Optimal Design and Operational Planning of Responsive Process Supply Chains

Abstract

This chapter addresses the problem of optimal design and operational planning of multi-echelon, multi-site process supply chain networks (PSCNs) with consideration of responsiveness and profitability. A quantitative characterization of responsiveness for PSCNs is presented, which measures the response time or lead time to changes in demands assuming zero inventories. This measure is incorporated in a multi-period mixed-integer non-linear programming (MINLP) model, which considers the selections of suppliers and manufacturing sites, process technology, production levels and scheduling. The problem is formulated as a bi-criterion optimization model in which the objectives are to maximize the net present value (NPV) and to minimize the lead time. This allows establishing trade-offs between the economics and responsiveness of the supply chain network. The model produces a Pareto-optimal curve, which reveals how the optimal net present value, and therefore the network structure of the PSCN, changes with different specifications of lead time. The application of this model is illustrated through an example. The modeling approach developed in this paper and the results obtained suggest that a new conceptual strategy has been developed that yields useful insights regarding the responsiveness of process supply chain networks.

Operability Analysis of Process Supply Chains—Toward the Development of a Sustainable Bioeconomy

“…In the worst case scenario when there is no stock of raw materials or intermediate products, the lead time is effectively the delay between a supplier and customer. You and Grossmann (2007) propose worst case lead time, i.e., length of the longest time path of chemical flow from a supplier to a customer by way of several manufacturing sites assuming zero inventory as a quantitative measure of supply chain responsiveness. Furthermore, the authors present a framework for strategic decision making, accounting for the selection of suppliers, manufacturing sites and process technology, and operational scheduling, under consideration of both profitability and responsiveness criteria through a bi-criterion optimization approach.…”

“…A quantitative approach to evaluate responsiveness is through the lead time, defined as the time for the supply chain to respond to customer demands (You and Grossmann, 2007). This definition associates a long lead time with less responsiveness.…”

Process Supply Chains Management – Where are We? Where to Go Next?

“…From that time onward, several works on the process SCs addressed the presence of production and distribution for simultaneous activities. This is the case of the works presented by Kallrath (2002), Tsiakis and Papageorgiou (2008), Jackson and Grossmann (2003), Naraharisetti et al (2008), You and Grossmann (2008), Kallrath (2008), Lainez et al (2009), and Naraharisetti and Karimi (2010). Different issues have been addressed such as facility location and relocation, production and distribution design and planning, supply contracts, and capital generation as well as investment and disinvestment.…”

Technology selection for bioethanol production: supply chain perspective

“…there is no sudden fluctuation in the market price of the products. This is shown in (8) and (9) below:…”

“…Zhang [5] presented a heterogeneous supply chain involving multiple products competing for multiple markets while Nagurney [6,7] proposed a new theoretical framework focusing on profit maximization in terms of horizontal merger. You et al [8] uses mixed integer non-linear programming for economic and responsiveness targets of multi-echelon supply chains. Similarly, when it comes to topics related to the economics of biofuel supply chain, a lot of researches are done, such as the work by Caputo et al [9] and Kumaran et al [10].…”