(181g) Surrogate-Based Derivative Free Optimization of a Multi-Enterprise Supply Chain | AIChE

(181g) Surrogate-Based Derivative Free Optimization of a Multi-Enterprise Supply Chain


Sahay, N. - Presenter, Aspen Technology
Dias, L. S., Rutgers, The State University of New Jersey
Ierapetritou, M. G., Rutgers, The State University of New Jersey
 Surrogate-based Derivative Free Optimization of a Multi-Enterprise Supply Chain

Nihar Sahay, Lisia Dias and Marianthi Ierapetritou

Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ

In the present scenario, with the growing interactions among the different enterprises constituting supply chain networks, it is rare for a single enterprise to operate its supply chain independently. The advancements in web technologies have also helped get rid of the limitations with multi-enterprise global supply chain networks. Multi-enterprise supply chain operations can be significantly different from that in a monolithic supply chain belonging to a single enterprise. It is important to take into consideration the effects of interactions among the different enterprises when modeling such networks.

Supply chain optimization strategies that consider centralized networks might not necessarily be suitable for the individual entities in the network. Such factors gain more importance when the network consists of more than one enterprise and the optimal solution for the overall network is not beneficial for all the participating enterprises. Under such circumstances, the model needs to take into consideration the interaction among the entities in terms of transfer prices, coalition formation, negotiations. Among the initial studies discussing multi-enterprise networks, Dâ??Amours et al.1 proposed a network approach with multiple firms. The firms are selected and scheduled with the objective of fulfilling demand at minimum cost and collaborative approach is shown to be more profitable. During the late 1990â??s, many new supply chain paradigms evolved which were studied by many authors.2-4 Among the different ways enterprises may interact with each other for transactions, auctions have been gained some emphasis recently.5-8Although buyers usually rely on contracts with suppliers for the critical products, auctions have become an efficient and effective means to procure non-critical products. They help achieve lower acquisition costs as well as enable new suppliers to enter the market. Another advantage such marketplaces offer is that they allow the buyers and sellers to determine price elasticity and dynamically adjust their prices based on supply and demand. In the recent past, there has been a growing body of literature in the field of operations management.

In this work we consider a multi-enterprise supply chain network. The production sites and warehouses are considered to belong to one particular enterprise while the retailers are different enterprises. The transaction between warehouses and retailers happens through auctions. A multi-attribute double auction is considered where the attributes of the auction are price and quantity. The auction mechanism consists of two phases where the first stage determines the buyer and the seller while the second stage uses the Nash Bargaining solution to determine the final trade. An agent-based simulation model is used to depict the representation of the supply chain dynamics and study the interactions among the enterprises. Additionally optimal warehouse capacities are obtained for the minimization of total cost. A kriging based derivative free optimization methodology is proposed. Different adaptive sampling methods including Expected Improvement and Goal Seeking are compared. Local search is performed using the trust region method starting from the different initial points obtained by clustering.


1. D'Amours S, Montreuil B, Lefrançois P, Soumis F. Networked manufacturing:: The impact of information sharing. International Journal of Production Economics. 1999;58(1):63-79.

2. Bartezzaghi E. The evolution of production models: is a new paradigm emerging? International Journal of Operations & Production Management. 1999;19(2):229-250.

3. Lehtinen U. Subcontractors in a partnership environment:: A study on changing manufacturing strategy. International Journal of Production Economics. 1999;60â??61:165-170.

4. Lindsey JH, Samuelson W, Zeckhauser R. Selling Procedures with Private Information and Common Values. Management Science. 1996;42(2):220-231.

5. Chen F. Auctioning Supply Contracts. Management Science. 2007;53(10):1562-1576.

6. Beil DR, Wein LM. An Inverse-Optimization-Based Auction Mechanism to Support a Multiattribute RFQ Process. Manage. Sci. 2003;49(11):1529-1545.

7. Gallien J, Wein LM. A Smart Market for Industrial Procurement with Capacity Constraints. Management Science. 2005;51(1):76-91.

8. Moyaux T, McBurney P, Wooldridge M. A supply chain as a network of auctions. Decision Support Systems. 2010;50(1):176-190.