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In the literature, there are many methods that can be helpful in strategic management of universities. Some of them are related to the aspect of sustainability, in terms of balancing the level of fulfillment of different, often conflicting objectives, which must be considered when building strategies. These methods include product/service portfolio ones. However, their use is often intuitive and detached from the quantitative aspects of management. The purpose of this paper is to present a proposal of the modification of the portfolio methods through the use of one of discrete optimization problems, namely, the multiple knapsack problem. The proposal is applied to a selected university. The results are presented and discussed.

The research methodology consists in a conceptual work: combining non-quantitative portfolio methods used in strategic management and the quantitative multiple knapsack problem. The analogy is established between a market sector (capacity) and a knapsack (capacity), a university department value and an object value, the university improvement budget and a knapsack, an object cost and an improvement measure cost. Then, the case study is used to conduct an initial validation of the proposed approach.

A quantitative model for strategic management of university as a whole or university departments is proposed. It allows to plan and control the application of improvement measures, allowing the university units to take on a better position in the educational market. It has been initially applied to a small university.

The model requires much more real-world case studies. Also, it will usually be difficult to establish the cost of running individual university units as well as the cost of corrective measures. The capacity of knapsacks – here of market sectors – will also be difficult to calculate. The method to be used here is activity-based costing, but it will not solve all the problems immediately, as its practical application is difficult.

The proposed model will allow to plan and evaluate strategic changes of university as a whole and its units’ position using quantitative values and to consider various strategic scenarios.

In order to establish the data necessary to construct the model, various stakeholders will have to cooperate (the university promotion department, the accounting representatives, the student and industry representatives etc.), probably for the first time. Such cooperation will improve the university position even if the model itself will not be able to be applied immediately.

The link between portfolio methods and a quantitative optimization method for university management purposes has been established in the paper for the first time in the literature.

The purpose of this paper is to provide an improved genetic algorithm to solve 0/1 multidimensional knapsack problem (0/1 MKP), by proposing new selection and crossover operators that cooperate to explore the search space.

The authors first present a new sexual selection strategy that significantly improves the one proposed by (Varnamkhasti and Lee, 2012), while working in phenotype space. Then they propose two variants of the two-stage recombination operator of (Aghezzaf and Naimi, 2009), while they adapt the latter in the context of 0/1 MKP. The authors evaluate the efficiency of both proposed operators on a large set of 0/1 MKP benchmark instances. The obtained results are compared against that of conventional selection and crossover operators, in terms of solution quality and computing time.

The paper shows that the proposed selection respects the two major factors of any metaheuristic: exploration and exploitation aspects. Furthermore, the first variant of the two-stage recombination operator pushes the search space towards exploitation, while the second variant increases the genetic diversity. The paper then demonstrates that the improved genetic algorithm combining the two proposed operators is a competitive method for solving the 0/1 MKP.

Although only 0/1 MKP standard instances were tested in the empirical experiments in this paper, the improved genetic algorithm can be used as a powerful tool to solve many real-world applications of 0/1 MKP, as the latter models several industrial and investment issues. Moreover, the proposed selection and crossover operators can be incorporated into other bio-inspired algorithms to improve their performance. Furthermore, the two proposed operators can be adapted to solve other binary combinatorial optimization problems.

This research study provides an effective solution for a well-known non-deterministic polynomial-time (NP)-hard combinatorial optimization problem; that is 0/1 MKP, by tackling it with an improved genetic algorithm. The proposed evolutionary mechanism is based on two new genetic operators. The first proposed operator is a new and deeply different variant of the so-called sexual selection that has been rarely addressed in the literature. The second proposed operator is an adaptation of the two-stage recombination operator in the 0/1 MKP context. This adaptation results in two variants of the two-stage recombination operator that aim to improve the quality of encountered solutions, while taking advantage of the sexual selection criteria to prevent the classical issue of genetic algorithm that is premature convergence.

The purpose of this paper is to test the capability of a new population‐based optimization algorithm for solving an NP‐hard problem, called “Multiple Knapsack Problem”, or MKP.

Here, the intelligent water drops (IWD) algorithm, which is a population‐based optimization algorithm, is modified to include a suitable local heuristic for the MKP. Then, the proposed algorithm is used to solve the MKP.

The proposed IWD algorithm for the MKP is tested by standard problems and the results demonstrate that the proposed IWD‐MKP algorithm is trustable and promising in finding the optimal or near‐optimal solutions. It is proved that the IWD algorithm has the property of the convergence in value.

This paper introduces the new optimization algorithm, IWD, to be used for the first time for the MKP and shows that the IWD is applicable for this NP‐hard problem. This research paves the way to modify the IWD for other optimization problems. Moreover, it opens the way to get possibly better results by modifying the proposed IWD‐MKP algorithm.

Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. The purpose of this paper is to investigate the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem

Early hyper-heuristics focused on selecting and applying a low-level heuristic at each stage of a search. Recent trends in hyper-heuristic research have led to a number of approaches being developed to automatically generate new heuristics from a set of heuristic components. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances.

The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results.

In this work the authors show that genetic programming is suitable as a method to generate reusable constructive heuristics for the multidimensional 0-1 knapsack problem. This is classified as a hyper-heuristic approach as it operates on a search space of heuristics rather than a search space of solutions. To our knowledge, this is the first time in the literature a GP hyper-heuristic has been used to solve the multidimensional 0-1 knapsack problem. The results suggest that using GP to evolve ranking mechanisms merits further future research effort.

In today’s highly competitive business environment, the main approach of all businesses is to optimally provide customers’ requirements and gain their satisfaction and trust. The process of value creation for customers consists of value chain activities which are concentrated on providing maximum level of customers’ needs. The purpose of this paper is to propose a holistic model by which the quality, the transferred value to customers and the firm’s competitive advantage would be improved simultaneously under budget constraint.

