Management by objectives | EPFL Graph Search

Management by objectives (MBO), also known as management by planning (MBP), was first popularized by Peter Drucker in his 1954 book The Practice of Management. Management by objectives is the process of defining specific objectives within an organization that management can convey to organization members, then deciding how to achieve each objective in sequence. This process allows managers to take work that needs to be done one step at a time to allow for a calm, yet productive work environment. In this system of management, individual goals are synchronized with the goals of the organization. An important part of MBO is the measurement and comparison of an employee's actual performance with the standards set. Ideally, when employees themselves have been involved with the goal-setting and choosing the course of action to be followed by them, they are more likely to fulfill their responsibilities. According to George S. Odiorne, the system of management by objectives can be described

Satisfaction des utilisateurs du dossier patient informatisé valaisan

David Marey, Philippe Wieser

A multidisciplinary and shared computerized patient record (CPR) has been progressively implemented since 2002 for all public hospitals in the State of Valais, Switzerland. This paper presents the results of a formal evaluation of user’s satisfaction made during the deployment and based on questionnaires and interviews. According to the results, the major objectives have been reached: 2/3 of users evaluate posi- tively the effect of the CPR on access to patient information as well as improvement of commu- nication between care providers. The perform- ance problems are clearly identified, such as time needed to start the software. These prob- lems govern the final adoption of the system, although 90% of users would not get back to paper. In conclusion, intrinsic quality of the CPR, such as ergonomics and structure, and technical aspects such as speed and availability are key factors for the success of such a deployment.

2006

An Extensive Group Decision Methodology for Alliance Partner Selection Problem in Collaborative Networked Organisations

Omar Ayadi, Naoufel Cheikhrouhou

More and more companies address collaborations and cooperation as strategic topics to build competitive networks and broaden the global competences provided by the community thus created. These companies can cooperate with partners to share resources, competences, risks, or costs. Besides, penetrating new markets can be easier when associating new partners. The selection process of an adequate partner considered for a specific objective is a key success factor. This paper proposes an evaluation methodology for selecting an alliance partner in the case of a network of enterprises, manufacturing high precision mechanical components. An extensive group decision methodology is developed using both quantitative data and qualitative judgments in the evaluation of criteria. Since some performances cannot be represented with crisp numbers, the proposed methodology allows experts to use linguistic variables to express their judgments for the assessment of qualitative criteria. Two main phases are integrated in this methodology. In the first phase, criteria and experts weightings are calculated to determine the criteria importance using fuzzy the analytical hierarchy process. In the second phase, a technique for order preference by similarity to ideal solution is proposed to rank the different alternatives, corresponding to the alternative partnerships.

2012

Active learning for multi-objective optimization of processes and energy systems

Julia Granacher, François Maréchal

In superstructure optimization of processes and energy systems, the design space is defined as the combination of unit considerations, process conditions and model parameters that might be subjected to uncertainty. Most of the time, decision makers are not looking for a single best solution, but rather are interested in analyzing a set of Pareto-optimal superstructure designs. The generation of Pareto-optimal solutions is computationally expensive, especially if nonlinear process evaluations or simulation is required. In our approach, we address the question of how to efficiently generate Paretooptimal sets of solutions by applying machine learning concepts. Using the criteria of Pareto-optimality to evaluate the performance of a set of design space variables and the corresponding solution, we train our algorithm on predicting if a solution is belonging to the Pareto frontier. Following the approach presented by Zuluaga et al., (2016) and applied to the design of materials by Jablonka et al., (2021), an adaptive learning concept is used to systematically identify the next best function evaluation to improve the confidence of the Pareto-frontier definition. Gaussian process surrogate models provide a prediction of the mean and the standard deviation of the relevant objectives. Design points with high probability to of being in the Pareto-optimal domain are evaluated by the original model, increasing the confidence with which the Pareto front is predicted. Simultaneously, the design space is continuously reduced by discarding the design points for which the probability of being in the set of Pareto-optimal solutions is low. The procedure is stopped when all points are labeled as Pareto-optimal or discarded. The algorithm is applied to the design of a utility superstructure for an industrial energy system. Our algorithm is compared and benchmarked with quasi random sampling of the design space.

Elsevier Science2022

Active learning for multi-objective optimization of processes and energy systems

Julia Granacher, François Maréchal

Elsevier Science2022