Optimal Solution Research Papers - Academia.edu (original) (raw)

Modular reconfigurable placement machines represent one of the most recent and most popular types of placement machines to respond to the needs for increased flexibility and productivity in automated printed circuit board (PCB) assembly. This paper studies the combined task of determining a favourable machine configuration and line balancing for an assembly line where a single type of PCB is

- by and +1
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- Peer Review, Printed Circuit Board, Cycle Time, Integer Program

This paper studies the combined task of determining a favorable machine configuration and line balancing (MCLB) for an assembly line where a single type of printed circuit board is assembled by a set of interconnected, reconfigurable machine modules. The MCLB problem has been solved previously by heuristic methods. In the present work, we give a mathematical formulation for it and transform the model into a linear integer programming model that can be solved using a standard solver for problems of moderate size. The model determines the best machine configuration and allocation of components to the machine modules with the objective of minimizing the cycle time. Because the solutions found in this way are globally optimal, they can be used to evaluate the efficiency of previous heuristics designed for the MCLB problem. In our experiments, an evolutionary algorithm gave near optimal results.

- by T. Knuutila
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- Linear Model, Evolutionary Algorithm, Cycle Time, Integer Program

The selection of entries to be included/excluded in Branch and Bound algorithms is usually done on the basis of cost values. We consider the class of Depth First Search algorithms, and we propose to use upper tolerances to guide the search for optimal solutions. In spite of the fact that it needs time to calculate tolerances, our computational experiments for Asymmetric Traveling Salesman Problems show that in most situations tolerance-based algorithms outperform cost-based algorithms. The solution time reductions are mainly caused by the fact that the branching process becomes much more effective, so that optimal solutions are found in an earlier stage of the branching process. The use of tolerances also reveals why the widely used choice for branching on a smallest cycle in assignment solutions is on average the most effective one. Moreover, it turns out that tolerance-based DFS algorithms are better in solving difficult instances than the Best First Search algorithm from Carpaneto et al. [Carpaneto, G., Dell'Amico, M., Toth, P., 1995. Exact solution of large-scale asymmetric traveling salesman problems. ACM Transactions on Mathematical Software 21 (4), 394-409].

- by Boris Goldengorin
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- Modeling, Multidisciplinary, Search Algorithm, Asymmetry

To exploit a heterogeneous computing (HC) environment, an application task may be decomposed into subtasks that have data dependencies. Subtask matching and scheduling consists of assigning subtasks to machines, ordering subtask execution for each machine, and ordering intermachine data transfers. The goal is to achieve the minimal completion time for the task. A heuristic approach based on a genetic algorithm is developed to do matching and scheduling in HC environments. It is assumed that the matcher/scheduler is in control of a dedicated HC suite of machines. The characteristics of this genetic-algorithm-based approach include: separation of the matching and the scheduling representations, independence of the chromosome structure from the details of the communication subsystem, and consideration of overlap among all computations and communications that obey subtask precedence constraints. It is applicable to the static scheduling of production jobs and can be readily used to collectively schedule a set of tasks that are decomposed into subtasks. Some parameters and the selection scheme of the genetic algorithm were chosen experimentally to achieve the best performance. Extensive simulation tests were conducted. For small-sized problems (e.g., a small number of subtasks and a small number of machines), exhaustive searches were used to verify that this genetic-algorithm-based approach found the optimal solutions. Simulation results for larger-sized problems showed that this genetic-algorithm-based approach outperformed two nonevolutionary heuristics and a random search.

A resource allocation framework is presented for spectrum underlay in cognitive radio networks. We consider both interference constraints for primary users and quality of service (QoS) constraints for secondary users. Specifically, interference from secondary users to primary users is constrained to be below a tolerable limit. Also, signal to interference plus noise ratio (SINR) of each secondary user is maintained higher than a desired level for QoS insurance. We propose admission control algorithms to be used during high network load conditions which are performed jointly with power control so that QoS requirements of all admitted secondary users are satisfied while keeping the interference to primary users below the tolerable limit. If all secondary users can be supported at minimum rates, we allow them to increase their transmission rates and share the spectrum in a fair manner. We formulate the joint power/rate allocation with proportional and max-min fairness criteria as optimization problems. We show how to transform these optimization problems into a convex form so that their globally optimal solutions can be obtained. Numerical results show that the proposed admission control algorithms achieve performance very close to that of the optimal solution. Also, impacts of different system and QoS parameters on the network performance are investigated for the admission control, and rate/power allocation algorithms under different fairness criteria.

