Good Solutions In Reasonable Time Research Articles

Load balancing scheduling algorithms for virtual computing laboratories in a Desktop-As-A-Service Cloud Computing Services

The present research work is interested in the study of the virtual computing laboratories in a Desktop-As-A-Service (DAAS) scheduling in a cloud computing environment. The issue emanates from a real-world study in which the Saudi Electronic University expresses a need to efficiently schedule the labs in predefined sessions for one of its colleges over all its branches. The problem under investigation consists, on the one hand, in load balancing the assignment of the labs to the sessions regarding the number of participants, and on the other hand, in determining the types and number of hosts required to ensure the best sessions hosts assignments. While the load balancing is evaluated by measuring the distance between the maximum and the minimum number of the virtual machines load, the maximum number of the virtual machines load, and the variance of number of the virtual machines load, the efficiency of the host selection and assignments are assessed in terms of the unbalance of the remaining capacity of each used host. We propose linearized mathematical models for the subproblems and the whole problem with the different objective functions. We firstly suggest a two-stage optimization approach in which the mathematical models via CPLEX are solved. Then, we deal with the combined method which does not divide the whole problem into its two sub-problems. Next, we run the combined model on CPLEX, and we propose two heuristics to have a feasible good solution in reasonable time. Subsequently, we compare the obtained results for diverse sizes of instances we generate according to the University features. The proposed heuristics are therefore proven to be a compelling solution to the powerful software CPLEX’s failure to solve the problem for large-scale cases. Moreover, the obtained results demonstrate that they outperform those currently in use in the University.

Heuristic Methods for Minimum-Cost Pipeline Network Design \u2013 a Node Valency Transfer Metaheuristic

Designing low-cost network layouts is an essential step in planning linked infrastructure. For the case of capacitated trees, such as oil or gas pipeline networks, the cost is usually a function of both pipeline diameter (i.e. ability to carry flow or transferred capacity) and pipeline length. Even for the case of incompressible, steady flow, minimizing cost becomes particularly difficult as network topology itself dictates local flow material balances, rendering the optimization space non-linear. The combinatorial nature of potential trees requires the use of graph optimization heuristics to achieve good solutions in reasonable time. In this work we perform a comparison of known literature network optimization heuristics and metaheuristics for finding minimum-cost capacitated trees without Steiner nodes, and propose novel algorithms, including a metaheuristic based on transferring edges of high valency nodes. Our metaheuristic achieves performance above similar algorithms studied, especially for larger graphs, usually producing a significantly higher proportion of optimal solutions, while remaining in line with time-complexity of algorithms found in the literature. Data points for graph node positions and capacities are first randomly generated, and secondly obtained from the German emissions trading CO2 source registry. As political will for applications and storage for hard-to-abate industry CO2 emissions is growing, efficient network design methods become relevant for new large-scale CO2 pipeline networks.

Solving the general employee scheduling problem

In many professions the demand for work requires employees to work in different shifts to cover varying requirements including areas like health care, protection services, transportation, manufacturing or call centers. However, there are many constraints that need to be satisfied in order to create feasible schedules. The demands can be specified in various ways, different legal requirements need to be respected and employee satisfaction has to be taken into account. Therefore, automated solutions are mandatory to stay competitive. However, even then it is often hard to provide good solutions in reasonable time as many of the problems are NP-hard.While not each problem will require the whole set of available restrictions, it is cumbersome to develop a new specification format and corresponding solver for each problem. Often these can not be well applied to similar problems differing in some requirements. On the other hand it is a challenging task to provide a general formulation and solution methods that can solve large integrated problems, as even several sub-problems on their own are known to be NP-hard.Therefore a new framework is proposed for the general employee scheduling problem that allows the implementation of various heuristic algorithms and their application to a wide range of problems. This is realized by proposing a unified handling of constraints and the possibility to implement various moves that can be reused across different algorithms. Further, a new search method is developed and implemented in the framework.In order to show the applicability to a wide range of problems, we take different problems from literature that cover different types of demand and constraints, translate their instances to our formulation and apply our solver to those instances as well as our own instances with good results. For one problem class our framework could obtain better solutions for several benchmark instances.

