Risk-averse two-stage stochastic programs in furniture plants

Alem, Douglas; Morábito, Reinaldo

doi:10.1007/s00291-012-0312-5

Cited by 39 publications

(28 citation statements)

References 81 publications

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“…This phenomenon is less evident in JSCM. "Phase transitions" in risk-averse models are relatively common; similar tradeoffs between cost and risk but in different production planning models were found in [36] and [38].…”

Section: Results Of the Risk-averse Modelsmentioning

confidence: 99%

“…Although some authors use the term robust optimization or robust stochastic programming to designate such methodology, more recently, there is a tendency to use the term risk-averse programming to allude to the adoption of control measures of robustness (or risk measures) in stochastic programming models [34][35][36].…”

Section: Deterministic Mixed-integer Programming Models For Lot Sizinmentioning

confidence: 99%

“…For this purpose, we need to define a measure to quantify this risk, i.e., a risk deviation measure. Among various risk deviation measures, we adopt the well-known upper partial mean [37], which results in an asymmetric and tractable model, and it provides an intuitive risk analysis [36,38]. The risk deviation term is then incorporated in the objective function, which results in the following mean-risk model:…”

Section: Deterministic Mixed-integer Programming Models For Lot Sizinmentioning

confidence: 99%

“…VSS is evaluated as follows: (i) solve the expected value problem (EV), where the random variables are placed by their expectations; (ii) solve one problem for each scenario ξ∈Ξ, with the first-stage variables fixed according to the EV problem, and obtain the expectation over the set of scenarios of these wait-and-see solutions (EEV); (iii) determine VSS= EEV−RP [36]. Notice that EEV is an approximation (upper bound) of the stochastic problem, and VSS then quantifies how good or bad this approximation is.…”

Section: Expected Value Of Perfect Information and Value Of The Stochmentioning

confidence: 99%

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Hybrid manufacturing and remanufacturing lot-sizing problem with stochastic demand, return, and setup costs

Macedo

Alem

Santos³

et al. 2015

Int J Adv Manuf Technol

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This paper presents mixed-integer programming models to hybrid manufacturing and remanufacturing lotsizing problems in which remanufactured products are treated as new ones, so that both manufactured and remanufactured products compete to meet the demands. Differently from previous studies, we consider an environment with multiple products, both manufacturing and remanufacturing costs, disposal, backlogging, and the inherent uncertainties of demands, return rates of usable products, and setup costs. In order to deal with these uncertainties, we propose a scenario-based two-stage stochastic programming model that assumes production and setup as first-stage decision variables, whereas inventory, disposal, and backlogging are defined as second-stage decision variables.We also analyze a risk-averse model in an attempt to reduce the dispersion of the second-stage costs. The main results of the present study indicate that setup costs for remanufacturing can be decisive in choosing between manufacturing or remanufacturing. Even though remanufacturing costs are lower, the process is still largely dependent on return rates and low storage costs for returned products.

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Section: Results Of the Risk-averse Modelsmentioning

confidence: 99%

Section: Deterministic Mixed-integer Programming Models For Lot Sizinmentioning

confidence: 99%

Section: Deterministic Mixed-integer Programming Models For Lot Sizinmentioning

confidence: 99%

Section: Expected Value Of Perfect Information and Value Of The Stochmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid manufacturing and remanufacturing lot-sizing problem with stochastic demand, return, and setup costs

Macedo

Alem

Santos³

et al. 2015

Int J Adv Manuf Technol

Self Cite

View full text Add to dashboard Cite

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“…There are other LS-CS problems in furniture and other practical settings that involve relevant setup time and costs in the cutting and drilling processes, which are different from the one studied in this paper. More details on some of these problems can be found in [5]. These problems are also very difficult to solve and could be interesting topics for future research, for instance, by appropriately modifying and applying the present proposed approach.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Progressive Selection Method for the Coupled Lot-Sizing and Cutting-Stock Problem

Akartunalı²,

Jans

et al. 2017

INFORMS Journal on Computing

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The coupled lot-sizing and cutting-stock problem has been a challenging and significant problem for industry, and has therefore received sustained research attention. The quality of the solution is a major determinant of cost performance in related production and inventory management systems, and therefore there is intense pressure to develop effective practical solutions. In the literature, a number of heuristics have been proposed for solving the problem. However, the heuristics are limited in obtaining high solution qualities. This paper proposes a new progressive selection algorithm that hybridizes heuristic search and extended reformulation into a single framework. The method has the advantage of generating a strong bound using the extended reformulation, which can provide good guidelines on partitioning and sampling in the heuristic search procedure so as to ensure an efficient solution process. We also analyze per-item and per-period Dantzig-Wolfe decompositions of the problem and present theoretical comparisons. The master problem of the per-period DantzigWolfe decomposition is often degenerate, which results in a tailing-off effect for column generation.We apply a hybridization of Lagrangian relaxation and stabilization techniques to improve the convergence. The discussion is followed by extensive computational tests, where we also perform detailed statistical analyses on various parameters. Comparisons with other methods indicate that our approach is computationally tractable and is able to obtain improved results.

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An uncertain furniture production planning problem with cumulative service levels

Yang

Zhao

2015

Soft Comput

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To investigate how the loss averse customer's psychological satisfaction affects the company's furniture production planning, we establish a furniture production planning model under uncertain environment, where customer demand and production costs are characterized by mutually independent uncertain variables. Based on prospect theory, customer's psychological satisfaction about stockout performance is measured by cumulative service levels in our model. In the framework of uncertainty theory, the proposed uncertain model can be transformed into an equivalent deterministic form. However, the transformed model is a nonlinear mixed integer programming problem, which cannot be solved by conventional optimization algorithms. To cope with this difficulty, a chemical reaction optimization algorithm integrated with LINGO software is designed to solve the proposed production planning problem. In order to verify the effectiveness of the designed hybrid chemical reaction optimization (CRO) algorithm, we conduct several numerical experiments via an application example and compare with a spanning tree-based genetic algorithm (hst-GA). The computational results show that our proposed CRO algorithm achieves better performance than hst-GA, and the results also provide several interesting managerial insights in production planning problems.

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Risk-averse two-stage stochastic programs in furniture plants

Cited by 39 publications

References 81 publications

Hybrid manufacturing and remanufacturing lot-sizing problem with stochastic demand, return, and setup costs

Hybrid manufacturing and remanufacturing lot-sizing problem with stochastic demand, return, and setup costs

Progressive Selection Method for the Coupled Lot-Sizing and Cutting-Stock Problem

An uncertain furniture production planning problem with cumulative service levels

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