Pith. sign in

REVIEW 2 major objections 7 minor 54 references

Dual Sourcing of Green Hydrogen: Balancing Local Production with Stochastic Capacity and Import with Random Yield

T0 review · 2 major / 7 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that jointly modeling stochastic local supply capacity and random import yield in a dual-sourcing Markov decision process yields an average cost benefit of about 8 percent over models that ignore both, and that flexible…

desk verdict Novel dual-sourcing MDP for green hydrogen with a serious case study, but the Bellman equation is sloppy and the FOQ+ description contradicts itself; worth refereeing after fixes. read the letter →

arxiv 2411.17583 v1 pith:TXZ5WVDP submitted 2024-11-26 math.OC

Sezen Ece Kayacik , Albert H. Schrotenboer , Iris F. A. Vis , Beste Basciftci , Evrim Ursavas This is my paper · ORCID
classification math.OC MSC 90C4090B05
keywords greenhydrogendualsourcingMarkovdecisionprocessstochasticsupplycapacityrandomyieldinventorycontrolvalueiterationimport
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

Green hydrogen supply is unreliable in two ways: local electrolysis output fluctuates with renewable availability, and imported hydrogen arrives only after a lead time and with a random fraction lost in transport and conversion. The paper models both uncertainties together in a Markov decision process and solves it with value iteration, arguing that a policy accounting for both is the right benchmark for dual sourcing decisions. In a case study of Dutch imports from Norway, Morocco, and the UAE, considering both stochastic supply capacity and random yield gives an average cost benefit of 8 percent compared to dual sourcing models that ignore them. The paper also proposes fixed and partially adjustable order policies; FOQ+, which lets order quantities vary inside narrow bands, stays within about 2 percent of optimal cost while giving exporters more stable order patterns. These results support feasibility assessments of Dutch climate scenarios that specify local production and import shares.

What carries the argument

The central object is a Markov decision process whose state records on-hand inventory and the pipeline of outstanding local and import orders, capturing general lead times. At each decision epoch the firm chooses a local order quantity and an import order quantity; the stochastic local capacity $K_t^l$ is realized after ordering, so the actual local delivery is $\min\{K_t^l, \hat{q}_t^l\}$, and the import order that arrives $\tau_i$ periods later is multiplied by a random yield factor $p_t$. The optimal policy is computed by value iteration (Algorithm 1), and four heuristic policies are derived by restricting actions: FOQ fixes both order quantities, FOQ+ allows each to vary within a two-step band around fixed levels, TBS keeps a fixed import order and uses local production as a backup triggered by an inventory threshold, and TBS+ adds a band of allowed import adjustments to TBS.

What would settle it

Re-solve the Netherlands case study with the same transition law using a standard average-cost MDP algorithm that includes a gain constant in the Bellman equation; if the long-run average cost of the Algorithm 1 policy differs from that average-cost optimum by more than numerical tolerance, then the reported 8% and 2% figures are not measured against the true optimal policy.

Watch

Extended reading notes

Core claim

The paper's central claim is that the optimal dual sourcing policy for green hydrogen must be computed with stochastic local supply capacity and random import yield modeled simultaneously, because ignoring either one biases the sourcing mix and adds cost. On the evidence of the Netherlands case study, jointly modeling both produces an average cost benefit of 8% relative to dual sourcing models that ignore both, with stochastic local capacity contributing more than random yield in these settings. The paper further claims that simple, more implementable policies are near-optimal: FOQ+ has an average optimality gap around 1.6–1.8% across the three exporting countries and cost settings, and TBS+ improves on TBS at higher cost ratios and storage costs, which agrees with the known result that tailored base-surge policies improve as slower-supplier lead times grow. These findings are put forward to guide order-structure negotiations in hydrogen trade agreements and to identify conditions under which Dutch climate scenarios' local production shares are achievable.

Load-bearing premise

The load-bearing premise is that Algorithm 1's value iteration really produces the optimal policy for the stated infinite-horizon problem, but Eq. (3) writes the Bellman equation with no discount factor and no average-cost gain term, so the optimality criterion behind all percentage gaps is not pinned down by the equations.

