Abstract
In this paper, the main complexities related to the modeling of production planning problems of food products are addressed. We start with a deterministic base model and build a road-map on how to incorporate key features of food production planning. The different "ingredients" are organized around the model components to be extended: constraints, objective functions and parameters. We cover issues such as expiry dates, customers' behavior, discarding costs, value of freshness and age-dependent demand. To understand the impact of these "ingredients", we solve an illustrative example with each corresponding model and analyze the changes on the solution structure of the production plan. The differences across the solutions show the importance of choosing a model suitable to the particular business setting, in order to accommodate the multiple challenges present in these industries. Moreover, acknowledging the perishable nature of the products and evaluating the amount and quality of information at hands may be crucial in lowering overall costs and achieving higher service levels. Afterwards, the deterministic base model is extended to deal with an uncertain demand parameter and risk management issues are discussed using a similar illustrative example. Results indicate the increased importance of risk-management in the production planning of perishable food goods.

Abstract
In food supply chain planning, the trade-off between expected profit and risk is emphasized by the perishable nature of the goods that it has to handle. In particular, the risk of spoilage and the risk of revenue loss are substantial when stochastic parameters related to the demand, the consumer behavior, and the spoilage effect are considered. This paper aims to expose and handle this trade-off by developing risk-averse production planning models that incorporate financial risk measures. In particular, the performance of a risk-neutral attitude is compared to the performance of models taking into account the upper partial mean and the conditional value-at-risk. Insights from an illustrative example show the positive impact of the-risk-averse models in operational performance indicators, such as the amount of expired products. Furthermore, through an extensive computational experiment, the advantage of the conditional value-at-risk model is evidenced, as it is able to dominate the solutions from the upper partial mean for the spoilage performance indicator. These advantages are tightly related to a sustainable view of production planning, and they can be achieved at the expense of controlled losses in the expected profit.

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
The pharmaceutical industry operates in a very competitive and regulated market The increased pressure of pharmacies to order fewer products and to receive them more frequently is overcharging the pharmaceutical's distribution network Furthermore, the tight margins and the continuous growth of generic drugs consumption are pressing wholesalers to optimize their supply chains. In order to survive, wholesalers are rethinking their strategies to increase competitiveness. This paper proposes an optimization-simulation approach to address the wholesalers network redesign problem, trading off the operational costs and customer service level. Firstly, at a strategic-tactical level, the supply chain network redesign decisions are optimized via a mixed integer programming model. Here, the number, location, function and capacity of the warehouses, the allocation of customers to the warehouses and the capacity and function of the distribution channels are defined. Secondly, at an operation level, the solution found is evaluated by means of a discrete event simulation model to assess the impact of the redesign in the wholesaler's daily activities. Computational results on a pharmaceutical wholesaler case-study are discussed and the benefits of this solution approach exposed.

Abstract
Mathematical formulations for production planning are increasing complexity, in order to improve their realism. In short-term planning, the desirable level of detail is particularly high. Exact solvers fail to generate good quality solutions for those complex models on medium- and large-sized instances within feasible time. Motivated by a real-world case study in the pulp and paper industry, this paper provides an efficient solution method to tackle the short-term production planning and scheduling in an integrated mill. Decisions on the paper machine setup pattern and on the production rate of the pulp digester (which is constrained to a maximum variation) complicate the problem. The approach is built on top of a mixed integer programming (MIP) formulation derived from the multi-stage general lotsizing and scheduling problem. It combines a Variable Neighbourhood Search procedure which manages the setup-related variables, a specific heuristic to determine the digester's production speeds and an exact method to optimize the production and flow movement decisions. Different strategies are explored to speed-up the solution procedure and alternative variants of the algorithm are tested on instances based on real data from the case study. The algorithm is benchmarked against exact procedures.

Abstract
Joint production and distribution planning at the operational level has received a great deal of attention from researchers. In most industries these processes are decoupled by means of final goods inventory that allow for a separated planning of these tasks. However, for example, in the catering industry, an integrated planning framework tends to be more favorable due to the perishable nature of the products that forces a make-to-order production strategy. So far this planning problem has only been addressed by allowing the batching of orders. The main contribution of this paper is to extend this approach and prove the importance of lot sizing for make-to-order systems when perishability is explicitly considered. The value of considering lot sizing versus batching is further investigated per type of production scenario. Overall, results indicate that lot sizing is able to deliver better solutions than batching. On average, for the improved instances, the cost savings ascend to 6.5% when using lot sizing. The added flexibility of lot sizing allows for a reduction on production setup costs and both fixed and variable distribution costs. The savings derived from lot sizing are enhanced by customer oriented time windows and production systems with non-triangular setups.