Modeling Cash Flow of Early Production in Optimization of Oil and Gas Development, Production, and Transmission

Lack of financial resources is a common dilemma in various types of projects which can result in project delays and, in some cases, can threaten project feasibility. One of the remedies for these situations is early production where a part of the project will be completed and commissioned. In this paper, a development, production, and transmission optimization model for oil and gas fields is extended to consider early production cash flows. A case study is analyzed which showed sooner completion of projects and also a significant improvement in the total net present value of considered projects. This research is applied, experimental, field, and long-term. A linear mathematical model is developed which is solved in GAMS software using the CPLEX algorithm

A multi-product multi-objective optimization model for aggregate production planning considering production preferences: A case study

Aggregate production planning is a multi-objective problem which is influenced by managerial preferences which is rarely considered with these preferences in many researches. In this paper, a multi-product multi-objective aggregate-production-planning model has been proposed and implemented in an industrial ball-valves manufacturing company. In the first phase, preferences of various product groups have been determined via a multiple-attribute-decision-making method which is used as an input for the second phase. To do this, one of the outranking methods has been used because of the variety in the dimension and the nature of different attributes. In the second phase, a deterministic multi-objective mixed-integer mathematical model has been designed considering the needs of the company. This model not only concentrates on the benefits, but also considers the preferences of the products. The third objective function is decreasing work in process. To solve this model, ϵ-constraint method has been used leading to a set of Pareto-optimal solutions, enabling the decision-maker to choose the best solution by trading off between the three objective functions. So top managers are able to decide how to provide product preferences and how to decrease WIP products while the benefits remain reasonable. The results show that using the proposed approach in the case study has improved 35%, 28%, and 56% total benefit, total utility, and WIP products, respectively

A Decision Support System for Stakeholder Management during Different Project Phases considering Stakeholders’ Personality Types and Available Resources (The Case of Behsama Web-Based Information System)

A common mistake in the management of information technology projects is paying attention only to the technical issues and neglecting other areas of the project management. A critical area in information technology projects is the stakeholder management, which has been the focal point of many recent researches in the project management field. Thus, a decision support system (DSS) has been developed in this paper, consisting of a 10-stage operational process that leads to an executive plan for responding to the stakeholders’ expectations. The proposed DSS benefits from an optimization model that considers not only cost and time constraints, but also the possibility of partly fulfilling the stakeholders’ needs and expectations. The most important innovations in the development of this DSS are: considering the different phases of the project (planning, implementation and closing); and also, considering the personality types of contact persons in each stakeholder group. The proposed system has been implanted in a national IT project and the results have been presented in three categories, i.e. criteria and stakeholders, cost and time, and the results of the proposed model

Development of sustainable and resilient healthcare and non-cold pharmaceutical distribution supply chain for COVID-19 pandemic: a case study

Purpose: This study evaluated the influence of the coronavirus pandemic on the healthcare and non-cold pharmaceutical care distribution supply chain. Design/methodology/approach: The model involves four objective functions to minimize the total costs, environmental impacts, lead time and the probability of a healthcare provider being infected by a sick person was developed. An improved version of the augmented e-constraint method was applied to solve the proposed model for a case study of a distribution company to show the effectiveness of the proposed model. A sensitivity analysis was conducted to identify the sensitive parameters. Finally, two robust models were developed to overcome the innate uncertainty of sensitive parameters. Findings: The result demonstrated a significant reduction in total costs, environmental impacts, lead time and probability of a healthcare worker being infected from a sick person by 40%, 30%, 75% and 54%, respectively, under the coronavirus pandemic compared to the normal condition. It should be noted that decreasing lead time and disease infection rate could reduce mortality and promote the model\u27s effectiveness. Practical implications: Implementing this model could assist the healthcare and pharmaceutical distributors to make more informed decisions to minimize the cost, lead time, environmental impacts and enhance their supply chain resiliency. Originality/value: This study introduced an objective function to consider the coronavirus infection rates among the healthcare workers impacted by the pharmaceutical/healthcare products supply chain. This study considered both economic and environmental consequences caused by the coronavirus pandemic condition, which occurred on a significantly larger scale than past pandemic and epidemic crises