Can Producers’ Price War End Up in an Optimal Allocation?

The paper presents a theoretical framework for the phenomenon of the price war in the context of general equilibrium, with special attention to the production system. The natural question that arises is whether Nash-optimal production plans being the reactions to the changing prices can finally approximate a Nash-optimal production plan at the end of this war. To provide an answer, the production system is described as a parametric-multicriteria game. Referring to some results on the lower semicontinuty of the parametric weak-multicriteria Nash equilibria, we provide a positive answer for the stated problem.

An optimal production plan for cashew nuts community enterprise using metaheuristic algorithms

The proper production plan plays an important role in the cashew nuts market enterprise in order to reduce cost. This study aims to find the optimal production plan for cashew nuts using ant lion optimization (ALO), symbiotic organisms search (SOS), particle swarm optimization (PSO) and artificial bee colony algorithm (ABC). The novel objective function is introduced in this study. Three input data set, including production cost, holding cost and inventory quantity are investigated. The experiment cases consist of the frequency of production cycle time in January, February and March, respectively. As a results, four algorithms are available to estimate not only the proper production plan of cashew nuts but also an ability in reducing the inventory and the holding costs. In summary, the ALO algorithm provides better predictive skill than others for the cashew nuts production plan with the lowest RMSE value of 0.0913.

Analysis of production capacity planning to meet the consumer request using the Rough Cut Capacity Planning (RCCP) method in PT. Maccon Generasi Mandiri Makassar, Indonesia

Maccon Generasi Mandiri Makassar company is one of a manufacturing company engaged in the production of light brick AAC (Autoclaved Aerated Concrete). PT. Maccon Generasi Mandiri Makassar has a production capacity of 15024 〖 m〗^3 in a month or 180288 〖 m〗^3 in a year. However, with this capacity, the company is often unable to meet high consumer demand of 181450 〖 m〗^3 in a year due to less than optimal engine performance, a number of hours of work and an unbalanced workforce in the producing light brick of ACC (Autoclaved Aerated Concrete). This requires the company to plan the optimal production of capacity in order to fulfill the consumer demand in a timely and appropriate amount so that the expected of company profits will be increased. The purpose of this research is to plan production capacity in the future based on the demand rate of the consumer using the Rough Cut Capacity Planning (RCCP) with the method is Bill of Labor Approach (BOLA) technique. Based on the data processing which has been done, the recommended made were a combination of engine additions and working time. This is realized to fulfill the lack of production capacity. For the January Period = 19872 hours/month, February = 19008 hours/month, March = 19872 hours/month, April = 19008 hours/month, May = 18144 hours/month, June = 18144 hours/month, July = 19872 hour/month, August = 18144 hours/month, September = 17280 hours/month, October = 18144 hours/month, November = 18144 hours/month, December = 17280 hours/month.

AI for Production Forecasting and Optimization of Gas Wells: A Case Study on a Middle-East Gas Field

Oil & Gas extraction companies are using a vast amount of capital and expertise on production optimization. The scale and diversity of information required for analysis is massive and often leading to a prioritization between time and precision for the teams involved in the process. This paper provides a success story of how artificial intelligence (AI) is used to dynamically and effeciently optimize and predict production of gas wells. In particular, we focus on the application of unsupervised machine learning to identify under different potential constraints the optimal production parameter settings that can lead to maximum production. A machine learning model is supported by a decision support system that can enhance future drilling operations and also help answer important questions such as why a particular well or group of wells is producing differently than others of the same type or what kind of parameters that work on different wells in different conditions. The model can be advanced to optimize within field constraints such as facility handling capacity, quotas, budget or emmisions. The methods used were a combination of similarity measures and unsupervised machine learning techniques which were effective in identifying wells and clusters of wells that have similar production and behavioral profiles. The clusters of wells were then used to identify the process path (specific drilling and completion, choke size, chemicals, etc processes) most likely to result in optimal production and to identify the most impactful variables on production rate or cumulative production via an additional clustering of the principle charactersitics of the well. The data sets used to build these models include but are not limited to gas production data (daily volume), drilling data (well logs, fluid summary etc.), completion data (frac, cement bond logs), and pre-production testing data (choke, pressure etc.) Initial results indicate that this approach is a feasible approach, on target in terms of accuracy with traditional methods and represents a novel, data driven, method of identifying optimal parameter settings for desired production levels; with the ability to perform forecasts and optimization scenarios in run-time. The approach of using machine learning for production forecasting and production optimization in run-time has immense values in terms of the ability to augment domain expertise and create detailed studies in a fraction of the time that is typically required using traditional approaches. Building on same approach to optimise the field to deliver most reliable or most effeciently against a parameter will be an invaluable feature for overall asset optimisation.