Lagrangean relaxation based heuristics for lot sizing with setup times

Süral, Haldun; Denizel, Meltem; Wassenhove, Luk N. Van

doi:10.1016/j.ejor.2007.11.052

Cited by 38 publications

(50 citation statements)

References 22 publications

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“…An evaluation of the hybrid ALNS heuristic is applied to the LSPST on a set of instances found in the literature. The ALNS heuristic outperforms ILOG CPLEX (applied to two state-of-the-art MIP formulations) and the current best heuristic of Sural et al (2009) both with respect to the quality of solutions and lower bounds. During the experiments we found 60 new best upper bounds (for the 100 instances also considered by Sural et al (2009)), and improved all lower bounds.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the problem appears frequently in the industry, where different tools are used to produce the items: The tools are a one-off investment so changes of tools only involve a setup time. Papers relating to the LSP with setup times and no setup costs include the MIP based heuristic of Denizel & Süral (2006), and the Lagrangian heuristic of Sural et al (2009). In the following we will refer to the LSP with setup times and no setup costs as the LSPST.…”

Section: Introductionmentioning

confidence: 99%

“…The paper is organized as follows: Section 2 gives an outline of the ALNS framework and describes the hybrid variant proposed in this paper, Section 3 presents an application of the hybrid ALNS heuristic to the LSPST, and Section 4 contains the experimental results performed on the instances of Sural et al (2009) which is an extension of the instances of Trigeiro et al (1989), and on a new set of larger instances. Section 5 concludes the paper and suggests new directions for future research.…”

Section: Introductionmentioning

confidence: 99%

“…A recent study by Sural et al (2009) shows that the standard benchmark instances of Trigeiro et al (1989) are considerably harder when setup costs are removed. Furthermore, Sural et al (2009) consider an extension of the standard (heterogeneous) instances denoted the homogeneous instances where all holding costs are equal.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, Sural et al (2009) consider an extension of the standard (heterogeneous) instances denoted the homogeneous instances where all holding costs are equal. Their experiments showed that the homogeneous instances have even larger integrality gaps than the heterogeneous instances.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Just-in-Time Planning and Lot-Sizing

Absi

Dauzère‐Pérès

Kedad-Sidhoum

2011

Just-in-Time Systems

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International audienceThe goal of this chapter is to show that production planning and lot-sizing models are not contradictory with a Just-In-Time (JIT) approach. Characteristics of these models are discussed to explain how they fit with JIT concepts. We first underline the importance of reducing setup times/costs in both lot-sizing and JIT models. We show that the definition of setup time/cost reduction can be different depending on the problem. We then discuss when demands cannot be delivered at their due dates (backlogs or lost sales). The relevance of time windows in a JIT environment is presented, followed by bounded production and inventory. The importance of capacity constraints and their impact on JIT systems are also discussed. Finally, the last section of the chapter is devoted to the interest of integrating lot-sizing and scheduling in a JIT approach

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Novel Pareto-based meta-heuristics for solving multi-objective multi-item capacitated lot-sizing problems

Hajipour

Kheirkhah

Tavana

et al. 2015

Int J Adv Manuf Technol

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International audienceCapacitated production lot-sizing problems (LSPs) are challenging problems to solve due to their combinatorial nature. We consider a multi-item capacitated LSP with setup times, safety stocks, and demand shortages plus lost sales and backorder considerations for various production methods (i.e., job shop, batch flow, or continuous flow among others). We use multi-objective mathematical programming to solve this problem with three conflicting objectives including: (i) minimizing the total production costs; (ii) leveling the production volume in different production periods; and (iii) producing a solution which is as close as possible to the just-in-time level. We also consider lost sales, backorders, safety stocks, storage space limitation, and capacity constraints. We propose two novel Pareto-based multi-objective meta-heuristic algorithms: multi-objective vibration damping optimization (MOVDO) and a multi-objective harmony search algorithm (MOHSA). We compare MOVDO and MOHSA with two well-known evolutionary algorithms called the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective simulated an-nealing (MOSA) to demonstrate the efficiency and effectiveness of the proposed methods

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Lagrangean relaxation based heuristics for lot sizing with setup times

Cited by 38 publications

References 22 publications

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

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

Just-in-Time Planning and Lot-Sizing

Novel Pareto-based meta-heuristics for solving multi-objective multi-item capacitated lot-sizing problems

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