Optimization of inventory levels and production effort in Hybrid Inventory-Production (HIP) systems

A production node within a network of interconnected production centers (as in supply chains) is considered in this paper, with the aim of optimizing inventory levels and production effort. The proposed Hybrid Inventory-Production model consists of two inventories (the input one for raw parts and the output one for processed parts) and a production resource which transforms parts and moves them from the input to the output inventory, at a certain rate. The production node interacts with other nodes in the network by the arrival process (raw parts entering the node) and the departure process (processed parts leaving the node). In HIP systems, arrival and departure processes, as well as the production process, are supposed to be piece-wise constant functions that change their values at asynchronous time instants. Based on this model and considering different cost terms, a mixed-integer nonlinear optimization problem has been defined. Then, some assumptions have been considered and the optimization problem has been simplified until obtaining a linear statement

The Modeling, Analysis and Control of Resilient Manufacturing Enterprises

The resilience of manufacturing enterprises is an important research topic, since disruptions have severe effects on the normal operation of manufacturing enterprises, especially as manufacturing supply chains become global. Although many case studies have been carried out to address resilience in organizations, a systematic method to model and analyze the resilience dynamics in manufacturing enterprises is not well developed. This study is intended to conduct research on quantitative analysis and control for resilience. After reviewing the literature addressing resilience, a modeling framework is presented to characterize the resilience of a manufacturing enterprise responding to disruptive events, which includes inventory ow between enterprise nodes, different costs, resource, demand, etc. Each node within the network is represented as a dynamic model with associated costs of production and inventory. This mathematical model is the foundation of quantitative analysis and control. With this model, an optimal control problem is formulated, by which the control can be solved to achieve minimum cost. Several different types of systems are defined and analyzed in this work. We develop the approach of aggregation to simplify the network structures. The study is mainly focused on two categories of network systems: serial network systems and assembly tree network systems. The analysis on these two categories covers two conditions: in discrete time domain without considering capacities, and in continuous time domain with considering capacities. The methods to determining optimal operations are developed under different conditions. In the serial network systems analysis, a practical case study is introduced to show the corresponding method developed. Finally, the problems are discussed for future research. Based on the results of these analyses, we present optimal control policies for resilience. Our method can support the analysis of the impact of disruptions, and the development of control strategies that reduce the impact of the disruption