- by Ekram Hossain
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- Distributed Computing, Resource Allocation, Cognitive radio, Optimization Problem

Abstract. We consider the problem of finding near-optimal solutions for a variety of A/I)-hard scheduling problems for which the objective is to minimize the total weighted completion time. Recent work has led to the development of several techniques that yield constant worst-case bounds in a number of settings. We continue this line of research by providing improved performance guarantees for several of the most basic scheduling models, and by giving the first constant performance guarantee for a number of more realistically constrained scheduling problems. For example, we give an improved performance guarantee for minimizing the total weighted completion time subject to release dates on a single machine, and subject to release dates and/or precedence constraints on identical parallel machines. We also give improved bounds on the power of preemption in scheduling jobs with release dates on parallel machines. We give improved on-line algorithms for many more realistic scheduling mod...

To illustrate and test the applicability and performance of the innovative honey-bee mating optimization (HBMO) algorithm in highly non-convex hydropower system design and operation, two problems are considered: single reservoir and multi-reservoir. Both hydropower problems are formulated to minimize the total present net cost of the system, while achieving the maximum possible ratio for generated power to installed capacity. The single hydropower reservoir problem is approached by the developed algorithm in 10 different runs. The first feasible solution was generated initially and later improved significantly and solutions converged to a near optimal solution very rapidly. In the application of the proposed algorithm to a five-reservoir hydropower system with 48 periods and a total of 230 decision variables, in early mating flights, the first feasible solution was identified and the results converged to a near optimal solution in later mating flights. In the case of the multi-reservoir problem, an efficient gradient-based nonlinear-programming solver (LINGO 8.0) failed to find a feasible solution and for the single hydropower reservoir design problem it performed much worse than the proposed algorithm.

The objective of this study is to develop the transmitting environment simulation of uncompressed highdefinition (HD) video system over Additive White Gaussian Noise (AWGN) channel. This system analyzes and compares the Peak Signal-to-Noise Ratio (PSNR) performance over 16-QAM and QPSK modulation. Equal Error Protection (EEP) and Unequal Error Protection (UEP) methods for video streaming are also incorporated. In this paper, a selection modulation method is proposed to achieve a high quality picture for video streaming. By using this approach, an optimal solution of PSNR values can be maintained over different kinds of channel conditions.

- by Ngoc Anh Nguyen
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- Video Transmission, PSNR, Decoding, Qam

This paper presents the SR-GCWS-CS probabilistic algorithm that combines Monte Carlo simulation with splitting techniques and the Clarke and Wright savings heuristic to find competitive quasi-optimal solutions to the Capacitated Vehicle Routing Problem (CVRP) in reasonable response times. The algorithm, which does not require complex fine-tuning processes, can be used as an alternative to other metaheuristics-such as Simulated Annealing, Tabu Search, Genetic Algorithms, Ant Colony Optimization or GRASP, which might be more difficult to implement and which might require non-trivial fine-tuning processes-when solving CVRP instances. As discussed in the paper, the probabilistic approach presented here aims to provide a relatively simple and yet flexible algorithm which benefits from: (a) the use of the geometric distribution to guide the random search process, and (b) efficient cache and splitting techniques that contribute to significantly reduce computational times. The algorithm is validated through a set of CVRP standard benchmarks and competitive results are obtained in all tested cases. Future work regarding the use of parallel programming to efficiently solve large-scale CVRP instances is discussed. Finally, it is important to notice that some of the principles of the approach presented here might serve as a base to develop similar algorithms for other routing and scheduling combinatorial problems.

