Robust Optimization for Smart Manufacturing Planning and Supply Chain Design in Chemical Industry

Robust Optimization for Smart Manufacturing Planning and Supply Chain Design in Chemical Industry

Tianxing Cai (Lamar University, USA)
DOI: 10.4018/978-1-4666-5836-3.ch002
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Abstract

The depletion of natural resource, the complexity of economic markets and the increased requirement for environment protection have increased the uncertainty of chemical supply and manufacturing. The consequence of short-time material shortage or emergent demand under extreme conditions, may cause local areas to suffer from delayed product deliveries and manufacturing disorder, which will both cause tremendous economic losses. In such urgent events, robust optimization for manufacturing planning and supply chain design in chemical industry, targeting the smart manufacturing, should be a top priority. In this chapter, a novel methodology is developed for robust optimization of manufacturing planning and supply chain design in chemical industry, which includes four stages of work. First, the network of the chemical supply chain needs to be characterized, where the capacity, quantity, and availability of various chemical sources is determined. Second, the initial situation under steady conditions needs to be identified. Then, the optimization is conducted based on a developed MILP (mixed-integer linear programming) model in the third stage. Finally, the sensitivity of the manufacturing and transportation planning with respect to uncertainty parameters is characterized by partitioning the entire space of uncertainty parameters into multiple subspaces. The efficacy of the developed methodology is demonstrated via a case study with in-depth discussions.
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1. Introduction

In recent years, the depletion of natural resources, the complexity of economic markets and the increased requirement for environment protection have increased the uncertainty of chemical supply and manufacturing: the natural inventory quantity of mineral material and crude oil will be continuously decreased, which pushes the refinery industry to innovate their current process performance in order to increase the operation capability to handle the raw material with the low quality; the speedy progress of advanced technology development has resulted in the fast revolution of supply-demand relationship in the chemical market, which directly impact the distribution and business operation of chemical supply chain; the global attention for environment protection and sustainable development has definitely formed the pattern of survival of the fittest, which means the process or product will be eliminated if it is harmful to the surrounding environment. The manufacturing planning and supply chain operation are tremendously impacted by these factors in terms of resource limit, economy and environmental sustainability, transportation, as well as their spontaneous characterizations of uncertainty. To restore the functionality and capability of these supply chain and manufacturing systems under the unexpected situation, the recovery of raw material delivery is one of the most important because all the other operations have to be supported by enough available raw materials. On the other hand, if the local energy shortage caused by an uncertain event cannot be effectively restored, the local areas will be at risks of delivery delay, economic losses, and even public issues. Therefore, the recovery and delivery time minimization of a suffered chemical supply chain should be a top priority of a smart manufacturing planning in the chemical industry.

Nowadays chemical supply chain is characterized by its diversity of chemical products and processes. It also involves a complex network system composed of chemical generation, chemical transformation, chemical transportation, and chemical consumption. The network does provide the great flexibility for chemical transformation and transportation; meanwhile, it presents a complex task for conducting agile dispatching when abnormal events have caused local material shortages that need to be restored timely. Conceivably, any type of dispatched chemical material under certain emergency condition has its own characteristics in terms of availability, quantity, transportation speed, and conversion rate and efficiency to other types of chemical manufacturing process. Thus, different types of chemical should be dispatched through a superior plan. For instance, raw material sources such as petroleum or coal can be directly transported to a suffered area; meanwhile, they can also be converted to a typical intermediate in a source region and then sent to the suffered area through an available supply chain. Sometimes, even the transportation of the same type of chemical may have different alternative routes for selection, which needs to be optimally determined from the view point of the entire energy system of chemical supply chain design and manufacturing planning.

Facing the challenges of emergency response to material shortage of chemical supply, decision makers often encounters various uncertainties that inevitably influence the performance of a being designated chemical dispatch plan. The uncertainties can upset the optimality and even the feasibility of the designed plan. Thus, quantitative analysis on the impact of uncertainties is of great significance for the study of robust optimization for smart manufacturing planning and supply chain design in the chemical industry. Technically, a viable approach is to conduct a full evaluation of the effects of uncertainties based on all their possibilities. This will provide decision makers a complete roadmap of the space of uncertainty parameters. Through this way, the objective function and the optimization parameters are represented as functions of uncertainty parameters; meanwhile, the regions in the space of the uncertainties characterized by these functions can be obtained.

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