Optimal Politics of Conflict over Physical-Industrial Development Using a Technique of Cooperative Game Theory in Iran

One of the most important parameters for economic growth is industrial development in many developing regions like Iran. The Markazi province in the center of Iran is one of the most important industrial areas in the country, where unplanned economic development in recent decades has led to many social and environmental problems. Accordingly, the main organizations involved in industrial development in this region are facing difficulties in the future development of industrial areas, which has become a complex problem. Therefore, the main purpose of this paper is to study the industrial development dispute in the Markazi province through a cooperative model of game theory in order to achieve conflict resolution through a comprehensive approach. In this research, the conflict has been analyzed through strategic analysis of stakeholders. For this purpose, a model of cooperative game theory and its bargaining analysis methods, including social choice rules (SCRs) and fallback bargaining (FB), and six available options were used. According to the six SCRs, the most likely option that can exist between the Department of the Environment (DOE) and the Industrial and Mining Organization (IMO) is compromise coordination (C). In addition, the results of the Fallback Bargaining (FB) rule in three different forms show that the most appropriate options for agreement between the IMO and DOE are the moderate version of construction through arbitration (TCa) and compromise coordination (C). In fact, the results indicate that if the actors involved in this conflict do not cooperate to resolve it, it can lead to more complex problems and the involvement of other groups who may not even have a proper perception of the conflict. In addition, the findings show that cooperation between the parties and understanding of their positions and views along with the policy coherence are necessary to strive for sustainable development and maintain economic growth and development.

Monthly Allocation of Water Resources and Pollutant Loads in a Basin Based on the Water Footprint and Fallback Bargaining

The refined allocation of water resources and pollutant loads in a basin according to seasonal changes is an important measure for sustainable management. This study proposes a monthly water resource and pollutant load allocation model that is based on the water footprint and fallback bargaining. First, the water utilization and pollutant discharge demand and allocable resources are accounted for by taking their water footprints as indicators. Subsequently, various initial allocation schemes are designed based on several typical bankruptcy rules. Finally, with the goal of resource sustainability, the initial schemes are optimized by applying the fallback bargaining approach. The Huangshui River basin, which is located in Qinghai, China, is a typical seasonal basin with water use conflicts and it is considered for verifying the proposed methodology. The results show that the monthly allocation framework can effectively balance the water use and pollutant discharge demand of regions upstream and downstream in different seasons, improve the overall resource utilization efficiency in the basin, and ensure that the allocation each month reaches the Pareto optimum.

A Game Theory Approach for Conjunctive Use Optimization Model Based on Virtual Water Concept

In this study to allocate the agricultural and environmental water, considering virtual water concept, a multi-objective optimization model based on NSGA-II is developed. The objectives consist of equity maximization, agricultural benefit maximization for each region, maximization of green water utilization and finally minimization of environmental shortage. Then a cooperative game (Grand Coalition) model is presented by forming all possible coalitions. By the game model including Nucleolus, Proportional Nucleolus, Normal Nucleolus and Shapley methods, the benefit is reallocated based on all Pareto optimal solutions obtained from multi-objective optimization model. Then using two famous fallback bargaining methods, Unanimity and q-Approval, preferable alternative (solution) for each of the cooperative games is determined. Finally, based on the obtained benefit for each selected alternatives, the two most beneficial alternatives are chosen. The proposed methodology applied for water allocation of Minoo-Dasht, Azad-Shahr and Gonbad-Kavoos cities in Golestan province, Iran for a 3-year period as a case study. Also, eight crops including Wheat, Alfalfa, Barley, Bean, Rice, Corn, Soya, and Cotton are selected based on local experts’ recommendations. The models’ results indicated no significant difference between the grand coalition model and the multi-objective optimization model in terms of the average cultivation area (a relative change of 2.1%), while lower agricultural water allocation occurred for the grand coalition model (about 10.35 percent average) compared with the multi-objective optimization model. It is also observed that more agricultural benefit gained by the grand coalition model (32 percent average). Finally, it is found that Wheat and Corn hold the most rates of import and export, respectively, and Rice was the crop which has the least shortage of production to supply food demand.

A Hybrid of Genetic Algorithm and Evidential Reasoning for Optimal Design of Project Scheduling: A Systematic Negotiation Framework for Multiple Decision-Makers

Traditional project scheduling methods inherently assume that the decision makers (DMs) are a unique entity whose acts are based on group rationality. However, in practice, DMs’ reliance on individual rationality and the wish to optimize their own objectives skew negotiations towards their preferred solutions. This makes conventional project scheduling solutions unrealistic. Here, a new two-step method is proposed that seeks to increase the overall efficiency of project schedules without violating individual rationality criteria, to find scheduling solutions that are acceptable to all DMs. First, a genetic algorithm is combined with evidential reasoning (ER) to obtain near optimal project schedule alternatives with respect to the priorities of each DM, separately. Second, the fallback bargaining method is used to help the DMs reach a consensus on an alternative with the highest group satisfaction. The proposed model is tested on a benchmark project scheduling problem with over 3.6 billion possible project scheduling alternatives. The results show that the model helps DMs when appointing their preferences using a well-organized procedure to provide a transparent view of each project schedule performance solution. Furthermore, the model is able to absorb the maximum support from the DMs, not necessarily a unique entity, by collecting all the self-optimizing DMs’ preferences and fairly allocating the benefits.