An integrated reliable four-level supply chain with multi-stage products under shortage and stochastic constraints

Purpose The main purpose is to minimize the total inventory cost of chain, whereas the stochastic constraints are satisfied. In other words, the goal is to find optimum agreed stockpiles and period length for products to minimize the total inventory cost of the chain while the stochastic constraints are fulfilled. Design/methodology/approach This paper designs and optimizes an integrated inventory model in a four-echelon supply chain that contains a supplier, a producer, a wholesaler and multiple retailers. All four levels agree with each other to make an integrated inventory system. Products in this model have a multi-stage production process, and the model is bounded by multiple stochastic constraints. The problem model is nonlinear and large. So, the interior point method as an effective algorithm is used for solving the recent convex nonlinear model. Two numerical examples are solved to demonstrate the application of this methodology and to evaluate the performance of the proposed approach. Findings The findings showed the model is applicable for real-world supply chain problems in the cases that echelons are going to do executive external integration. Also, the Interior Point algorithm has a satisfactory performance and a high efficiency in terms of optimum solution for solving nonlinear and large models. Originality/value The authors designed and optimized the inventory cost in a four-level integrated supply chain in stochastic conditions. The new decision variables, number of chain levels, multi-products, stochastic constraints and multi-stage products in four-level integrated supply chain are other novelties of this paper. The authors provided an efficient algorithm for solving a large-scale and nonlinear model in this research, too.

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A COVID-19 Supply Chain Management Strategy Based on Variable Production under Uncertain Environment Conditions

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18041662 ◽

2021 ◽

Vol 18 (4) ◽

pp. 1662

Author(s):

Mohammed Alkahtani ◽

Muhammad Omair ◽

Qazi Salman Khalid ◽

Ghulam Hussain ◽

Imran Ahmad ◽

...

Keyword(s):

Supply Chain ◽

Supply Chain Management ◽

Production Rate ◽

Manufacturing System ◽

Working Environment ◽

Data Set ◽

Manufacturing Firm ◽

Chain Management ◽

Ripple Effects ◽

Multi Stage

The management of a controllable production in the manufacturing system is essential to achieve viable advantages, particularly during emergency conditions. Disasters, either man-made or natural, affect production and supply chains negatively with perilous effects. On the other hand, flexibility and resilience to manage the perpetuated risks in a manufacturing system are vital for achieving a controllable production rate. Still, these performances are strongly dependent on the multi-criteria decision making in the working environment with the policies launched during the crisis. Undoubtedly, health stability in a society generates ripple effects in the supply chain due to high demand fluctuation, likewise due to the Coronavirus disease-2019 (COVID-19) pandemic. Incorporation of dependent demand factors to manage the risk from uncertainty during this pandemic has been a challenge to achieve a viable profit for the supply chain partners. A non-linear supply chain management model is developed with a controllable production rate to provide an economic benefit to the manufacturing firm in terms of the optimized total cost of production and to deal with the different situations under variable demand. The costs in the model are set as fuzzy to cope up with the uncertain conditions created by lasting pandemic. A numerical experiment is performed by utilizing the data set of the multi-stage manufacturing firm. The optimal results provide support for the industrial managers based on the proactive plan by the optimal utilization of the resources and controllable production rate to cope with the emergencies in a pandemic.

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