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:10.1007/s40092-014-0050-1

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…(2009) present a solution procedure based on a steady‐state genetic algorithm with a new encoding structure for the design of a single‐source, multiproduct, multistage supply chain network and extend the priority‐based encoding of the transportation tree to a multiproduct case, Firoozi et al. (2013) solve a three‐level hierarchical supply chain that is modeled as a nonlinear MIP using a genetic algorithm, and Izadi and Kimiagari (2014) solve the location–allocation problem with an unknown demand function using a genetic algorithm and a Monte Carlo simulation approach.…”

Section: Problem Description and Prior Workmentioning

confidence: 99%

Redesign of vaccine distribution networks

Lim

Norman

Rajgopal

2019

Int Trans Operational Res

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In most low and middle income countries supported by the World Health Organization's Expanded Programme on Immunization, vaccines are distributed through a legacy medical supply chain that is typically not costefficient. Vaccines require storage and transport in a temperature controlled environment; this requires a "cold" distribution chain with capacity constraints on cold storage and cold transport. We propose an approach to redesigning the vaccine distribution chain that includes locating a set of intermediate distribution centers and determining the flow paths from the central store (where vaccines are received into a country) through one or more of these to health clinics where vaccination actually occurs. In addition, the transport vehicles to allocate to each flow path, and the cold storage devices to use at each clinic or intermediate distribution center are determined. The redesigned network does not have to follow the current four-tiered, arborescent structure commonly found in practice but can use alternative network structures. To redesign this network optimally, we develop a mixed-integer programming model that can be used for small to medium sized problems and also present a hybrid heuristic-MIP method to obtain good solutions for larger problems. Numerical results are shown using data reflecting distribution networks in several countries in sub-Saharan Africa.

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Section: Problem Description and Prior Workmentioning

confidence: 99%

Redesign of vaccine distribution networks

Lim

Norman

Rajgopal

2019

Int Trans Operational Res

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“…Other studies utilized a process simulator developed in MATLAB for optimization of Generalized Predictive Control (GPC) tuning parameters [33]. Izadi and 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 [34]. 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 [35].…”

Section: The Selected Approachmentioning

confidence: 99%

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

et al. 2018

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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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“…The facility location problems have been formulated and solved mathematically using a variety of methodologies and solution techniques based on the use of various objective functions to reduce costs while improving access to healthcare services 17 – 19 . The literature contains a comprehensive review of healthcare location decisions 20 – 22 .…”

Section: Introductionmentioning

confidence: 99%

Location management for the supply of PD fluid via large neighborhood search based virus optimization algorithm

Atthirawong

Luangpaiboon

2022

Sci Rep

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The facility location problem is extended by a new two-stage zero-one programming system (2S-ZOPS). It is a type of design optimization issue that exists in logistics implementations such as supply chain planning in healthcare or agriculture. Along with concerns regarding PD delivery time manner for connecting logistics centers and customers, recent studies have considered the zero-one location design model. This research discussed a route selection model for the 2S-ZOPS that did not exist in the published studies by taking into account the level of risk associated with physical appearance. The mathematical models were developed in response to a PD supply chain design that occurred in Thailand’s National Health Insurance Program. By combining the virus optimization algorithm (VOA) with a large neighborhood search (LNS), we created a hybrid metaheuristic method for solving the 2S-ZOPS. Experiments with real-world data demonstrated that the hybrid algorithm was efficient in terms of time consumption and solution quality, saving approximately 6% on total costs. The presented practice benefits not only the healthcare industry but also various other businesses.

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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 10 publications

References 27 publications

Redesign of vaccine distribution networks

Redesign of vaccine distribution networks

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

Location management for the supply of PD fluid via large neighborhood search based virus optimization algorithm

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