This paper investigates the joint pricing and ordering decisions in a two-level supply chain with remanufacturing operations. The supply chain consists of a manufacturer performing both manufacturing and remanufacturing activities and two competing retailers serving overlapping market segments, one for new products and another for remanufactured products. The objective of this study is to determine the optimal decisions that maximize the profits of all stakeholders while accounting for competitive interactions and coordination mechanisms. To model the decision-making process, a hierarchical coordination framework is adopted: (1) A Stackelberg game is formulated between the manufacturer and each retailer, where the manufacturer acts as the leader and the retailers respond as followers, while (2) the two competing retailers seek a Nash equilibrium in their pricing and ordering strategies. Additionally, inventory and flow coordination is captured through a Joint Economic Lot Sizing (JELS) approach. The resulting model is a non-linear program solved using a general solver. A numerical study is conducted to validate the resolution and to analyze the impact of retailer competition.

This paper introduces a novel pre-positioned inventory model aimed at mitigating supply chain disruptions by enhancing the resilience of supply networks characterized by multiple facilities subject to disruptions. Based on a Time-To-Recover model, we explore a single-tier supply chain framework, incorporating real-world disruption scenarios to assess the efficacy of pre-positioned inventories in disruption mitigation. A two-stage stochastic programming approach is used to formulate the problem, incorporating a special case scenario that allows for the development of a closed-form equation. This enables a detailed analysis of the impact of pre-positioned inventory on supply chain resilience, examining various scenarios to ascertain the optimal inventory levels required to mitigate disruption risks effectively. Some numerical examples are presented to illustrate the practical application of the model, offering valuable insights into the strategic positioning of inventories and the implications for supply chain design.

In response to the ongoing global energy crisis, particularly prevalent in Europe, there is a critical need to identify alternative energy sources and reduce energy consumption. This study presents an integrated inventory model that incorporates energy consumption from a supplier’s production and storage operations. The model focuses on a manufacturing system consisting of a single machine for production and a warehouse responsible for storing a single product. Energy consumption in the production process is attributed to the machine’s operation, which can be either in a production or non-production phase. Additionally, energy is utilized to maintain goods at the required temperature, accounting for ambient temperature fluctuations and varying levels of warehouse occupancy. The objective is to determine optimal decision variables, including production rate, cycle time, and machine status in the non-production phase, to minimize the overall system cost, encompassing energy costs. The proposed optimisation procedure simplifies solution identification for the nonlinear programming (NLP) problem. A realistic numerical example illustrates the model’s applicability and reveals that optimal machine status during non-production phases significantly reduces both energy costs and total costs compared to the remaining status. Sensitivity analysis explores the effects of different parameters on the model, emphasizing the considerable impact of factors like energy consumption per unit, energy cost, and demand rate on the total cost.

International audience

As companies strive to reduce energy consumption and minimize their carbon footprint, there is a growing trend among customers to choose eco-friendly products. In order to address these concerns, the present study puts forth a new economic production quantity (EPQ) model that takes into account various factors such as energy usage, carbon emissions, and market demand in relation to product price and environmental impact. This is achieved through an analysis of the working phases of the manufacturing machine. By maximizing the overall profitability of the system, optimal decisions can be made regarding cycle time, production rate, demand rate, and machine states during non-production phases (standby or powered off). A Mixed integer nonlinear programming (MINLP) problem is proposed and analyzed. A case study demonstrates that meeting customer expectations for sustainable products can lead to lower profits for businesses, but it also results in reduced energy consumption and environmental emissions. The sensitivity analysis demonstrates that market demand, price sensitivity coefficient, and unit production cost have a more substantial impact on profit compared to the other parameters.