Solving a multi-level capacitated lot sizing problem with multi-period setup carry-over via a fix-and-optimize heuristic

Sahling, Florian; Buschkühl, Lisbeth; Tempelmeier, Horst; Helber, Stefan

doi:10.1016/j.cor.2008.10.009

Cited by 171 publications

(111 citation statements)

References 28 publications

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“…As for the heuristics-based approaches, Sahling et al (2009) propose a new algorithm to solve the MLCLSP as a big bucket problem, allowing to produce any number of products within a period, however, partial sequencing of the orders is solved by determining the release of the first and last orders in each period. Helber and Sahling (2010) apply the same fix-and-optimise heuristics as it provides a flexible and most efficient known solution for the MLCLSP.…”

Section: Production Planning In Multi-stage Systemsmentioning

confidence: 99%

Robust production planning and control for multi-stage systems with flexible final assembly lines

Gyulai

Pfeiffer

Monostori

2016

International Journal of Production Research

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Production planning of final assembly systems is a challenging task, as the often fluctuating order volumes require flexible solutions. Besides, the calculated plans need to be robust against the process-level disturbances and stochastic nature of some parameters like manual processing times or machine availability. In the paper, a simulation-based optimisation method is proposed that utilises lower level shop floor data to calculate robust production plans for final assembly lines of a flexible, multi-stage production system. In order to minimise the idle times when executing the plans, the capacity control that specifies the proper operator-task assignments is also determined. The analysed multi-stage system is operated with a pull strategy, which means that the production at the final assembly lines generates demands for the preceding stages providing the assembled components. In order to guarantee the feasibility of the plans calculated for the final assembly lines, a decomposition approach is proposed to optimise the production plan of preceding stages. By this way, the robust production can be ensured resulting in reduced losses and overall production costs even though the system is exposed to changes and disturbances.

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Section: Production Planning In Multi-stage Systemsmentioning

confidence: 99%

Robust production planning and control for multi-stage systems with flexible final assembly lines

Gyulai

Pfeiffer

Monostori

2016

International Journal of Production Research

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“…Therefore, we use an adapted version of the flexible Fix-and-Optimize heuristic proposed by Helber and Sahling (2010) as it leads to high-quality solutions for the multilevel version of the deterministic CLSP, see also Sahling et al (2009) andSahling (2010).…”

Section: Formulation Of the Sclsp-plamentioning

confidence: 99%

Dynamic capacitated lot sizing with random demand and dynamic safety stocks

2012

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Suggested Citation: Helber, Stefan; Sahling, Florian; Schimmelpfeng, Katja (2011) AbstractWe present a stochastic version of the single-level, multi-product dynamic lotsizing problem subject to a capacity constraint. A production schedule has to be determined for random demand so that expected costs are minimized and a constraint based on a new backlog-oriented δ-service-level measure is met. This leads to a non-linear model that is approximated by two different linear models. In the first approximation, a scenario approach based on random samples is used. In the second approximation model, the expected values of physical inventory and backlog as functions of the cumulated production are approximated by piecewise linear functions. Both models can be solved to determine efficient, robust and stable production schedules in the presence of uncertain and dynamic demand. They lead to dynamic safety stocks that are endogenously coordinated with the production quantities. A numerical analysis based on a set of (artificial) problem instances is used to evaluate the relative performance of the two different approximation approaches. We furthermore show under which conditions precise demand forecasts are particularly useful from a production-scheduling perspective.

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“…The approach is somewhat similar to the hybrid method presented in this paper although it has no adaptiveness or randomness builtin, instead Sahling et al (2009) consider a large number of predetermined subproblems and optimize over all of them. For some comparisons see for instance Jans & Degraeve (2007) or Buschkühl et al (2010).…”

Section: Introductionmentioning

confidence: 99%

“…In the multi-period carry-over variant Sahling et al (2009) present a fixand-optimize heuristic that repeatedly solves a series of subproblems with a MIP solver. The approach is somewhat similar to the hybrid method presented in this paper although it has no adaptiveness or randomness builtin, instead Sahling et al (2009) consider a large number of predetermined subproblems and optimize over all of them.…”

Section: Introductionmentioning

confidence: 99%

A hybrid adaptive large neighborhood search heuristic for lot-sizing with setup times

Muller

Spoorendonk

Pisinger

2012

European Journal of Operational Research

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This paper presents a hybrid of a general heuristic framework and a general purpose mixed-integer programming (MIP) solver. The framework is based on local search and an adaptive procedure which chooses between a set of large neighborhoods to be searched. A mixed integer programming solver and its built-in feasibility heuristics is used to search a neighborhood for improving solutions. The general reoptimization approach used for repairing solutions is specifically suited for combinatorial problems where it may be hard to otherwise design suitable repair neighborhoods. The hybrid heuristic framework is applied to the multi-item capacitated lot sizing problem with setup times, where experiments have been conducted on a series of instances from the literature and a newly generated extension of these. On average the presented heuristic outperforms the best heuristics from the literature, and the upper bounds found by the commercial MIP solver ILOG CPLEX using state-of-the-art MIP formulations. Furthermore, we improve the best known solutions on 60 out of 100 and improve the lower bound on all 100 instances from the literature.

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Solving a multi-level capacitated lot sizing problem with multi-period setup carry-over via a fix-and-optimize heuristic

Cited by 171 publications

References 28 publications

Robust production planning and control for multi-stage systems with flexible final assembly lines

Robust production planning and control for multi-stage systems with flexible final assembly lines

Dynamic capacitated lot sizing with random demand and dynamic safety stocks

A hybrid adaptive large neighborhood search heuristic for lot-sizing with setup times

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