Scheduling internal audit activities:A stochastic combinatorial optimization problem

The problem of finding the optimal timing of audit activities within an organisation has been addressed by many researchers. We propose a stochastic programming formulation with Mixed Integer Linear Programming (MILP) and Constraint Programming (CP) certainty-equivalent models. In experiments neither approach dominates the other. However, the CP approach is orders of magnitude faster for large audit times, and almost as fast as the MILP approach for small audit times. This work generalises a previous approach by relaxing the assumption of instantaneous audits, and by prohibiting concurrent auditin

OPTIMAL DESIGN OF ENERGY COMMUNITIES Multi-objective design of multi-vector energy hubs integrated with electric mobility charging systems and acting as an energy community

The present thesis has the aim to develop a tool based on prescriptive analytics to perform the optimal design of several multi-vector energy hubs, integrated with electric mobility charging infrastructures, jointly acting as a local energy community through a posteriori multi-objective function. In Chapter 1 after having introduced the scope of the study, the justification of its relevance, and the main objectives, a brief summary of the publications of the author and his main activities during the PhD program course is reported. In Chapter 2 the energy transition is introduced, underlining the EU environmental targets by 2030 and the main energy trends which the energy sector is facing. Then the main incentive policies which are used to reach the environmental targets are reported and briefly analysed. The focus is moved on the newly introduced concepts of energy communities and collective self-consumers at the EU and at the State Member level. The preliminary implementation of the EU directives in Italy and Spain are evaluated and commented. Finally, the concept of microgrid and nanogrid is reported, as an actual and real representation of integrated energy systems characterized by multiple energy demands and different technologies. Chapter 3 recalls the concept of traditional design and compare it with optimal design. After a brief introduction on the different analytics techniques (descriptive, predictive, prescriptive) the focus is moved to the MILP (Mixed-Integer Linear Programming) problem as a tool of prescriptive analytics which can be used to perform the optimal design. Finally, a review of the state of the art of optimal design algorithms and case studies are reported and the main contributions of the present work are underlined. Chapter 4 introduces the first step towards this thesis objective. At first a deterministic mathematical model capable of performing the optimal design of a single-vector (electricity) energy hub integrated with EVs (Electric Vehicles) infrastructure is reported and applied to the case of a single-family dwelling. The considered technologies are photovoltaic, electric storage systems and charging infrastructures. Later the complexity of the model is increased, by proposing a stochastic mathematical model capable of performing the optimal design of a single-vector energy hub integrated with EVs infrastructure. The model is applied to the Mensa building of the Savona Campus of the University of Genova. Several objective functions are considered and the results are reported and commented. Chapter 5 increases the complexity of the study by introducing a deterministic mathematical model to perform the optimal design of a multi-vector energy hub. Several energy demands are considered (electricity, space heating and cooling, domestic hot water) and the portfolio of technologies is significantly expanded involving electric and thermal RES (Renewable Energy Sources), micro cogeneration units, trigeneration units, conversion units (reversible heat pumps), electric and thermal storage systems and EVs charging infrastructures. A multi-objective function is implemented. The model is applied to the entirety of the Savona Campus of the University of Genova. Chapter 6 reports the final and complete version of the developed mathematical model. This model is able to perform the optimal design of several multi-vector energy hubs, integrated with EVs charging stations, jointly acting as an energy community. The model is then applied to the Opera Pia Engineering compound of the University of Genova through the analysis of two different cases. At first a purely virtual relationship between several hubs is considered similarly to the Italian implementation of the renewable energy community concept. Later, a physical relationship between hubs is investigated similarly to the Spanish implementation of the renewable energy community configuration. Finally, Chapter 7 reports the conclusions and possible future research activities

Energy Optimization and Management of Demand Response Interactions in a Smart Campus

The proposed framework enables innovative power management in smart campuses, integrating local renewable energy sources, battery banks and controllable loads and supporting Demand Response interactions with the electricity grid operators. The paper describes each system component: the Energy Management System responsible for power usage scheduling, the telecommunication infrastructure in charge of data exchanging and the integrated data repository devoted to information storage. We also discuss the relevant use cases and validate the framework in a few deployed demonstrators

Efficient product allocation strategy to enable network-wide risk mitigation

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 65-66).Amgen Inc. currently manufactures, formulates and fills substantially all of their global drug product units in a single primary facility ("Site 1A"). Concerned about the inherent risks posed by the geographic concentration of these activities, Amgen has decided to acquire a new international Risk Mitigation Site ("RMS"), expand existing bulk manufacturing infrastructure at Site 1A, and construct a new formulation and filling facility colocated with Site 1A ("Site IB"). Bringing both sites online in the near future will create a novel operational challenge for Amgen, as it will present a broad range of formulation/fill production allocation decisions that did not previously exist. If per-unit costs (production, logistics, etc.) were considered to be typically higher at either RMS or Site 1A/B, an unconstrained optimization model might suggest filling/finishing all product at whichever site has the lowest average cost. However, we assume that RMS should be able to ramp up to full capacity within 3 months of an adverse occurrence at Site 1A. This translates to a minimum product flow constraint through RMS, irrespective of per unit costs, that will keep the facility sufficiently staffed to prepare for a fast ramp-up. Furthermore, helping Amgen mitigate the risks of geographic concentration, RMS may typically produce only a portion of global demand for any product. Given this situation, this thesis develops a product allocation strategy that will: 1) minimize the financial cost of filling various quantities of drug product at the new facility, yet 2) maintain at RMS the expertise required begin manufacturing all drugs in a short period of time. A mixed-integer linear program ("MILP") was developed to capture variable costs of the formulation & fill process for each drug product ("DP") and market combination. The objective of this model is to minimize total supply chain costs subject to meeting market demand and maintaining a sufficient amount of product flow through the RMS facility. The analysis assumes that the decision to develop fill capacity at both RMS and Site lB is complete and that both facilities will be licensed to fill all products that currently run through Site 1A (i.e. capital investment decisions will not be analyzed in this study). The outcome of this study is a product allocation strategy that minimizes network costs as well as a tool that will enable Amgen to solve for minimal network costs under additional future scenarios.by Roy J. Lehman, III.S.M.M.B.A