Editorial extensions

If this is right

  • Energy planners who neglect either stochastic local capacity or random import yield should expect to pay about 8 percent more than necessary under the Dutch 2030 parameter settings.
  • A fixed-order contract with a narrow adjustment band (FOQ+) is a strong candidate for hydrogen trade agreements: it stabilizes order quantities and stays within roughly 2 percent of the optimal policy across countries and cost ratios.
  • Tailored base-surge contracts become more attractive as the import lead time and the local-to-import cost ratio grow, which guides when exporters should push for fixed base volumes.
  • Achieving the National Drivers climate scenario, with high local production, requires low local-to-import production costs and low variability in local capacity; the International Ambition scenario becomes feasible when local production costs exceed import costs by about 20 percent or more.

Reading between the lines

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

  • Editorial inference: the same MDP could be re-estimated for other intermittent local generators and other hydrogen carriers such as ammonia or liquid organic hydrogen carriers; only the lead-time, yield, and cost distributions change.
  • Editorial inference: the sensitivity result that higher demand variability shifts sourcing toward local production suggests a direct counterfactual test—if the import lead time were equalized with the local lead time, that shift should weaken; the paper does not run this experiment.
  • Editorial inference: the Bellman equation in Eq. (3) is written without a discount factor or an average-cost gain term, so certifying the 'optimal' label would require restating it as an average-cost optimality equation and checking that the value iteration stopping rule converges to that gain.
  • Editorial inference: in practice, the width of the FOQ+ bands is set heuristically to two steps in the action grid; an obvious refinement is to optimize band width jointly with base quantities for each counterparty.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 7 minor

Summary. The paper studies a dual-sourcing inventory problem for green hydrogen in which local production is subject to stochastic capacity and imports are subject to random yield, with general lead times. The authors formulate an infinite-horizon MDP, solve it by value iteration, and propose four heuristics (FOQ, FOQ+, TBS, TBS+) that stabilize order quantities. A Netherlands case study with Norway, Morocco, and the UAE as suppliers reports an average cost benefit of 8% for considering both uncertainties relative to models that ignore them, heuristic optimality gaps of about 2% for FOQ+, and sensitivity analyses linking local supply rates to Dutch climate scenarios.

Significance. The application is timely and the modeling combination of stochastic capacity and random yield in dual sourcing is a reasonable extension of the inventory literature. The case study uses externally grounded cost data, and the headline 8% and roughly 2% figures are outputs of the proposed MDP and simulations, not inputs; I see no circularity. If the numerical claims are reproducible and the optimality criterion is made rigorous, the paper would offer useful, decision-relevant insights for hydrogen trade negotiations. However, the paper provides no formal structural results, no code or data, and the current statement of the Bellman equation is not a well-posed optimality condition for the stated average-cost objective, so the quantitative claims need verification before publication.

