Planning is one of the most important components of gas industry production. Most big gas companies usually look for effective planning approaches to accomplish the organizational objectives including cost and time reduction as well as enhancing quality and efficiency. For planning gas refinery production, important parameters including production time, production volume, production cost and storage need to be considered. Planning different units ought to be integrated and coordinated with other departments. This article presents an intensive arithmetic model to determine the production of gas derivatives. The proposed model of this paper is formulated as a mixed integer programming and the resulted problem is solved using NSGA-II algorithm and a hybrid method called BBO/NSGA-II. The problem is also applied for a real-world case study and the results are discussed.

This paper presents a unified multi items general inventory model for integrated production of new items and remanufacturing of returned and defected items for a finite planning horizon. In this paper, a production model that takes into account learning, instantaneous deterioration rate and inflation is proposed. In addition, we also consider that the holding cost is a non-negative, non-decreasing and continuous function of time. In this model, the preservation technology is used to reduce the rate of product deterioration. A theory is developed to find the optimal solution of the proposed model; it is then exemplified with the help of several numerical examples. An efficient solution procedure is also provided to find the optimal strategy. Finally, sensitivity of the optimal solution to changes in the values of different parameters of the system and the convexities of the cost functions are also studied and represented through the graphs.

Supply chain management is important for companies and organizations to improve their business and lead competitiveness in the global marketplace. But demand variations in the supply chain are significant problem for most practitioners, planners, demand managers, and operations managers. Demand variations make forecasting and inventory management more difficult and tend to increase inventory levels. The supply chain (SC) profitability can be affected by the cost associated with large inventories, transportation, and production due to the bullwhip effect. Only bullwhip effect can lead to reduce the supply chain profitability in great amount. This paper represents a computational intelligence approach, which addresses the bullwhip effect in multistage supply chain. As a computational intelligence approach, Genetic Algorithm (GA) is employed to reduce the bullwhip effect. Through this approach, optimal order quantity in each stage is to be calculated by considering cost associated with bullwhip effect. Distorted information from one end of a supply chain can lead to tremendous inefficiencies to other end. In this paper it is shown that if each player of the supply chain orders or transfers optimum quantities for the upcoming period then the bullwhip effect can be reduced significantly.

Manufacturing industries are consistently working on improving their operational performance to remain competitive in the market. LM is a well-recognized approach for improving the overall performance. It contains several elements covered under a few lean attributes. This paper presents the application of Graph Theory and Matrix Approach (GTMA) for the identification of relative importance of different lean attributes in a lean environment using qualitative and quantitative factors. The Lean Manufacturing Attributes (LMA’s), affecting the overall LM environment, of a manufacturing industry were identified and analyzed for the implications on the managerial decisions. .In this proposed study, The GTMA approach is applied to prioritize the LMA’s based on their relative importance.

Bullwhip effect is one of the serious problems caused by uncertainty in supply chain management. Graph theoretical approach has been utilized by some of the authors towards evaluation of various phenomena performance. This paper has been an attempt to develop bullwhip effect mitigation index (BWEMI), which is a single numerical benchmarking index utilizing graph theory and matrix (GTM) method. Review of literature and subsequent discussions with experts to obtain their valuable opinions regarding managing supply chains have been utilized to complement and supplement the variables. Digraph theory has been used to develop various matrices showing interactions among bullwhip effect mitigation factors. The tool so developed may help supply chain managers to analyze and quantify efforts towards bullwhip effect mitigation.