This study uses a combination of several quality management (QM) tools including SERVQUAL, Kano’s model, quality function deployment and knapsack problem. Moreover, the triangular fuzzy logic is used throughout the model to address data uncertainty and increase the model flexibility. The proposed model includes five steps which are implemented in the case study of life-insurance services.

The lack of coordination and cooperation between the people working in the inherently related sections leads to incorrect decisions and also the failure in implementation of adopted decisions. Hence, the interface between quality and strategic management should be well considered in organizations. The model generates an integrated vision to the process of decision making in this interface. The framework has several significant outcomes which would be used by both researchers and practitioners.

The study shows that the individual elements of decision-making process in the interface between quality and strategic management are related to each other, recommending the need to coordinated and consistent effort between different parts of a firm. The results are limited by the sample size and geography of the survey.

This paper is among the few in the literature that have presented a holistic and step-by-step approach to the decisions on the intersection between two areas of quality and strategic management, recommending the managers to not have insular look to the issues and try to make a sufficient and efficient relationship between the different sections. This study is an important step in reflecting these relations and the need to create an integrated decision model.

The purpose of this paper is to apply a recent kind of neural networks in a reliability optimization problem for a series system with multiple‐choice constraints incorporated at each subsystem, to maximize the system reliability subject to the system budget and weight. The problem is formulated as a non‐linear binary integer programming problem and characterized as an NP‐hard problem.

The design of neural network to solve this problem efficiently is based on a quantized Hopfield network (QHN). It has been found that this network allows one to obtain optimal design solutions very frequently and much more quickly than other Hopfield networks.

For systems more complex than series systems considered in this paper, the proposed approach needs to be adapted. The QHN‐based solution approach can be applied in many industrial systems where reliability is considered as an important design measure, e.g. in manufacturing systems, telecommunication systems and power systems.

The paper develops a new efficient method for reliability optimization. The most interesting characteristic of this method is related to its high‐speed computation, since the practical importance lies in the short computation time needed to obtain an optimal or nearly optimal solution for large industrial problems.

The successful provision of context‐awareness in pervasive environments requires the support of autonomic management facilities that provide ways to efficiently acquire and use contextual information. This paper claims that in order to offer viable context‐aware services, the issue of context imperfection and aging as well as the alignment of the context information that is used by a service with the customized service objectives should be taken into account. It presents an approach for managing the selection of context sources considering the freshness and actuality of the available information, and dynamically adapting to any source change and failure. Accordingly, there is no need to know beforehand the context sources to obtain the required information, but a quality‐aware discovery of the sources is envisioned. Finally, the proposed approach allows services to be ported easily to an environment with a different set of context sources.

Project selection management is a matter of challenge for project-oriented organizations, particularly, if the decision-makers are confronted with limited resources. One of the main concerns is selecting an optimal subset that can successfully satisfy the requirements of the organization providing enough resources to the best subset of the project. The projects for which there are not enough resources or those requiring whole resources of the organization will collapse soon after failed to success. Therefore, the issue is in the risk of choosing a set of projects so that can make a balance in investment versus on collective benefit.

A model is presented for project selection and has been tested on the 37 available projects. This model could increase the efficiency of the whole subset of the project significantly in comparison to the other model and it was because of choosing a diverse subset of projects.

Provides a general framework for project selection and a diverse and balanced subset of projects to increase the efficiency of the selected subset. Also, reduces the impact of uncertainty risk on the project selection process.

For the purposes of project selection, any project whose results are uncertain is a risky project because, if the project fails, it will reduce combined project value. For example, a pharmaceutical company’s R&D project is affected by the uncertain results of a specific compound. If the company invests in different compounds, a failure with one will be offset by a good result on another. Therefore, with selecting a diverse set of projects, this paper will have a different set of risks.

This paper discusses the risk of selecting or being responsible for selecting a project under uncertainty. Most of the projects in the field of project selection generally consider the risks facing the projects or existing models that do not take into account the risk.

This study aims to show how cost savings can be achieved through optimizing the scheduling of e-commerce enablements. The University of California is one of the largest, most prestigious public education and research systems in the world, yet diminished state support is driving the search for system-wide cost savings.

This study documents the preparation for and rollout of an e-procurement system across a subset of campuses. A math programing tool was developed for prioritizing the gradual rollout to generate the greatest expected savings subject to resource constraints.

The authors conclude by summarizing the results of the rollout, discussing lessons learned and their benefit to decision-makers at other public institutions.

The pilot program comprising three campuses has been predicted to yield $1.2m in savings over a one-year period; additional sensitivity analysis with respect to savings, project timelines and other rollout decisions illustrate the robustness of these findings.

The purpose of this paper is to deal with a problem of admission control in computer networks when some of their parameters are uncertain. The case is considered when the most common probabilistic description of the uncertainty cannot be used and another approach should be applied.

The uncertain versions of admission control problem with quality of service requirements are considered. The uncertain variables are used to describe possible values of the unknown parameters in computer networks.

Given are formulations for the admission control problem in computer networks with unknown values of the capacities based on the network utility maximization concept. Solution algorithms for all these problems are proposed.

It is assumed that an expert can describe possible values of uncertain network parameters in the form of a certainty distribution. Then the formalism of uncertain variables is applied and the knowledge of an expert is modelled with the use of certainty distributions. Decisions strongly depends on the quality of an expert's knowledge.

Obtained admission control algorithms can be useful for planning and designing of computer networks.

A new approach to the admission control problem in computer networks in the presence of uncertainty, in the case when the uncertain variable can be applied, is proposed and discussed.