remove blocks of actions, repair operators (max-profit, regret-$k$, closeness, and price) reinsert orders and charge or discharge actions, an adaptive weighting scheme learns which strategies work, and a small MIP re-optimises the charging schedule on the repaired path with a 5% suboptimality gap. That MIP-in-the-loop repair step is what the paper credits for LNS's advantage in discharge decisions.

What would settle it

Re-run the 30-order comparisons with $\delta$ and $|T|$ fixed to explicit values, such as 15-minute slots over a 24-hour horizon, and with $\lambda$ set to 1, then check whether the LNS and EA profits still sit within the reported margin of the MIP optimum; alternatively, benchmark the two heuristics against a stronger non-greedy planner with lookahead rather than the paper's greedy baseline, and see whether the profit advantage survives.

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Extended reading notes

Core claim

The paper's central claim is that bidirectional charging changes the economics of EV route planning: a driver's daily profit is not just order fares minus charging costs but includes an arbitrage term from discharging when prices are high, so route, order selection, and charge/discharge schedule must be optimised together. On instances built from real ride-hailing trip records and real charging and feed-in tariffs, the paper reports that its two heuristics achieve more than twice the profit of a greedy baseline; that V2G contributes about 20% of total profit under default settings, rising to 47.7% for the evolutionary algorithm and 66.4% for the large-neighbourhood search when charging prices and rates are tripled; and that the large-neighbourhood search solves 900-order, 70-station instances quickly while staying near the MIP optimum on 30-order instances. The paper also argues that LNS is the stronger heuristic in most settings because it re-optimises charging decisions with the MIP inside each repair step, while the evolutionary algorithm wins on some instances where random exploration helps escape local optima.

Load-bearing premise

The 'near-optimal' measurement is anchored to the MIP treated as the exact optimum, yet the model's time-slot length $\delta$ and horizon size $|T|$ are never given values, and an efficiency parameter $\lambda$ appears in the battery constraints without being defined; if that exact model is not a faithful or correctly instantiated version of the problem, the near-optimality of the heuristics is not anchored to a true optimum.

Editorial extensions

If this is right

  • Accepted orders are no longer chosen by fare alone: orders that keep the vehicle near a cheap-charging, expensive-discharging station can be worth more, so route plans and energy trades have to be priced jointly.
  • At city scale (900 orders, 70 charging stations) a few minutes of computation suffices to produce a schedule that beats the greedy baseline by a factor of two, which makes the approach a plausible daily-scheduling tool for a single commercial driver.
  • Because the V2G share of profit climbs from about 20% to between 48% and 66% when charging prices and rates are tripled, the value of bidirectional scheduling depends mainly on the spread between peak and off-peak tariffs, not on absolute price levels.
  • The MIP allows at most two revisits to any charging station in the exact comparison, while the heuristics revisit cheap stations up to three times, so the reported near-optimality margins inherit that cap on station revisits.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension the paper leaves implicit: re-running the same pipelines on tariff structures with larger peak/off-peak spreads, or on real-time prices, would separate how much of the profit gain is energy arbitrage versus better order selection; the 20%-to-66% V2G share range suggests arbitrage, not routing, is the dominant driver.
  • The LNS design of heuristic routing with an exact solver reserved for the energy subproblem is a modular recipe that could transfer to fleet-scale ride-hailing, where each vehicle's charge/discharge schedule becomes an independent small MIP once orders are assigned.
  • The unspecified slot length and horizon raise a reproducibility question: on markets with rapidly changing real-time prices, the granularity of $\delta$ would directly control how much price volatility the driver can exploit, so results on time-of-use tariffs may not predict real-time-pricing performance.
  • The few instances where EA beats LNS share longer time windows, hinting that a hybrid that lets random exploration dominate early and switches to LNS-style exploitation later could outperform either method alone.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper introduces the Electric Vehicle Orienteering Problem with V2G (EVOP-V2G): a single-EV profit-maximization problem in which a driver selects customer orders and decides when and where to charge or discharge a battery under time-varying electricity prices, station-dependent charging rates, time windows, and range constraints. The authors propose a Mixed Integer Programming (MIP) model, an Evolutionary Algorithm (EA), and a Large Neighborhood Search (LNS) with adaptive strategy selection and a MIP-based refinement step. The experiments use real-world ride and charging-station data from Melbourne, reporting that EA and LNS achieve near-optimal profit versus the MIP on small instances, that all proposed methods more than double the profit of a greedy baseline, and that V2G contributes about 20% of profit under default settings.