major comments (2)
  1. [§3.1, Eq. (3); §4.1, Algorithm 1] The optimality equation is not well-posed for the stated objective of minimizing expected cost per period. Equation (3) writes V(S_t) = min E[C_O + C_I + V(S_{t+1})] with no discount factor and no average-cost gain term; for positive costs no finite V satisfies this equation. Algorithm 1's span-based stopping rule resembles average-cost value iteration, but the paper never states the normalized relative value iteration, the gain constant, or the conditions under which span-based stopping certifies optimality. Because the 8% benefit and the 2% heuristic gaps in Sections 6.1 and 6.2 are all measured against policies labeled optimal by this algorithm, the central quantitative claims rest on an unstated optimality criterion. Please either introduce a discount factor and report discounted results, or state and solve the average-cost optimality equation with the gain term and a normalization, and confirm that the reported numbers are unchanged.
  2. [§5.2, §5.3, §6] The exact probability distributions driving the experiments are not specified. Section 5.2 gives support sets for local capacity and demand but no probability masses; Section 6 says these are normal with VarL=0.5 but does not describe the discretization/truncation used for capacity and demand, while the corresponding procedure for random yield is only described in Section 5.3. As a result, the numerical results—including the headline 8% benefit and all heuristic gaps—cannot be reproduced or independently checked. Please provide the full probability mass functions (or a data/code repository) and state how the pmfs are derived from the normal distributions.
minor comments (7)
  1. [§4.1, Algorithm 1] Please report the normalization used for the value function, the value of epsilon, and the number of iterations needed for convergence; without these details the 'optimal' label is difficult to assess.
  2. [§4.2] FOQ+ and TBS+ are obtained by re-running Algorithm 1 on restricted action spaces; the paper should state explicitly that these are optimal policies for the constrained MDP, so the reported gaps are constrained-optimality gaps rather than heuristic approximations.
  3. [§5.2, Figure 2] The storage-cost units should be clarified. Section 5.2 says per-day costs are multiplied by 7 to obtain weekly ch, but Figure 2's axis is labeled '€/kg' and the text refers to '4 €/kg' as a threshold; specify whether the axis and thresholds are per-day or per-week costs.
  4. [§6.1, Table 2] At ρl/i=1.0 with salt-cavern storage for Norway, the 'Yes/No' deviation (9.21%) exceeds the 'No/No' deviation (7.38%), which contradicts the general claim that the Yes/No case decreases deviations; please address this exception.
  5. [§6.2, Tables 5–7] Tables 5–7 report gaps such as 0.00% and 0.01%; because the heuristic parameters are selected by simulation over 100,000 periods, please provide standard errors or confidence intervals so that small values can be distinguished from simulation noise.
  6. [Abstract and §7] The 'within 2% of optimal' claim should be qualified as an average; individual FOQ+ gaps in Table 5 reach 4.39% (ρl/i=1.2, compressed gas, Norway).
  7. [Eq. (1)–(2)] Equation (1) uses the symbol s′0_t before it is introduced in Eq. (2); please define the interim inventory level before using it.

Circularity Check

0 steps flagged · score 2.0 of 10

No load-bearing circularity: the 8% benefit and the ~2% heuristic gaps are computed outputs, not inputs; the only self-citations are introductory and non-load-bearing.

full rationale

The paper's central quantitative claims are derived from solving MDPs and simulating the resulting policies, not from fitting to those claims. The 8% average benefit is computed as the average deviation of the simplified dual-sourcing benchmarks (No/No, Yes/No, No/Yes) from the full stochastic-capacity/random-yield policy in Tables 2-4; each benchmark policy is obtained by solving a separate MDP and then evaluated under the full stochastic model (Section 6.1), so the contrast is an output of the model rather than an equivalence by construction. The heuristic gaps in Tables 5-7 are likewise computed by comparing simulated long-run costs of FOQ/FOQ+/TBS/TBS+ against the value-iteration policy; parameter tuning by 100,000-period simulation (FOQ, TBS) or by restricting the action space around those parameters (FOQ+, TBS+) may make the gaps optimistic in-sample, but it is not circular because the cost figures are not assumed. Model parameters (capacities, yields, costs, lead times) come from external reports and expert-based sources, not from the target result. The two self-citations (Basciftci et al. 2024; Kayacik et al. 2025) appear only in the introduction to motivate the stability-flexibility trade-off and are not used to derive optimality, so they are not load-bearing. One non-circular weakness worth flagging: Eq. (3) writes V(S_t)=min E[CO+CI+V(S_{t+1})] with neither a discount factor nor an average-cost gain term, although the stated objective is expected cost per period; the 'optimal' label in Algorithm 1 therefore rests on an unstated optimality-equation normalization. That is a correctness/rigor concern about the Bellman recursion, not a self-referential reduction of the predictions to the inputs, and it does not raise the circularity score.

