A location-inventory model for the sustainable supply chain of perishable products based on pricing and replenishment decisions: A case study

Abstract

In recent decades, changes in people’s lifestyles and occupations have led to new food consumption patterns around the world, with a notable growth in the demand for ready meals and meat products. Accordingly, the food industry has tried to transform the global food culture by promoting its more profitable products to become able to set a higher price for meat. Considering the short lifetime of products in perishable food supply chains, inventory decisions are considered crucial. In addition, the demand for perishable food products is greatly affected by their freshness. In this paper, we develop a multi-objective mixed-integer non-linear programming model for a four-level sustainable supply chain (SC) of a perishable product with price-dependent demand and deterioration rates. The SC consists of suppliers, a production center (PC), distribution centers (DCs), and retailers. We aim to ascertain the optimal pricing policy and cycle length to maximize profit, achieve specific social objectives, and minimize the total cost and adverse environmental impacts. The proposed model determines the deterioration rate of each product according to its expiration date, the optimal location of supply-side facilities, and the flow rate between the facilities based on the selected mode of delivery. The products are delivered to the retailers by a mechanized transportation system either directly from the PC or indirectly through the DCs. Finally, an actual case study is provided to demonstrate the applicability of the model and our theoretical results under real-world conditions. We solve the case study by a preemptive fuzzy goal programming method and perform several sensitivity analyses on the results. By performing simultaneous sensitivity analyses on the demand and expiration dates, the optimal values of the problem’s parameters are determined. The optimal values help decision-makers make optimal decisions regarding the selling price of products and replenishment times. The model is applicable to supply chains of any perishable items with expiration dates.

Optimization of “vehicle-UAV” joint distribution routing for cold chain logistics considering risk of epidemic spreading and green cost

“…Pan [ 17 ] established a cold chain logistics route optimization model from the perspective of low-carbon, and solved the model by using Wolf pack algorithm and ant colony algorithm. Mohammadi et al [ 18 ] developed a multi-objective mixed-integer non-linear programming model for a four-level sustainable supply chain of a perishable product with price-dependent demand and deterioration rates. In addition, some scholars have carried out extended studies on the influence of factors such as facility capacity limitation [ 19 ], influence of multiple vehicle types [ 20 ], and inventory cost [ 21 , 22 ] on LRP.…”

R&D mode and coordination of green products in sustainable supply chain considering power structures

“…It was found that whether the supply chain carried out green product R&D would be affected by the competitive supply chain selection strategy and the cost of green product R&D. Zhang et al [ 52 ] established a mixed integer programming model to study the optimal design of sustainable supply chain network, and put forward a chaotic particle-ant colony algorithm to solve multi-objective functions. Mohammadi et al [ 53 ] established a multi-objective mixed integer nonlinear programming model for the sustainable supply chain of perishable products with price-dependent demand and deterioration rate, determined the optimal pricing strategy and cycle length, and achieved profit maximization and specific social goals. Fu et al [ 54 ] discussed the influence of non-subsidy policy, fixed subsidy policy and agricultural risk coverage (ARC) subsidy policy on government costs, farmers and enterprise profits.…”