Approaches to integrated strategic/tactical forest planning

Traditionally forest planning is divided into a hierarchy of planning phases. Strategic planning is conducted to make decisions about sustainable harvest levels while taking into account legislation and policy issues. Within the frame of the strategic plan, the purpose of tactical planning is to schedule harvest operations to specific areas in the immediate few years and on a finer time scale than in the strategic plan. The operative phase focuses on scheduling harvest crews on a monthly or weekly basis, truck scheduling and choosing bucking instructions. Decisions at each level are to a varying degree supported by computerized tools. A problem that may arise when planning is divided into levels and that is noted in the literature focusing on decision support tools is that solutions at one level may be inconsistent with the results of another level. When moving from the strategic plan to the tactical plan, three sources of inconsistencies are often present; spatial discrepancies, temporal discrepancies and discrepancies due to different levels of constraint. The models used in the papers presented in this thesis approaches two of these discrepancies. To address the spatial discrepancies, the same spatial resolution has been used at both levels, i.e., stands. Temporal discrepancies are addressed by modelling the tactical and strategic issues simultaneously. Integrated approaches can yield large models. One way of circumventing this is to aggregate time and/or space. The first paper addresses the consequences of temporal aggregation in the strategic part of a mixed integer programming integrated strategic/tactical model. For reference, linear programming based strategic models are also used. The results of the first paper provide information on what temporal resolutions could be used and indicate that outputs from strategic and integrated plans are not particularly affected by the number of equal length strategic periods when more than five periods, i.e. about 20 year period length, are used. The approach used in the first paper could produce models that are very large, and the second paper provides a two-stage procedure that can reduce the number of variables and preserve the allocation of stands to the first 10 years provided by a linear programming based strategic plan, while concentrating tactical harvest activities using a penalty concept in a mixed integer programming formulation. Results show that it is possible to use the approach to concentrate harvest activities at the tactical level in a full scale forest management scenario. In the case study, the effects of concentration on strategic outputs were small, and the number of harvest tracts declined towards a minimum level. Furthermore, the discrepancies between the two planning levels were small

Inventory drivers in a pharmaceutical supply chain

In recent years, inventory reduction has been a key objective of pharmaceutical companies, especially within cost optimization initiatives. Pharmaceutical supply chains are characterized by volatile and unpredictable demands –especially in emergent markets-, high service levels, and complex, perishable finished-good portfolios, which makes keeping reasonable amounts of stock a true challenge. However, a one-way strategy towards zero-inventory is in reality inapplicable, due to the strategic nature and importance of the products being commercialised. Therefore, pharmaceutical supply chains are in need of new inventory strategies in order to remain competitive. Finished-goods inventory management in the pharmaceutical industry is closely related to the manufacturing systems and supply chain configurations that companies adopt. The factors considered in inventory management policies, however, do not always cover the full supply chain spectrum in which companies operate. This paper works under the pre-assumption that, in fact, there is a complex relationship between the inventory configurations that companies adopt and the factors behind them. The intention of this paper is to understand the factors driving high finished-goods inventory levels in pharmaceutical supply chains and assist supply chain managers in determining which of them can be influenced in order to reduce inventories to an optimal degree. Reasons for reducing inventory levels are found in high inventory holding and scrap related costs; in addition to lost sales for not being able to serve the customers with the adequate shelf life requirements. The thesis conducts a single case study research in a multi-national pharmaceutical company, which is used to examine typical inventory configurations and the factors affecting these configurations. This paper presents a framework that can assist supply chain managers in determining the most important inventory drivers in pharmaceutical supply chains. The findings in this study suggest that while external and downstream supply chain factors are recognized as being critical to pursue inventory optimization initiatives, pharmaceutical companies are oriented towards optimizing production processes and meeting regulatory requirements while still complying with high service levels, being internal factors the ones prevailing when making inventory management decisions. Furthermore, this paper investigates, through predictive modelling techniques, how various intrinsic and extrinsic factors influence the inventory configurations of the case study company. The study shows that inventory configurations are relatively unstable over time, especially in configurations that present high safety stock levels; and that production features and product characteristics are important explanatory factors behind high inventory levels. Regulatory requirements also play an important role in explaining the high strategic inventory levels that pharmaceutical companies hold

