Journal of Quality Engineering and Management

Abstract Purpose: This study aims to explore and assess situations in international markets. In this context, the primary objective is to identify the factors influencing the gas market and analyze the internal dynamics of each factor. Subsequently, based on the recognized elements' factor-driven model, the interplay and behavior of these elements as parts of the model are investigated.
Methodology: This research employs the problem structuring approach, also known as soft operational research, specifically the Graph Model for Conflict Resolution (GMCR) method. Additionally, in the quantitative part of this research, factor-based modeling is utilized. In this research, an attempt is first made to obtain a clear understanding of the gas market, and then a model is built based on this understanding. By identifying the key and leverage components of the factor-based model and simulating it over the long term, we generate possible scenarios or states.
Findings: In the first step, key players in the gas market, including the United States, the European Union, Russia, China, India, Iran, Qatar, and the Renewable Energy Group, were identified, and the conflicts between them were modeled. The GMCR analysis identified 28 equilibrium points, which were subsequently clustered into five distinct scenarios. In the next step, an agent-based simulation model was developed based on these scenarios.
Originality/Value: By integrating GMCR and an Agent-Based Model (ABM), this study has successfully addressed aspects of strategic conflict, market dynamics, and participants' gradual learning, which were previously overlooked in earlier research.

Abstract In this paper, a mathematical model for predicting expected costs to the manufacturer and buyer in pro-rata warranty policy under inflationary conditions is developed. In this model, the cost of individual claims to the manufacturer is limited to fixed cost 𝐶𝐼 whereby the manufacturer carries out all rectification action with a portion of the cost to the customer if the cost of rectification is below a limit 𝐶𝐼. If the cost of rectification exceeds 𝐶𝐼 then the customer pays the difference between the costs of rectification and 𝐶𝐼.based on this model we analyze respectively the expected warranty costs from the perspectives of the manufacturer and the consumer. A real data are executed to demonstrate the applicability of the model. It is proved that, inflation rate, warranty period length and failure parameters impacts the manufacturer and the consumer expected costs. 
 
 

Abstract The design of the closed loop supply chain network is one of the most important and fundamental strategic decisions, with the proper design of which creates a desirable structure and facilitates the efficient management of the chain effectively. One of the most fundamental problems in the design of the supply chain network is the closed loop of uncertainty in the quality of the return products, due to the emergence of this problem, the lack of accurate and accurate information as well as the dynamics and complexity of the chain components. This research in the design space the closed loop supply chain network is for durable products, which in the short term cannot be damaged and allow the reuse of parts in the production of new products, recycling or sales on the secondary market. The main purpose of this study is to use a two-stage randomized programming model and maximize expected earnings for all of the quality status scenarios in which the target function is a combination of revenue from the sale of products and recycled materials and components Recovered, in addition to fixed costs for centers, processes, logistics and transportation. Due to the complexity of the model, the problem was used with the Lp-shape and CPLEX algorithms and the GAMS software was used to solve the problem. Based on the results of the research, the substantive response introduced by CPLEX for the C3 to C6 test questions is significantly far from the optimal responses obtained by the L-Shape method.