Fix‐and‐Optimize and Variable Neighborhood Search Approaches for Stochastic Multi‐Item Capacitated Lot‐Sizing Problems

Li, Liuxi; Song, Sejun; Wu, Cheng; Rui, Wang

doi:10.1155/2017/7209303

Cited by 6 publications

(6 citation statements)

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“…As mentioned before, the underlying constraints to consider vary significantly from one industry to another. Recent papers dealing with simultaneous lot sizing and scheduling (LSS) problems put more focus on particular features of these industries [7]: the beverage industry [10], [11] and [12], steel manufacturing [13], [14] and [15], the automotive industry [16] and [17], glass-container manufacturing [18], the tile industry [19], the chemical industry [20], and the paper industry [21]. Additionally some research has been conducted in the tire industry [22] and [23] and more specifically in the off-the-road tire industry [24] and [25].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Toscano et al [36] have recently developed a F&O algorithm to solve a synchronized two-stage CLSP with mandatory temporal cleaning and sequence-dependent changeover in a soft drink company. F&O heuristics have also been combined with local search such variable neighborhood search as in [37] and [13]. Li et al [13] dealt with a multi-item lot sizing problem in the steel industry with demand class formulation and stochastic demand where backlogging and overtime costs were also incurred.…”

Section: Literature Reviewmentioning

confidence: 99%

“…F&O heuristics have also been combined with local search such variable neighborhood search as in [37] and [13]. Li et al [13] dealt with a multi-item lot sizing problem in the steel industry with demand class formulation and stochastic demand where backlogging and overtime costs were also incurred. An original F&O approach based on "k-degree decomposition" that proposes several decomposition among products, resources and time horizon was presented.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Matheuristic Approach for Solving a Simultaneous Lot Sizing and Scheduling Problem with Client Prioritization in Tire Industry

Koch,

Arbaoui,

Ouazene

et al. 2022

Preprint

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This paper introduces an integrated lot sizing and scheduling problem inspired from a real-world application in off-the-road tire industry. This problem considers the assignment of different items on parallel machines with complex eligibility constraints within a finite planning horizon. It also considers a large panel of specific constraints such as: backordering, a limited number of setups, upstream resources saturation and customers prioritization. A novel mixed integer formulation is proposed with the objective of optimizing different normalized criteria related to the inventory and service level performance. Based on this mathematical formulation, a problem-based matheuristic method that solves the lot sizing and assignment problems separately is proposed to solve the industrial case. A computational study and sensitivity analysis are carried out based on real-world data with up to 170 products, 70 unrelated parallel machines and 42 periods. The obtained results show the effectiveness of the proposed approach on improving the company's solution. Indeed, the two most important KPIs for the management have been optimized of respectively 32% for the backorders and 13% for the overstock. Moreover, the computational time have been reduced significantly.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A Matheuristic Approach for Solving a Simultaneous Lot Sizing and Scheduling Problem with Client Prioritization in Tire Industry

Koch,

Arbaoui,

Ouazene

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It is shown that this formulation has a very tight linear relaxation that provides a far superior computational performance as compared to traditional period based formulations present in the literature. It should also be remarked that there are number of recent studies addressing the stochastic multi-item CLSP with setup carryovers ( [25]), energy concerns ( [26]), and rolling horizon framework under service level constraints ( [27], [28]).…”

Section: *Corresponding Authormentioning

confidence: 99%

A fix-and-optimize heuristic for the capacitated multi-item stochastic lot-sizing problem

Gurkan

Tunç²

2020

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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This study addresses the stochastic multi-item capacitated lot-sizing problem. Here, it is assumed that all items are produced on a single production resource and unmet demands are backlogged. The literature shows that the deterministic version of this problem is NP-Hard. We consider the case where period demands are time-varying random variables. The objective is to determine the minimum expected cost production plan so as to meet stochastic period demands over the planning horizon. We extend the mixed integer programming formulation introduced in the literature to capture the problem under consideration. Further, we propose a fix-and-optimize heuristic building on an item-period oriented decomposition scheme. We then conduct a numerical study to evaluate the performance of the proposed heuristic as compared to the heuristic introduced by Tempelmeier and Hilger [16]. The results clearly show that the proposed fix-and-optimize heuristic arises as both cost-efficient and time-efficient solution approach as compared to the benchmark heuristic.

