A Lagrangean heuristic for a two-echelon storage capacitated lot-sizing problem

Kebe, S. Abdoudrahamane; Sbihi, Najiba; Penz, Bernard

doi:10.1007/s10845-011-0514-8

Cited by 10 publications

(7 citation statements)

References 14 publications

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“…For instance, Xiao et al [4] study the price-dependent demand in a multi-product Newsvendor model and propose corresponding methods of obtaining the optimal order quantity and optimal sales price. Kébé et al [5] consider a two-echelon inventory lot-sizing problem with price-dependent demand and develop a mixed-integer programming formulation as well as a Lagrangian relaxation solution procedure for solving this problem. Secondly, considering the profound influence of demand variation on expected profits or costs, plenty of scholars focus on the modelling of demand when studying Newsvendor model.…”

Section: Research Backgroundmentioning

confidence: 99%

Determining the Optimal Order Quantity with Compound Erlang Demand under (T,Q) Policy

Jiang

Tam

Bao

et al. 2018

Mathematical Problems in Engineering

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Management of electric equipment has a direct impact on companies' performance and profitability. Considering the critical role that electric power materials play in supporting maintenance operations and preventing equipment failure, it is essential to maintain an inventory to a reasonable level. However, a majority of these electric power materials exhibit an intermittent demand pattern characterized by random arrivals interspersed with time periods with no demand at all. These characteristics cause additional difficulty for companies in managing these electric power material inventories. In response to the above problem, this paper, based on the electric power material demand data of Shanghai Electric Power Company, develops a new method to determine the optimal order quantity * in a cost-oriented periodic review ( , ) system, whereby unsatisfied demands are backordered and demand follows a compound Erlang distribution.* corresponds to the value of that gives the minimum expected total inventory holding and backordering cost. Subsequently, an empirical investigation is conducted to compare this method with the Newsvendor model. Results verify its superiority in cost savings. Ultimately, considering the complicated calculation and low efficiency of that algorithm, this paper proposes an approximation and a heuristic algorithm which have a higher level of utility in a real industrial context. The approximation algorithm simplifies the calculation process by reducing iterative times while the heuristic algorithm achieves it by generalizing the relationship between the optimal order quantity * and mean demand interarrival rate .

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Section: Research Backgroundmentioning

confidence: 99%

Determining the Optimal Order Quantity with Compound Erlang Demand under (T,Q) Policy

Jiang

Tam

Bao

et al. 2018

Mathematical Problems in Engineering

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show abstract

“…is considered as the unconditional probability of the n p th possibility of the capability requirements in each node. Constraints (14,15) (14,15) locates the variables of the ancestor node in the th p′ period. Furthermore, the variables in all leaf nodes (circular nodes) must share the same variables of the weapon models in the root node (rectangular node).…”

Section: Multistage Stochastic Programming Modelmentioning

confidence: 99%

“…Chen et al introduced a binary particle swarm optimization algorithm (PSO) with dynamic inertia weight and mutation mechanism for the production planning problem in the thin film transistor Array process [12]. Interested readers may refer to [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A Parallel Evolution based Intelligent Optimization Algorithm to Solve Large-scale Production Planning Problem in Defense Industry

Jiang¹

2013

JSW

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Since the multistage production planning problem possesses high-dimensional variables and large-scale solution space, it is hard to be solved in an acceptable time. To deal with this challenge, we propose a parallel evolution based intelligent optimization algorithm. The proposed algorithm employs the differential evolution as the algorithm framework to implement the primary mutation and crossover operations, then the entire variables are clustered into several sub-populations according to the problem structure, finally a parallel evolution strategy is proposed to speed up the convergence and progress the search precision of the sub-population. A case of weapons production planning is studied to validate the proposed algorithm. The results show that this algorithm has the fastest convergence and the best global searching capability, compared with classical differential evolution algorithm, genetic algorithm and particle swarm optimization algorithm

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“…To compensate for the computational shortcomings of exact methods and to provide real time solutions to industrial-size problems, heuristic methods have been extensively used in this area, from very simple frameworks to very sophisticated ones, see, e.g., Van Vyve and Pochet (2004), Wu et al (2011), Kébé et al (2012), Absi et al (2013), , Baki et al (2014). We also refer the interested reader to Ball (2011) for a recent literature review on general mathematical programming heuristics.…”

Section: Introductionmentioning

confidence: 99%

A relax-and-fix with fix-and-optimize heuristic applied to multi-level lot-sizing problems

et al. 2015

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In this paper, we propose a simple but efficient heuristic that combines construction and improvement heuristic ideas to solve multi-level lot-sizing problems. A relax-and-fix heuristic is firstly used to build an initial solution, and this is further improved by applying a fix-and-optimize heuristic. We also introduce a novel way to define the mixed-integer subproblems solved by both heuristics. The efficiency of the approach is evaluated solving two different classes of multi-level lot-sizing problems: the multi-level capacitated lot-sizing problem with backlogging and the two-stage glass container production scheduling problem (TGCPSP). We present extensive computational results including four test sets of the Multi-item Lot-Sizing with Backlogging library, and real-world test problems defined for the TGCPSP, where we benchmark against state-of-the-art methods from the recent literature. The computational results show that our combined heuristic approach is very efficient and competitive, outperforming benchmark methods for most of the test problems

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A Lagrangean heuristic for a two-echelon storage capacitated lot-sizing problem

Cited by 10 publications

References 14 publications

Determining the Optimal Order Quantity with Compound Erlang Demand under (T,Q) Policy

Determining the Optimal Order Quantity with Compound Erlang Demand under (T,Q) Policy

A Parallel Evolution based Intelligent Optimization Algorithm to Solve Large-scale Production Planning Problem in Defense Industry

A relax-and-fix with fix-and-optimize heuristic applied to multi-level lot-sizing problems

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