Clothing production is an important sector within manufacturing.It includes the sewing, knitting and leather industries.It is important for a manufacturer to organize the production process well.This organization includes personnel allocation, machine loading, and material allocation.The goal is to complete the given order in the shortest time and, if possible, at the lowest cost.This paper addresses the task of producing knitted shirts.An ant colony optimization algorithm has been proposed to solve the problem.The objective is to complete the order in the shortest possible time.

The correct organization of medical assistance after the occurrence of a major disaster is very important for saving the lives of the victims.Earthquakes are natural phenomena/disasters in which there are many victims.The timely provision of medical assistance to the injured is an important element of their service.It is good to divide them into types of injuries and severity of injuries.Thus, the medical teams will be prepared for how many people need outpatient treatment and how many need hospital treatment.Rapid distribution of victims to hospitals according to their injuries can reduce the number of deaths and people with serious consequences.In this article, we present a breakdown of the injured by hospitals and medical facilities near the earthquake site.The type of injuries and the capacity and equipment of hospital facilities are taken into account.

There is great potential in leveraging Artificial Intelligence (AI) systems to optimize complex infrastructures, automate difficult tasks, or support autonomy and coordination between networked devices. However, advances in state-of-the-art AI often neglect features and/or requirements that businesses care deeply about, namely traceability and explainability. While majority of research concerning Explainable AI remains focused on weight modelling and timid gray-box approaches, the state-of-the-art has not explored much the deployment of semi-physical architectures combining fuzzy rule-based systems with more opaque models to improve explainability. This contribution aims to explore and make the case for a middle ground of mixed AI architectures that combine the performance of black-box AI models with a more explainable overall architecture, enabling operators to use them, while still retaining the core aspects of explainability, when compared to full black-box AI systems. This work contextualizes a potential application of such approach to the problem of Service Level Agreement compliance, in a case of microservice allocation decision over cloud (and cloud-like) infrastructures.

In recent years, there has been an increase in interest in carbon-aware computing. Here, the Green Edge Processing project inspired the development of the Extended Green Cloud Simulator (EGCS), which serves as an agent-based digital twin, facilitating the simulation of cloud infrastructure powered, in part, by renewable energy sources. Given that cloud systems must operate efficiently for a large number of clients, the following contribution briefly describes the design of the EGCS and discusses the results of the experimental assessment of its scalability.

Production organization plays a key role in the success of any enterprise.Optimizing workforce planning can improve the overall organization of production.The main goal is to minimize the assignment cost of the workers who will perform the planned work.The problem is known to be NPhard, therefore we will apply methods from the field of artificial intelligence.For this reason, most of the existing methods hardly find feasible solutions.We propose Ant Colony Optimization Algorithm with hybridization, combination with local search procedures.We compare and analyze their performance.

Cereals contribute significantly to humanity's livelihood.They are a source of more food energy worldwide than any other group of crops.Their production contributes considerably to the total global anthropogenic greenhouse gas (GHGs) emissions.In this study we propose a basic bio-economic farm model (BEFM) solved with the help of Ant Colony Optimization (ACO) methodology.We aim to assess farm profits and risks considering various types of policy incentives and adverse weather events.The proposed model can be applied to any annual crop.

The local search procedure is a method for hybridization and improvement of the main algorithm, when complex problems are solved.It helps to avoid local optimums and to find faster the global one.In this paper we apply InterCriteria analysis (ICrA) on hybrid Ant Colony Optimization (ACO) algorithm for Multiple Knapsack Problem (MKP).The aim is to study the hybrid algorithm behavior comparing with traditional ACO algorithm.Based on the obtained numerical results and on the ICrA approach the efficiency and effectiveness of the proposed hybrid ACO, combined with appropriate local search procedure are confirmed.

