Effective replenishment planning and inventory control are essential for the smooth operation and adaptability of supply chains. These aspects play a pivotal role in upholding a company’s competitiveness and triumph in today’s fiercely competitive markets. Supply chain planners encounter significant hurdles in choosing the most appropriate suppliers in diverse scenarios, reducing average inventory levels, and determining optimal safety lead times. This research tackles these challenges by examining and evaluating a multi-period replenishment planning issue within the framework of dynamic demand and multiple suppliers. The suppliers are pre-selected and defined by procurement costs, with lead times considered as independent discrete random variables with known and limited probability distributions. The goal is to optimize the distribution of order quantities among these pre-selected suppliers while minimizing the anticipated total cost. Two strategies and corresponding linear models are suggested to investigate the impact of dividing orders between suppliers, order crossover, and order flexibility. Numerical experiments provide evidence that concurrently considering splitting and flexibility yields benefits in terms of cost optimization.

This article studies a bi-objective stochastic optimization problem for multi-period multi-sourcing supply planning. The formulated problem accounts for stochastic lead times, degressive pricing, holding and backlog costs, delivery flexibility costs, as well as both holding and transportation carbon footprint. The first objective is to minimize the expected total cost, while the second objective is to minimize the expected total footprint. These objectives must be achieved while adhering to suppliers’ capacity constraints and meeting customer demand. In this paper, the proposed stochastic integer linear program is detailed, and the ϵ-constraint method used to solve it is described. The first results of experiments are presented and discussed.

In order to meet item demands, end-of-life (EOL) product and subassembly ordering and disassembly schedules are determined by disassembly lot sizing, which is the subject of this study. We take into consideration a stochastic version with undetermined ordering lead time (OLT). In this case, OLT stands for the amount of time that passes between placing and receiving an order (we can only order EOL products). Throughout the planning horizon, scenarios are used to model the stochasticity. The objective is to reduce the expected total of setup, purchasing, inventory, and backlog expenses. This is achieved by expressing the problem as a two-stage mixed integer linear programming (2S-MILP) model across all potential scenarios. The 2S-MILP is unsolvable since it is predicated on every scenario conceivable. A Simultaneous Backward Reduction approach is proposed to make it tractable. To confirm the suggested method’s efficacy, it is assessed in a variety of environments.