scholarly journals Waste load allocation under uncertainty using game theory approach and simulation-optimization process

2020 ◽  
Vol 22 (4) ◽  
pp. 815-841 ◽  
Author(s):  
Behnam Andik ◽  
Mohammad Hossein Niksokhan
Keyword(s):  
Game Theory ◽  
Sensitivity Analysis ◽  
Simulation Optimization ◽  
Graph Model ◽  
Decision Makers ◽  
Optimization Process ◽  
Theory Approach ◽  
Waste Load Allocation ◽  
Waste Load ◽  
The Status

Abstract This article aims to present a new methodology for waste load allocation (WLA) in a riverine system considering the uncertainty and achieve the lowest amount of inequity index, cost, and fuzzy risk of standard violation. To find a surface of undominated solutions, a new modified PAWN method, initially designed for sensitivity analysis, was developed and coupled with a simulation-optimization process using multi-objective particle swarm optimization (MOPSO) algorithm, to consider the uncertainty of all affecting variables and parameters by using their probability distribution. The proposed methodology applied to Sefidrood River in the northern part of Iran. Graph model for conflict resolution (GMCR) as a subset of game theory was implemented to attain a compromise on WLA among the stakeholders of a river system's quality in Iran: Department of Environment, Municipal Waste Water, and Private Sector. Some undominated solutions were used in GMCR model and modeling the conflict among decision makers reveals that their preferences and the status quo do not lead to a solely stable equilibrium; thus the intervention of a ruler as arbitrator leads them to reach a compromise on a scenario that has a median FRVS and cost. Sensitivity analysis was done using the PAWN method to assess the sensitivity of three intended objectives to all variables and parameters.

Optimal waste load allocation using graph model for conflict resolution

2017 ◽  
Vol 75 (6) ◽  
pp. 1512-1522 ◽  
Author(s):  
Leila Saberi ◽  
Mohammad Hossein Niksokhan
Keyword(s):  
Conflict Resolution ◽  
Oxygen Demand ◽  
Graph Model ◽  
Waste Load Allocation ◽  
Swarm Optimization ◽  
Penalty Costs ◽  
Waste Load ◽  
Total Treatment ◽  
Stability Definitions

In this paper, a new methodology is proposed for waste load allocation in river systems using the decision support system (DSS) for the graph model for conflict resolution II (GMCRII), multi-criteria decision making (MCDM) analysis and the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Minimization of total treatment and penalty costs and minimization of biological oxygen demand violation of standards at the check point are considered as the main objectives of this study. At first, the water quality along the river was simulated using the Streeter-Phelps (S-P) equation coupled with the MOPSO model. Thereby a trade-off curve between the objectives is obtained and a set of non-dominated solutions is selected. In the next step, the best alternative is chosen using MCDM techniques and the GMCRII DSS package and non-cooperative stability definitions. The applicability and efficiency of the methodology are examined in a real-world case study of the Sefidrud River in the northern part of Iran.

Simulation–optimization method based on rationality evaluation for waste load allocation in Daliao river

2015 ◽  
Vol 73 (9) ◽  
pp. 5193-5209 ◽  
Author(s):  
Kun Lei ◽  
Gang Zhou ◽  
Fu Guo ◽  
Soon-Thiam Khu ◽  
Guangjun Mao ◽  
...  
Keyword(s):  
Simulation Optimization ◽  
Optimization Method ◽  
Waste Load Allocation ◽  
Waste Load

scholarly journals Sensor Communication Rate Control Scheme Based on Inference Game Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Sungwook Kim
Keyword(s):  
Game Theory ◽  
Real Life ◽  
Decision Makers ◽  
Optimal Decision ◽  
Game Model ◽  
Uncertain Information ◽  
Theory Approach ◽  
Communication Paradigm ◽  
Control Scheme ◽  
Traditional Game

In real-life situations, decisions must be made even when limited or uncertain information is available. Therefore, the payoff of an action is not clearly known when the decision is made. Recently, game theory has become a powerful tool for analyzing the interactions between decision makers in many domains. However, the traditional game theory approach assumes that a player belief about the payoff of a strategy taken is accurate. To address this problem, we introduce a new kind of game, called an inference game, and study how degrees of uncertainty of belief about payoffs impact the outcomes of real-world games. To approximate an optimal decision, our proposed inference game model can clarify how to better manage ambiguous information. In this study, we apply our inference game model to the sensor communication paradigm and confirm that our approach achieves better performance than other existing sensor communication schemes in widely diverse Internet of Things (IoT) environments.

Heuristic Algorithms for Waste Load Allocation in a River Basin

1989 ◽  
Vol 21 (8-9) ◽  
pp. 1057-1064 ◽  
Author(s):  
Vijay Joshi ◽  
Prasad Modak
Keyword(s):  
Dynamic Programming ◽  
Heuristic Algorithms ◽  
Water Quality Management ◽  
User Preferences ◽  
Theory And Practice ◽  
Waste Load Allocation ◽  
Practical Applications ◽  
Waste Load ◽  
On Line ◽  
The One

Waste load allocation for rivers has been a topic of growing interest. Dynamic programming based algorithms are particularly attractive in this context and are widely reported in the literature. Codes developed for dynamic programming are however complex, require substantial computer resources and importantly do not allow interactions of the user. Further, there is always resistance to utilizing mathematical programming based algorithms for practical applications. There has been therefore always a gap between theory and practice in systems analysis in water quality management. This paper presents various heuristic algorithms to bridge this gap with supporting comparisons with dynamic programming based algorithms. These heuristics make a good use of the insight gained in the system's behaviour through experience, a process akin to the one adopted by field personnel and therefore can readily be understood by a user familiar with the system. Also they allow user preferences in decision making via on-line interaction. Experience has shown that these heuristics are indeed well founded and compare very favourably with the sophisticated dynamic programming algorithms. Two examples have been included which demonstrate such a success of the heuristic algorithms.

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