Abstract
Managing perishability may represent a remarkable problem in supply chain management of a varied set of industries. In fact, perishability can influence, for example, productivity or customer service and it may happen to occur in one or more processes throughout the supply chain. In this paper a review on planning models that handle perishability issues in production and distribution is conducted. The contribution of this paper is three-fold. First, a new framework for classifying perishability models based on multiple process features is presented. Second, it draws the community attention to the importance of managing perishability in many different industries' supply chains by showing its relevance and by reviewing the literature related to production and distribution planning. Finally, it points towards research opportunities so far not addressed by the research community in this challenging field.

Abstract
The operating theater is the biggest hospital budget expenditure. The usage of surgery related resources and its intrinsic planning must be carefully devised in order to achieve better operational performance. However, from long to short term planning, the decision processes inherent to the operating theater are often the subject of empiricism. Moreover, the current hospital information systems available in Portuguese public hospitals lack a decision support system component, which could assist in achieving better planning solutions. This work reports the development of a centralized system for the operating theater planning to support decision-making tasks of surgeons, chief specialty managers, and hospital administration. Its main components concern surgery scheduling, operating theater's resource allocation and performance measurement. The enhancement of the planning processes, the increase of policy compliance, and the overall performance of the operating theater compared to the former methodologies are also discussed. © 2013, IGI Global.

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
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.

Abstract
In the last years, several researchers from two different academic communities, the Operations Research and the Process Systems Engineering, have been developing mathematical formulations for the lot-sizing and scheduling of single-stage continuous processes with complex setup structures. This problem has been intensively studied due to its importance to a wide range of industries where a single-stage approach is suitable for production planning. This is the case of the glass container, beer, and dairy production. Recent works have been performed by both mentioned communities, however, no intense communication between these research efforts has been observed. This work attempts a systematic analysis on recent formulation developments of both communities. Based on the result of this comparison, a reformulation is proposed that outperforms in the majority of the cases the previous existent formulations for a set of systematically generated random instances.