- by J. Jorba and +1
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- Monte Carlo Simulation, Tabu Search, Ant Colony Optimization, Simulated Annealing

The Metric Traveling Salesman Problem (TSP) is a classical NP-hard optimization problem. The double-tree shortcutting method for Metric TSP yields an exponentially-sized space of TSP tours, each of which approximates the optimal solution within at most a factor of 2. We consider the problem of finding among these tours the one that gives the closest approximation, i.e. the minimum-weight double-tree shortcutting. Burkard et al. gave an algorithm for this problem, running in time O(n 3 + 2 d n 2 ) and memory O(2 d n 2 ), where d is the maximum node degree in the rooted minimum spanning tree. We give an improved algorithm for the case of small d (including planar Euclidean TSP, where d ≤ 4), running in time O(4 d n 2 ) and memory O(4 d n). This improvement allows one to solve the problem on much larger instances than previously attempted. Our computational experiments suggest that in terms of the time-quality tradeoff, the minimum-weight double-tree shortcutting method provides one of the best known tour-constructing heuristics.

We bring some market segmentation concepts into the statement of the ''new product introduction'' problem with Nerlove-ArrowÕs linear goodwill dynamics. In fact, only a few papers on dynamic quantitative advertising models deal with market segmentation, although this is a fundamental topic of marketing theory and practice. In this way we obtain some new deterministic optimal control problems solutions and show how such marketing concepts as ''targeting'' and ''segmenting'' may find a mathematical representation. We consider two kinds of situations. In the first one, we assume that the advertising process can reach selectively each target group. In the second one, we assume that one advertising channel is available and that it has an effectiveness segment-spectrum, which is distributed over a non-trivial set of segments. We obtain the explicit optimal solutions of the relevant problems.

- by Alessandra Buratto
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- Marketing, Advertising, Control, Multidisciplinary

This paper deals with the problem of designing a least-cost digital data service (DDS) network that connects a given set of locations through digital switching offices with bridging capabilities. We present several alternative mixed 0-1 integer programming formulations and evaluate analytically their relative strengths by comparing their respective linear programming relaxations. By exploiting the structures inherent in a particularly strong formulation, we develop several classes of valid inequalities and cutting planes in order to tighten the initial formulation. For several problems of real-world data, computational results show that the strong formulation with valid inequalities and cutting planes generates a very tight lower bound (over 98% of the optimality) and so finds an optimal solution well within an acceptable time bound.

- by lu lu
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- Distributed Computing, Telecommunication Systems, Lower Bound, Integer Program

A method and software are proposed for optimal assignment of vehicles to transportation tasks in terms of total cost and emission. The assignment problem is transformed into a process-network synthesis problem that can be algorithmically handled by the P-graph framework. In the proposed method, each task is given by a set of attributes to be taken account in the assignment; this is also the case for each vehicle. The overall mileage is calculated as the sum of the lengths of all the routes to be travelled during, before, after, and between the tasks . Cost and emission are assigned to the mileages of each vehicle type. In addition to the globally optimal solution of the assignment problem, the P-graph framework provides the n-best suboptimal solutions that can be ranked according to multiple criteria. The viability of the proposed method is illustrated by an example.

- by Ferenc Friedler
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- Engineering, Biological Sciences, Environmental Sciences, Clean Technologies

This paper addresses the problem of scheduling n jobs on m identical parallel machines so as to minimize the completion time variance. Properties of optimal solutions are derived first. Then, complexity results are obtained, which show that the problem is NP-complete in the strong sense when m is arbitrary, and NP-complete in the ordinary sense when m is fixed.

This paper concerns project scheduling under resource constraints. Traditionally, the objective is to find a unique solution that minimizes the project makespan, while respecting the precedence constraints and the resource constraints. This work focuses on developing a model and a decision support framework for industrial application of the cumulative global constraint. For a given project scheduling, the proposed approach allows the generation of different optimal solutions relative to the alternate availability of outsourcing and resources. The objective is to provide a decision-maker an assistance to construct, choose, and define the appropriate scheduling program taking into account the possible capacity resources. The industrial problem under consideration is modeled as a Constraint Satisfaction Problem (CSP). It is implemented under the constraint programming language CHIP V5. The provided solutions determine values for the various variables associated to the tasks realized on each resource, as well as the curves with the profile of the total consumption of resources on time.

