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The use of improved storage procedures for route generation for demand actuated systems is necessary for practical implementation of many routing systems. Neighborhood Storage is a method of storing the system information which requires a covering to be generated for a number of sets of points which are generated by the method. The set covering problem is important to the overall practical implementation of the method. The method requires M coverings to be generated for each application. Thus, computational efficiency is of considerable importance in obtaining the required coverings. The problem is defined and formulated as a set covering problem. Solutions are carried out for a number of examples and the results for the optimum covering are reported. An algorithm is then presented for obtaining a suboptimum covering with considerable efficiency in computation and overall data manipulation. The example results are also included. The algorithm presented is applicable to any (V;A,B) modeled in R2.

This paper attempts to establish the conceptional and computational systems of grey determinant and apply it to solve n grey equations with n grey linear equations, which can be viewed as the important parts of grey mathematics.

Starting from the fact that missing information often appears in complex systems, the true values of elements when constructing a determinant and of coefficients when solving n equations with n linear equations cannot be obtained, so they are uncertain. However, their ranges can be obtained by using correct investigation methods. The uncertain elements and coefficients are grey and their ranges are number‐covered sets. On the basis of the results of Li and Wang, the paper systematically proposes the definition system of grey determinant and n grey linear equations, and utilizes the computational rules of grey determinant to solve the n grey equations with n grey linear equations. Some numerical examples are computed to illustrate the results in this paper.

The results show that the ranges of grey value of grey determinant and grey solutions of grey equations with n grey linear equations can be obtained by using computational rules proposed.

Because the determinant and the linear equations have been widely used in many fields such as system controlling, economic analysis and social management, and the missing information is a general phenomenon for complex systems, grey determinant and grey linear equations may have great potential application in the real world. The method realizes the feasibility of system analysis under uncertain situations.

The paper succeeds in providing systematic results of computation of uncertain determinant and n linear equations by using grey systems theory and enriches the contents of grey mathematics.

This paper aims to investigate to what extent hybrid differential evolution (DE) algorithms can be successful in solving the optimal camera placement problem.

This problem is stated as a unicost set covering problem (USCP) and 18 problem instances are defined according to practical operational needs. Three methods are selected from the literature to solve these instances: a CPLEX solver, greedy algorithm and row weighting local search (RWLS). Then, it is proposed to hybridize these algorithms with two hybrid DE approaches designed for combinatorial optimization problems. The first one is a set-based approach (DEset) from the literature. The second one is a new similarity-based approach (DEsim) that takes advantage of the geometric characteristics of a camera to find better solutions.

The experimental study highlights that RWLS and DEsim-CPLEX are the best proposed algorithms. Both easily outperform CPLEX, and it turns out that RWLS performs better on one class of problem instances, whereas DEsim-CPLEX performs better on another class, depending on the minimal resolution needed in practice.

Up to now, the efficiency of RWLS and the DEset approach has been investigated only for a few problems. Thus, the first contribution is to apply these methods for the first time in the context of camera placement. Moreover, new hybrid DE algorithms are proposed to solve the optimal camera placement problem when stated as a USCP. The second main contribution is the design of the DEsim approach that uses the distance between camera locations to fully benefit from the DE mutation scheme.

The paper aims to provide an efficient meta-heuristic algorithm to solve the partial set covering problem (PSCP). With rich application scenarios, the PSCP is a fascinating and well-known non-deterministic polynomial (NP)-hard problem whose goal is to cover at least k elements with as few subsets as possible.

In this work, the authors present a novel variant of the ant colony optimization (ACO) algorithm, called Argentine ant system (AAS), to deal with the PSCP. The developed AAS is an integrated system of different populations that use the same pheromone to communicate. Moreover, an effective local search framework with the relaxed configuration checking (RCC) and the volatilization-fixed weight mechanism is proposed to improve the exploitation of the algorithm.

A detailed experimental evaluation of 75 instances reveals that the proposed algorithm outperforms the competitors in terms of the quality of the optimal solutions. Also, the performance of AAS gradually improves with the growing instance size, which shows the potential in handling complex practical scenarios. Finally, the designed components of AAS are experimentally proved to be beneficial to the whole framework. Finally, the key components in AAS have been demonstrated.

At present, there is no heuristic method to solve this problem. The authors present the first implementation of heuristic algorithm for solving PSCP and provide competitive solutions.

