Douglas Alem

Production planning in furniture settings via robust optimization

Production planning procedures in small-size furniture companies commonly consists of decisions with respect to production level and inventory policy, while attempting to minimize trim-loss, backlogging and overtime usage throughout the planning horizon. Managing these decisions in a tractable way is often a challenge, especially considering the uncertainty of data. In this study, we employ robust optimization tools to derive robust combined lot-sizing and cutting-stock models when production costs and product demands are uncertainty parameters. Our motivation over the traditional two-stage stochastic programming approach is the absence of an explicit probabilistic description of the input data, as well as avoiding to deal with a large number of scenarios. The results concerning uncertainty in the cost coefficients were illustrative and confirmed previous studies. Regarding uncertainty in product demands, we provide some insights into the relationship between the budgets of uncertainty, fill rates and optimal values. Moreover, when uncertainty affects both costs and demands simultaneously, optimal values are worse for large variability levels. Numerical evidence indicated that less conservative budgets of uncertainty result in reasonable service levels with cheaper global costs, while worst-case deterministic approaches generate relatively good fill rates, but with prohibitive global costs.

Stochastic network models for logistics planning in disaster relief

Emergency logistics in disasters is fraught with planning and operational challenges, such as uncertainty about the exact nature and magnitude of the disaster, a lack of reliable information about the location and needs of victims, possible random supplies and donations, precarious transport links, scarcity of resources, and so on. This paper develops a new two-stage stochastic network flow model to help decide how to rapidly supply humanitarian aid to victims of a disaster within this context. The model takes into account practical characteristics that have been neglected by the literature so far, such as budget allocation, fleet sizing of multiple types of vehicles, procurement, and varying lead times over a dynamic multiperiod horizon. Attempting to improve demand fulfillment policy, we present some extensions of the model via state-of-art risk measures, such as semideviation and conditional value-at-risk. A simple two-phase heuristic to solve the problem within a reasonable amount of computing time is also suggested. Numerical tests based on the floods and landslides in Rio de Janeiro state, Brazil, show that the model can help plan and organise relief to provide good service levels in most scenarios, and how this depends on the type of disaster and resources. Moreover, we demonstrate that our heuristic performs well for real and random instances.

Effective location models for sorting recyclables in public management

The recycling of urban solid wastes is a critical point for the “closing supply chains” of many products, mainly when their value cannot be completely recovered after use. In addition to environmental aspects, the process of recycling involves technical, economic, social and political challenges for public management. For most of the urban solid waste, the management of the end-of-life depends on selective collection to start the recycling process. For this reason, an efficient selective collection has become a mainstream tool in the Brazilian National Solid Waste Policy. In this paper, we study effective models that might support the location planning of sorting centers in a medium-sized Brazilian city that has been discussing waste management policies over the past few years. The main goal of this work is to provide an optimal location planning design for recycling urban solid wastes that fall within the financial budget agreed between the municipal government and the National Bank for Economic and Social Development. Moreover, facility planning involves deciding on the best sites for locating sorting centers along the four-year period as well as finding ways to meet the demand for collecting recyclable materials, given that economic factors, consumer behavior and environmental awareness are inherently uncertain future outcomes. To deal with these issues, we propose a deterministic version of the classical capacity facility location problem, and both a two-stage recourse formulation and risk-averse models to reduce the variability of the second-stage costs. Numerical results suggest that it is possible to improve the current selective collection, as well as hedge against data uncertainty by using stochastic and risk-averse optimization models.

Risk-averse two-stage stochastic programs in furniture plants

We present two-stage stochastic mixed 0–1 optimization models to hedge against uncertainty in production planning of typical small-scale Brazilian furniture plants under stochastic demands and setup times. The proposed models consider cutting and drilling operations as the most limiting production activities, and synchronize them to avoid intermediate work-in-process. To design solutions less sensitive to changes in scenarios, we propose four models that perceive the risk reductions over the scenarios differently. The first model is based on the minimax regret criteria and optimizes a worst-case scenario perspective without needing the probability of the scenarios. The second formulation uses the conditional value-at-risk as the risk measure to avoid solutions influenced by a bad scenario with a low probability. The third strategy is a mean-risk model based on the upper partial mean that aggregates a risk term in the objective function. The last approach is a restricted recourse approach, in which the risk preferences are directly considered in the constraints. Numerical results indicate that it is possible to achieve significant risk reductions using the risk-averse strategies, without overly sacrificing average costs.

