Pallavi Manohar

Job Routing in Multi-site Print Shop Environment

Xerox is a market leader in managing print services and provides enhanced print-shop productivity through its LDP Lean Document Production solution. Geographically distributed and multi-site service operations present challenges that are quite different from stand-alone print shops. There is a need to address other aspects for the multi-site enterprise such as optimal routing of print jobs and capacity planning. This paper presents a system that can assist the enterprise manager to make efficient job routing decisions within the enterprise. The system described is part of the LDP suite enabling multi-site enterprise print management.

Cost Efficient Short Term Capacity Planning for MTO Enterprises

A Make-to-Order (MTO) set-up is one in which production starts only on receiving confirmed orders, and there is no inventory. An enterprise is a group of production sites that may be geographically separated. This paper considers enterprises that operate on the MTO principles. Upon receiving orders for products, the orders are assigned to the different production sites that are geographically distributed. In order to have an efficient production plan, the decision support system must address challenges at the enterprise level, as well as at the individual production site level. In this paper our interest is in an optimal distribution of orders across sites while exploiting individual capacities at those sites. The enterprise level decisions are referred to as routing decisions, while the site level decisions are called scheduling decisions. The planning horizon for these decisions is typically short, that is, a day or a week and hence the proposed solution approach falls under short term capacity planning. A central router is assumed to receive orders on behalf of the enterprise, and is then expected to divide the order workload amongst the sites, in order to minimize the overall costs (production as well as transportation). At each site, an optimal schedule is determined to minimize the maximal completion time, as well as maximize remaining capacity on each machine, after satisfying the workload assigned to it. A framework for cost efficient routing and scheduling while ensuring job deadlines is proposed which allows iterative interaction of routing and scheduling modules. The feedback on capacity based on deadline violations at each site is used to iteratively optimize routing of jobs while ensuring job deadlines, along with the objective of cost efficient capacity planning. We present two iterative frameworks one using Mixed Integer Program (MIP) and the second using MIP and discrete event simulation model. The proposed frameworks have been implemented and computational results are presented to compare their performances.