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“…poses the original problem by fixing values of binary variables based on their interrelatedness. That approach has also been integrated into a variable neighborhood search framework, achieving excellent results for lot sizing (Chen, 2015;Li et al, 2017) and transportation (Toschi et al, 2018) problems. introduced a hybrid metaheuristic called Construct, Merge, Solve & Adapt (CMSA).…”

Section: Introductionmentioning

confidence: 99%

MineReduce: An approach based on data mining for problem size reduction

Maia

Plastino

Penna

2020

Computers & Operations Research

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Hybrid variations of metaheuristics that include data mining strategies have been utilized to solve a variety of combinatorial optimization problems, with superior and encouraging results. Previous hybrid strategies applied mined patterns to guide the construction of initial solutions, leading to more effective exploration of the solution space. Solving a combinatorial optimization problem is usually a hard task because its solution space grows exponentially with its size. Therefore, problem size reduction is also a useful strategy in this context, especially in the case of large-scale problems. In this paper, we build upon these ideas by presenting an approach named MineReduce, which uses mined patterns to perform problem size reduction. We present an application of MineReduce to improve a heuristic for the heterogeneous fleet vehicle routing problem. The results obtained in computational experiments show that this proposed heuristic demonstrates superior performance compared to the original heuristic and other state-of-the-art heuristics, achieving better solution costs with shorter run times. . This manuscript version is made available under the CC BY-NC-ND 4.0 license. Guerine et al., 2016;Maia et al., 2018;Martins et al., 2018a). Data-mining-hybridized heuristics were able to find solutions of higher quality while spending less computational time when compared to their non-hybridized counterparts and other state-of-the-art heuristics. They applied patterns (found by data mining procedures) to guide the construction of initial solutions.The difficulty in solving a COP is due to the fact that its solution space grows exponentially with its size. Therefore, problem size reduction (PSR) -which consists of reducing the size of a problem instance, then solving the reduced instance and expanding it back -has been found to be a very useful strategy in this context, especially in the case of large-scale problems, since it can significantly reduce the search space of a COP (Gavish & Srikanth, 1986;Fischer & Merz, 2007;Delgadillo et al., 2016).Building upon these ideas, in this paper, we present an approach named MineReduce, which uses mined patterns to perform problem size reduction. Previous methods that also incorporate data mining techniques into metaheuristics, in general, use the mined patterns as a starting point for the construction of initial solutions (Martins et al., 2018b). MineReduce, on the other hand, performs a PSR procedure by removing or merging elements that are in a pattern, finding a solution to the reduced instance, and expanding the solution found, which will be used as the starting point for a local search on the original solution space.In order to validate the MineReduce approach, we have applied it to extend a previous and state-of-theart heuristic for the heterogeneous fleet vehicle routing problem (HFVRP), obtaining significantly better results in terms of both solution quality and computational time.So, the main contribution of this work is twofold: an approach based on the idea of using mi...

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Fix‐and‐Optimize and Variable Neighborhood Search Approaches for Stochastic Multi‐Item Capacitated Lot‐Sizing Problems

Cited by 6 publications

References 30 publications

A Matheuristic Approach for Solving a Simultaneous Lot Sizing and Scheduling Problem with Client Prioritization in Tire Industry

A Matheuristic Approach for Solving a Simultaneous Lot Sizing and Scheduling Problem with Client Prioritization in Tire Industry

A fix-and-optimize heuristic for the capacitated multi-item stochastic lot-sizing problem

MineReduce: An approach based on data mining for problem size reduction

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