Andrei Sleptchenko (Department of mechanical and industrial engineering, College of Engineering, Qatar University)
6 February 2015
Maintaining Secure and Reliable Distributed Control Systems
We consider the role of security in the maintenance of an automated system, controlled by a network of sensors and simple computing devices. Such systems are widely used in transportation, utilities, healthcare, and manufacturing. Devices in the network are subject to traditional failures that can lead to a larger system failure if not repaired. However, the devices are also subject to security breaches that can also lead to catastrophic system failure. These security breaches could result from either cyber attacks (such as viruses, hackers, or terrorists) or physical tampering. We formulate a stochastic model of the system to examine the repair policies for both real and suspected failures. We develop a linear programming-based model for optimizing repair priorities. We show that, given the state of the system, the optimal repair policy follows a unique threshold indicator (either work on the real failures or the suspected ones). We examine the behavior of the optimal policy under different failure rates and threat levels. Finally, we examine the robustness of our model to violations in the underlying assumptions and find the model remains useful over a range of operating assumptions.
Haoxuan (Howard) Xu (School of Management, Huazhong University of Science & Technology)
20 February 2014
Dynamic Lot-Sizing Models with Advance Demand Information for Online Retailers
This paper studies inventory replenishment planning problems for online retailers able to obtain advance demand information (ADI) in an environment of time-varying demands. We incorporate ADI into dynamic lot-sizing models to formulate the replenishment planning problems for online retailers in three scenarios: (1) companies act as pure-play online retailers with customers homogeneous in demand lead time, (2) online customers are heterogeneous in demand lead time with priorities, and (3) online retailers operate in a bricks and clicks structure, in which demands come from online and offline channels, with either independent or interactive channels. We formulate the problem in the general scenario of interactive demand channels as a mixed-integer linear programming model, and then develop a unified model through reformulation. Based on the optimality properties, we design a dynamic programming algorithm with polynomial running time to solve the unified model. The numerical experiments for several online retailers find that the method can significantly reduce the total inventory cost.
Theresia van Essen (Department of mathematics and computer science, Delft University of Technology)
25 March 2015
Improve the operating room schedule to reduce the number of required beds
After surgery most of the surgical patients have to be admitted and treated at one of the wards in the hospital. Due to financial and other reasons, it is important to reduce the number of required beds as much as possible. Each feasible operating room schedule leads to a number of required beds at the ward. Due to the stochastic nature of the length of stay of patients, the analytical calculation of this number results in a complex formulation which involves convolutions of discrete distributions. I will present two different approaches to deal with this complexity. The first approach is based on a local search heuristic which takes into account the detailed formulation of the objective. The second approach reduces the complexity by simplifying the objective function. This allows modeling and solving the resulting problem as an ILP. The computational results show that the second approach provides better solutions to the original problem for instances based on a Dutch hospital. By using this approach, the number of required beds for this hospital can be reduced by almost 20%.
Chiel van Oosterom (School of industrial engineering, Eindhoven university of technology)
8 April 2015
Maintenance Optimization with Imperfect Information
Condition-based maintenance is an approach that recommends taking maintenance decisions based on measurements of the condition of a system. Ideally, decisions are based on full knowledge of a system’s condition and a completely specified (stochastic) deterioration model to describe how its condition will evolve in the future. However, in reality often one or more quantities of interest are only partially known. In this talk, we will discuss maintenance optimization problems that arise in settings with different types of imperfect information.
In the main part of the talk, we focus on the problem of optimally scheduling replacements for a Markovian deteriorating system under population heterogeneity. Heterogeneities in the population of components can arise, for example, because of variations in the production process of components. We assume that the population of spare components is composed of multiple component types that cannot be distinguished by their exterior appearance, but deteriorate according to different transition probability matrices. We formulate the problem using a partially observable Markov decision process (POMDP) model, and provide a set of conditions for which we characterize the structure of the optimal policy that minimizes the total expected discounted operating and replacement cost over an infinite horizon. By a numerical experiment, we benchmark the optimal policy against a heuristic policy that neglects population heterogeneity.
Judith Mulder (ESE/Econometrie)
22 May 2015
Joint optimization of sailing speed and buffer times in liner shipping
Liner shipping networks consist of fixed ship routes and time schedules that are published beforehand. However, delays are inevitable while executing the timetables, introducing uncertainty in sailing and port times. To maintain schedule reliability and reduce delay cost, liner companies will try to limit the amount of delay with respect to the schedule. Delays can for example be managed by adding buffer times in the timetables (prevention) or by increasing the sailing speed of the ship (recovery). Our goal is to jointly optimize the buffer times and sailing speed. Buffer time allocation is a problem on the tactical planning level, which has to be made in the scheduling problem before routes are executed. Adjusting the sailing speed, on the contrary, is a decision on the operational planning level. Hence, the optimal sailing times given a buffer allocation should already be available when determining the quality of the given buffer allocation, while in practice these decisions are taken much later. In our solution approach, we use that the optimal cost associated with a fixed buffer allocation can be obtained by solving a finite state Markov Decision Process and is convex in the buffer allocation. Hence, we can use a subgradient-based approach to find an optimal solution to the joint problem.
Kevin Dalmeijer (ESE/Econometrie)
29 May 2015
A branch-and-cut algorithm for the Time Window Assignment Vehicle Routing Problem
In distribution networks, decisions are often made on both the tactical and the operational level. On the tactical level, it is decided in which time windows deliveries will be made to the clients. On the operational level it is determined which vehicles will be used, which goods they will be transporting, and which routes they will use. If the demand on the operational level is uncertain, the Time Window Assignment Vehicle Routing Problem (TWAVRP) can be solved to determine the optimal time window assignments. The TWAVRP has been introduced by Spliet and Gabor (2014), who use a branch-price-and-cut approach to solve it. In this paper, we take a branch-and-cut approach and show that this approach is highly competitive: the same set of test-instances considered by Spliet and Gabor (2014) can now be solved over 40 times faster. To achieve this result we make use of both the well-studied rounded capacity cuts, as do we introduce a novel class of valid inequalities and separation heuristics.
José Núñez Ares (MeBioS, KU Leuven)
19 June 2015
A Column Generation Approach for Locating Roadside Clinics in Africa based on Effectiveness and Equity
Long distance truck drivers in Sub-Saharan Africa are extremely vulnerable to HIV and other infectious diseases. The NGO North Star Alliance (North Star) aims to alleviate this situation by placing so-called Roadside Wellness Centers (RWCs) at busy truck stops along major routes. Currently, locations for new RWCs are chosen so as to maximize the expected patient volume and to ensure continuity of access along the routes the truck drivers use. As North Star's network grows larger, the objective to provide equal access to healthcare along the different truck routes gains importance. To solve the problem to locate a fixed number of RWCs based on these effectiveness and equity objectives, new models and solution methods are needed.
Our contributions are fourfold. First, we introduce and motivate the equity criterion. Second, we propose and analyze a novel set partitioning formulation for the location problem. Third we propose and analyze a column generation approach to solve it and show how the pricing problem can be heuristically solved by using a sequence of shortest path problems. Moreover, we investigate several acceleration techniques, including dual stabilization, column pool management and a two-stage algorithm. Last, we numerically assess the trade-off between the equity criterion and North Star's current criteria based on randomly generated instances. Our results show that the column generation approach efficiently solves large problem instances to near-optimality and that solutions that are close to optimal with respect to each of the effectiveness and equity objectives are likely to be attainable.