A framework for investigating optimization of service parts performance with big data

As national economies continue to evolve across the globe, businesses are increasing their capacity to not only generate new products and deliver them to customers, but also to increase levels of after-sales service. One major component of after-sale service involves service parts management. However, service parts businesses are typically seen as add-ons to existing business models, and are not well integrated with primary businesses. Consequently, many service parts operations are managed using ad-hoc practices that are often subordinated to primary businesses. Early research in this area has been instrumental in assisting organizations to begin optimizing some aspects of service parts management. However, performance goals for service parts management are often ill-defined. Further, because these service parts businesses are often subordinated to primary businesses within a firm, the use of newer big data applications to help manage these processes is almost completely absent. Herein, we develop a framework that seeks to define service parts performance goals for the purpose of outlining where scholars and practitioners can further examine where, how, and why big data applications can be employed to enhance service parts management performance

A novel modeling approach for express package carrier planning

Express package carrier networks have large numbers of heavily-interconnected and tightly-constrained resources, making the planning process difficult. A decision made in one area of the network can impact virtually any other area as well. Mathematical programming therefore seems like a logical approach to solving such problems, taking into account all of these interactions. The tight time windows and nonlinear cost functions of these systems, however, often make traditional approaches such as multicommodity flow formulations intractable. This is due to both the large number of constraints and the weakness of the linear programming (LP) relaxations arising in these formulations. To overcome these obstacles, we propose a model in which variables represent combinations of loads and their corresponding routings, rather than assigning individual loads to individual arcs in the network. In doing so, we incorporate much of the problem complexity implicitly within the variable definition, rather than explicitly within the constraints. This approach enables us to linearize the cost structure, strengthen the LP relaxation of the formulation, and drastically reduce the number of constraints. In addition, it greatly facilitates the inclusion of other stages of the (typically decomposed) planning process. We show how the use of templates, in place of traditional delayed column generation, allows us to identify promising candidate variables, ensuring high-quality solutions in reasonable run times while also enabling the inclusion of additional operational considerations that would be difficult if not impossible to capture in a traditional approach. Computational results are presented using data from a major international package carrier. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/60965/1/20310_ftp.pd

Optimization Models and Algorithms for Truckload Relay Network Design

Driver turnover is a significant problem for full truckload (TL) carriers that operate using point-to-point (PtP) dispatching. The low quality of life of drivers due to the long periods of time they spend away from home is usually identified as one of the main reasons for the high turnover. In contrast, driver turnover is not as significant for less-than-truckload (LTL) carriers that use hub-and-spoke transportation networks which allow drivers to return home more frequently. Based on the differences between TL and LTL, the use of a relay network (RN) has been proposed as an alternative dispatching method for TL transportation in order to improve driver retention. In a RN, a truckload visits one or more relay points (RPs) where drivers and trailers are exchanged while the truckload continues its movement to the final destination. In this research, we propose a new composite variable model (CVM) to address the strategic TL relay network design (TLRND) problem. With this approach, we capture operational considerations implicitly within the variable definition instead of adding them as constraints in our model. Our composites represent feasible routes for the truckloads through the RN that satisfy limitations on circuity, number of RPs visited, and distances between RPs and between a RP and origin-destination nodes. Given a strict limitation on the number of RPs allowed to be visited, we developed a methodology to generate feasible routes using predefined templates. This methodology was preferred over an exact feasible path enumeration algorithm that was also developed to generate valid routes. The proposed approach was successfully used to obtain high quality solutions to largely-sized problem instances of TLRND. Furthermore extending the original CVM formulation, we incorporate mixed fleet dispatching decisions into the design of the RN. This alternative system allows routing some truckloads through the RN while the remaining truckloads are dispatched PtP. We analyze the performance of our models and the solutions obtained for TLRND problems through extensive computational testing. Finally, we conclude with a description of directions for future research

Human machine collaborative decision making in a complex optimization system

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2005.Includes bibliographical references (p. 149-151).Numerous complex real-world applications are either theoretically intractable or unable to be solved in a practical amount of time. Researchers and practitioners are forced to implement heuristics in solving such problems that can lead to highly sub-optimal solutions. Our research focuses on inserting a human "in-the-loop" of the decision-making or problem solving process in order to generate solutions in a timely manner that improve upon those that are generated either scolely by a human or solely by a computer. We refer to this as Human-Machine Collaborative Decision-Making (HMCDM). The typical design process for developing human-machine approaches either starts with a human approach and augments it with decision-support or starts with an automated approach and augments it with operator input. We provide an alternative design process by presenting an 1HMCDM methodology that addresses collaboration from the outset of the design of the decision- making approach. We apply this design process to a complex military resource allocation and planning problem which selects, sequences, and schedules teams of unmanned aerial vehicles (UAVs) to perform sensing (Intelligence, Surveillance, and Reconnaissance - ISR) and strike activities against enemy targets. Specifically, we examined varying degrees of human-machine collaboration in the creation of variables in the solution of this problem. We also introduce an IIHMCDM method that combines traditional goal decomposition with a model formulation into an Iterative Composite Variable Approach for solving large-scale optimization problems.(cont.) Finally, we show through experimentation the potential for improvement in the quality and speed of solutions that can be achieved through the use of an HMCDM approach.by Jeremy S. Malasky.S.M