Abstract

Least squares Monte Carlo (LSM) is an approximate dynamic programming technique commonly used for the valuation of high dimensional financial and real options, but has broader applicability. It is known that the regress-later version of this method is an approximate linear programming (ALP) relaxation that implicitly provides a potential solution to a familiar ALP deficiency. We provide numerical backing for the usefulness of this solution using a numerical study dealing with merchant ethanol production, an energy real option application, based on an ALP heuristic that we propose. When both methodologies are applicable, our research supports the use of regress-later LSM rather than this ALP technique to approximately solve intractable Markov decision processes. Our findings motivate additional research to obtain even better methods than the regress-later version of LSM.

Emerging Advances in Supply Chain Finance and FinTech Innovations

Advances in Supply Chain Finance and FinTech Innovations examines three themes:

Financing Issues in Supply Chains look into popular working capital management financing practices: trade credits and guarantor practices including advanced trade credit practices in supply chains, guarantor financing practices for capital constrained retailers, and innovative practices of joint financing of capital constrained firms by a bank.

FinTech Innovations for Supply Chains examines business model innovations for supply chain financing supported through new platform technologies (such as blockchain), and simple financial technologies effectively implemented for high impact in supply chain risk management.

Advances in Risk Management of Operational Systems provide state-of-the art thinking on many risk issues in supply chain operations including disruption strategies over the product life cycle, the production planning complexities for a capital constrained manufacturer that uses Inventory Based Financing (IBF) scheme to fund its working capital needs, capacity procurement decision, capacity planning in the presence of demand and price uncertainty, and valuing complex real options in dynamic operational settings.