A sustainable supply chain network considering lot sizing with quantity discounts under disruption risks: centralized and decentralized models

Abstract

This study proposes a framework for the main parties of a sustainable supply chain network considering lot-sizing impact with quantity discounts under disruption risk among the first studies. The proposed problem differs from most studies considering supplier selection and order allocation in this area. First, regarding the concept of the triple bottom line, total cost, environmental emissions, and job opportunities are considered to cover the criteria of sustainability. Second, the application of this supply chain network is transformer production. Third, applying an economic order quantity model lets our model have a smart inventory plan to control the uncertainties. Most significantly, we present both centralized and decentralized optimization models to cope with the considered problem. The proposed centralized model focuses on pricing and inventory decisions of a supply chain network with a focus on supplier selection and order allocation parts. This model is formulated by a scenario-based stochastic mixed-integer non-linear programming approach. Our second model focuses on the competition of suppliers based on the price of products with regard to sustainability. In this regard, a Stackelberg game model is developed. Based on this comparison, we can see that the sum of the costs for both levels is lower than the cost without the bi-level approach. However, the computational time for the bi-level approach is more than for the centralized model. This means that the proposed optimization model can better solve our problem to achieve a better solution than the centralized optimization model. However, obtaining this better answer also requires more processing time. To address both optimization models, a hybrid bio-inspired metaheuristic as the hybrid of imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) is utilized. The proposed algorithm is compared with its individuals. All employed optimizers have been tuned by the Taguchi method and validated by an exact solver in small sizes. Numerical results show that striking similarities are observed between the results of the algorithms, but the standard deviations of PSO and ICA–PSO show better behavior. Furthermore, while PSO consumes less time among the metaheuristics, the proposed hybrid metaheuristic named ICA–PSO shows more time computations in all small instances. Finally, the provided results confirm the efficiency and the performance of the proposed framework and the proposed hybrid metaheuristic algorithm.

Analyzing Quantity-based Strategies for Supply Chain Sustainability and Resilience in Uncertain Environment

“…Broadly, there are two categories of uncertainties: operational and disasters [13]. Operational uncertainties pertain to the configuration of activities, encompassing factors like order timing and product prices [16]. The literature shows that various studies focus on disruption risks in supply chain systems through risk mitigation strategies, and aimed at identifying suitable measures throughout different stages, pre-disaster, during-disaster, and post-disaster [17], [18].…”

Flexible supplier selection and order allocation in the big data era with various quantity discounts

“…Models formed with one or two types often cannot describe those with more. Supplier selection models are NP-hard [ 19 ]. As the number of discounts increases, modeling complexity and solution difficulty increase.…”

Optimization of Total Cost of Production for a Mixed Make-To-Order (MTO) and Make-To-Stock (MTS) Production System with Lot Sizing for the RMG Industry in Bangladesh

“…Just-in-time (JIT), Materials Requirements Planning (MRP), Vendor Management Inventory (VMI), and Distribution Resource Planning are examples of inventory control techniques (DRP). Three different order size models are available for replenishing inventory: the quantity discount model, economic production quantity, and basic economic order quantity (EOQ) (Rafigh et al, 2022). However, the most crucial step is the one that decides whether an operation will be profitable or not.…”