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arxiv: 1907.11789 · v1 · pith:MVQL5WGFnew · submitted 2019-07-26 · 🧮 math.OC

A Dynamic Sustainable Competitive Petroleum Supply Chain Model for Various Stakeholders with Shared Facilities

Nazanin Moradinasab , Hassan Jafarzadeh , M. R. Amin-Naseri , Cody H. Fleming This is my paper

Pith reviewed 2026-05-24 15:13 UTC · model grok-4.3

classification 🧮 math.OC
keywords petroleum supply chainmulti-objective optimizationMILPsustainabilitystakeholdersshared facilitiessensitivity analysisGAMS
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The pith

A multi-period multi-objective MILP model optimizes petroleum supply chains for profit, jobs, and pollution while handling multiple stakeholders and shared facilities.

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

The paper develops a dynamic sustainable competitive petroleum supply chain model formulated as a multi-period, multi-objective, multi-level, multi-product mixed-integer linear program. It incorporates various stakeholders with shared facilities and is solved using GAMS on a portion of real industry data. Sensitivity analysis then identifies which cost coefficients most affect the overall objective value. The work aims to support planning that simultaneously addresses economic profit, job creation, and environmental pollution under dynamic and competitive conditions.

Core claim

The authors propose the DSCPSC model as an MILP that can be solved with GAMS 24.1.2 on real petroleum supply chain data; sensitivity analysis shows the objective function varies most with changes in variable costs, facility installation costs, and pipeline transportation costs.

What carries the argument

The DSCPSC model, a multi-objective MILP that simultaneously maximizes profit and jobs while minimizing air pollution across periods, levels, products, and stakeholders with shared facilities.

If this is right

  • Decision makers can use the model to select facility installations and transportation routes that jointly improve profit, employment, and emission targets.
  • Data collection efforts should prioritize accurate estimates of variable operating costs, installation costs, and pipeline tariffs.
  • Standard MILP solvers can produce feasible plans for realistic network sizes within the petroleum sector.
  • Plans can be updated periodically as new data on costs or demand becomes available.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same modeling structure could be tested on other energy networks such as natural gas or biofuels to check transferability.
  • Adding stochastic elements for uncertain future costs or regulations would test whether the deterministic solutions remain stable.
  • Linking the model outputs to regulatory compliance metrics could reveal policy levers that most influence the three objectives.

Load-bearing premise

The real petroleum supply chain data supplied to the model accurately represents the costs, capacities, stakeholder interactions, and environmental impacts that would occur in the actual system.

What would settle it

Running the model on a new set of real operational data from the same petroleum network and checking whether the optimized facility locations, flows, and objective values match observed decisions or expert-validated benchmarks over the modeled time periods.

Figures

Figures reproduced from arXiv: 1907.11789 by Cody H. Fleming, Hassan Jafarzadeh, M. R. Amin-Naseri, Nazanin Moradinasab.

Figure 1
Figure 1. Figure 1: Stakeholders costs costs for the first stakeholder are greater compared with those of the second and third stakeholders. It is also observed that fixed and variable, transportation and facility construction and expansion costs are remarkable in comparison with other costs [PITH_FULL_IMAGE:figures/full_fig_p011_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Flow of products transported by stakeholders [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Rate of change in objective function values of the [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Rate of change in objective function values of the [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
read the original abstract

Petroleum industry is the world's biggest energy source, and its associated industries such as production, distribution, refining and retail are considered as the largest ones in the world. Having the increasing price and governments job creation and international environmental policies, the petroleum companies try to maximize the number of created job, and their profit and minimize the air pollution simultaneously. To meet these objectives, an effective detailed and precise planning is needed. On the other hand, the dynamic environment and the presence of various stakeholders add to the complexity of planning and design of petroleum supply chain. Therefore, the multi-period, multi-objective, multi-level and multi-product dynamic sustainable competitive petroleum supply chain (DSCPSC) model taking into consideration the various stakeholders have been proposed in this paper. The proposed model is an MILP model and GAMS 24.1.2 software has been used to run it for a part of real petroleum supply chain data. Sensitivity analysis was then performed to determine the sensitivity of the results to the variation of the coefficients in objective function. Sensitivity analysis reveals that the highest variations of the objective function were observed with respect to the variable costs, facility installation costs and pipeline transportation costs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper proposes a multi-period, multi-objective, multi-level, multi-product dynamic sustainable competitive petroleum supply chain (DSCPSC) model formulated as an MILP. It reports solving the model with GAMS 24.1.2 on a portion of real petroleum supply chain data and performing sensitivity analysis on objective-function coefficients, concluding that the objective is most sensitive to variable costs, facility installation costs, and pipeline transportation costs.