Essays on Supply Chain Economic Networks for Disaster Management Inspired by the COVID-19 Pandemic

The COVID-19 pandemic, which was declared by the World Health Organization on March 11, 2020, negatively impacted virtually all economic and social activities across the globe. As of March 7, 2022, more than 6 million deaths have been associated with COVID-19 disease. This health disaster, unlike many other disasters, is not limited to time or location. It has resulted in intense global competition for many essential products, from Personal Protective Equipment (PPE) to ventilators and vaccines and food products. In this dissertation, I construct, analyze, and quantitatively solve a spectrum of supply chain economic network models inspired by realities in the COVID-19 pandemic in four essays. In this dissertation, I first develop a game theory network model for integrating financial and logistical challenges that humanitarian organizations involved in disaster management are faced with. This part of the dissertation illustrates how game theory can be utilized in the modeling and analysis of the behavior of multiple decision-makers that interact with each other in disaster supply chain economic networks under different constraints. I, subsequently, construct the first Generalized Nash Equilibrium (GNE) model with stochastic demands to model competition among organizations at demand points for medical supplies inspired by the COVID-19 pandemic. The theoretical constructs are provided, and a Variational Equilibrium is utilized to enable alternative variational inequality formulations. Then, I delve more deeply into an important characteristic of disasters, that of uncertainty, by developing a two-stage stochastic game theory network model. Specifically, the first multistage stochastic GNE model is constructed for the study of competition among multiple countries for limited supplies of medical items in the disaster preparedness and response phases in the COVID-19 pandemic. Illustrative examples and algorithmically solved numerical examples, inspired by the need for N95 masks and ventilators, are presented. Finally, I turn to a key aspect of pandemic disaster management, which is the evaluation of trade instruments that governments have been applying during the pandemic to protect their citizens. Specifically, a unified variational inequality framework in the context of spatial price network equilibrium problems is constructed that focuses on a plethora of essential products, that are in high demand in the pandemic, but short in supply globally. The model allows one to seamlessly introduce various trade measures, including tariffs, quotas, as well as price floors and ceilings

Sustainability Assessment of Community Scale Integrated Energy Systems: Conceptual Framework and Applications

abstract: One of the key infrastructures of any community or facility is the energy system which consists of utility power plants, distributed generation technologies, and building heating and cooling systems. In general, there are two dimensions to “sustainability” as it applies to an engineered system. It needs to be designed, operated, and managed such that its environmental impacts and costs are minimal (energy efficient design and operation), and also be designed and configured in a way that it is resilient in confronting disruptions posed by natural, manmade, or random events. In this regard, development of quantitative sustainability metrics in support of decision-making relevant to design, future growth planning, and day-to-day operation of such systems would be of great value. In this study, a pragmatic performance-based sustainability assessment framework and quantitative indices are developed towards this end whereby sustainability goals and concepts can be translated and integrated into engineering practices. New quantitative sustainability indices are proposed to capture the energy system environmental impacts, economic performance, and resilience attributes, characterized by normalized environmental/health externalities, energy costs, and penalty costs respectively. A comprehensive Life Cycle Assessment is proposed which includes externalities due to emissions from different supply and demand-side energy systems specific to the regional power generation energy portfolio mix. An approach based on external costs, i.e. the monetized health and environmental impacts, was used to quantify adverse consequences associated with different energy system components. Further, this thesis also proposes a new performance-based method for characterizing and assessing resilience of multi-functional demand-side engineered systems. Through modeling of system response to potential internal and external failures during different operational temporal periods reflective of diurnal variation in loads and services, the proposed methodology quantifies resilience of the system based on imposed penalty costs to the system stakeholders due to undelivered or interrupted services and/or non-optimal system performance. A conceptual diagram called “Sustainability Compass” is also proposed which facilitates communicating the assessment results and allow better decision-analysis through illustration of different system attributes and trade-offs between different alternatives. The proposed methodologies have been illustrated using end-use monitored data for whole year operation of a university campus energy system.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201