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In many decision‐making problems under parameter uncertainty, the most popular stochastic approach is not used because of its serious drawbacks. The purpose of this paper is to present another approach, which copes with the uncertainty of parameters. It uses a precise criterion evaluating a decision with respect to uncertain parameters. This precision by the maximum operator is performed on a term based on the criterion and called the relative regret. The approach is applied to the allocation problems in a complex of operations.

The resource allocation problems in a complex of operations of independent and dependent structures to minimize a total execution time of all operations are investigated. Then, the results are extended for the problem of a task allocation in the complex of independent operations. The case is considered when the parameters in the functional models of the operations are uncertain, and their values belong to the intervals of known bounds. The solution algorithms for the uncertain problems are based on known solution algorithms for the corresponding deterministic problems. The solution algorithms for the latter problems are outlined in the paper.

The main contribution of the paper consists in presenting the property that it is possible for the uncertain problems considered to replace the solution of the uncertain allocation problems by solving a number of corresponding deterministic problems.

The useful and interesting property of the solution algorithm for the allocation problems, in general, cannot be applied to the other decision‐making problems under uncertainty. As an example of such a problem, a simple routing‐scheduling problem is presented for which, however, a number of possible parameter scenarios can be substantially limited.

The allocation problems addressed in the paper have a variety of applications in computer systems and in manufacturing systems. Moreover, a lack of crisp values for the parameters in models of individual operations is rather common.

The paper extends previous results for the allocation problems in a complex of operations.

Rapid advancements in internet technology have made it possible to develop electronic commerce in general and internet shopping in particular. Easy access to a vast number of existing internet stores enables buyers to customize their shopping processes to minimize the total purchase cost. This paper aims to investigate a novel internet shopping problem, which consists of the diversification of a given list of products to buy among many stores and to use discounts offered by the stores.

The adequate discrete optimization problem referred to as internet shopping optimization problem with price sensitivity discounts (ISOPwD) is investigated, which turned out to be strongly nondeterministic polynomial (NS)-hard. Two heuristic solution algorithms have been derived using the tabu search (TS) and the simulated annealing (SA) metaheuristics for having a solution in a reasonable time. The algorithms have been assessed via computational experiments, and they have been compared with another algorithm known from the literature that has been elaborated for a simpler version of ISOPwD.

The conducted evaluation has shown the advantage of both heuristic algorithms on the algorithm known from the literature. Moreover, the TS-based algorithm outperformed the other one in terms of the total cost incurred by customers and the computational time.

The special primary piecewise linear discounting function is only taken into account. Other possible discounts connected, for example, with bundles of products and (or) coupons are not considered.

The elaborated algorithms can be recommended for internet shopping providers who want to introduce the ability to search a cost-optimized set of products in their databases or for applications that combine offers from various online retailers, e.g. internet price comparison services and auction sites.

The novelty of considered ISOPwD, in comparison with similar problems discussed in the literature, deals with an arbitrary number of purchased products, the possibility to buy an identical product in different stores and the consideration of the weight, the amount and the availability of goods as parameters of ISOPwD.

The purpose of this paper is to deal with a decision‐making problem in a complex operation system. Two levels of the system are made up of two different decision problems, i.e. task scheduling and task execution where by the latter an executor's movement control problem is understood. Interconnection of both levels creates a new problem that requires a new solution algorithm.

With use of a model of a moving vehicle in the state space, an offline movement control algorithm, is developed. Moreover, the concept of rescheduling to improve the solution through repeated execution of both, the movement control and the scheduling algorithms is used.

Decision‐making problem, and its substitutive version is defined. A solution is given for the substitutive approach along with its analytical evaluation. Furthermore, significant improvement of the solution through rescheduling has been achieved.

Proposed approach to decision making creates a difficulty for generalization of the results on cases with a different movement model.

The methodology introduced in the paper can be applied prominently in flexible manufacturing systems with moving executors where it is either unfeasible to move the assemblage or the executors are capable of performing multiple tasks.

Solution to a decision‐making problem in a two‐level system, with the given vehicle model, and use of rescheduling for quality improvement was not considered beforehand.

The purpose of this paper is to consider selected optimization problems with parameter uncertainty. A case is studied when uncertain parameters in functions undergoing the optimization belong to intervals of known bounds as well as the absolute regret based approach for coping with such an uncertainty is applied. The paper presents three different cases depending on properties of optimization problems and proposes which method can be used to solve corresponding problems.

The worst‐case absolute regret method is employed to manage interval uncertainty in functions to be optimized. To solve resulting uncertain optimization problems, optimal, approximate as well as heuristic solution algorithms have been elaborated for particular problems presented and described in the paper. The latter one is based on Scatter Search metaheuristics.

Solution algorithms for worst‐case absolute regret versions of the following optimization problems have been determined: resource allocation in a complex of independent operations and two task scheduling problems Q‖∑C_i and P‖C_max.

It is very difficult to generalize the results obtained and to use them for solving other optimization problems which correspond to real‐world applications. Such new cases require separate investigations.

The considered allocations as well as task scheduling problems have plenty of applications, for example in computer and manufacturing systems. Their versions with not precisely known parameters can be met commonly.

The investigations presented correspond to previous works on so‐called minimax regret problems and extend them for new optimization problems.

The purpose of this paper is to present research in the area of the modeling of complex systems using feed‐forward neural network.

Applications of multilayer neural networks with supervisor learning on the own simulator program wrote in Borland^® Pascal Language. Series‐parallel identification method is applied. Tapped delay lines (TDL) in static neural networks for modeling of dynamic plants are used. Gradient and heuristic learning algorithms are applied. Three kinds of calibration of learning and testing data are used.

This paper illustrates that feed‐forward multilayer neural networks can model complex systems. Feed‐forward multilayer neural networks with TDL can be used to build global dynamic models of complex systems. It is possible to compare the quality both models.

The learning and testing data from real systems to tune neuronal models require use of calibrating these data to range 0‐1.

The models quality depends on kind of calibration learning data from real system and depends on kind of learning algorithms.

The method and the learning algorithms discussed in the paper can be used to create global models of complex systems. The multilayer neural network with TDL can be used to model complex dynamic systems with low dynamics.

The concept of utility was the first time applied in Economics. The purpose of this paper is to report its usefulness for the decision making in complex technological systems, in general and in computer networks, in particular. Three selected decision-making problems are considered, corresponding solution algorithms are explained and results of numerical experiments are presented for the selected real-world case study.

Referring to similar decision-making problems in Economics, three problems of different time horizon are investigated: strategic investment planning, short-term network rate allocation and on-line network operating. Deterministic and uncertain versions are taken into account, and the latter one is handled more thoroughly. The formalism of uncertain variables is used to represent the parameter uncertainty which concerns users’ demands for services in computer networks as well as network links’ capacities. Corresponding optimization tasks are presented. Numerical experiments concerning a part of the computer network Pionier working in Poland confirmed the usefulness of the solution algorithms proposed.

The carried out numerical experiments verified the importance and worth of the decision-making algorithms for the Pionier computer network. It particularly concerns the game theory-based algorithm solving the on-line network operating problem which enables calculating the rates for computer links distinctly, i.e., separately for every link.

More case studies should be considered to formulate more general corollaries. The application of utility concept for wireless sensor networks needs further studies on solution algorithms.

The results can be directly applied to a class of modern computer networks, e.g., content delivery networks, self-managing networks, context aware networks, multilevel virtual networks.

The paper presents the unified and systematic approach for individual results previously obtained, and it considers one case study.