- by Trojette Meriam
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- Engineering, Computer Science, Artificial Intelligence, Chemistry

Multimedia technology is increasingly being used as an alternative way of delivering instruction. According to researchers, the success of multimedia is due to the dualcoding aspect of the information processing theory. In applying dual-coding principles, different information has to be coded in different media in order for people to learn effectively. Designers of interactive multimedia applications are faced with thousands of different combinations of input and/or output modalities of information representation. Each single modality or multimodal combination has its own specific capabilities and limitations for representing or conveying information. It is important to be able to select the right combination of modalities for a given application. This paper describes Modality Theory and how it can be used to guide designers developing interactive multimedia applications. Given any particular set of information which needs to be exchanged between the user and system during task perf...

- by Lorna Uden
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- Education, Theory of modality, Interactive Multimedia, Information Processing
- by F. Dragan
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- Algorithms, Distributed Computing, Networks, Performance Evaluation

Constructing parsimonious phylogenetic trees from species data is a central problem in phylogenetics, and has diverse applications, even outside biology. Many variations of the problem, including the cladistic Camin-Sokal (CCS) version, are NP-complete. We present Answer Set Programming (ASP) models for the binary CCS problem, as well as a simpler perfect phylogeny version, along with experimental results of applying the models to biological data. Our contribution is threefold. First, we solve phylogeny problems which have not previously been tackled by ASP. Second, we report on variants of our CCS model which significantly affect run time, including the interesting case of making the program "slightly tighter". This version exhibits some of the best performance, in contrast with a tight version of the model which exhibited poor performance. Third, we are able to find proven-optimal solutions for larger instances of the CCS problem than the widely used branch-and-bound-based PHYLIP package.

- by David Mitchell
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- Bioinformatics, Artificial Intelligence, Modeling, Biology

The obvious advantages of digital audio technology have up to now being manifested mainly in media storage and processing sub-components, witch are parts of a more elaborate audio / acoustic analog reproduction chain. It is envisaged that the remaining components such as cables, amplifiers and transducers will soon be also implemented in digital form, potentially leading to networked, integrated and highly optimized solutions. The paper examines theoretical and implementation aspects related to each of the modules that could constitute such an all-digital audio / acoustic transducer, namely: (a) the digital wireless receiver, via the Bluetooth and IEEE 802.11 protocols, (b) audio decoding and format adaptation (c) DSP for acoustic compensation, based on measured results for smoothed response equalization, (d) digital amplification, and, (e) all-digital transduction.

- by John Mourjopoulos
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- Optimal Solution

This paper presents the optimal sequence of a set of jobs for a single machine with idle insert, in which the objective function is to minimize the sum of maximum earliness and tardiness (n/1/I/ET max ). Since this problem tries to minimize and diminish the values of earliness and tardiness, the results can be useful for different production systems such as just in time (JIT). Special case of determining the optimal sequence, considering common due date, is investigated and the structure of optimal solution is introduced, using some simple orders. In the general case, the neighborhood conditions are developed and the dominant set for any optimal solution is determined. The branch-and-bound (B&B) method is used to solve the problem, and the proper upper and lower bounds are also 0096-3003/$ -see front matter Ó (R. Tavakkoli-Moghaddam), azaron@msl.sys.hiroshimau. ac.jp (A. Azaron).

- by Peter Brucker and +1
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- Applied Mathematics, Logistics, Scheduling, Dynamic programming

This paper proposes the real-coded clonal selection algorithm (RCSA) for use in electromagnetic design optimization. Some features of the algorithm, such as the number of clones, mutation range, and the fraction of the population selected each generation are discussed. The TEAM Workshop problem 22 is investigated, in order to illustrate the performance of the algorithm in a real electromagnetic problem. The results obtained, a set of optimal solutions representing a broader range of options for the designer, are compared with those achieved by a genetic algorithm, showing the efficiency of the RCSA in practical optimization problems.

- by Jaime Ramirez
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- Engineering, Informatics, Artificial Immune Systems, Genetic Algorithms

Synthesis of asynchronous circuits from Signal Transition Graphs (STGs) involves resolving state coding conflicts. The refinement process is generally done automatically using heuristics and often produces sub-optimal solutions, which have to be corrected manually. This paper presents a framework for an interactive refinement process aimed to help the designer. It is based on the visualization of conflict cores, i.e., sets of transitions causing coding conflicts, which are represented at the level of finite and complete prefixes of STG unfoldings.

