Resumo em Inglês:

ABSTRACT During the last two decades, integrated production-distribution problems have attracted a great deal of attention in the operations research literature. Within a short period, a large number of papers have been published and the field has expanded dramatically. The purpose of this paper is to provide a comprehensive review of the existing literature by classifying the existing models into several different categories based on multiple characteristics. The paper also discusses some trends and list promising avenues for future research.

Resumo em Inglês:

ABSTRACT Industries conduct the Sales and Operations Planning (S&OP) to balance demand and supply aligned to business targets. This study aims at proposing a model and an algorithm for the tactical supply chain planning admitting uncertainty and reflecting the peculiar S&OP aspect of rolling horizon planning. Therefore, a two-stage stochastic programming model is developed and solved via a multi-cut Benders decomposition algorithm. The model and the solution method are evaluated by numerical experiments and a case study. Results show that the optimal supply chain profit is not proportional to demand, in fact, an increase in demand can even decrease the optimal profit due to capacity constraints along the supply chain. Such findings reinforce that profitability and service level are increased with the synergy of the sales team with production, distribution and procurement team on establishing which demand should be satisfied - or not - in each period. The stochastic solution is compared to deterministic approaches.

Resumo em Inglês:

ABSTRACT In some industrial contexts, such as in the molded pulp, paper, furniture and electro fused grain industries, items or final products are obtained by processes that can produce several types of products simultaneously. These processes are considered here as any specific mode of operation or configuration of a production system that can produce several different items simultaneously and in varied quantities. The production planning and scheduling in these industrial contexts involve decisions of: i) configuration selection of these processes; ii) production lot sizing of selected configurations; and iii) decisions of scheduling these selected configurations. In this work, a mixed integer programming model (MIP) is presented to adequately represent this integrated problem, called the general lot sizing and scheduling problem with process configuration selection. In order to achieve effective production plans at acceptable computational times in practice, Relax-and-Fix and Fix-and-Optimize heuristics based on this model are also presented. Computational experiments were performed solving the model with an optimization solver and applying the proposed heuristics in examples inspired in molded pulp, furniture and electro fused grain packaging companies. The results show that MIP-heuristics can solve the problem more effectively (good quality solution in acceptable time) than simply using the solver for the mathematical model.

Resumo em Inglês:

ABSTRACT In many manufacturing industries, the production process involves the production of objects and the cutting of such objects into smaller pieces in order to meet a specific demand. In the optimization of these processes, one can identify the lot sizing and the cutting stock problems. In the literature, these problems are mostly separately dealt. However, treating these two problems in an integrated approach can decrease overall costs. In this research, we deal with the coupled lot sizing and cutting stock problem. It is proposed a mathematical formulation for the production and cutting of paper based on a real case, which considers setup costs and limited machine capacity in the production process of the objects. For the solution of the proposed model, we used a column generation approach and a relax-and-fix heuristic. Computational tests were carried out in order to analyze the methodology used in the resolution of the model. The results showed to be competitive in a reasonable computational time.

Resumo em Inglês:

ABSTRACT In this paper, the relevance of integrated planning concerning decisions of production and blending in a spinning industry is studied. The scenario regards a plant that produces several yarn packages over a planning horizon. Each yarn type is produced using a blend of several cotton bales that must contain attributes to ensure the quality of the produced yarns. Three approaches to managing production and blending are compared; the first deals with the solution to the production scheduling and blending problems in a single integrated model. The second approach hierarchically addresses these problems. The third procedure combines features from the integrated and hierarchical approaches. These approaches are applied to a real-world problem, and their respective performances are analyzed. The third approach proved to deal with lot sizing, scheduling and blending in the spinning industry more efficiently. Moreover, the results indicate the importance of coordinating production and blending decisions.

Resumo em Inglês:

ABSTRACT In this paper, we propose a novel MIP-based heuristic method to deal with a lot sizing and scheduling problem with multiple heterogeneous production lines in a production setting with perishable items. The problem is inspired by the production processes adopted by some Brazilian food industries and it considers that several production lines share the same scarce production resources. Therefore, only a subset of those lines can simultaneously operate in each production period. Moreover, the production environment is characterized by the existence of sequence-dependent setup times and costs, and by the production of perishable items which can be stocked for a short period only. Firstly, we propose a facility location reformulation for a model previously proposed in the literature. Secondly, we propose a heuristic composed of two phases. The first phase has an elaborated approach to building feasible solutions solving initially an aggregated lot sizing problem to decide which production lines to assemble, followed by the resolution of the various single line lot sizing and scheduling problems. The second phase applies improvement heuristics exploring principles of fix-and-optimize and local branching procedures. Computational results carried out using a data set proposed in the literature are presented in order to study the efficiency of the proposed approach. The results demonstrate that our heuristics provide superior results when benchmarked with a heuristic from the literature specifically developed to solve the problem under consideration, and with a commercial MIP solver.