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Sequential testing of complex systems: a review

We consider the problem of testing sequentially the components of a multi-component system in order to learn the state of the system, when the tests are costly. In this review paper, we describe and analyze a framework for the problem, results for certain classes of problems and related widespread applications, including distributed computing, artificial intelligence, manufacturing and telecommunications. We also report variations and possible extensions of the problem.

A multi-period double coverage approach for locating the emergency medical service stations in Istanbul

We consider the multi-period location planning problem of emergency medical service (EMS) stations. Our objective is to maximize the total population serviced by two distinct stations within two different response time limits over a multi-period planning horizon. Our aim is to provide a backup station in case no ambulance is available in the closer station and to develop a strategic plan that spans multiple periods. In order to solve this problem, we propose a Tabu Search approach. We demonstrate the effectiveness of the proposed approach on randomly generated data. We also implement our approach to the case of Istanbul to determine the locations of EMS stations in the metropolitan area.

A taxonomy for emergency service station location problem

The emergency service station (ESS) location problem has been widely studied in the literature since 1970s. There has been a growing interest in the subject especially after 1990s. Various models with different objective functions and constraints have been proposed in the academic literature and efficient solution techniques have been developed to provide good solutions in reasonable times. However, there is not any study that systematically classifies different problem types and methodologies to address them. This paper presents a taxonomic framework for the ESS location problem using an operations research perspective. In this framework, we basically consider the type of the emergency, the objective function, constraints, model assumptions, modeling, and solution techniques. We also analyze a variety of papers related to the literature in order to demonstrate the effectiveness of the taxonomy and to get insights for possible research directions.

Diagnosing double regular systems

We consider the problem of testing sequentially the components of a multi-component system, when the testing of each component is costly. We propose a new testing policy, that can be executed in polynomial time in the input size, and show that it is cost-minimal in the average case sense, for certain double regular systems that include regular (in particular, threshold) systems with identical components. This result generalizes known results for series, parallel, and, more generally, for k-out-of-n systems.

Bottom-Up Construction of Minimum-Cost and / or Trees for Sequential Fault Diagnosis

The problem of generating the sequence of tests required to reach a diagnostic conclusion with minimum average cost, which is also known as a test-sequencing problem, is considered. The traditional test-sequencing problem is generalized here to include asymmetrical tests. In general, the next test to execute depends on the results of previous tests. Hence, the test-sequencing problem can naturally be formulated as an optimal binary AND/OR decision tree construction problem, whose solution is known to be NP-hard. Our approach is based on integrating concepts from one-step look-ahead heuristic algorithms and basic ideas of Huffman coding to construct an AND/OR decision tree bottom-up as opposed to heuristics proposed in the literature that construct the AND/OR trees top-down. The performance of the algorithm is demonstrated on numerous test cases, with various properties.

Hop Constrained Energy-Efficient Broadcasting: Insights from Massively Dense Ad Hoc Networks

We consider source-initiated broadcast session traffic in an ad hoc wireless network operating under a hard constraint on the end-to-end delay between the source and any node in the network. We measure the delay to a given node in the number of hops data travels from the source to that node, and our objective in this paper is to construct an energy-efficient broadcast tree that has a maximum depth Delta, where Delta; represents the end-to-end hop constraint in the network. We characterize the optimal solution to a closely related problem in massively dense networks using a dynamic programming formulation. We prove that the optimal solution can be obtained by an algorithm of polynomial time complexity O(Delta2). The solution to the dynamic program indicates that there is a single optimal policy applicable to all massively dense networks. Elaborating on the insights provided by the structure of the problem in massively dense networks, we design an algorithm for finding a solution to the hop constrained minimum power broadcasting problem in general networks. By extensive simulations, we demonstrate that our proposed optimization-based algorithm generates broadcast trees within 20% of optimality for general dense networks.

Sequential Testing of Series-Parallel Systems of Small Depth

We consider the problem of testing sequentially the components of a multi-component system, when testing the components is costly. We consider a polynomial time testing policy for series-parallel systems, and prove, generalising earlier results that it is cost-minimal in the average case sense, for two sub-families of series-parallel systems. We also demonstrate via examples that neither this algorithm nor some of its improved versions are optimal for general series-parallel systems, disproving some published claims.

Lookahead-Based Approaches for Minimizing Adaptive Distinguishing Sequences

For Finite State Machine (FSM) based testing, it has been shown that the use of shorter Adaptive Distinguishing Sequences (ads) yields shorter test sequences. It is also known, on the other hand, that constructing a minimum cost ADS is an NP-hard problem and it is NP-hard to approximate. In this paper, we introduce a lookahead-based greedy algorithm to construct reduced ADSs for FSMs. The greedy algorithm inspects a search space to make a decision. The size of the search space is adjustable, allowing a trade-off between the quality and the computation time. We analyse the performance of the approach on randomly generated FSMs by comparing the ADSs constructed by our algorithm with the ADSs that are computed by the existing algorithms.

A Decomposition-Based Approach for the Multiperiod Multiproduct Distribution Planning Problem

We address the most general case of multiperiod, multiproduct network planning problems, where we allow spoilage on arcs and storage at nodes. In our models, all network parameters change over time and products. The minimum-cost flow problem in the discrete-time model with varying network parameters is investigated when we allow storage and/or spoilage, and some reformulation techniques employing polyhedrals are developed to obtain optimal solutions for a predefined horizon. Our methods rely on appropriate definitions of polyhedrals and matrices that lead to LP problems comprising a set of sparse subproblems with special structures. Knowing that computational expenses of solving such a large-scale planning problem can be decreased by using decomposition techniques, the special structure of polyhedrals is utilized to develop algorithmic approaches based on decomposition techniques to handle the global problem aiming to save computational resources.

A New Model and Tabu Search Approach for Planning the Emergency Service Stations

The location planning of emergency service stations is crucial, especially in the populated cities with heavy trafic conditions such as Istanbul. In this paper, we propose a Backup Double Covering Model (BDCM), a variant of the well-known Maximal Covering Location Problem, that requires two types of services to plan the emergency service stations. The objective of the model is to maximize the total population serviced using two distinct emergency service stations in different time limits where the total number of stations is limited. We propose a Tabu Search (TS) approach to solve the problem. We conduct an extensive experimental study on randomly generated data set with different parameters to demonstrate the effectiveness of the proposed algorithm. Finally, we apply our TS approach for planning the emergency service stations in Istanbul.