- by Alex Bystrov
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- Visualization, Graph Theory, Petri Nets, Asynchronous circuit design

This paper investigates the capability of Sweep Algorithm in solving the vehicle routing problem for public transport The Sweep Algorithm is firstly introduced as a method to search shortest route in the vehicle routing problem. In order to evaluate the result of the algorithm, current routes profile of a public transport is presented. An application is constructed based on the algorithm and tested using current routes data. The route generation is performed repeatedly using different constraints in order to obtain the optimal solution. A route is selected based on shortest distance and capacity constraint. Each constraint affects the route selection to gain different combination of routes. Revenue and operational cost are considered to select the best combination of routes. Two methods in sweep algorithm are implemented and compared to find the better method. The result shows that Sweep Algorithm is capable of solving vehicle routing problem for public transport under certain constraints.

In this paper, we introduce a new condition on functions of a control problem, for which we define a KT-invex control problem. We prove that a KT-invex control problem is characterized in order that a Kuhn–Tucker point is an optimal solution. We generalize optimality results of known mathematical programming problems. We illustrate these results with examples.

In today's business transactions, there are various reasons, namely, bulk purchase discounts, re-ordering costs, seasonality of products, inflation induced demand, etc., which force the buyer to order more than the warehouse capacity. Such situations call for additional storage space to store the excess units purchased. This additional storage space is typically a rented warehouse. Inflation plays a very interesting and significant role here: It increases the cost of goods. To safeguard from the rising prices, during the inflation regime, the organisation prefers to keep a higher inventory, thereby increasing the aggregate demand. This additional inventory needs additional storage space, which is facilitated by a rented warehouse. Ignoring the effects of the time value of money and inflation might yield misleading results. In this study, a two-warehouse inventory model with linear trend in demand under inflationary conditions having different rates of deterioration has been developed. Shortages at the owned warehouse are also allowed subject to partial backlogging. The solution methodology provided in the model helps to decide on the feasibility of renting a warehouse. Finally, findings have been illustrated with the help of numerical examples. Comprehensive sensitivity analysis has also been provided.

- by Chandra K. Jaggi
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- Engineering, Operations Research, Systems Science, Sensitivity Analysis

We study the multidimensional knapsack problem, present some theoretical and empirical results about its structure, and evaluate different Integer Linear Programming (ILP) based, metaheuristic, and collaborative approaches for it. We start by considering the distances between optimal solutions to the LP-relaxation and the original problem and then introduce a new core concept for the MKP, which we study extensively. The empirical analysis is then used to develop new concepts for solving the MKP using ILP-based and memetic algorithms. Different collaborative combinations of the presented methods are discussed and evaluated. Further computational experiments with longer run-times are also performed in order to compare the solutions of our approaches to the best known solutions of another so far leading approach for common MKP benchmark instances. The extensive computational experiments show the effectiveness of the proposed methods, which yield highly competitive results in significantly shorter run-times than previously described approaches.

- by Ulrich Pferschy
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- Heuristics, Mathematical Sciences, Memetic Algorithm, Integer Linear Programming

For a given graph G over n vertices, let OPT G denote the size of an optimal solution in G of a particular minimization problem (e.g., the size of a minimum vertex cover). A randomized algorithm will be called an α-approximation algorithm with an additive error for this minimization problem, if for any given additive error parameter > 0 it computes a value OPT such that, with probability at least 2/3, it holds that OPT G ≤ OPT ≤ α · OPT G + n .

- by Time Connect
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- Distributed Algorithms, Theoretical Computer Science, Probability, Optimization

It is widely believed that IP over optical networks will be a major component of the next generation Internet. However, it is not efficient to map a single multicast IP flow into one light-tree, since the bandwidth of an IP flow required... more

- by Yaohui Jin
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- Distributed Computing, Graph Theory, Linear Programming, Integer Programming

Consider a family of sets and a single set, called the query set. How can one quickly find a member of the family which has a maximal intersection with the query set? Time constraints on the query and on a possible preprocessing of the set family make this problem challenging. Such maximal intersection queries arise in a wide range of applications, including web search, recommendation systems, and distributing on-line advertisements. In general, maximal intersection queries are computationally expensive. We investigate two well-motivated distributions over all families of sets and propose an algorithm for each of them. We show that with very high probability an almost optimal solution is found in time which is logarithmic in the size of the family. Moreover, we point out a threshold phenomenon on the probabilities of intersecting sets in each of our two input models which leads to the efficient algorithms mentioned above. *

