Differential evolution to solve the lot size problem in stochastic supply chain management systems

Lieckens, Kris; Vandaele, Nico

doi:10.1007/s10479-014-1778-0

Cited by 10 publications

(4 citation statements)

References 23 publications

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“…It was first introduced by Storn and Price (1997), and has been extensively applied to a wide domain of optimization problems due to its ability to efficiently handle non-differentiable, nonlinear and multimodal cost functions (for a state of the art survey see Das and Suganthan, 2011). Recently, DE has been implemented to solve the lot size problem in stochastic supply chain management systems (e.g., Lieckens and Vandaele, 2015), and also identify optimal groups of assets in active portfolio management (e.g., Krink et al, 2009). …”

Section: Methodsmentioning

confidence: 99%

Private labels and retail assortment planning: a differential evolution approach

et al. 2015

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Despite the longstanding recognition of the importance of product assortment planning (PAP), existing literature has failed to provide satisfactory solutions to a great deal of problems that reside in this area of research. The issue of optimal assortment planning in the retail sector becomes even more important in periods of economic crisis, as retailers must adapt their product portfolios to new evolving patterns of consumer buying behaviour and reduced levels of consumer's purchasing power.Private labels (PLs) typically experience significant growth in times of recession, due to their low prices, and the reduced disposable income of households. In this direction, the present paper introduces Differential Evolution (DE) to assist retailers in adapting their product portfolios in periods of economic recession and facilitate strategic PAP decisions, related to a) optimal variety of PL product categories, b) optimal service level of PL merchandise within a product category, and hence, c) optimal balance between PLs and National Brands (NBs) in a retailer's product portfolio. The interrelated issue of assortment adaptation across different store formats is also considered. Economic recessions contribute to the prolonged upward evolution in PL share, and hence, our mechanism facilitates decisions that are nowadays more important than ever before. The proposed mechanism is illustrated through an implementation to an empirical dataset derived from a random sample of 1,928 consumers who participated in a large-scale computer assisted telephone survey during the current economic crisis period.

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Section: Methodsmentioning

confidence: 99%

Private labels and retail assortment planning: a differential evolution approach

et al. 2015

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“…Furthermore, they have few control parameters (i.e., population size, scaling factor, and crossover rate) (Tanabe & Fukunaga, 2014 ). Lastly, DE has been applied in many real-world applications (Wang et al, 2012b; Li et al, 2018 ; Wang et al, 2012a; Lieckens & Vandaele, 2016 ).…”

Section: Solution Approachmentioning

confidence: 99%

A mathematical model for managing the multi-dimensional impacts of the COVID-19 pandemic in supply chain of a high-demand item

et al. 2022

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The COVID-19 pandemic has wreaked havoc across supply chain (SC) operations worldwide. Specifically, decisions on the recovery planning are subject to multi-dimensional uncertainty stemming from singular and correlated disruptions in demand, supply, and production capacities. This is a new and understudied research area. In this study, we examine, SC recovery for high-demand items (e.g., hand sanitizer and face masks). We first developed a stochastic mathematical model to optimise recovery for a three-stage SC exposed to the multi-dimensional impacts of COVID-19 pandemic. This allows to generalize a novel problem setting with simultaneous demand, supply, and capacity uncertainty in a multi-stage SC recovery context. We then developed a chance-constrained programming approach and present in this article a new and enhanced multi-operator differential evolution variant-based solution approach to solve our model. With the optimisation, we sought to understand the impact of different recovery strategies on SC profitability as well as identify optimal recovery plans. Through extensive numerical experiments, we demonstrated capability towards efficiently solving both small- and large-scale SC recovery problems. We tested, evaluated, and analyzed different recovery strategies, scenarios, and problem scales to validate our approach. Ultimately, the study provides a useful tool to optimise reactive adaptation strategies related to how and when SC recovery operations should be deployed during a pandemic. This study contributes to literature through development of a unique problem setting with multi-dimensional uncertainty impacts for SC recovery, as well as an efficient solution approach for solution of both small- and large-scale SC recovery problems. Relevant decision-makers can use the findings of this research to select the most efficient SC recovery plan under pandemic conditions and to determine the timing of its deployment.

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“…The algorithm has been applied to solve problems from different domains, as documented in the literature (Das & Suganthan, 2010 ). There are few applications of DE in the area of SCM (Routroy & Kodali, 2005 ; Yu et al, 2020 ) and stochastic SCM (Lieckens & Vandaele, 2016 ), two-stage humanitarian logistic under uncertainty (Tofighi et al, 2016 ), and supply allocation for disaster relief operations (Chen et al, 2020 ). So, there are ample scopes of application of DE for solving the problem related to the supply of emergency RM and set the motivation for selecting the technique.…”

Section: Literature Reviewmentioning

confidence: 99%

A probabilistic fuzzy goal programming model for managing the supply of emergency relief materials

2021

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The post-disaster humanitarian logistic operations deal with the supply of emergency relief materials to mitigate damages in the affected areas. Immediately after the disaster, it is challenging to estimate the demand for emergency relief materials. As a result, the demand for such materials at the point of demand and the corresponding transportation costs for the entire supply chain network becomes uncertain. This paper proposes a new probabilistic fuzzy goal programming model for making decisions to manage the post-disaster supply of emergency relief materials. A suggested procedure converts the proposed model to its deterministic equivalent when the demands for the relief materials follow uniform distributions. We implement the differential evolution, a metaheuristic technique, for analyzing demand satisfaction for relief materials under various scenarios. A case example based on the Nepal Earthquake in 2015 demonstrates the usefulness of the proposed approach. The solution of the model will help the Disaster Management Agency coordinate with the humanitarian organizations and foreign countries to provide the required emergency relief materials so that an adequate level of supply can be assured to the affected areas with the least possible transportation cost.

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Differential evolution to solve the lot size problem in stochastic supply chain management systems

Cited by 10 publications

References 23 publications

Private labels and retail assortment planning: a differential evolution approach

Private labels and retail assortment planning: a differential evolution approach

A mathematical model for managing the multi-dimensional impacts of the COVID-19 pandemic in supply chain of a high-demand item

A probabilistic fuzzy goal programming model for managing the supply of emergency relief materials

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