Operating room scheduling under waiting time constraints: the Chilean GES plan

In 2000, Chile introduced profound health reforms to achieve a more equitable and fairer system (GES plan). The reforms established a maximum waiting time between diagnosis and treatment for a set of diseases, described as an opportunity guarantee within the reform. If the maximum waiting time is exceeded, the patient is referred to another (private) facility and receives a voucher to cover the additional expenses. This voucher is paid by the health provider that had to do the procedure, which generally is a public hospital. In general, this reform has improved the service for patients with GES pathologies at the expense of patients with non-GES pathologies. These new conditions create a complicated planning scenario for hospitals, in which the hospital’s OR Manager must balance the fulfillment of these opportunity guarantees and the timely service of patients not covered by the guarantee. With the collaboration of the Instituto de Neurocirugia, in Santiago, Chile, we developed a mathematical model based on stochastic dynamic programming to schedule surgeries in order to minimize the cost of referrals to the private sector. Given the large size of the state space, we developed an heuristic to compute good solutions in reasonable time and analyzed its performance. Our experimental results, with both simulated and real data, show that our algorithm performs close to optimum and improves upon the current practice. When we compared the results of our heuristic against those obtained by the hospital’s OR manager in a simulation setting with real data, we reduced the overtime from occurring 21% of the time to zero, and the non-GES average waiting list’s length from 71 to 58 patients, without worsening the average throughput.

An Improved Generalized Quantum-Inspired Evolutionary Algorithm for Multiple Knapsack Problem

This article describes how the 0/1 Multiple Knapsack Problem (MKP), a generalization of popular 0/1 Knapsack Problem, is NP-hard and harder than simple Knapsack Problem. Solution of MKP involves two levels of choice – one for selecting an item to be placed and the other for selecting the knapsack in which it is to be placed. Quantum Inspired Evolutionary Algorithms (QIEAs), a subclass of Evolutionary algorithms, have been shown to be effective in solving difficult problems particularly NP-hard combinatorial optimization problems. QIEAs provide a general framework which needs to be customized according to the requirements of a given problem to obtain good solutions in reasonable time. An existing QIEA for MKP (QIEA-MKP) is based on the representation where a Q-bit collapse into a binary number. But decimal numbers are required to identify the knapsack where an item is placed. The implementation based on such representation suffers from overhead of frequent conversion from binary numbers to decimal numbers and vice versa. The generalized QIEA (GQIEA) is based on a representation where a Q-bit can collapse into an integer and thus no inter conversion between binary and decimal is required. A set of carefully selected features have been incorporated in proposed GQIEA-MKP to obtain better solutions in lesser time. Comparison with QIEA-MKP shows that GQIEA-MKP outperforms it in providing better solutions in lesser time for large sized MKPs. The generalization proposed can be used with advantage in other Combinatorial Optimization problems with integer strings as solutions.

A hybrid VNS-HS algorithm for a supply chain scheduling problem with deteriorating jobs

This paper investigates a coordinated scheduling problem in a two stage supply chain where parallel-batching machine, deteriorating jobs and transportation coordination are considered simultaneously. During the production stage, jobs are processed by suppliers and there exists one parallel-batching machine in each supplier. The actual processing time of a job depends on its starting time and normal processing time. The normal processing time of a batch is equal to the largest normal processing time among all jobs in its batch. During the transportation stage, the jobs are then delivered to the manufacturer. Since suppliers are distributed in different locations, the transportation time between each supplier and the manufacturer is different. Based on some structural properties of the studied problem, an optimal algorithm for minimising makespan on a single supplier is presented. This supply chain scheduling problem is proved to be NP-hard, and a hybrid VNS-HS algorithm combining variable neighbourhood search (VNS) with harmony search (HS) is proposed to find a good solution in reasonable time. Finally, some computational experiments are conducted and the results demonstrate the effectiveness and efficiency of the proposed VNS-HS.