- by Mikhail Lifshits
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- Applied Mathematics, Distributed Computing, Web search, Nearest Neighbor

Previous research on the newsboy problem is based on the assumption that in case of a shortage, unsatisfied demand is lost. Such an assumption is inappropriate for items that have a close substitute. In this paper, we formulate a two-item newsboy problem with substitutability (TINPS). Upper and lower bounds on the optimal order quantities of the two items are derived. Since analytical solutions to the problem are difficult to obtain, a Monte Carlo simulation is used to identify the optimal solution to the TINPS. Order quantities identified by the simulation provide higher expected profit than would have been obtainable without considering substitutability.

- by Gadi Rabinowitz
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- Production, Monte Carlo Simulation, Multidisciplinary, Production economics

A comprehensive study of capacitor placements and real-time control in general unbalanced distribution systems is undertaken. New developments in a loss reduction formula, problem formulations, solution methodology and mathematical justification are presented. The problem is decoupled into two subproblems: the capacitor placement subproblem and the real-time control subproblem. An effective solution algorithm for placing capacitors and determining their real-time control

In the real world, we have to frequently deal with searching for and tracking an optimal solution in a dynamic environment. This demands that the algorithm not only find the optimal solution but also track the trajectory of the solution in a dynamic environment. Particle Swarm Optimization (PSO) is a populationbased stochastic optimization technique, which can find an optimal, or near optimal, solution to a numerical and qualitative problem. However, the traditional PSO algorithm lacks the ability to track the optimal solution in a dynamic environment. In this paper, we present a modified PSO algorithm that can be used for tracking a non-stationary optimal solution in a dynamically changing environment.

- by Rammohan Ragade
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- Stochastic Optimization, First, Dynamic Environment, Particle Swarm Optimizer

In this paper, we empirically investigate the NP-hard problem of finding sparsest solutions to linear equation systems, i.e., solutions with as few nonzeros as possible. This problem has received considerable interest in the sparse approximation and signal processing literature, recently. We use a branch-and-cut approach via the maximum feasible subsystem problem to compute optimal solutions for small instances and investigate the uniqueness of the optimal solutions. We furthermore discuss five (modifications of) heuristics for this problem that appear in different parts of the literature. For small instances, the exact optimal solutions allow us to evaluate the quality of the heuristics, while for larger instances we compare their relative performance. One outcome is that the so-called basis pursuit heuristic performs worse, compared to the other methods. Among the best heuristics are a method due to Mangasarian and a bilinear approach.

- by Sadegh Jokar
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- Applied Mathematics, Signal Processing, Numerical Analysis, Numerical Linear Algebra

A pseudospectral method for generating optimal trajectories of linear and nonlinear constrained dynamic systems is proposed. The method consists of representing the solution of the optimal control problem by an mth degree interpolating polynomial, using Chebyshev nodes, and then discretizing the problem using a cell-averaging technique. The optimal control problem is thereby transformed into an algebraic nonlinear programming problem. Due to its dynamic nature, the proposed method avoids many of the numerical difficulties typically encountered in solving standard optimal control problems. Furthermore, for discontinuous optimal control problems, we develop and implement a Chebyshev smoothing procedure which extracts the piecewise smooth solution from the oscillatory solution near the points of discontinuities. Numerical examples are provided, which confirm the convergence of the proposed method. Moreover, a comparison is made with optimal solutions obtained by closed-form analysis and/or other numerical methods in the literature.

- by Mohammad Kazemi
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- Applied Mathematics, Optimal Control, Nonlinear Programming, Numerical Method

In this paper, we present a simulated annealing algorithm for solving multi-objective simulation optimization problems. The algorithm is based on the idea of simulated annealing with constant temperature, and uses a rule for accepting a candidate solution that depends on the individual estimated objective function values. The algorithm is shown to converge almost surely to an optimal solution. It is applied to a multi-objective inventory problem; the numerical results show that the algorithm converges rapidly.