In this work we investigate advanced stochastic methods for solving a specific multidimensional problem related to neural networks.Monte Carlo and quasi-Monte Carlo techniques have been developed over many years in a range of different fields, but have only recently been applied to the problems in neural networks.As well as providing a consistent framework for statistical pattern recognition, the stochastic approach offers a number of practical advantages including a solution to the problem for higher dimensions.For the first time multidimensional integrals up to 100 dimensions related to this area will be discussed in our numerical study.

AbstractÐRecently stochastic methods have become very important tool for high performance computing of very high dimensional problems in computational finance.The advantages and disadvantages of the different highly efficient stochastic methods for multidimensional integrals related to evaluation of European style options will be analyzed.Multidimensional integrals up to 100 dimensions related to European options will be computed with highly efficient optimized lattice rules.

An optimized Monte Carlo approach (OPTIMIZED MC) for a Fredholm integral equations of the second kind is presented and discussed in the present paper.Numerical examples and results are discussed and MC algorithms with various initial and transition probabilities are compared.

We study an optimized Adaptive Monte Carlo algorithm for the Wigner kernel -an important problem in quantum mechanics.We will compare the results with the basic adaptive approach and other stochastic approaches for computing the Wigner kernel represented by difficult multidimensional integrals in dimension d up to 12.The higher cases d > 12 will be considered for the first time.A comprehensive study and an analysis of the computational complexity of the optimized Adaptive MC algorithm under consideration has also been presented.

In this paper, we present a fast Belief Function based Inter-Criteria Analysis (BF-ICrA) method based on the canonical decomposition of basic belief assignments defined on a dichotomous frame of discernment.This new method is then applied for evaluating the Multiple-Objective Ant Colony Optimization (MO-ACO) algorithm for Wireless Sensor Networks (WSN) deployment.

Public transport plays an important role in our live.It is very important to have a reliable service.Up to 1000 km, trains and buses play the main role in the public transport.The number of the people and which kind of transport they prefer is important information for transport operators.In this paper is proposed algorithm for transport modeling and passenger flow, based on Ant Colony Optimization method.The problem is described as multi-objective optimization problem.There are two optimization purposes: minimal transportation time and minimal price.Some fuzzy element is included.When the price is in a predefined interval it is considered the same.Similar for the starting traveling time.The aim is to show how many passengers will prefer train and how many will prefer buses according their preferences, the price or the time.

In this paper we propose a two-stage lattice Monte Carlo approach for optimization of bimetallic nanoalloys: simulated annealing on a larger lattice, followed by simulated diffusion.Both algorithms are fairly similar in structure, but their combination was found to give significantly better solutions than simulated annealing alone.We also discuss how to tune the parameters of the algorithms so that they work together optimally.

Stochastic techniques have been developed over many years in a range of different fields, but have only recently been applied to the problems in machine learning.A fundamental problem in this area is the accurate evaluation of multidimensional integrals.An introduction to the theory of the stochastic optimal generating vectors has been given.A new optimized lattice sequence with a special choice of the optimal generating vector has been applied to compute multidimensional integrals up to 30-dimensions.Clearly, the progress in the area of machine learning is closely related to the progress in reliable algorithms for multidimensional integration.

Optimization of the production process is important for every factory or organization.The better organization can be done by optimization of the workforce planing.The main goal is decreasing the assignment cost of the workers with the help of which, the work will be done.The problem is NP-hard, therefore it can be solved with algorithms coming from artificial intelligence.The problem is to select employers and to assign them to the jobs to be performed.The constraints of this problem are very strong and for the algorithms is difficult to find feasible solutions.We apply Ant Colony Optimization Algorithm to solve the problem.We investigate the algorithm performance according evaporation parameter.The aim is to find the best parameter setting.