Mean-VaR portfolio optimization: A nonparametric approach

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk. We consider an alternative Markowitz’s mean–variance model in which the variance is replaced with an industry standard risk measure, Value-at-Risk (VaR), in order to better assess market risk exposure associated with financial and commodity asset price fluctuations. Realistic portfolio optimization in the mean-VaR framework is a challenging problem since it leads to a non-convex NP-hard problem which is computationally intractable. In this work, an efficient learning-guided hybrid multi-objective evolutionary algorithm (MODE-GL) is proposed to solve mean-VaR portfolio optimization problems with real-world constraints such as cardinality, quantity, pre-assignment, round-lot and class constraints. A learning-guided solution generation strategy is incorporated into the multi-objective optimization process to promote efficient convergence by guiding the evolutionary search towards promising regions of the search space. The proposed algorithm is compared with the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm (SPEA2). Experimental results using historical daily financial market data from S & P 100 and S & P 500 indices are presented. The results show that MODE-GL outperforms two existing techniques for this important class of portfolio investment problems in terms of solution quality and computational time. The results highlight that the proposed algorithm is able to solve the complex portfolio optimization without simplifications while obtaining good solutions in reasonable time and has significant potential for use in practice.

A two-echelon location-routing model for designing recycling logistics networks with profit: MILP and heuristic approach

This paper presents a mathematical formulation of a two-echelon location-routing problem in case of non-hazardous recyclables collection with a profit and distance dependent collection rate. The proposed model simultaneously determines collection points' locations respecting the dependency of collected recyclables’ quantities on the distance between end users and collection points, the location of intermediate consolidation points (transfer stations) and routing of collection vehicles. For solving large problem instances, we have developed heuristics for obtaining good solutions in reasonable time. We tested our approach for different system parameters in order to get better insight into the system performance.

Discussion of operators involved in a process of calibration using genetic algorithms for a surface water quality model to work with the tool Qual2Kw

At the beginning of the process of calibration of a water quality model, using the computational tool Qual2kw that includes a genetic algorithm as a mathematical tool for calibration, it is necessary to introduce some operators for the start of the calibration process that seeks the best combination of constants that represent the reality of the current in terms of water quality. In this work are made general recommendations on three operators that uses genetic algorithm: the seed used, the number of generations and the numberof populations; mainly the latter two are important because they involve partners computational times, since a combination that creates many runs could not present significant variations in the total adjustment of the model, so that a combination “optimal” could give good solutions in reasonable time. This study found that indeed there are points where the improvement in the quality of adjustment does not increase more than 5% variation in the value obtained by the function of error, so it is possible to recommend certain values for use by the modeler at the time of use this tool.

Parallel hybrid heuristics for the permutation flow shop problem

This paper addresses the Permutation Flowshop Problem with minimization of makespan, which is denoted by Fm|prmu|Cmax. In the permutational scenario, the sequence of jobs has to remain the same in all machines. The Flowshop Problem (FSP) is known to be NP-hard when more than three machines are considered. Thus, for medium and large scale instances, high-quality heuristics are needed to find good solutions in reasonable time. We propose and analyse parallel hybrid search methods that fully use the computational power of current multi-core machines. The parallel methods combine a memetic algorithm (MA) and several iterated greedy algorithms (IG) running concurrently. Two test scenarios were included, with short and long CPU times. The tests were conducted on the set of benchmark instances introduced by Taillard (Eur. J. Oper. Res. 64:278–285, 1993), commonly used to assess the performance of new methods. Results indicate that the use of the MA to manage a pool of solutions is highly effective, allowing the improvement of the best known upper bound for one of the instances.