Chemical process control requires intelligent monitoring due to the dynamic nature of the chemical reactions and the non-linear functional relationship between the input and output variables involved. CSTR is one of the major processing unit in many chemical, pharmaceutical and petroleum industries as well as in environmental and waste management engineering. In spite of continuing advances in optimal solution techniques for optimization and control problems, many of such problems remain too complex to be solved by the known techniques. Thus, a heuristic approach is often a viable alternative. Neural Network models offer the most unified approach to building truly intelligent systems, which can provide good optimal solution for many applications. In this work we propose a hybrid (KohKal) neural network algorithm which is being used to model and solve a continuous stirred tank mixer/reactor (CSTM/R) problem which is non-linear and stochastic in nature. This hybrid algorithm is robust...

- by Emuoyibofarhe Justice
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- Process Control, Waste Management, Neural Network, ANN

Peter Checkland and his colleagues developed Soft System Methodology (SSM) (associated with the department of systems at Lancaster University in UK) with the intention to fetch an organized technique towards the explanation of imprecise management-type problems. The rationale of a rich picture is to help out the analyst to gain a deep insight of the complex problem situations. The paper opted for a case study in which the rich pictures are compared with other tools and techniques. The research will disclose facts about the effectiveness of rich pictures in organizations to solve problem. It will also help to investigate that by using symbols and diagrammatic conventions which are used to signify any messy situation must be unambiguous and understandable by the users can fill in the gaps of understanding problem first and its better solution. This paper includes the implications for the implementation and incorporation of rich pictures in order to get optimal solutions in time.

- by Navid Ehsan
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- Computer Science, Decision Making, Systems Thinking, Tools and Techniques

The problem addressed in this paper is the allocation of multiple advertisements on a Web banner, in order to maximize the revenue of the allocated advertisements. It is essentially a two-dimensional, single, orthogonal, knapsack problem, applied to pixel advertisement. As this problem is known to be NP-hard, and due to the temporal constraints that Web applications need to fulfill, we propose several heuristic algorithms for generating allocation patterns. The heuristic algorithms presented in this paper are the left justified algorithm, the orthogonal algorithm, the GRASP constructive algorithm, and the greedy stripping algorithm. We set out an experimental design using standard banner sizes, and primary and secondary sorting criteria for the set of advertisements. We run two simulations, the first simulation compares the heuristics with an optimal solution found using brute force search, and the second simulation compares the heuristic algorithms to gain a better insight into their performance. Finding a suitable pattern generating algorithm is a trade-off between effectiveness and efficiency. Results indicate that allocating advertisements with the orthogonal algorithm is the most effective. In contrast, allocating advertisements using the greedy stripping algorithm is the most efficient. Furthermore, the best settings per algorithm for each banner size are given.

- by Flavius Frasincar
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- Applied Mathematics, Heuristics, Advertising, Experimental Design

In this research, a special form of Automated Guided Vehicle (AGV) routing problem is investigated. The objective is to find the shortest tour for a single, free-ranging AGV that has to carry out multiple pick and deliver (P&D) requests. This problem is an incidence of the asymmetric traveling salesman problem which is known to be NP-complete. An artificial neural network algorithm based on Kohonen's self-organizing feature maps is developed to solve the problem, and several improvements on the basic features of self-organizing maps are proposed. Performance of the algorithm is tested under various parameter settings for different P&D request patterns and problem sizes, and compared with the optimal solution and the nearest neighbor rule. Promising results are obtained in terms of solution quality and computation time.

- by Sinan Kayaligil
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- Neural Network, Multidisciplinary, Nearest Neighbor, Self Organization
- by Gh Omran
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- Engineering, Evolutionary algorithms, Engineering Optimization, Computer Simulation

This paper presents extensive computational experiments to compare 10 heuristics and 20 metaheuristics for the maximum diversity problem (MDP). This problem consists of selecting a subset of maximum diversity from a given set of elements. It arises in a wide range of real-world settings and we can find a large number of studies, in which heuristic and metaheuristic methods are proposed. However, probably due to the fact that this problem has been referenced under different names, we have only found limited comparisons with a few methods on some sets of instances. This paper reviews all the heuristics and metaheuristics for finding near-optimal solutions for the MDP. We present the new benchmark library MDPLIB, which includes most instances previously used for this problem, as well as new ones, giving a total of 315. We also present an exhaustive computational comparison of the 30 methods on the MDPLIB. Non-parametric statistical tests are reported in our study to draw significant conclusions.