In this paper we present an application of a new Belief Function-based Inter-Criteria Analysis (BF-ICrA) approach for Global Positioning System (GPS) Surveying Problems (GSP). GPS surveying is an NP-hard problem. For designing Global Positioning System surveying network, a given set of earth points must be observed consecutively. The survey cost is the sum of the distances to go from one point to another one. This kind of problems is hard to be solved with traditional numerical methods. In this paper we use BF-ICrA to analyze an Ant Colony Optimization (ACO) algorithm developed to provide near-optimal solutions for Global Positioning System surveying problem.

Every organization and factory optimize their production process with a help of workforce planing.The aim is minimization of the assignment costs of the workers, who will do the jobs.The problem is very complex and needs exponential number of calculations, therefore special algorithms are developed to be solved.The problem is to select employers and to assign them to the jobs to be performed.This problem has very strong constraints and it is difficult to find feasible solutions.The objective is to fulfil the requirements and to minimize the assignment cost.We propose a hybrid Ant Colony Optimization (ACO) algorithm to solve the workforce problem, which is a combination between ACO and an appropriate local search procedure.

The workforce planning helps organizations to optimize the production process with aim to minimize the assigning costs.A workforce planning problem is very complex and needs special algorithms to be solved.The problem is to select set of employers from a set of available workers and to assign this staff to the jobs to be performed.Each job requires a time to be completed.For efficiency, a worker must performs a minimum number of hours of any assigned job.There is a maximum number of jobs that can be assigned and a maximum number of workers that can be assigned.There is a set of jobs that shows the jobs on which the worker is qualified.The objective is to minimize the costs associated to the human resources needed to fulfill the work requirements.On this work we propose a variant of Ant Colony Optimization (ACO) algorithm to solve workforce optimization problem.The algorithm is tested on a set of 20 test problems.Achieved solutions are compared with other methods, as scatter search and genetic algorithm.Obtained results show that ACO algorithm performs better than other two algorithms.

In combinatorial optimization, the goal is to find the optimal object from a finite set.Since such problems are hard to be solved, usually some metaheuristics is applied.One of the most successful techniques for a number of classes of problems is Ant Colony Optimization (ACO).Some start strategies can be applied, to the ACO algorithms, to improve their performance.Here, the InterCriteria Analysis (ICrA) is applied to the ACO algorithm.On the basis of the ICrA, we examine and analyse the ACO performance according to the different start strategies.

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In this paper we apply an approach based on the apparatus of the Index Matrices and the Intuitionistic Fuzzy Sets -namely InterCriteria Analysis.The main idea is to use the InterCriteria Analysis to establish the existing relations and dependencies of defined parameters in non-linear model of an E. coli fed-batch cultivation process.Moreover, based on results of series of identification procedures we observe the mutual relations between model parameters and considered optimization techniques outcomes, such as execution time and objective function value.Based on InterCriteria Analysis we examine the obtained identification results and discuss the conclusions about existing relations and dependencies between defined, in terms of InterCriteria Analysis, criteria.

In this paper, a hybrid scheme, to solve optimization problems, using a Genetic Algorithm (GA) and an Ant Colony Optimization (ACO) is introduced.In the hybrid GA-ACO approach, the GA is used to find a feasible solutions to the considered optimization problem.Next, the ACO exploits the information gathered by the GA.This process obtains a solution, which is at least as good as-but usually better than-the best solution devised by the GA.To demonstrate the usefulness of the presented approach, the hybrid scheme is applied to the parameter identification problem in the E. coli MC4110 fedbatch fermentation process model.Moreover, a comparison with both the conventional GA and the stand-alone ACO is presented.The results show that the hybrid GA-ACO takes the advantages of both the GA and the ACO, thus enhancing the overall search ability and computational efficiency of the solution method.

This paper introduces a linear array for spelling correction using 15 processors. Many architectures have been proposed to solve similar string correction problems such as speech recognition or nucleic acid sequence computation. It is known that the hypercube, de Bruijn and grid networks contain a Hamiltonian path, a path which contains all the vertices of the network. The execution time of spelling correction on all of these networks is equal. © 1997 John Wiley & Sons, Ltd.