Significance. If the model formulation is correct, the data representative, and the sensitivity results reproducible, the work could contribute to multi-stakeholder optimization tools for petroleum networks balancing profit, employment, and emissions. The use of real data and post-hoc sensitivity is a positive step, but the absence of any model equations, constraint details, data provenance, or validation against benchmarks means the claimed results cannot be assessed or extended by others.

major comments (2)
  1. [Abstract] Abstract (paragraph on model application and sensitivity analysis): the central claim that sensitivity analysis identifies variable costs, facility installation costs, and pipeline transportation costs as having the highest impact rests entirely on the supplied real data accurately encoding capacities, cost structures, stakeholder interactions, and environmental impacts, yet the manuscript provides no description of data sources, derivation of coefficients, or any validation steps.
  2. [Abstract] Abstract: the statement that 'the proposed model is an MILP model' is presented without any equations, objective function, or constraint formulations, making it impossible to verify whether the multi-period, multi-stakeholder, and sustainability features are correctly encoded or whether the sensitivity ranking follows from the model structure rather than from the particular coefficient values chosen.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. The two major comments correctly identify gaps in transparency that prevent independent assessment of the model and results. We address each point below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract (paragraph on model application and sensitivity analysis): the central claim that sensitivity analysis identifies variable costs, facility installation costs, and pipeline transportation costs as having the highest impact rests entirely on the supplied real data accurately encoding capacities, cost structures, stakeholder interactions, and environmental impacts, yet the manuscript provides no description of data sources, derivation of coefficients, or any validation steps.

    Authors: We agree that the absence of data provenance, coefficient derivation, and validation details prevents readers from evaluating the sensitivity results. In the revised manuscript we will add a new subsection (likely in the computational experiments section) that explicitly describes the source of the real petroleum supply chain data, how the cost, capacity, employment, and emission coefficients were obtained or estimated, and any cross-checks performed against industry reports or partial benchmarks. This addition will directly support the central claim about the sensitivity ranking. revision: yes

  2. Referee: [Abstract] Abstract: the statement that 'the proposed model is an MILP model' is presented without any equations, objective function, or constraint formulations, making it impossible to verify whether the multi-period, multi-stakeholder, and sustainability features are correctly encoded or whether the sensitivity ranking follows from the model structure rather than from the particular coefficient values chosen.

    Authors: The referee is correct that the current manuscript states the model is an MILP but does not display the mathematical formulation. We will insert a complete model-formulation section that presents the multi-period, multi-objective, multi-level, multi-product objective functions together with all constraints that encode the stakeholder interactions, shared-facility rules, and sustainability criteria. With the explicit model available, readers will be able to confirm the encoding of the claimed features and to assess whether the reported sensitivity ordering is a structural consequence or data-specific. revision: yes

Circularity Check

0 steps flagged

No circularity: MILP model solved on external data with independent sensitivity analysis

full rationale

The paper formulates an MILP optimization model for the DSCPSC, solves it via GAMS on supplied real petroleum supply chain data, and performs sensitivity analysis on objective-function coefficients. No derivation step, equation, or result reduces to its own inputs by construction; the model outputs are determined by externally provided capacities, costs, and parameters rather than self-referential definitions or fitted predictions. No self-citation chains or ansatzes are invoked as load-bearing premises. The analysis is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Abstract-only review; the model necessarily rests on standard MILP axioms plus domain assumptions about cost linearity, capacity limits, and stakeholder objective weights, but none are enumerated in the provided text.

free parameters (1)
  • objective-function coefficients
    Sensitivity analysis performed on variable costs, facility installation costs, and pipeline transportation costs implies these weights are treated as tunable inputs.
axioms (1)
  • domain assumption The supply chain can be represented as a multi-period multi-level network with linear costs and discrete facility decisions.
    Implicit in any MILP supply-chain formulation; location in abstract description of the DSCPSC model.

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