Presents an inductive machine learning algorithm called CLILP2 that learns multiple covers for a concept from positive and negative examples. Although inductive learning is an error‐prone process, multiple meaning interpretation of the examples is utilized by CLILP2 to compensate for the narrowness of induction. The algorithm is tested on data sets representing three different domains. Analyses the complexity of the algorithm and compares the results with those obtained by others. Employs measures of specificity, sensitivity, and predictive accuracy which are not usually used in presenting machine learning results, and shows that they evaluate better the “correctness” of the learned concepts. The study is published in two parts: I – the CLILP2 algorithm; II – experimental results and conclusions.

This paper proposes a new framework for optimizing investment decisions when deciding about information security remedies.

The framework assumes that the organization is aware of a set of remedies that can be employed to address end‐effects that have been identified. The framework also assumes that the organization defines its information security policy by setting a minimum level of protection for each end‐effect. Given the two sets of costs, that of the end‐effect and the potential damage it can cause and that of the remedy and the required level of protection from each end‐effect, this framework can be used to identify the optimal set of remedies for a given budget that complies with the organization's information security policy. The framework is illustrated using a practical example concerning investment decision optimization in a financial organization.

The paper shows that exhausting the information security budget does not assure a higher level of security required by the organisation.

Concentrating on end‐effects and on the organizational requirements eases the process of remedy selection. The proposed methodology circumvents the common process of assuming probabilities of information security events.

This research proposes a practical and an easily implementable framework, enabling the information security manager to align the information security remedies and best practice methodological requirements with organizational budget constraints and business requirements while maintaining a required level of security.

It's been three years since my previous survey in RSR. Superb reference books in pop music have been appearing so frequently that I've been having trouble keeping up. Let's hope “next year's” survey will only be 12 months in the making and not 36.

It is essential to provide drinking water to affected population directly after a disaster. The purpose of this paper is to develop an optimization methodology that helps in the distribution of drinking water in post-disaster situations.

The research was conducted on two phases: phase 1 aims at identifying an appropriate way to deliver drinking water to refugee camps from external sources, considering required drinking water quantities and four possible sources of water with respect to cost and risk assessments. Phase 2 investigates drinking water distribution within a refugee camp using covering models. The MCLP–optimal number of facilities model is proposed to ensure that the water is distributed and delivered to all individuals in a camp with minimum number of water storage tanks required. A control policy is proposed to ensure the fair distribution of water to all targeted individuals.

Al-Za’atari refugee camp, located in northeast of Jordan, was considered as the case study for this research. The result showed that the appropriate way to deliver water to the camp is by using tanker–trucks, and a minimum number of five tanks are required to distribute water to individuals inside the camp with respect to tank locations and the allocation of tank of each area.

The proposed methodology is essential in decision making for the distribution of drinking water in refugee camps in short-term needs. The model adds important value to the literature as the proposed problem has no solution in the literature before.

Based on some of the results and definitions provided in the paper “system linkage: structural functions and hierarchies” and adding new definitions that are in keeping with the spirit of the same paper, new results have been obtained that explore the utility of the structural input‐output function.

Our approach is based principally on the application of graph theory to the study of relationships between variables using specific set theory concepts.

A result such as the fact that A covers B, for example, can be interpreted in terms of the latter set being formed of direct influences from elements in the former set in relation to one or more than one relationships. Analogously, the invariant set concept may be interpreted as the set maintaining its structure and status, remaining constant with respect to any possible relationships.

From a practical point of view, in the context of the study of networks within an ecosystem, authors (such as Patten et al. and Patten) have demonstrated that indirect effects between the variables of an ecosystem outweigh direct effects. This is the notion that the present authors have borne in mind in order to extend previous results to indirect influences from a discrete perspective.

This paper seeks to analyze ecological networks from a different viewpoint, using the concepts of orbits, coverage, invariant sets, etc.

A very simple view of ecosystems is presented, without delving into their multiple properties. Specifically, an ecosystem will be considered a system containing living and non‐living entities whose interrelationships include those of predator‐prey and competition. On interpreting that orbits are associated with ecosystems' direct and indirect paths, it is possible to present a much more conceptual design of ecological networks.

An ecosystem's two most important relationships, those of predator‐prey and competition, appear to be interconnected with the concepts of coverage, invariance and orbit, with multiple properties.

There are many interesting analyses of ecological networks' indirect effects that develop the quantitative and qualitative theory of ecological networks. What is new in this study is that it is the first dealing with orbits in the field of ecosystems.