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.

Abstract
We present a novel mathematical model and a mathematical programming based approach to deliver superior quality solutions for the single machine capacitated lot sizing and scheduling problem with sequence-dependent setup times and costs. The formulation explores the idea of scheduling products based on the selection of known production sequences. The model is the basis of a matheuristic, which embeds pricing principles within construction and improvement MIP-based heuristics. A partial exploration of distinct neighborhood structures avoids local entrapment and is conducted on a rule-based neighbor selection principle. We compare the performance of this approach to other heuristics proposed in the literature. The computational study carried out on different sets of benchmark instances shows the ability of the matheuristic to cope with several model extensions while maintaining a very effective search. Although the techniques described were developed in the context of the problem studied, the method is applicable to other lot sizing problems or even to problems outside this domain.

Abstract
Disturbance Management is a major issue in process industries like the pulp and paper (P&P) industry. In this paper, a case study in an integrated P&P mill is examined. Production plans for the whole mill need not only to be optimized concerning company's indicators, but also to be robust so that disturbances can be avoided. We present a simulation-optimization approach that generates plans, correctly weighting their quality (regarding various indicators) and robustness. A discrete-event simulation model replicates the dynamics of implementation and adaptation of production plans in practice. The simulation model gives then feedback to optimization, in order to enhance the analytical model, which is thus able to generate robust plans. © IFAC.

Abstract
In this paper a new heuristic is proposed to solve general multi-level lot-sizing and scheduling problems. The idea is to cross-fertilize the principles of the meta-heuristic Variable Neighborhood Decomposition Search (VNDS) with those of the MIP-based Fix&Optimize heuristic. This combination will make it possible to solve the kind of problems that typically arise in the consumer goods industry due to sequence-dependent setups and shifting bottlenecks. In order to demonstrate the strength of this procedure, a GLSP variant for multiple production stages is chosen as a representative. With the help of artificial and real-world instances, the quality of the solution as well as the computational performance of the new procedure is tested and compared to a standard MIP-solver.

Abstract
In this work we present single-stage formulations for the integrated soft drink lot-sizing and scheduling problem with two-stage synchronization. It is a multi-product, multi-machine problem, with sequence-dependent setup times and costs. Without loss of generality, these single-stage reformulations address the problem correctly and, in general, reduce the size of the synchronized two-stage model of Ferreira, Morabito e Rangel (2009), regarding the number of variables and constraints. The preliminary computational experiments on real-world instances from a soft-drink company show the competitiveness of the single-stage models against other formulations and solution approaches reported in the literature.