Significance. The problem variant is timely and practically relevant, and the paper's use of realistic data from Melbourne and PlugShare is a strength. The LNS design—adaptive strategy weighting, randomized greedy insertion, and MIP post-optimization—is methodologically plausible. If the formulation and numerical claims were corrected, the paper would provide a useful benchmark problem and evidence that V2G-aware scheduling can materially increase EV driver profit. However, the printed MIP is dimensionally inconsistent, the 'near-optimal' claim rests on a restricted MIP benchmark, and several key parameters are unspecified; these issues are load-bearing for the central claims.

major comments (5)
  1. [4.1, Eqs. (1) and (17)–(20)] The MIP model as printed does not define the objective in monetary units. In Eq. (1), charging/discharging decisions are binary indicators rc_tk_i and dc_tk_i multiplied by prices in $/kWh, with no charging rate P_i (kW) or timeslot length δ (h); the resulting terms have units of $/kWh, not $. In Eq. (17), the battery update adds/subtracts δ·rc and δ·dc, which have units of hours, to b_j, which is in kWh, and Constraints (19)–(20) repeat the same unit error. The charging rate P_i introduced in Section 3.1 appears nowhere in the constraints, and the efficiency parameter λ in Eqs. (16)–(17) is never defined or assigned a value. As written, the MIP cannot determine how much energy is transferred in a charging or discharging timeslot, so the 'optimal' objective and the V2G profit decomposition in Section 5.2.4 are not computed from the physical quantities the paper says it optimizes. This must be corrected (e.g., by multiplying each charging/discharging binary by P_i·δ) and the experiments rerun.
  2. [5.2.1] The small-instance optimality anchor is not the true EVOP-V2G optimum: the authors state that EA/LNS solutions used up to three station revisits while the MIP was restricted to at most two revisits because of solver time. The 'near-optimal' gap is therefore measured against a restricted MIP, not against the unrestricted optimum of the problem. The claim would be sound only if the heuristics never benefit from a third revisit or if the gap to the unrestricted optimum is otherwise bounded; neither is shown. Please report the distribution of the number of station revisits in heuristic solutions and quantify the profit gap between the 2-revisit MIP and the heuristics, or restrict the heuristics to two revisits to make the comparison symmetric.
  3. [5.1] Several parameters required to instantiate the MIP are never specified: the timeslot length δ, the number of slots |T|, and the efficiency λ in Eqs. (16)–(17). Section 3.1 says the planning horizon T is 'set to 24 hours' by default, but δ (and hence |T|) is not reported, and the timing constraints (11)–(13) and battery constraints (17)–(20) depend directly on δ and |T|. Without these values, the reported MIP runtimes, profits, and the comparison in Section 5.2.1 are not reproducible. Please state δ and |T| explicitly in Section 5.1 and, if δ is part of the experimental design, explain how it was chosen.
  4. [4.3.4] The LNS 'Improving Solution Using MIP Solver' step reuses the MIP model of Section 4.1 on a path graph, so it inherits the dimensional and parameter issues described above. If the implementation actually uses a corrected model (e.g., with P_i·δ factors), the paper should present that corrected model explicitly, because it is the model whose solutions are reported. As printed, the MIP refinement step is not well defined.
  5. [4.2.1, Eq. (24)] Equation (24) defines the EA/LNS fitness as a sum of prices over timeslots without multiplying by the energy transferred per timeslot (P_i·δ). This is consistent with the unit error in the MIP, but it is inconsistent with the paper's statement that charging rates are station-dependent. Consequently, the reported profit values (including the V2G profit share in Section 5.2.4) are not actual dollar amounts unless an implicit normalization is being used. Please clarify the exact formula used in the implementation and ensure it matches the corrected model.
minor comments (6)
  1. [4.3.2, 'Worst Profit Removal' bullet] In the third bullet of the Worst Profit Removal strategy, the discharging profit is written as Σ (PC_tk_i); it should be PD_tk_i, the discharging price, rather than the charging price PC_tk_i.
  2. [5.1] The text says the home charging infrastructure has a 'charging rate of 7 kw/h'; the unit should be kW (kilowatts), not kW/h, because the charging rate is a power, not an energy rate per hour.
  3. [5.1] The reproducibility link is given as the placeholder 'https://will-publish-after-acceptance.com'; a working repository is needed for the experimental results to be verifiable.
  4. [4.1] The text mentions 'two redundant vertices in Vc' for source and destination charging, but no such dummy vertices are introduced in the formal model; please clarify that these are experimental constructs used to model station revisits.
  5. [4.1, Eq. (13)] Constraint (13) writes τ_d, whereas arrival time at the destination is defined as τ_i for vi∈Vd; please use consistent notation.
  6. [Throughout] There are numerous typographical artifacts in the typeset text (e.g., 'A ffordable', 'e fficient', 'board stoke') that should be cleaned up in a final version.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: V2G profit and near-optimality claims are computed from independent inputs, not recovered from fitted parameters or self-citations.