Assumptions & free parameters 10 free parameters · 6 assumptions · 0 invented entities

The central results depend on several hand-chosen numerical inputs and standard stochastic inventory assumptions. No new physical entities, forces, or conserved quantities are introduced; the model uses standard inventory abstractions.

free parameters (10)
  • Mean local supply capacity = 10,000 t/week
    Set from 4 GW electrolyzer at 50 percent capacity factor; base value for the 8 percent cost-benefit claim.
  • Mean demand = 14,000 t/week
    Midpoint of 75-100 PJ/year projections for 2030; base value.
  • Mean random yield loss = 17.5%
    From IRENA 2022c; import arrives at 82.5 percent of order.
  • Variability level VarL = 0.5
    Controls standard deviation of supply, demand, and yield; sensitivity explores 0-1.
  • Lost sales penalty cp = 30 EUR/kg
    Chosen as roughly four times average import unit cost, per Section 5.2.
  • Holding cost by storage type = SC 2.8, CG 16.1, LH 24.5 EUR/kg/week
    Averages of projected ranges multiplied by 7 for weekly periods.
  • Import unit costs = Norway 8.62, Morocco 5.76, UAE 6.27 EUR/kg
    Derived from SeaRates lead times and IEA/IRENA transport and conversion costs.
  • Cost ratios rho_l/i = 0.6, 0.8, 1.0, 1.2, 1.4
    Grid over which the 8 percent average benefit is computed; no single base value.
  • Order increment = 2000 t
    Discretization needed for MDP solution.
  • Lead times = Norway 1, Morocco 2, UAE 3 weeks
    Computed with SeaRates; difference affects policy.
assumptions (6)
  • domain assumption Demand realizations are independent and identically distributed across periods with a stationary discrete distribution.
    Used in the transition Eq. (1) and throughout the MDP; serial correlation or nonstationarity is not modeled.
  • domain assumption Local supply capacity is stochastic and revealed only after an order is placed.
    Section 3.1: delivered local quantity is min{K_l, qhat_l}; the decision cannot condition on K_l.
  • domain assumption Import yield is a random proportion applied linearly to the order quantity.
    Section 3.1: arrived amount is p_t times q_i_{t-tau_i}; the yield is independent of quantity.
  • domain assumption Unmet demand is lost and excess inventory is held at linear costs.
    Section 3.1 cost function C_I; no backlogging, no nonlinear holding costs.
  • standard math The problem is an infinite-horizon stationary MDP with an average-cost optimality criterion.
    Section 3 states objective of minimizing expected cost per period; Eq. (3) does not explicitly include a discount factor or average-cost gain.
  • standard math Value iteration converges to the optimal value function.
    Algorithm 1 relies on standard value iteration (Puterman 2014), though the span-based stopping condition is unusual.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Dual Sourcing of Green Hydrogen: Balancing Local Production with Stochastic Capacity and Import with Random Yield." pith.science (2026). https://pith.science/paper/TXZ5WVDP

@misc{pith2026241117583,
  author       = {Pith},
  title        = {Pith review of: Dual Sourcing of Green Hydrogen: Balancing Local Production with Stochastic Capacity and Import with Random Yield},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TXZ5WVDP}},
  note         = {Machine review of arXiv:2411.17583}
}
read the original abstract

Green hydrogen is a critical component for achieving the European Union's 2050 net-zero emissions goal. However, ensuring a reliable and stable supply is challenging, particularly when local production of green hydrogen is subject to high variability due to fluctuating renewable energy output. Although importing from regions with stable renewable resources may offer greater reliability, they introduce longer lead times and potential energy losses during transportation and conversion. To address this issue, we develop optimal dual sourcing policies for green hydrogen through modeling and solving a Markov Decision Process that integrates general lead times, stochastic local supply capacity, and random yield from import. Alongside optimal dual sourcing policies, we propose heuristic policies that offer both flexibility and stability, enabling practical implementation while achieving near-optimal performance. We test our framework on a case study for the Netherlands and obtain the following insights: (i) based on the results across different countries and cost settings, our dual sourcing model demonstrates an average cost benefit of 8 percent compared to models that ignore stochastic supply capacity and random yield, (ii) the proposed heuristic policies can perform comparably to optimal policies under varying country-specific conditions and cost settings, offering insights for shaping hydrogen trade agreements between importing and exporting countries, (iii) sensitivity analyses on production and storage costs, as well as variability in supply capacity, demand, and random yield, reveal the conditions needed to achieve specific local production levels. These results thereby support feasibility of the Netherlands' climate scenarios and ambitions while guiding green hydrogen investment planning.

