*2.2. Research Gap*

Hashemipour et al. [53] presented a mathematical model to optimize the NG allocation to an oil field of Iran by using genetic algorithms. The objective function included (1) maximizing the production rate and (2) maximizing the profit. Alikhani and Rshidi [54] proposed a stochastic programming model for NG allocation with an energy security cost approach. They concluded that the priorities of NG resources allocations are as follows: domestic and commercial sections, power plants, industries, gas reinjection, exports, refinery, road transport, and agriculture, respectively. Kazemi et al. [55] comprehensively studied the problem of NG allocation models by AHP approach and 13 models were ranked in this study. Using multi-objective goal programming, Chedid et al. [56] suggested a model for NG allocation in Lebanon. Borges and Antunes [57] formulated energy allocation to the domestic sector using fuzzy multi-objective programming. Hatagalung [47] analyzed the optimization allocation of sustainable energy source by a non-linear mathematical model. Li et al. [58] examined the energy allocation problem by minimizing errors of budget. Maroufmashat and Sattari [59] presented a linear programing model to allocate NG resources to Iran's various demand sectors. Alikhani and Azar [60] employed a fuzzy goal programming model for allocating NG to different sectors. It was shown that NG injection, export, and road transportation are the most important sectors, respectively.

Some other studies investigated the NG prices' effects on the economics of importers and exporters since this influences the cycle of energy demand and supply [61–63]. Orlov [64] studied the Russian government's policy to reduce domestic NG price regulation and concluded that the domestic NG price should be 55% of the export netback price. Wang and Lin [61] presented a dynamic model to analyze the effect of NG price increase on the economy indexes (GDP, imports, and household income) in China. The effects of NG pricing were not presented or researched in any of the studies analyzed by the authors (or in systematic literature review made by the authors). Hence, some literature with their features is listed in Table 3 for a better idea of research trends needed in this field.


**Table 3.** Modeling approaches.

Note: NLP: none-linear programing, LP: linear programing, CGC: computational general equilibrium, FGP: fuzzy goal programming, SLP: stochastic linear programing, SD: system dynamic, DP: dynamic programing, and GP: goal programing.

In this paper, the optimal allocation of natural gas resources to different sectors of consumption is investigated by applying multi-objective goal programming decisionmaking techniques. Therefore, the objective function is the goal and system constraints of the multi-goal programming decision-making technique based on the relative weights assigned to different sectors of consumption.

#### **3. Methods and Material**