Enhanced simulated-annealing-based algorithms and their applications to process planning in reconfigurable manufacturing systems

Capabilities of enhanced simulated-annealing-based algorithms in solving process planning problems in reconfigurable manufacturing are investigated. The algorithms are enhanced by combining variants of the simulated annealing technique with other algorithm concepts such as (i) knowledge exploitation and (ii) parallelism. Four configurations of simulated annealing algorithms are devised and engaged to solve an instance of a process planning problem in reconfigurable manufacturing systems. These configurations include; a basic simulated annealing algorithm, a variant of the basic simulated annealing algorithm, a variant of the simulated annealing algorithm coupled with auxiliary knowledge and a variant of the simulated annealing algorithm implemented in a quasi-parallel architecture. Although differences in performances were observed, the implemented algorithms are capable of obtaining good solutions in reasonable time. Experimental results show that the performances of the variants of simulated annealing based algorithms are better in comparison to a basic simulated annealing algorithm. A computational analysis and comparison using ANOVA indicates that improvements towards a better optimal solution can be gained by implementing variants of the simulated annealing algorithm. In addition, little speed gains can be obtained by implementing variants of the simulated annealing algorithms that are coupled with other algorithmic concepts.

Simulated annealing with auxiliary knowledge for process planning optimization in reconfigurable manufacturing

In this paper, three simulated annealing based algorithms that exploit auxiliary knowledge in different ways are devised and employed to handle a manufacturing process planning problem for reconfigurable manufacturing. These algorithms are configured based on a generic combination of the simulated annealing technique with; (a) heuristic knowledge, and (b) metaknowledge. Capabilities of the implemented algorithms are tested and their performances compared against a basic simulated annealing algorithm. Computational and optimization performances of the implemented algorithms are investigated and analyzed for two problem sizes. Each problem size consists of five different forms of a manufacturing process planning problem. The five forms are differentiated by five alternative objective functions. Experimental results show that the implemented simulated annealing algorithms are able to converge to good solutions in reasonable time. A computational analysis indicates that significant improvements towards a better optimal solution can be gained by implementing simulated annealing based algorithms that are supported by auxiliary knowledge.

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.

A new mathematical model for a competitive vehicle routing problem with time windows solved by simulated annealing

This paper presents an extension of a competitive vehicle routing problem with time windows (VRPTW) to find short routes with the minimum travel cost and maximum sale by providing good services to customers before delivering the products by other rival distributors. In distribution of the products with short life time that customers need special device for keeping them, reaching time to customers influences on the sales amount which the classical VRPs are unable to handle these kinds of assumptions. Hence, a new mathematical model is developed for the proposed problem and for solving the problem, a simulated annealing (SA) approach is used. Then some small test problems are solved by the SA and the results are compared with obtained results from Lingo 8.0. For large-scale problems, the, Solomon's benchmark instances with additional assumption are used. The results show that the proposed SA algorithm can find good solutions in reasonable time.

A Method of Using Sub Graph Based on the Characteristics of the Optima for the TSPs

Using the characteristics of the optima is so effective to solve the traveling salesman problems. In our study, the edges on the optimal tours calculated completely have been analyzed to have the characteristics of their mutual relationship. The edge set based on the characteristics can construct the sub graph including all or almost the edges selected as optimal edges. If a sub graph has all or almost the edges of constructing the optimum, the investigation on this graph will lead to discover a good solution in reasonable time. Therefore, such sub graphs are the effective tools for existing methods to become improved methods. In this paper, Delaunay graph and two graphs, called Fragmented Nearest Neighbor graph, are proposed as the special sub graphs. And the experiments by the simulated annealing method that is executable on sub graph explain that Delaunay graph as a triangulated graph is the most effective graph among the three sub graphs to find optimum or sub optimum tour of the traveling salesman problems.

Theory of Evolutionary Computation

Evolutionary algorithms are general-purpose randomized search heuristics. They have been successfully applied to many different kinds of search and optimization problems. They are popular in practice since they are easy to implement and easy to apply, and yet often find good solutions in reasonable time. This lead to an increased interest in gaining a thorough understanding of their strengths (and limitations). In the past decades, the theory of evolutionary algorithms has become an important and accepted field of research. This special issue Theory of Evolutionary Computation of Algorithmica contains expanded research from the theory track of the Genetic and Evolutionary Computation Conference and the International Conference on Parallel Problem Solving From Nature. All papers have been enhanced, extended, and undergone a critical journal reviewing process. The result is a collection of seven papers that all deal with theoretical aspects of evolutionary computation in a rigorous and accessible way. It contributes to the development of a better understanding of evolutionary algorithms, paving the way for a more informed application of these algorithms. Ten years after the first publication of a run-time analysis of the most simple evolutionary algorithm, the (1 + 1) evolutionary algorithm, Jens Jagerskupper reconsiders the result. By using the drift analysis method introduced by He and Yao in a sophisticated way, he obtains much more precise bounds on the expected optimization time. Drift analysis is also the topic of Pietro S. Oliveto’s and Carsten Witt’s article. They present a theorem that yields exponential lower bounds on the expected op-