Figures

Figures reproduced from arXiv: 2411.17583 by the authors.

Figure 2
Figure 2. 0 2 4 6 8 0 20 40 60 80 100 Storage cost (€/kg) Percentage of local supply (%) ρl/i 0.6 0.8 1 1.2 1.4 [PITH_FULL_IMAGE:figures/full_fig_p021_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

54 extracted references · 53 canonical work pages

  1. [1]

    and Van Mieghem, J

    Allon, G. and Van Mieghem, J. A. (2010). Global dual sourcing: Tailored base-surge allocation to near-and offshore production. Management Science , 56(1):110--124

    work page 2010

  2. [2]

    Renewable hydrogen imports could compete with eu production by 2030

    Aurora Energy Research (2024). Renewable hydrogen imports could compete with eu production by 2030

    work page 2024

  3. [3]

    Barankin, E. (1961). A delivery-lag inventory model with an emergency provision. Naval Research Logistics Quarterly , 8(3):285--311

    work page 1961

  4. [4]

    Basciftci, B., Ahmed, S., and Gebraeel, N. (2024). Adaptive two-stage stochastic programming with an analysis on capacity expansion planning problem. Manufacturing & Service Operations Management , 26(6):2121--2141

    work page 2024

  5. [5]

    Burke, A., Ogden, J., Fulton, L., and Cerniauskas, S. (2024). Hydrogen storage and transport: Technologies and costs. Technical Report UCD-ITS-RR-24-17, Institute of Transportation Studies, UC Davis

    work page 2024

  6. [6]

    S., Jeong, H., Han, J., Nam, S

    Cha, J., Jo, Y. S., Jeong, H., Han, J., Nam, S. W., Song, K. H., and Yoon, C. W. (2018). Ammonia as an efficient cox-free hydrogen carrier: Fundamentals and feasibility analyses for fuel cell applications. Applied energy , 224:194--204

    work page 2018

  7. [7]

    Chen, W., Feng, Q., and Seshadri, S. (2013). Sourcing from suppliers with random yield for price-dependent demand. Annals of Operations Research , 208:557--579

    work page 2013

  8. [8]

    and Yang, H

    Chen, W. and Yang, H. (2019). A heuristic based on quadratic approximation for dual sourcing problem with general lead times and supply capacity uncertainty. IISE Transactions , 51(9):943--956

    work page 2019

Show all 54 references
  1. [9]

    Chen, X., Gao, X., and Pang, Z. (2018). Preservation of structural properties in optimization with decisions truncated by random variables and its applications. Operations Research , 66(2):340--357

    2018

  2. [10]

    Cheng, C., van Greevenbroek, K., and Viole, I. (2024). The competitive edge of norway's hydrogen by 2030: Socio-environmental considerations. International Journal of Hydrogen Energy , 85:962--975

    2024

  3. [11]

    Daniel, K. H. (1962). A delivery-lag inventory model with emergency order. In Scarf, H., Gilford, D., and Shelly, M., editors, Multistage Inventory Models and Techniques , chapter 2. Stanford University Press, Stanford, CA

    1962

  4. [12]

    Den Ouden, B., Kerkhoven, J., Warnaars, J., Terwel, R., Coenen, M., Verboon, T., Tiihonen, T., and Koot, A. (2020). Klimaatneutrale energiescenario’s 2050: Scenariostudie ten behoeve van de integrale infrastructuurverkenning 2030-2050

    2020

  5. [13]

    Detz, R., Weeda, M., and van Stralen, J. (2020). Hydrogen in the netherlands: A review of recent dutch scenario studies. Technical report, TNO

    2020

  6. [14]

    Eckl, F., Eltrop, L., Moita, A., and Neto, R. C. (2022). Techno-economic evaluation of two hydrogen supply options to southern germany: On-site production and import from portugal. International Journal of Hydrogen Energy , 47(60):25214--25228

    2022

  7. [15]

    European hydrogen backbone

    EHB (2020). European hydrogen backbone

    2020

  8. [16]

    Energy: Roadmap 2050

    European Union (2012). Energy: Roadmap 2050 . Publications Office of the European Union

