Location and allocation decisions in a two-echelon supply chain with stochastic demand – A genetic-algorithm based solution

Recently, Shankar et al. (Expert Syst Appl 40(2):551-562, 2013; Expert Syst Appl 40(14):5730-5739, 2013) proposed a bi-objective mixed integer linear programming model for a multi-echelon supply chain network problem. The current technical paper identifies some inconsistencies in the research works of Shankar et al. (Expert Syst Appl 40(2):551-562, 2013; Expert Syst Appl 40 (14):5730-5739, 2013) and provides some observations and corrections to the shortcommings. Basically, the inconsistencies are: on one hand, contradictions between constraints 10 and 6; on the other hand, contradictions between constrainsts 10 and 4. Another shortcoming is the unsatisfaction of the lower bound on the fill rate in constraint 10 in some cases.

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“…It is also suitable to model the problem in which the demands should not necessarily be satisfied (e.g. Wang et al [4]). …”

Section: Discussionmentioning

confidence: 99%

Some Observations on “Location and Allocation Decisions for Multi-echelon Supply Chain Network: A Multi-objective Evolutionary Approach”

Nobil

Niaki

Cárdenas–Barrón

2016

Int. J. Appl. Comput. Math

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“…The SCN design optimization problems with uncertainty have been addressed and developed in a wide variety of past researches (Taleizadeh et al 2008a(Taleizadeh et al , b, 2010(Taleizadeh et al , 2011Olivares-Benitez et al 2012;Wang et al 2011). These researches typically considered demand as the uncertainty parameter of the supply chain (El-Sayed et al 2010;Rodriguez et al 2014;Georgiadis et al 2011;Cardona-Valdés and Alvarez 2011).…”

Section: Supply Chain Network Design Under Uncertaintymentioning

confidence: 99%

Optimizing decentralized production–distribution planning problem in a multi-period supply chain network under uncertainty

et al. 2017

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Decentralized supply chain management is found to be significantly relevant in today's competitive markets. Production and distribution planning is posed as an important optimization problem in supply chain networks. Here, we propose a multi-period decentralized supply chain network model with uncertainty. The imprecision related to uncertain parameters like demand and price of the final product is appropriated with stochastic and fuzzy numbers. We provide mathematical formulation of the problem as a bi-level mixed integer linear programming model. Due to problem's convolution, a structure to solve is developed that incorporates a novel heuristic algorithm based on Kth-best algorithm, fuzzy approach and chance constraint approach. Ultimately, a numerical example is constructed and worked through to demonstrate applicability of the optimization model. A sensitivity analysis is also made.

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“…For SA, the values were initial temperature (50 to 300), absolute temperature (0.01), and cooling rate (0.8 to 0.95). These rates are taken from previous research in the literature [19,20,33].…”

Section: Sensitivity Analysis and Tuning The Model Parametersmentioning

confidence: 99%

The Ordered Capacitated Multi-Objective Location-Allocation Problem for Fire Stations Using Spatial Optimization

Bolouri

Vafaeinejad

Alesheikh

et al. 2018

IJGI

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Determining the positions of facilities, and allocating demands to them, is a vitally important problem. Location-allocation problems are optimization NP-hard procedures. This article evaluates the ordered capacitated multi-objective location-allocation problem for fire stations, using simulated annealing and a genetic algorithm, with goals such as minimizing the distance and time as well as maximizing the coverage. After tuning the parameters of the algorithms using sensitivity analysis, they were used separately to process data for Region 11, Tehran. The results showed that the genetic algorithm was more efficient than simulated annealing, and therefore, the genetic algorithm was used in later steps. Next, we increased the number of stations. Results showed that the model can successfully provide seven optimal locations and allocate high demands (280,000) to stations in a discrete space in a GIS, assuming that the stations' capacities are known. Following this, we used a weighting program so that in each repetition, we could allot weights to each target randomly. Finally, by repeating the model over 10 independent executions, a set of solutions with the least sum and the highest number of non-dominated solutions was selected from among many non-dominated solutions as the best set of optimal solutions.

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Location and allocation decisions in a two-echelon supply chain with stochastic demand – A genetic-algorithm based solution

Cited by 74 publications

References 14 publications

Some Observations on “Location and Allocation Decisions for Multi-echelon Supply Chain Network: A Multi-objective Evolutionary Approach”

Some Observations on “Location and Allocation Decisions for Multi-echelon Supply Chain Network: A Multi-objective Evolutionary Approach”

Optimizing decentralized production–distribution planning problem in a multi-period supply chain network under uncertainty

The Ordered Capacitated Multi-Objective Location-Allocation Problem for Fire Stations Using Spatial Optimization

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