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.

Abstract
Driven by a real-world application in the capital-intensive glass container industry, this paper provides the design of a new hybrid evolutionary algorithm to tackle the short-term production planning and scheduling problem. The challenge consists of sizing and scheduling the lots in the most cost-effective manner on a set of parallel molding machines that are fed by a furnace that melts the glass. The solution procedure combines a multi-population hierarchically structured genetic algorithm (GA) with a simulated annealing (SA), and a tailor-made heuristic named cavity heuristic (CH). The SA is applied to intensify the search for solutions in the neighborhood of the best individuals found by the GA, while the CH determines quickly values for a relevant decision variable of the problem: the processing speed of each machine. The results indicate the superior performance of the proposed approach against a state-of-the-art commercial solver, and compared to a non-hybridized multi-population GA.

Abstract