The application of preprocessing and cutting plane techniques for a class of production planning problems

This paper investigates properties of integer programming models for a class of production planning problems. The models are developed within a decision support system to advise a sales team of the products on which to focus their efforts in gaining new orders in the short term. The products generally require processing on several manufacturing cells and involve precedence relationships. The cells are already (partially) committed with products for stock and to satisfy existing orders and therefore only the residual capacities of each cell in each time period of the planning horizon are considered. The determination of production recommendations to the sales team that make use of residual capacities is a nontrivial optimization problem. Solving such models is computationally demanding and techniques for speeding up solution times are highly desirable. An integer programming model is developed and various preprocessing techniques are investigated and evaluated. In addition, a number of cutting plane approaches have been applied. The performance of these approaches which are both general and application specific is examined.

A linear optimization approach to the combined production planning model

Two fundamental processes usually arise in the production planning of many industries. The first one consists of deciding how many final products of each type have to be produced in each period of a planning horizon, the well-known lot sizing problem. The other process consists of cutting raw materials in stock in order to produce smaller parts used in the assembly of final products, the well-studied cutting stock problem. In this paper the decision variables of these two problems are dependent of each other in order to obtain a global optimum solution. Setups that are typically present in lot sizing problems are relaxed together with integer frequencies of cutting patterns in the cutting problem. Therefore, a large scale linear optimizations problem arises, which is exactly solved by a column generated technique. It is worth noting that this new combined problem still takes the trade-off between storage costs (for final products and the parts) and trim losses (in the cutting process). We present some sets of computational tests, analyzed over three different scenarios. These results show that, by combining the problems and using an exact method, it is possible to obtain significant gains when compared to the usual industrial practice, which solve them in sequence. (C) 2010 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

A Fuzzy Goal Programming model for solving aggregate production-planning problems under uncertainty: A case study in a Brazilian sugar mill

This paper proposes a Fuzzy Goal Programming model (FGP) for a real aggregate production-planning problem. To do so, an application was made in a Brazilian Sugar and Ethanol Milling Company. The FGP Model depicts the comprehensive production process of sugar, ethanol, molasses and derivatives, and considers the uncertainties involved in ethanol and sugar production. Decision-makings, related to the agricultural and logistics phases, were considered on a weekly-basis planning horizon to include the whole harvesting season and the periods between harvests. The research has provided interesting results about decisions in the agricultural stages of cutting, loading and transportation to sugarcane suppliers and, especially, in milling decisions, whose choice of production process includes storage and logistics distribution. (C)2014 Elsevier B.V. All rights reserved.

A Study of Different Subsequence Elimination Strategies for the Soft Drink Production Planning

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Strategies to model scheduling decisions to plan the soft drink production process

In this paper we present a mixed integer model that integrates lot sizing and lot scheduling decisions for the production planning of a soft drink company. The main contribution of the paper is to present a model that differ from others in the literature for the constraints related to the scheduling decisions. The proposed strategy is compared to other strategies presented in the literature.

A genetic algorithm/mathematical programming approach to solve a two-level soft drink production problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Otimização do processo de MRP (Material Requirement Planning) de embalagens: uma aplicação da metodologia lean six sigma em uma indústria de agronegócios

There is a growing search for continuous improvement within the companies which creates an obligation of reducing and when it is possible eliminating waste. Production Planning and Control Department (PCP) is not out of this question, making necessary the application of methods and creation of tools that eliminate steps which do not add value to the planning process. This paper aims to develop a tool which concentrates in just one place all the necessary information to make the packaging material requirement planning (MRP) in a agribusiness company. Besides, it also aims, in a more visual way and using devices that prevent mistakes (Poka-Yoke), to reduce the number of reviews and mistakes made by analysts. As a result, an Excel spreadsheet was developed. This spreadsheet shows what happens with the status of planning and receiving of packaging, giving some advices when some critical situation happens. The use of Lean Manufacturing Method and the action research method helped to well define the problem and to reduce the number of steps, spreadsheets and time of process in 80%, 60% and 75%, respectively

Multi-population genetic algorithm to solve the synchronized and integrated two-level lot sizing and scheduling problem

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Proposta de avanço para o método Tambor-Pulmão-Corda Simplificado aplicado em ambientes de produção sob encomenda

Recentes aplicações da Teoria das Restrições em Gestão de Operações têm adotado o chamado método Tambor-Pulmão-Corda Simplificado (S-DBR - Simplified Drum-Buffer-Rope) como padrão para planejamento e controle da produção. Contudo, poucas são as referências bibliográficas, especialmente em periódicos nacionais e internacionais, que relatam seus principais conceitos e premissas. Este artigo se propõe a apresentar o método S-DBR para planejamento da produção e sua correspondente abordagem de controle da produção denominado Gerenciamento do Pulmão, aqui abordados em ambientes de produção sob encomenda (MTO - make to order), suas principais características, pressupostos e diferenças em relação ao método DBR clássico. É também apresentada uma proposta complementar ao método S-DBR para estabelecer promessas de entregas urgentes mantendo elevada probabilidade de entrega no prazo.

