Distribution network design under demand uncertainty using genetic algorithm and Monte Carlo simulation approach: a case study in pharmaceutical industry

Izadi, Arman; Kimiagari, Ali Mohammad

doi:10.1186/2251-712x-10-1

Cited by 30 publications

(15 citation statements)

References 27 publications

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“…Building robustness requires the strategic planning to construct supply chain network (Ehrenhuber et al 2015). For that, it is needed to design a value-creating supply chain network which will able to resist the operation before and after the unwanted event (Izadi and Kimiagari 2014). A robust supply chain can work in spite of a few unsettling influences, as it withstands and adapts to stuns by holding its dependability when changes happen (Tang 2006;Shishebori and Babadi 2018).…”

Section: Robustnessmentioning

confidence: 99%

Performance indicators for supply chain resilience: review and conceptual framework

2019

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In the current competitive and dynamic market, customer demands and interests are changing continuously, and hence, risk of disruption in the supply chain is also increasing. To be successful in this scenario, supply chain of a firm should be resilient. Most of the firms realize that with a specific end goal to develop a resilient supply chain, there is a need for assessment of performance. The purpose of this paper is to discuss supply chain resilience and identify indicators which can help in increasing the performance and making a supply chain resilient. Articles published on this issue were collected and classified to draw out some meaningful information. After classification and analyses of the collected literature, seventeen performance indicators for supply chain resilience are found. A supply chain resilience framework is developed using these indicators to assist the supply chain managers to examine and withstand the disruption.

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Section: Robustnessmentioning

confidence: 99%

Performance indicators for supply chain resilience: review and conceptual framework

2019

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“…Other studies utilized a process simulator developed in MATLAB for optimization of Generalized Predictive Control (GPC) tuning parameters [25]. Izadi & Kimiagari were able to specify the optimal number and location of distribution centers to determine the allocation of customer demand to DC with a model based on Monte Carlo Simulation [26]. Chackelson use a discrete event simulation model to evaluate order picking performance in a warehouse operation and propose a new picking design process to improve performance [27].…”

Section: Techniques For Optimization In Logisticsmentioning

confidence: 99%

Integrating Simulation-Based Optimization for Lean Logistics: A Case Study

González-Reséndiz¹,

Arredondo-Soto²,

Realyvásquez-Vargas³

et al. 2018

Preprint

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The present work aims at the comprehensive application of stochastic and optimization tools with the support of Information and Communication Technologies (ICT) through a case study in a logistics process for electronic goods; simulation and Response Surface Methodology (RSM) are applied for this purpose. The problem to be evaluated is to define an optimal distribution cost for products shipped to wholesale customers located in different cities in Mexico from a manufacturing plant in Tijuana, Mexico. The factors under study are the product allocation for each distribution center, finished good inventory level and on time deliveries, which are supposed to be significant to get the objective. The methodology applied for this problem considers the design of a discrete event simulation model to represent virtually the real life of logistics process, which is considered a complex system due to different activities are interrelated to carry it out. This model is used to execute the different experiments proposed by the RSM. The results obtained from simulation model were analyzed with the RSM to define the mathematical model that allows identifying the parameters of the factors in order to optimize the process. The findings prove how the ICT facilitate the application of stochastic tools with the purpose of process optimization.

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“…Genetic algorithm as an evolutionary algorithm has been successfully used for solving NP-hard problems, such as different supply chain management problems (Izadi and Kimiagari 2014;Kannan et al 2010;Kuo and Han 2011;Raj and Rajendran 2012) and different VRP problems (Elhassania et al 2014;Karakatič and Podgorelec 2015). In a GA, we have an initial population including individuals that evolves during the algorithm by genetic operators, i.e., selection, cross over and mutation.…”

Section: Proposed Genetic Algorithmmentioning

confidence: 99%

Two phase genetic algorithm for vehicle routing and scheduling problem with cross-docking and time windows considering customer satisfaction

2017

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Cross-docking is a new warehousing policy in logistics which is widely used all over the world and attracts many researchers attention to study about in last decade. In the literature, economic aspects has been often studied, while one of the most significant factors for being successful in the competitive global market is improving quality of customer servicing and focusing on customer satisfaction. In this paper, we introduce a vehicle routing and scheduling problem with cross-docking and time windows in a three-echelon supply chain that considers customer satisfaction. A set of homogeneous vehicles collect products from suppliers and after consolidation process in the cross-dock, immediately deliver them to customers. A mixed integer linear programming model is presented for this problem to minimize transportation cost and early/tardy deliveries with scheduling of inbound and outbound vehicles to increase customer satisfaction. A two phase genetic algorithm (GA) is developed for the problem. For investigating the performance of the algorithm, it was compared with exact and lower bound solutions in small and large-size instances, respectively. Results show that there are at least 86.6% customer satisfaction by the proposed method, whereas customer satisfaction in the classical model is at most 33.3%. Numerical examples results show that the proposed two phase algorithm could achieve optimal solutions in small-size instances. Also in large-size instances, the proposed two phase algorithm could achieve better solutions with less gap from the lower bound in less computational time in comparison with the classic GA.

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Distribution network design under demand uncertainty using genetic algorithm and Monte Carlo simulation approach: a case study in pharmaceutical industry

Cited by 30 publications

References 27 publications

Performance indicators for supply chain resilience: review and conceptual framework

Performance indicators for supply chain resilience: review and conceptual framework

Integrating Simulation-Based Optimization for Lean Logistics: A Case Study

Two phase genetic algorithm for vehicle routing and scheduling problem with cross-docking and time windows considering customer satisfaction

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