full rationale

The paper's derivation chain is not circular in any load-bearing sense. The central claims are that EA and LNS produce near-optimal solutions relative to an exact MIP solved by Gurobi, and that V2G contributes roughly 20% of profit under default settings. The MIP objective and constraints are stated independently of the heuristics, and the heuristics are evaluated against the MIP on small instances rather than being calibrated to match it. The only self-citation, reference [4], is a vision paper used in the introduction as background motivation; it supplies no equation, theorem, or parameter used in the formulation or experiments, so it does not create circularity. The use of the MIP model inside LNS (Section 4.3.4) is a subroutine applied to a fixed single-path subproblem to refine charging/discharging decisions; the full MIP benchmark optimizes routing and energy decisions jointly, so the near-optimality comparison is not tautological. The reviewer's concern about the dimensional consistency of Equation (17) and the unspecified efficiency parameter lambda is a correctness or reproducibility issue with the formal model, not a circularity issue: even if the MIP is mis-specified, that does not mean its outputs are equal to its inputs by construction. No fitted parameter is renamed as a prediction, no uniqueness theorem is imported from the authors' prior work, and no known result is disguised as a new one. Accordingly, a low circularity score is appropriate.

Assumptions & free parameters 8 free parameters · 5 assumptions · 0 invented entities

The central claims rest on a small number of modeling conventions and on the correctness of the MIP benchmark, plus algorithm hyperparameters chosen by hand. No new physical or conceptual entity is introduced, but several load-bearing parameters (delta, |T|, lambda, m) are left unspecified in the manuscript.

