Year: 2006
Type: dissertation
Abstract: Make-to-order (MTO) systems have been traditionally popular in manufacturing\nindustries that either seek to provide greater variety to their customers or make\nproducts that are unique to their customers. More recently, with shrinking product\nlife cycles, there is an increasing interest in operating as MTO systems. With the\ntremendous success of revenue management techniques in the service industries over\nthe last three decades, there is a growing interest in applying these techniques in\nMTO manufacturing industries.\nIn the present work, we consider three problems that apply revenue management\n(RM) to on-date delivery MTO systems. In the first problem, we assume that all\norders completed in advance of their due-dates are stored at third party warehouses\nand apply RM in computing efficient order acceptance and scheduling policies. We\ndevelop an optimal solution scheme, and based on the insights gained on the structural\nproperties of the optimal solution, we develop a stochastic approximation scheme for\nfinding efficient solutions. Through computational studies on simulated problems, we\nillustrate the potential of RM in improving net profits over popular practices.\nIn our second problem, we extend the RM model to consider presence of a certain\namount of first party warehousing capacity for storing the orders completed in advance\nof their due-dates. We study the conditions under which it is desirable to consider the\nholding cost aspects in the RM model. In our third problem, we develop a scheme for determining an efficient capacity of the first party warehouse that is used for\nstoring the orders completed in advance of their due-dates at an on-date delivery\nMTO system. This scheme captures the completed orders storage demand resulting\nfrom a RM based order acceptance and scheduling policy. We illustrate that when\nbooking horizon is large, considerable amount of savings in the holding costs can be\nmade with an efficiently sized first party warehouse.
Author: Anshu Jalora
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2277767281