A Simulation Framework for Real-Time Management and Control of Inventory Routing Decisions

We consider a logistics network where a single warehouse distributes a single item to multiple retailers. Retailers in the network participate in a Vendor Managed Inventory (VMI) program with the warehouse, where the warehouse is responsible for tracking and replenishing the inventory at various retailer locations. The information update occurs every time a vehicle reaches a location and the decision on the delivery quantity and the next location to visit is made. For a small increase of locations in the network, the state space for the solution increases exponentially, making this problem NP-hard. Thus, we propose a solution methodology where in the size of the state space is reduced at each stage. In this work, we use simulation to develop the framework for the real-time control and management of inventory and routing decisions, given this scenario

Public-Private Partnerships for Technology Growth in the Public Sector

Public-private partnerships (PPP) are a mechanism for financing large infrastructure development such as transportation projects, hospitals, schools, and public works facilities. In addition, the benefits of PPP stretch well into the realm of engineering management. Most notably, PPPs provide the opportunity for more efficient project management, proficient risk mitigation, and enhanced technological innovation. This paper provides a general description of the typical PPP process and how this process can be used to improve management of technology in the public sector

SR-2: A Hybrid Algorithm for the Capacitated Vehicle Routing Problem

During the last decades a lot of work has been devoted to develop algorithms that can provide near-optimal solutions for the capacitated vehicle routing problem (CVRP). Most of these algorithms are designed to minimize an objective function, subject to a set of constraints, which typically represents aprioristic costs. This approach provides adequate theoretical solutions, but they do not always fit real-life needs since there are some important costs and some routing constraints or desirable properties that cannot be easily modeled. In this paper, we present a new approach which combines the use of Monte Carlo simulation and parallel and grid computing techniques to provide a set of alternative solutions to the CVRP. This allows the decision-maker to consider multiple solution characteristics other than just aprioristic costs. Therefore, our methodology offers more flexibility during the routing selection process, which may help to improve the quality of service offered to clients

A Generic, Adaptive Systems Engineering Information Model I

This paper proposes a new network centric architecture that can be used by first responders to effectively respond to crisis situations. The powerful network-centric concept originally developed for and mainly used in the military environment, can be effectively used for civilian security and emergency response missions. This paper also proposes the use of a swarm of intelligent robots as a part of the network-centric architecture to aid the first responders. The swarm of robots works in tandem with the first responders and provides them with the necessary information on a real time basis. The proposed network centric architecture with a swarming robot entity is explained in detail using C4ISR framework. The proposed architecture if implemented successfully will result in solving crisis situations, may it be natural calamity or terrorist attacks, more efficiently and effectively

Alternative Energy Sources for MoDOT

This research investigates environmentally friendly alternative energy sources that could be used by MoDOT in various areas, and develops applicable and sustainable strategies to implement those energy sources

Alternative Energy Resources for the Missouri Department of Transportation

This research investigates environmentally friendly alternative energy sources that could be used by MoDOT in various areas, and develops applicable and sustainable strategies to implement those energy sources

Enhancing Undergraduate Engineering Education of Lean Methods using Simulation Learning Modules Within a Virtual Environment

This paper highlights the use of an integrated user-centered virtual learning environment throughextensible simulation learning modules that is currently being developed to enhance undergraduate curricula to meet the industrial needs for engineers with education in lean. The purpose of the research is to address these expectations by developing learning modules that incorporate lean simulation models into various Engineering Management, Industrial Engineering, and Mechanical Engineering courses at Missouri S&T, Texas Tech, and South Dakota State, respectively. In recent years, increasing global competition, rapidly changing technology, and a deficit of U.S. engineering graduates have intensified the need to produce graduating engineers who are effective problem solvers and analytical thinkers, yet who can also collaborate on interdisciplinary teams to address complex, real-world systems. A key area of competence for many engineering undergraduate, as well as graduate, disciplines is the application of structured problem solving methods, e.g., lean, to improve the performance of organizational processes. This virtual learning environment will enhance undergraduate engineering education by utilizing technology as a learning tool in lean, by fostering student development through active learning in the classroom, and through projects based on current real-world challenges, thus improving student learning, motivation, and retention. The paper highlights the learning modules to be developed in the virtual learning environment. The long-term goal is to evaluate the impact of the curriculum changes on student learning, outreach, and industrial collaboration

A review of simheuristics: extending metaheuristics to deal with stochastic combinatorial optimization problems

Many combinatorial optimization problems (COPs) encountered in real-world logistics, transportation, production, healthcare, financial, telecommunication, and computing applications are NP-hard in nature. These real-life COPs are frequently characterized by their large-scale sizes and the need for obtaining high-quality solutions in short computing times, thus requiring the use of metaheuristic algorithms. Metaheuristics benefit from different random-search and parallelization paradigms, but they frequently assume that the problem inputs, the underlying objective function, and the set of optimization constraints are deterministic. However, uncertainty is all around us, which often makes deterministic models oversimplified versions of real-life systems. After completing an extensive review of related work, this paper describes a general methodology that allows for extending metaheuristics through simulation to solve stochastic COPs. ‘Simheuristics’ allow modelers for dealing with real-life uncertainty in a natural way by integrating simulation (in any of its variants) into a metaheuristic-driven framework. These optimization-driven algorithms rely on the fact that efficient metaheuristics already exist for the deterministic version of the corresponding COP. Simheuristics also facilitate the introduction of risk and/or reliability analysis criteria during the assessment of alternative high-quality solutions to stochastic COPs. Several examples of applications in different fields illustrate the potential of the proposed methodology.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (grant TRA2013-48180-C3-P), FEDER, and the Ibero-American Programme for Science and Technology for Development (CYTED2014-515RT0489). Likewise we want to acknowledge the support received by the Department of Universities, Research & Information Society of the Catalan Government (Grant 2014-CTP-00001) and the CAN Foundation (Navarre, Spain) (Grant 3CAN2014-3758)

Production strategies for random yield processes.

Classical inventory models provide optimal results for a wide variety of problems but do not apply directly to random yield systems. While random yield problems have been extensively studied, little attention has been given to multiple product systems. We present a queueing analysis of multiple product systems with setups and random yield and utilize these results to develop basestock production strategies for both backlogging and lost sales (or expediting). These results are related to bounds on the optimal production strategy. We present theoretical and applicable results for manufacturing environments and discuss other areas of application.Ph.D.Applied SciencesIndustrial engineeringOperations researchUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/132576/2/9977163.pd