Sustainable vegetable crop supply problem with perishable stocks

In this paper, we deal with a vegetable crop supply problem with two main particularities: (i) the production must respect certain ecologically-based constraints and (ii) harvested crops can be stocked but only for a limited period of time, given that they are perishable. To model these characteristics, we develop a linear formulation in which each variable is associated to a crop rotation plan. This model contains a very large number of variables and is therefore solved with the aid of a column generation approach. Moreover, we also propose a two-stage stochastic programming with recourse model which takes into consideration that information on the demands might be uncertain. We provide a discussion of the results obtained via computational tests run on instances adapted from real-world data.

On the cutting stock problem under stochastic demand

This paper addresses the one-dimensional cutting stock problem when demand is a random variable. The problem is formulated as a two-stage stochastic nonlinear program with recourse. The first stage decision variables are the number of objects to be cut according to a cutting pattern. The second stage decision variables are the number of holding or backordering items due to the decisions made in the first stage. The problem’s objective is to minimize the total expected cost incurred in both stages, due to waste and holding or backordering penalties. A Simplex-based method with column generation is proposed for solving a linear relaxation of the resulting optimization problem. The proposed method is evaluated by using two well-known measures of uncertainty effects in stochastic programming: the value of stochastic solution—VSS—and the expected value of perfect information—EVPI. The optimal two-stage solution is shown to be more effective than the alternative wait-and-see and expected value approaches, even under small variations in the parameters of the problem.

A robust optimization approach for cash flow management in stationery companies

We present an effective optimization approach to support decisions in cash management.The robust model is represented by means of network flows with gains and losses.It considers uncertainty in the parameters that define the financial flows over time.A case study was conducted in the cash flow of a typical stationery company.Several results are presented analyzing the trade-off between risk and return. This paper proposes an effective optimization approach based on mixed integer linear programming and robust optimization to support decisions in the cash management problem of stationery companies. The approach represents the problem by means of network flows with gains and losses in an environment with uncertainty in the parameters that define financial flows over time. A case study was conducted in the cash flow of a typical company in the stationery sector with different grace periods and piecewise linear yields. Several results and analysis are presented by applying this robust optimization approach to support the decision maker in relation to the trade-off between risk and return, showing that the approach is able to generate solutions as good as, or better than, the ones of the treasury of the stationery company. It is in these conditions of uncertainty that the motivation of this research can be found, addressing how financial managers project their cash flows in order to maximize their uncertain monetary resources in a given multi-period and finite planning horizon.

A computational study of the general lot-sizing and scheduling model under demand uncertainty via robust and stochastic approaches

Abstract This paper presents an empirical assessment of the General Lot-Sizing and Scheduling Problem (GLSP) under demand uncertainty by means of a budget-uncertainty set robust optimization and a two-stage stochastic programming with recourse model. We have also developed a systematic procedure based on Monte Carlo simulation to compare both models in terms of protection against uncertainty and computational tractability. The extensive computational experiments cover different instances characteristics, a considerable number of combinations between budgets of uncertainty and variability levels for the robust optimization model, as well as an increasing number of scenarios and probability distribution functions for the stochastic programming model. Furthermore, we have devised some guidelines for decision-makers to evaluate a priori the most suitable uncertainty modeling approach according to their preferences.

Robust Optimization for Petroleum Supply Chain Collaborative Design and Planning

Abstract Network design and planning is instrumental to improve the Petroleum Supply Chains (PSC) competitiveness, affected nowadays by the economic crisis, alternative energy competition and price related uncertainties. This poses the need of developing stochastic models for simultaneous profit maximization and risk minimization, however these stance difficult to solve due to problem complexity and representation issues. We propose a tractable robust optimization (RO) downstream PSC planning model to determine the depot and the transportation capacities to install, operate and close between the refineries and retail filling stations; the fair price costs and tariffs per product, company, location and route; and the multi-stage product transfer and inventory volumes per period while considering uncertainties in crude oil costs and refined product prices. Results are presented for the real case Portuguese PSC, identifying insights and opportunities for further research.

O problema combinado de planejamento da produção e corte de estoque sob incertezas: aplicação em fábricas de móveis de pequeno porte

This paper investigates an integrated production planning and cutting stock problem that is common in small-scale furniture plants, in which the production costs involved in the manufacturing process and the demands for the products are not known precisely. To deal with these uncertainties, robust optimization models that control the conservatism of the solution according to the attitude of the decision maker towards risk were proposed. Computational experiments based on real data from a furniture plant showed that it is possible to obtain robust solutions without overly sacrificing the total cost. In addition, it was observed that commonly used strategies to deal with uncertainty issues can result in production plans with prohibitive total costs.