Relax and fix heuristics to solve one-stage one-machine lot-scheduling models for small-scale soft drink plants

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A Lagrangian relaxation approach to a coupled lot-sizing and cutting stock problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Heuristics and meta-heuristics for lot sizing and scheduling in the soft drinks industry: A comparison study

This chapter studies a two-level production planning problem where, on each level, a lot sizing and scheduling problem with parallel machines, capacity constraints and sequence-dependent setup costs and times must be solved. The problem can be found in soft drink companies where the production process involves two interdependent levels with decisions concerning raw material storage and soft drink bottling. Models and solution approaches proposed so far are surveyed and conceptually compared. Two different approaches have been selected to perform a series of computational comparisons: an evolutionary technique comprising a genetic algorithm and its memetic version, and a decomposition and relaxation approach. © 2008 Springer-Verlag Berlin Heidelberg.

Reformulação para um problema integrado de dimensionamento e sequenciamento de lotes

In this paper, some new constraints and an extended formulation are presented for a Lot Sizing and Scheduling Model proposed in the literature. In the production process considered a key material is prepared and is transformed into different final items. The sequencing decisions are related to the order in which the materials are processed and the lot sizing decisions are related to the final items production. The mathematical formulation considers sequence-dependent setup costs and times. Results of the computational tests executed using the software Cplex 10.0 showed that the performance of the branch-and-cut method can be improved by the proposed a priori reformulation.

Measuring supply chain management practices

Purpose: This paper aims to perform an empirical investigation about the constructs and indicators of the supply chain management practices framework. Design/methodology/approach: The measuring framework proposed is based on a survey that was carried out on 107 Brazilian companies. Statistical techniques were employed to verify, validate, and test the reliability of the constructs and their indicators. To validate this framework principal component analysis and structural equation modeling techniques were used. Findings: In general, previous studies suggest six constructs for measuring the supply chain management practices framework. However, in this study a framework was achieved with four constructs of supply chain management practices, namely, supply chain (SC) integration for production planning and control (PPC) support, information sharing about products and targeting strategies, strategic relationship with customer and supplier, and support customer order. This framework has adequate levels of validity and reliability. Research limitations/implications: The main limitation of this study was that only a small sample of companies in a single sector and country were surveyed, and therefore there needs to be further research considering the special conditions in other countries. Originality/value: This study investigated statistically set indicators to discuss the topic supply chain management practices. The framework obtained has good quality of validity and reliability indicators. Thus, an alternative framework has been added to measure supply chain management practices, which is currently a popular topic in the supply chain mainstream literature. Both defined constructs and the validated indicators can be used in other studies on supply chain management. © Emerald Group Publishing Limited.

Multi-choice mixed integer goal programming optimization for real problems in a sugar and ethanol milling company

Goal Programming (GP) is an important analytical approach devised to solve many realworld problems. The first GP model is known as Weighted Goal Programming (WGP). However, Multi-Choice Aspirations Level (MCAL) problems cannot be solved by current GP techniques. In this paper, we propose a Multi-Choice Mixed Integer Goal Programming model (MCMI-GP) for the aggregate production planning of a Brazilian sugar and ethanol milling company. The MC-MIGP model was based on traditional selection and process methods for the design of lots, representing the production system of sugar, alcohol, molasses and derivatives. The research covers decisions on the agricultural and cutting stages, sugarcane loading and transportation by suppliers and, especially, energy cogeneration decisions; that is, the choice of production process, including storage stages and distribution. The MCMIGP allows decision-makers to set multiple aspiration levels for their problems in which the more/higher, the better and the less/lower, the better in the aspiration levels are addressed. An application of the proposed model for real problems in a Brazilian sugar and ethanol mill was conducted; producing interesting results that are herein reported and commented upon. Also, it was made a comparison between MCMI GP and WGP models using these real cases. © 2013 Elsevier Inc.