An analysis of optimal ordering policies for a two-supplier system with disruption risk

Luo, Sha; Ahiska, S. Sebnem; Fang, Shu‐Cherng; King, Robert E.; Warsing, Donald P.; Wu, Shuohao

doi:10.1016/j.omega.2021.102517

Cited by 9 publications

(3 citation statements)

References 55 publications

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“…Mirjalili [45] explains how this optimizer was inspired by transverse orientation. The benchmark test function, which is specified as F8 [35], is used to verify the findings, and the dimensions' number in this optimization is regarded to be six for this approach.…”

Section: Mfo Solution Methodsmentioning

confidence: 99%

“…However, the theory of the Markov chain is utilized in all the aforementioned literature, all of which solely deals with a single item or multiple items with independent demand. Luo [35] proposed a single-product mathematical model for controlling and predicting inventory systems, which includes a stable and unstable supplier. Asadi [36] investigated a stochastic model to control inventory with respect to the Markov chain.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, investigating the performance of different optimization methods to find more suitable methods to solve inventory models is vital [38]. Uncertainty in demand and other parameters, which has an undeniable effect on the models, is not widely considered [35].…”

Section: Introductionmentioning

confidence: 99%

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Using Four Metaheuristic Algorithms to Reduce Supplier Disruption Risk in a Mathematical Inventory Model for Supplying Spare Parts

et al. 2022

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Due to the unexpected breakdowns that can happen in various components of a production system, failure to reach production targets and interruptions in the process of production are not surprising. Since this issue remains for manufactured products, this halting results in the loss of profitability or demand. In this study, to address a number of challenges associated with the management of crucial spare parts inventory, a mathematical model is suggested for the determination of the optimal quantity of orders, in the case of an unpredicted supplier failure. Hence, a production system that has various types of equipment with crucial components is assumed, in which the crucial components are substituted with spare parts in the event of a breakdown. This study’s inventory model was developed for crucial spare parts based on the Markov chain process model for the case of supplier disruption. Moreover, for optimum ordering policies, re-ordering points, and cost values of the system, four metaheuristic algorithms were utilized that include Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Moth–Flame Optimization (MFO) Algorithm, and Differential Evolution (DE) Algorithm. Based on the results, reliable suppliers cannot meet all of the demands; therefore, we should sometimes count on unreliable suppliers to reduce unmet demand.

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Section: Mfo Solution Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%