- by Rafael Martí
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- Applied Mathematics, Heuristics, Heuristic Optimization, Non parametric statistics

I n fluidized-bed gas-phase polymerization reactors, several grades of polyethylene are produced in the same equipment by changing the operating conditions. Transitions between the different grades are rather slow and result in the production of a considerable amount of off-specification polymer. Grade transition improvement is viewed here as a dynamic optimization problem, for which numerous approaches exist. Numerical optimization based on a nominal process model is typically insufficient due to the presence of uncertainty in the form of model mismatch and process disturbances. This paper proposes to implement optimal grade transition using a measurement-based approach instead. It is based on tracking the necessary conditions of optimality (NCO tracking) using a decentralized control scheme. For this, the nominal input profiles are dissected into arcs and switching times that are assigned to the various parts of the NCO. These input elements are then adapted using appropriate measurements. NCO tracking is used to determine optimal grade transition in polyethylene reactors. The problem of minimizing the transition time from a steady state of low melt index to that of high melt index is studied, with the feeds of hydrogen and inert and the output flow rate considered as manipulated variables. In the optimal solution, all arcs are determined by path constraints, and all switching times are determined by path and terminal constraints, which significantly eases the adaptation. The on-line and run-to-run adaptation of these parameters is illustrated in simulation.

- by Dominique Bonvin
- •
- Chemical Engineering, Dynamic Optimization, Polyethylene, Fluidized Bed

The problem of solving the optimal (minimumnoise) error feedback coefficients for recursive digital filters is addressed in the general high-order case. It is shown that when minimum noise variance at the filter output is required, the optimization problem leads to a set of familiar Wiener-Hopf or Yule-Walker equations, demonstrating that the optimal error feedback can be interpreted as a special case of Wiener filtering.

- by I. Hartimo
- •
- Multidisciplinary, Optimization Problem, Noise reduction, Second Order

In this paper we present the initial experimental results that we obtained with deploying our distributed agent-based system for Ant Colony Optimization (ACODA) on a computer cluster. The novelty of ACODA consists in agent-based modeling and distribution of the problem environment that is explored by the ants to determine an optimal solution. The effect of this approach is that ants' migration is reduced to asynchronous messages exchanged between the agents that compose the problem environment. The deployment of ACODA on a computer cluster benefits from an automated setup that simplifies the installation and configuration of the necessary software packages.

- by Sorin Ilie
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- Computer Architecture, Ant Colony Optimization, Synchronization, Computers

A6stract-Frequency-domain adaptive filters have long been recognized as an attractive alternative to time-domain algorithms when dealing with systems with large impulse response and/or correlated input. Recently, new frequency-domain LMS adaptive schemes have been proposed. These algorithms essentially retain the attractive features of frequency-domain implementations, while requiring a processing delay considerably smaller than the length of the impulse response. The first purpose of this contribution is to show that these algorithms can be seen as particular implementations of a more general scheme, the generalized multidelay filter (GMDF). Within this general class of algorithms, we focus on implementations based on the weighted overlap and add reconstruction algorithms; these variants, overlooked in previous contributions, provide an independent control of the overall processing delay and of the rate of update of the filter coefficients, allowing a trade-off between the computational complexity and the rate of convergence. The second purpose of this work is to present a comprehensive analysis of the performance of this new scheme and to provide insight into the influence of impulse response segmentation on the behavior of the adaptive algorithm. Exact analytical expressions for the steady-state mean-square error are first derived. Necessary and sufficient conditions for the convergence of the algorithm to the optimal solution within finite variance are then obtained, and are translated into bounds for the stepsize parameter. Simulations are presented to support our analysis and to demonstrate the practical usefulness of the GMDF algorithm in applications where large impulse response has to be processed. 'Making use of the CCITT recommendations for evaluation of echocanceller in ISDN hands-free terminal [29].

- by omar AIT AMRANE
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- Computational Complexity, Multidisciplinary, Mean square error, Impulse response
- by Janine Aronson and +2
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- Tabu Search, Multidisciplinary, Local Search, European
- by James Patterson
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- Management Science, Production Scheduling, Group work, Cycle Time