Abstract
The recent evidence demonstrating the importance of perishables in terms of store choice and shopping experience makes these products a very interesting topic in many different research areas. Nevertheless, the production planning research has not been paying the necessary attention to the complexities of production systems of such items. The evidence that consumers of perishable goods search for visual and other cues of freshness, such as the printed expiry dates, triggered the development of a multi-objective lot-sizing and scheduling model taking this relevant aspect into account by considering it explicitly as an objective function. A hybrid genetic algorithm based on NSGA-II was developed to allow the decision maker a true choice between different trade-offs from the Pareto front. Computational experiments were based on a case study, reported in the literature, concerning a diary company producing yogurt.

Abstract
Integrated production and distribution planning have received a lot of attention throughout the years and its economic advantages are well documented. However, for highly perishable products this integrated approach has to include, further than the economic aspects, the intangible value of freshness. We explore, through a multi-objective framework, the advantages of integrating these two intertwined planning problems at an operational level. We formulate models for the case where perishable goods have a fixed and a loose shelf-life (i.e. with and without a best-before-date). The results show that the economic benefits derived from using an integrated approach are much dependent on the freshness level of products delivered.

Abstract
This study deals with industrial processes that produce soft drink bottles in different flavours and sizes, carried out in two synchronised production stages: liquid preparation and bottling. Four single-stage formulations are proposed to solve the synchronised two-stage lot sizing and scheduling problem in soft drink production synchronising the first stage's syrup lots in tanks with the second stage's soft drink lots on bottling lines. The first two formulations are variants of the General Lot Sizing and Scheduling Problem (GLSP) with sequence-dependent setup times and costs, while the other two are based on the Asymmetric Travelling Salesman Problem (ATSP) with different subtour elimination constraints. All models are computationally tested and compared to the original two-stage formulation introduced in Ferreira et al. (2009), using data based on a real-world bottling plant. The results show not only the superiority of the single-stage models if compared to the two-stage formulation, but also the much faster solution times of the ATSP-based models.

Abstract
We propose a general-purpose heuristic approach combining metaheuristics and mixed integer programming to find high quality solutions to the challenging single- and parallel-machine capacitated lotsizing and scheduling problem with sequence-dependent setup times and costs. Commercial solvers fail to solve even medium-sized instances of this NP-hard problem; therefore, heuristics are required to find competitive solutions. We develop construction, improvement and search heuristics all based on MIP formulations. We then compare the performance of these heuristics with those of two metaheuristics and other MIP-based heuristics that have been proposed in the literature, and to a state-of-the-art commercial solver. A comprehensive set of computational experiments shows the effectiveness and efficiency of the main approach, a stochastic MIP-based local search heuristic, in solving medium to large size problems. Our solution procedures are quite flexible and may easily be adapted to cope with model extensions or to address different optimization problems that arise in practice.

Abstract
In this paper we present a genetic algorithm with new components to tackle capacitated lot sizing and scheduling problems with sequence dependent setups that appear in a wide range of industries, from soft drink bottling to food manufacturing. Finding a feasible solution to highly constrained problems is often a very difficult task. Various strategies have been applied to deal with infeasible solutions throughout the search. We propose a new scheme of classifying individuals based on nested domains to determine the solutions according to the level of infeasibility, which in our case represents bands of additional production hours (overtime). Within each band, individuals are just differentiated by their fitness function. As iterations are conducted, the widths of the bands are dynamically adjusted to improve the convergence of the individuals into the feasible domain. The numerical experiments on highly capacitated instances show the effectiveness of this computational tractable approach to guide the search toward the feasible domain. Our approach outperforms other state-of-the-art approaches and commercial solvers.

Abstract
This article describes a real-world production planning and scheduling problem occurring at an integrated pulp and paper mill (P&P) which manufactures paper for cardboard out of produced pulp. During the cooking of wood chips in the digester, two by-products are produced: the pulp itself (virgin fibers) and the waste stream known as black liquor. The former is then mixed with recycled fibers and processed in a paper machine. Here, due to significant sequence-dependent setups in paper type changeovers, sizing and sequencing of lots have to be made simultaneously in order to efficiently use capacity. The latter is converted into electrical energy using a set of evaporators, recovery boilers and counter-pressure turbines. The planning challenge is then to synchronize the material flow as it moves through the pulp and paper mills, and energy plant, maximizing customer demand (as backlogging is allowed), and minimizing operation costs. Due to the intensive capital feature of P&P, the output of the digester must be maximized. As the production bottleneck is not fixed, to tackle this problem we propose a new model that integrates the critical production units associated to the pulp and paper mills, and energy plant for the first time. Simple stochastic mixed integer programming based local search heuristics are developed to obtain good feasible solutions for the problem. The benefits of integrating the three stages are discussed. The proposed approaches are tested on real-world data. Our work may help P&P companies to increase their competitiveness and reactiveness in dealing with demand pattern oscillations.