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

Alem, Douglas; Curcio, Eduardo; Amorim, Pedro; Almada-Lobo, Bernardo

doi:10.1016/j.cor.2017.09.005

Cited by 43 publications

(20 citation statements)

References 36 publications

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“…In this context, it would be interesting to study a systematic way to compare our stochastic programming approach for the cash-flow problem under uncertainty to an alternate robust optimization method (as in Righetto et al 2016) in order to understand the relative strengths and weaknesses of each approach in terms of managerial implications (Alem et al, 2018;Cuvelier et al;. Other interesting research perspectives arose from this study.…”

Section: Resultsmentioning

confidence: 99%

Cash flow management by risk-neutral and risk-averse stochastic approaches

Righetto

Morábito

Alem

2019

Journal of the Operational Research Society

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This paper presents a dynamic cash flow management problem with uncertain parameters in a finite planning horizon via two-stage stochastic programming. We propose a risk-neutral mixed-integer twostage stochastic programming model and risk-averse versions based on the minimax regret and conditional value-at-risk criteria. The models support decisions in cash management that deals with different grace periods, piecewise linear yields and uncertainty in the exchange rate of external sales. The developed approach is applied to a real-world stationery company in Brazil. Numerical results assess the trade-off between risk and return, showing that the optimization models generate effective solutions for the company's treasury with reduced risks, which might be appealing for companies from other sectors as well. Keywords: Cash flow management, mixed-integer programming, stochastic programming, minimax with regret, conditional value-at-risk, stationery industry.During the 2008 financial crisis, the Federal Reserve (Fed) cut interest rates rapidly to zero, resulting in the dollar being at its most undervalued level in decades. In 2014, when the economy was recovering, the Fed began to show signs that it would normalize its monetary policy, causing an intensive realignment of exchange rates. In less than a year, the dollar appreciated no less than 20% against the major global currencies, a trend that naturally affected emerging currencies such as the Brazilian Real. Thus, large exchange variations have significant impacts on companies, especially when considering the predictability of their cash flows. There are many risks and uncertainties on the horizon and the volatility of the exchange rate will continue at high levels.In order to handle uncertainty and risk in this context, this paper develops risk-neutral and risk-averse stochastic programming (SP) models to support tactical decisions in cash-flow management problems that encompass grace periods, piecewise linear yields and uncertainty in the exchange rate of external sales. The risk-averse models are based on the minimax with regret and on the conditional value-atrisk (CVaR) criteria. Whereas the minimax with regret model provides solutions from a worst-case perspective when the probabilities of the scenarios are not known (or not reliable), the CVaR model aims at minimizing the risk of a solution influenced by a bad scenario with a low probability of occurrence. The risk-averse models are particularly appealing for providing less risky solutions, which might be reflected by the mitigation of the profit dispersion across a finite set of scenarios. The performance of the models is analyzed vis-a-vis a real-world stationery company in Brazil.The developed risk-neutral and risk-averse stochastic programming models are based on deterministic network flow models with gains and losses, which have been used to represent cash flow management problems in different settings, as discussed in Golden et al. (1979), Crum et al. (1979, Srinivasan and Kim (1986), Pacheco andMorabito...

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Section: Resultsmentioning

confidence: 99%

Cash flow management by risk-neutral and risk-averse stochastic approaches

Righetto

Morábito

Alem

2019

Journal of the Operational Research Society

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“…Reference [47] study a robust optimization and a scenario-based two-stage stochastic programming model for the General Lot-Sizing and Scheduling Problem (GLSP) under demand uncertainty. The authors propose an extensive simulation experiment based on Monte Carlo to evaluate different characteristics of the solutions, such as average costs, worst-case costs, and standard deviation.…”

Section: Solution Approaches and Model Extensionsmentioning

confidence: 99%

Late Acceptance Hill-Climbing Matheuristic for the General Lot Sizing and Scheduling Problem with Rich Constraints

2020

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This paper considers the general lot sizing and scheduling problem with rich constraints exemplified by means of rework and lifetime constraints for defective items (GLSP-RP), which finds numerous applications in industrial settings, for example, the food processing industry and the pharmaceutical industry. To address this problem, we propose the Late Acceptance Hill-climbing Matheuristic (LAHCM) as a novel solution framework that exploits and integrates the late acceptance hill climbing algorithm and exact approaches for speeding up the solution process in comparison to solving the problem by means of a general solver. The computational results show the benefits of incorporating exact approaches within the LAHCM template leading to high-quality solutions within short computational times.

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“…In production, demand uncertainty is a common problem faced by many companies. 12,13 Combined with the stochastic degradation process of production system, the integration of CBM and tactical production planning becomes a stochastic programming (SP) problem. Since product demands and system degradation level are important input information to the integration problem, managers want to make full use of this information to prepare relevant production and maintenance plans, regardless of whether the information is currently uncertain.…”

Section: Introductionmentioning

confidence: 99%

A rolling horizon approach for production planning and condition-based maintenance under uncertain demand

Lin

Liu

Ren

2019

Proceedings of the IMechE

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In reality, the forecast of uncertainties often becomes more accurate with the approaching of the forecasted period. This article proposes a rolling horizon approach to dynamically determine the production plan and the maintenance plan for a degradation system under uncertain environment. In each rolling horizon, demand forecasts are updated with new information from customers, and the degradation level of system is confirmed by inspection. By taking advantage of the updated uncertainties, at each decision point, the maintenance plan is determined by an advance-postpone balancing approach and the production plan is optimized by a heuristic algorithm in a two-stage stochastic model. Numerical results validate that the rolling horizon approach has great superiority over traditional stochastic programming approach in terms of real total cost and service level.

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

Cited by 43 publications

References 36 publications

Cash flow management by risk-neutral and risk-averse stochastic approaches

Cash flow management by risk-neutral and risk-averse stochastic approaches

Late Acceptance Hill-Climbing Matheuristic for the General Lot Sizing and Scheduling Problem with Rich Constraints

A rolling horizon approach for production planning and condition-based maintenance under uncertain demand

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