    2012

  9. [17]

    and Shanthikumar, J

    Feng, Q. and Shanthikumar, J. G. (2018). Supply and demand functions in inventory models. Operations Research , 66(1):77--91

    2018

  10. [18]

    and H \"a rtel, P

    Frischmuth, F. and H \"a rtel, P. (2022). Hydrogen sourcing strategies and cross-sectoral flexibility trade-offs in net-neutral energy scenarios for europe. Energy , 238:121598

    2022

  11. [19]

    Fukuda, Y. (1964). Optimal policies for the inventory problem with negotiable leadtime. Management Science , 10(4):690--708

    1964

  12. [20]

    Gaster, R. (2024). A realist approach to hydrogen. Technical report, Information Technology and Innovation Foundation. Retrieved from https://itif.org

    2024

  13. [21]

    Hystock: Hydrogen storage in the netherlands

    Gasunie (2023). Hystock: Hydrogen storage in the netherlands

    2023

  14. [22]

    Gong, X., Chao, X., and Zheng, S. (2014). Dynamic pricing and inventory management with dual suppliers of different lead times and disruption risks. Production and Operations Management , 23(12):2058--2074

    2014

  15. [23]

    Official dutch government website

    Government of the Netherlands (2024). Official dutch government website. https://www.government.nl/. Accessed: 2024-11-11

    2024

  16. [24]

    and Grand-Clement, J

    Goyal, V. and Grand-Clement, J. (2023). A first-order approach to accelerated value iteration. Operations Research , 71(2):517--535

    2023

  17. [25]

    Hydrogen energy applications in valley environments for northern netherlands

    HEAVENN (2023). Hydrogen energy applications in valley environments for northern netherlands

    2023

  18. [26]

    Hosseini, S. E. and Wahid, M. A. (2016). Hydrogen production from renewable and sustainable energy resources: Promising green energy carrier for clean development. Renewable and Sustainable Energy Reviews , 57:850--866

    2016

  19. [27]

    Hua, Z., Yu, Y., Zhang, W., and Xu, X. (2015). Structural properties of the optimal policy for dual-sourcing systems with general lead times. IIE Transactions , 47(8):841--850

    2015

  20. [28]

    The future of hydrogen

    International Energy Agency (2019). The future of hydrogen

    2019

  21. [29]

    Green hydrogen supply: A guide to policy making

    International Renewable Energy Agency (2021). Green hydrogen supply: A guide to policy making

    2021

  22. [30]

    Geopolitics of hydrogen

    International Renewable Energy Agency (2022a). Geopolitics of hydrogen

    2022

  23. [31]

    Global hydrogen trade outlook

    International Renewable Energy Agency (2022b). Global hydrogen trade outlook

    2022

  24. [32]

    Global hydrogen trade to meet the 1.5°c climate goal: Green hydrogen cost and potential

    International Renewable Energy Agency (2022c). Global hydrogen trade to meet the 1.5°c climate goal: Green hydrogen cost and potential

    2022

  25. [33]

    and Fransoo, J

    Jak s i c , M. and Fransoo, J. C. (2018). Dual sourcing in the age of near-shoring: trading off stochastic capacity limitations and long lead times. European Journal of Operational Research , 267(1):150--161

    2018

  26. [34]

    Janakiraman, G., Seshadri, S., and Sheopuri, A. (2015). Analysis of tailored base-surge policies in dual sourcing inventory systems. Management Science , 61(7):1547--1561

    2015

  27. [35]

    F., and van Ommeren, J

    Ju, W., Gabor, A. F., and van Ommeren, J. C. (2015). An approximate policy for a dual-sourcing inventory model with positive lead times and binomial yield. European Journal of Operational Research , 244(2):490--497

    2015

  28. [36]

    E., Basciftci, B., Schrotenboer, A

    Kayac k, S. E., Basciftci, B., Schrotenboer, A. H., and Ursavas, E. (2025). Partially adaptive multistage stochastic programming. European Journal of Operational Research , 321(1):192--207

    2025

  29. [37]

