Abstract
The possibilities of combining simulation and optimization are vast and the appropriate design highly depends on the problem characteristics. Therefore, it is very important to have a good overview of the different approaches. The taxonomies and classifications proposed in the literature do not cover the complete range of methods and overlook some important criteria. We provide a taxonomy that aims at giving an overview of the full spectrum of current simulation-optimization approaches. Our study may guide researchers who want to use one of the existing methods, give insights into the cross-fertilization of the ideas applied in those methods and create a standard for a better communication in the scientific community. Future reviews can use the taxonomy here described to classify both general approaches and methods for specific application fields.

Abstract
Several production environments require simultaneous planing of sizing and scheduling of sequences of production lots. Integration of sequencing decisions in lotsizing and scheduling problems has received an increased attention from the research community due to its inherent applicability to real world problems. A two-dimensional classification framework is proposed to survey and classify the main modeling approaches to integrate sequencing decisions in discrete time lotsizing and scheduling models. The Asymmetric Traveling Salesman Problem can be an important source of ideas to develop more efficient models and methods to this problem. Following this research line, we also present a new formulation for the problem using commodity flow based subtour elimination constraints. Computational experiments are conducted to assess the performance of the various models, in terms of running times and upper bounds, when solving real-word size instances.

Abstract
Disturbance Management is a major issue in process industries like the pulp and paper (P&P) case and is mostly performed in the execution/control level. That approach is confined to the amendment of plans sent by upper levels and can thus be problematic. This paper moves towards the integration of planning and control, starting from the planning's point of view. The application of Simulation-Optimization (S-O) allows considering uncertainty, but keeping a deterministic tractable optimization model. Indeed, it is the simulation model that incorporates more complex elements such as stochastic variables, as well as integrates (with more or less detail) the execution/control behaviour. In this work, we present a case study of a P&P mill, focusing on the two most critical production resources (the digester and the paper machine). The feedback obtained by simulating their interaction is used to adjust the slacks introduced in the intermediate tank. In this way, we are able to generate plans that are not only optimized concerning company's indicators, but also robust against disturbances.

Abstract
From long to short term planning, decision processes inherent to operating theater organization are often subject of empiricism, leading to far from optimal results. Waiting lists for surgery have always been a societal problem, which governments have been fighting with different management and operational stimulus plans. The current hospital information systems available in Portuguese public hospitals, lack a decision support system component that could help achieve better planning solutions. Thus, an intelligent decision support system has been developed, allowing the centralization and standardization of planning processes, improving the efficiency of the operating theater and tackling the waiting lists for surgery fragile situation. The intelligence of the system derives from data mining and optimization techniques, which enhance surgery duration predictions and operating rooms surgery schedules. Experimental results show significant gains, reducing overtime, undertime, and better resource utilization. Note to Practitioners-The Operating Theater (OT) is often considered hospitals' biggest budget consumer and revenue center in a hospital. This paper was motivated by a project that aims to reduce expenses and surgery waiting lists in Portuguese public hospitals, by developing an Intelligent Decision Support System (DSS) to support surgery scheduling. Prior to this research, decision makers (Surgeons, Department managers, Operating theatre managers) used their experience to make allocation, scheduling and estimation decisions. Since many of these decisions are made without analyzing past results, mistakes occur frequently, affecting the OT performance. With the help of business intelligence, data mining and optimization algorithms, surgeons' estimations can be more precise and the operating room schedule can be optimized. Preliminary experiments on the usage of DSS reveal a remarkable increase of the efficiency of the whole OT. In future research, we will extend the DSS and the techniques used to address the tactical master surgery scheduling problem, which aims to perform a better allocation of the different specialties to the operating rooms along the week. In addition, upstream and downstream resources shall be considered in the optimization module, as well as a simulation component to better evaluate generated solutions.

Abstract
Background: As patient's length of stay in waiting lists increases, governments are looking for strategies to control the problem. Agreements were created with private providers to diminish the workload in the public sector. However, the growth of the private sector is not following the demand for care. Given this context, new management strategies have to be considered in order to minimize patient length of stay in waiting lists while reducing the costs and increasing (or at least maintaining) the quality of care. Method: Appointment scheduling systems are today known to be proficient in the optimization of health care services. Their utilization is focused on increasing the usage of human resources, medical equipment and reducing the patient waiting times. In this paper, a simulation-based optimization approach to the Patient Admission Scheduling Problem is presented. Modeling tools and simulation techniques are used in the optimization of a diagnostic imaging department. Results: The proposed techniques have demonstrated to be effective in the evaluation of diagnostic imaging workflows. A simulated annealing algorithm was used to optimize the patient admission sequence towards minimizing the total completion and total waiting of patients. The obtained results showed average reductions of 5% on the total completion and 38% on the patients' total waiting time.

Abstract
Highly perishable food products can lose an important part of their value in the distribution process. We propose a novel multi-objective model that decouples the minimization of the distribution costs from the maximization of the freshness state of the delivered products. The main objective of the work is to examine the relation between distribution scenarios and the cost-freshness trade-off. Small size instances adapted from the vehicle routing problem with time windows are solved with an epsilon-constraint method and for large size instances a multi-objective evolutionary algorithm is implemented. The computational experiments show the conflicting nature of the two objectives.