free parameters (8)
  • time_slot_length_delta = unspecified
    Introduced in Section 3.1 as the length of each discretized timeslot; never assigned a value. The MIP model and all charging decisions depend on it.
  • planning_horizon_number_of_slots_|T| = unspecified
    The horizon is set to 24 hours by default, but the number of slots is never given, which affects MIP size and solution times.
  • efficiency_lambda = undefined in paper
    Appears in Constraints (16) and (17) dividing the energy consumption term; not defined in the notation table or text. If not equal to 1, the battery constraints are mis-specified.
  • max_charging_actions_m = unspecified
    Controls the maximum number of consecutive charging/discharging actions inserted in EA and LNS, referenced in Algorithm 1 and Section 4.3.3, but no value is set in Section 5.
  • LNS_battery_threshold_theta = 0.15
    Hand-chosen threshold B times theta in Algorithm 2 and Section 4.3.3 to prioritize charging actions; not tuned systematically.
  • ALNS_reaction_factor_alpha = 0.01
    User-defined reaction factor in equation (29); hand-chosen.
  • randomness_degree_m = 5
    Parameter in equation (30) controlling exploration versus exploitation in LNS action selection; hand-chosen.
  • EA_population_size = 2000
    Set in Section 5.1; authors note varying it did not significantly affect results, but it is still a hand-chosen hyperparameter.
assumptions (5)
  • domain assumption EVOP-V2G is NP-hard
    Section 4.2 states 'Similar to the NP-hard EVRP and EVOP problems' as motivation for heuristics, but no reduction is proved for this specific variant.
  • domain assumption Energy consumption is linear in distance with constant gamma
    Section 3.1 defines e = gamma times d_ij with constant gamma, ignoring speed, traffic, and regenerative braking. Stated as a simplification.
  • domain assumption Charging and discharging are discretized into slots with piecewise constant prices
    Section 3.1 models prices as piecewise constant over slots and represents charging as binary per slot; this assumes charging can commence only at slot boundaries and at a constant rate.
  • standard math Big-M reformulations are valid with sufficiently large M
    Constraints (10)-(13) use Big-M notation without specifying M values; standard, but correctness relies on M being large enough.
  • domain assumption All charging stations support V2G discharging at the residential feed-in tariff
    Section 5.1 assigns Energy Australia residential feed-in discharge prices to all stations, assuming public chargers pay drivers to export energy, which is not generally true today.

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Cite this review

Pith. "Pith review of Smart Ride and Delivery Services with Electric Vehicles: Leveraging Bidirectional Charging for Profit Optimisation." pith.science (2026). https://pith.science/paper/ALV7EUN7

@misc{pith2026250620401,
  author       = {Pith},
  title        = {Pith review of: Smart Ride and Delivery Services with Electric Vehicles: Leveraging Bidirectional Charging for Profit Optimisation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ALV7EUN7}},
  note         = {Machine review of arXiv:2506.20401}
}
read the original abstract

With the rising popularity of electric vehicles (EVs), modern service systems, such as ride-hailing delivery services, are increasingly integrating EVs into their operations. Unlike conventional vehicles, EVs often have a shorter driving range, necessitating careful consideration of charging when fulfilling requests. With recent advances in Vehicle-to-Grid (V2G) technology - allowing EVs to also discharge energy back to the grid - new opportunities and complexities emerge. We introduce the Electric Vehicle Orienteering Problem with V2G (EVOP-V2G): a profit-maximization problem where EV drivers must select customer requests or orders while managing when and where to charge or discharge. This involves navigating dynamic electricity prices, charging station selection, and route constraints. We formulate the problem as a Mixed Integer Programming (MIP) model and propose two near-optimal metaheuristic algorithms: one evolutionary (EA) and the other based on large neighborhood search (LNS). Experiments on real-world data show our methods can double driver profits compared to baselines, while maintaining near-optimal performance on small instances and excellent scalability on larger ones. Our work highlights a promising path toward smarter, more profitable EV-based mobility systems that actively support the energy grid.

Figures

Figures reproduced from arXiv: 2506.20401 by the authors.

Figure 1
Figure 1. Representation of Electric Vehicle Orienteering Problem with V2G [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Illustration of the EA process, showing the stages of initialization, crossover, mutation, and replacement. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Illustration of the LNS process, showing the stages of initialization, destroy operator, replacement operator, and optimization using MIP. [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Small scaled instances. The x-axis represents the variation of parameters for each criterion. Fig. 4a shows the cluster bounding box as percentage %, [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]
Figure 5
Figure 5. Figure 5: Large-scale instances. The x-axis represents the variation of parameters for each criterion. Fig. 5a shows the bounding box size as percentage % of the [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 6
Figure 6. Figure 6: Convergence graphs for small and large instances comparing our [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 7
Figure 7. Figure 7: Variation of charging rates, prices, and order fares for 2-hour time period instances. The x-axis represents the varying factors of both charging rate and [PITH_FULL_IMAGE:figures/full_fig_p016_7.png]

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Pith tools

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