    Li, J., Luo, X., Wang, Q., and Zhou, W. (2021). Supply chain coordination through capacity reservation contract and quantity flexibility contract. Omega , 99:102195

    2021

  30. [38]

    and Deshmukh, A

    Lian, Z. and Deshmukh, A. (2009). Analysis of supply contracts with quantity flexibility. European Journal of Operational Research , 196(2):526--533

    2009

  31. [39]

    Global energy perspective 2023: Hydrogen outlook

    McKinsey & Company (2023). Global energy perspective 2023: Hydrogen outlook

    2023

  32. [40]

    Government strategy on hydrogen: Towards a climate-neutral energy supply

    Ministry of Economic Affairs and Climate Policy of the Netherlands (2020). Government strategy on hydrogen: Towards a climate-neutral energy supply. Government Report

    2020

  33. [41]

    Scenario’s investeringsplannen 2024

    Netbeheer Nederland (2023). Scenario’s investeringsplannen 2024

    2023

  34. [42]

    Klimaat- en energieverkenning 2022 (climate and energy outlook 2022)

    PBL Netherlands Environmental Assessment Agency (2022). Klimaat- en energieverkenning 2022 (climate and energy outlook 2022)

    2022

  35. [43]

    Climate and energy outlook of the netherlands 2023: An overview of projections of greenhouse gas emissions, energy savings, and renewable energy

    PBL Netherlands Environmental Assessment Agency (2023). Climate and energy outlook of the netherlands 2023: An overview of projections of greenhouse gas emissions, energy savings, and renewable energy

    2023

  36. [44]

    and Mulder, M

    Perey, P. and Mulder, M. (2023). International competitiveness of low-carbon hydrogen supply to the northwest european market. international Journal of Hydrogen Energy , 48(4):1241--1254

    2023

  37. [45]

    Puterman, M. L. (2014). Markov decision processes: discrete stochastic dynamic programming . John Wiley & Sons

    2014

  38. [46]

    Sheopuri, A., Janakiraman, G., and Seshadri, S. (2010). New policies for the stochastic inventory control problem with two supply sources. Operations Research , 58(3):734--745

    2010

  39. [47]

    J., Lee, Y., Yu, Y., Ko, M., Lee, C., and Song, H

    Shin, W. J., Lee, Y., Yu, Y., Ko, M., Lee, C., and Song, H. H. (2023). Comparative life cycle greenhouse gas analysis of clean hydrogen pathways: Assessing domestic production and overseas import in south korea. Journal of Cleaner Production , 425:138907

    2023

  40. [48]

    V., Balcombe, P., Dodds, P

    Staffell, I., Scamman, D., Abad, A. V., Balcombe, P., Dodds, P. E., Ekins, P., Shah, N., and Ward, K. R. (2019). The role of hydrogen and fuel cells in the global energy system. Energy & Environmental Science , 12(2):463--491

    2019

  41. [49]

    and Van Mieghem, J

    Sun, J. and Van Mieghem, J. A. (2019). Robust dual sourcing inventory management: Optimality of capped dual index policies and smoothing. Manufacturing & Service Operations Management , 21(4):912--931

    2019

  42. [50]

    Tsay, A. A. and Lovejoy, W. S. (1999). Quantity flexibility contracts and supply chain performance. Manufacturing & Service Operations Management , 1(2):89--111

    1999

  43. [51]

    and Scheller-Wolf, A

    Veeraraghavan, S. and Scheller-Wolf, A. (2008). Now or later: A simple policy for effective dual sourcing in capacitated systems. Operations Research , 56(4):850--864

    2008

  44. [52]

    Whittemore, A. S. and Saunders, S. (1977). Optimal inventory under stochastic demand with two supply options. SIAM Journal on Applied Mathematics , 32(2):293--305

    1977

  45. [53]

    and Goldberg, D

    Xin, L. and Goldberg, D. A. (2018). Asymptotic optimality of tailored base-surge policies in dual-sourcing inventory systems. Management Science , 64(1):437--452

    2018

  46. [54]

    Zhu, S. X. (2015). Analysis of dual sourcing strategies under supply disruptions. International Journal of Production Economics , 170:191--203

    2015

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.