A variable neighborhood search based approach for uncapacitated multilevel lot-sizing problems

Xiao, Yiyong; Kaku, Ikou; Zhao, Qiuhong; Zhang, Renqian

doi:10.1016/j.cie.2010.11.003

Cited by 26 publications

(19 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hindi et al [21] proposed a heuristic that combines the VNS and Lagrangian relaxation for the CLSP with setup times. Xiao et al [22] used the VNS with distance-based neighbourhood structures to solve the uncapacitated multilevel lot-sizing problem, a problem much easier than the MLCLSP. Zhao et al [5] used the variable neighbourhood decomposition search (VNDS), a variant of the VNS, to solve the MLCLSP.…”

Section: Related Literaturementioning

confidence: 99%

Fix-and-optimize and variable neighborhood search approaches for multi-level capacitated lot sizing problems

Chen

2015

Omega

View full text Add to dashboard Cite

Section: Related Literaturementioning

confidence: 99%

Fix-and-optimize and variable neighborhood search approaches for multi-level capacitated lot sizing problems

Chen

2015

Omega

View full text Add to dashboard Cite

“…The forward dynamic programming algorithm was used to solve all these test problem instances. After that, we used commercial software AMPL/CPLEX (Version 12.0) to solve these problem instances with the linear UDSLLS-TVE model formulated in Equations (17)- (23). All tested instances were optimally solved by the AMPL/CPLEX software.…”

Section: Comparison Through Large-scale Experimentsmentioning

confidence: 99%

“…Furlan and Santos [19] proposed a hybrid heuristic based on the bees algorithm combined with the fix-and-optimize heuristic to solve the multi-level capacitated lot-sizing problem. For large-sized problems, meta heuristics algorithms were reported to obtain the near-optimal solutions with good computational efficiencies, such as the Genetic Algorithm [20], the particle swarm optimization (PSO) algorithm [21], and Variable Neighborhood Search [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

The Uncapacitatied Dynamic Single-Level Lot-Sizing Problem under a Time-Varying Environment and an Exact Solution Approach

et al. 2018

Self Cite

View full text Add to dashboard Cite

The dynamic lot-sizing problem under a time-varying environment considers new features of the production system where factors such as production setup cost, unit inventory-holding cost, and unit price of manufacturing resources may vary in different periods over the whole planning horizon. Traditional lot-sizing theorems and algorithms are no longer fit for these situations as they had assumed constant environments. In our study, we investigated the dynamic lot-sizing problem with deteriorating production setup cost, a typical time-varying environment where the production setup is assumed to consume more preparing time and manufacturing resources as the production interval lasts longer. We proposed new lot-sizing models based on the traditional lot-sizing model considering the changing setup cost as a new constraint, called uncapacitatied dynamic single-level lot-sizing under a time-varying environment (UDSLLS-TVE for short). The UDSLLS-TVE problem has a more realistic significance and higher research value as it is closer to reality and has higher computational complexity as well. We proposed two mathematical programming models to describe UDSLLS_TVE with or without nonlinear components, respectively. Properties of the UDSLLS-TVE models were extensively analyzed and an exact algorithm based on forward dynamic programming (FDP) was proposed to solve this problem with a complexity of O (n2). Comparative experiments with the commercial MIP solver CPLEX on synthesized problem instances showed that the FDP algorithm is a global optimization algorithm and has a high computational efficiency.

show abstract

“…optimization (IPO) on the ILP model under the framework of variable neighborhood searches (VNS), known as IPO-ILP-VNS, to obtain a near-optimal solution whose quality can be controlled by the given CPU time. VNS is a new top-level meta-heuristics approach proposed byMladenovic and Hansen (1997) and it has been successfully applied to combinatorial optimization problems in various fields, such as the travelling salesman problem, the Pmedian problem, the vehicle routing problem, and the multi-level lot-sizing problem(Hansen et al, 2010;Xiao et al, 2011aXiao et al, , 2014a. The mechanism of VNS is to perform local search with designed changes in multilevel neighborhoods, which gives many desirable properties of meta-heuristics such as simplicity, robustness, userfriendliness and generality(Hansen et al, 2008).…”

mentioning

confidence: 99%

“…based on the framework of the variable neighborhood search (VNS)Hansen, 1997, Hansen andMladenović, 2001;Mladenovic et al, 2012;Xiao et al, 2011a Xiao et al, , 2014b, the original task to optimize the entire medium and large-sized problem at a single instant is decomposed to optimize multiple small-sized problems at multiple times.To implement the IPO under a VNS framework, we first define a distance metric of solutions based on variable w ic , and then define the neighborhood structure of solutions based on the distance metric. The distance metric: For any two solutions, say x and y, of the ILP model formulated in Eq.…”

mentioning

confidence: 99%

Optimal mathematical programming and variable neighborhood search for k-modes categorical data clustering

Xiao

Huang

et al. 2019

Pattern Recognition

Self Cite

View full text Add to dashboard Cite

The conventional k-modes algorithm and its variants have been extensively used for categorical data clustering. However, these algorithms have some drawbacks, e.g. they can be trapped into local optima and sensitive to initial clusters/modes. Our numerical experiments even showed that the k-modes algorithm cannot identify the optimal clustering results for some special datasets regardless the selection of the initial centers. In this paper, for small-sized datasets we developed an optimal programming approach based on integer linear programming (ILP) for k-modes clustering, which is independent to the initial solution;. For medium and large sized datasets, we developed a heuristic algorithm that implements iterative partial optimization in the ILP approach based on a framework of variable neighborhood search, known as IPO-ILP-VNS, to search for nearoptimal results with controlled computing time. Experiments on 38 datasets from the UCI site, including 27 synthesized small datasets and 11 known benchmark datasets, were carried out to test the proposed ILP approach and the IPO-ILP-VNS algorithm. The experiment results outperformed the conventional and all other existing enhanced k-modes algorithms in literature.

show abstract

A variable neighborhood search based approach for uncapacitated multilevel lot-sizing problems

Cited by 26 publications

References 30 publications

Fix-and-optimize and variable neighborhood search approaches for multi-level capacitated lot sizing problems

Fix-and-optimize and variable neighborhood search approaches for multi-level capacitated lot sizing problems

The Uncapacitatied Dynamic Single-Level Lot-Sizing Problem under a Time-Varying Environment and an Exact Solution Approach

Optimal mathematical programming and variable neighborhood search for k-modes categorical data clustering

Contact Info

Product

Resources

About