A hybrid simulated annealing for capacitated vehicle routing problems with the independent route length

This paper presents a linear integer model of capacitated vehicle routing problems (VRP) with the independent route length to minimize the heterogeneous fleet cost and maximize the capacity utilization. In the proposed model, the fleet cost is independent on the route length and there is a hard time window over depot. In some real-world situations, the cost of routes is independent on their length, but it is dependent to type and capacity of vehicles allocated to routes where the fleet is mainly heterogeneous. In this case, the route length or travel time is expressed as restriction, that is implicated a hard time window in depot. The proposed model is solved by a hybrid simulated annealing (SA) based on the nearest neighborhood. It is shown that the proposed model enables to establish routes to serve all given customers by the minimum number of vehicles and the maximum capacity used. Also, the proposed heuristic can find good solutions in reasonable time. A number of small and large-scale problems in little and large scale are solved and the associated results are reported.

Application of the out-of-kilter algorithm to the asymmetric traveling salesman problem

This paper presents a heuristic method that finds optimum or near-optimum solutions to the asymmetric traveling salesman problem. The method uses the out-of-kilter algorithm to search for a neighbourhood. When subtours are produced by a flow-augmenting path of the out-of-kilter algorithm, it patches them into a Hamiltonian cycle. It extends the neighbourhood space by exchanging an even number of arcs, and it also exchanges arcs by a non-sequential primary change. Instances from real applications were used to test the algorithm, along with randomly generated problems. The new heuristic algorithm produced optimum solutions for 16 out of 28 real-world instances from TSPLIB and other sources. Also, compared with four efficient heuristics, it produced the best solutions for all except six instances. It also produced relatively good solutions in reasonable times for 216 randomly generated instances from nine instance generators.

An approach for transmission expansion planning using neurocomputing hybridized with a genetic algorithm

AbstractThe aim of transmission expansion planning is to determine which right‐of‐way to use when constructing new lines in order to meet a forecasted load in the most economical way. This problem has been solved previously by mathematical sensitivity analysis (which finds a single nonoptimal solution). It is difficult to plan for economical and reliable expansion due to its discrete and combinatorial nature. Although another method that has efficiency for combinatorial problems is neurocomputing, this approach saves computational efforts but obtains poor solutions. The most desirable approach for this planning problem is one in which many good solutions are found in reasonable time, because planning experts will then be able to plan economical and reliable expansion according to these solutions.This paper presents an approach for solving transmission expansion planning based on neurocomputing hybridized with a genetic algorithm. This approach generates suitable initial states, which include past information, of neural networks utilizing genetic algorithm. Mingling neurocomputing and a genetic algorithm, the proposed approach can find many good solutions in reasonable time making full use of their merits. Computational examples show the effectiveness of the proposed approach by comparison with conventional approaches.

Design of material flow networks in manufacturing facilities

In this paper we consider the design of material handling flow paths in a discrete parts manufacturing facility. A fixed-charge capacitated network design model is formulated, and two efficient heuristics are proposed to determine near-optimal solutions to the resulting NP-hard problem. The first heuristic iteratively adjusts the arc fixed costs and selectively includes arcs in the flow network until a feasible network is obtained. The second heuristic partially explores the problem state-space. Both heuristics are tested against an implicit enumeration scheme used to obtain optimal solutions for small examples. For more realistic cases, solutions are compared to lower bounds obtained by linear programming relaxation of the mixed integer program or by an iterative dual-ascent algorithm. Results indicate that, on average, the heuristics provide good solutions in reasonable time. The proposed methodology is applied to design flow paths of an existing manufacturing facility. The role of the flow path network problem in integrated shop design is also discussed.