Abstract:
This paper explores new dimension in optimal path planning, Here we are proposing a new algorithm ‘OPTIMIZED PATH PLANING ALGORITHM’, which optimizes the path between single source to single destination. The proposed algorithm optimizes the path found by Dijkstra’s algorithm, it is proven in this paper that the new path is better than the path found by Dijkstra’s algorithm. The simulator is developed in C- language, on the basis of which, comparison table has been constructed which shows better performance of the proposed algorithm. First step is Object Growing in which the object is grown to its configuration space and produces the actual space for robot navigation. Next step is possible complete graph generation, in which a graph is drawn connecting all the obstacles, this is a complete graph and better than those of convex polygon graph in Meadow Maps technique as it has more edges producing more information. Mid point network generation, connects the midpoints of all these edges and creates the path for robot movement. Then we find a path using Dijkstra’s algorithm [8], the best in single source and single destination problems. We applied our algorithm “Optimized Algorithm” and hence creating a more optimized path, which is approximate to ideal path, consist of less turns and hence saves energy.

Abstract:
Designing of modern digital circuits require high performance with reduced cost and minimal time to market. In order to achieve greater performance, timing analysis is done to meet all the timing constraints. It also leads to increase the complexity of emerging Very Large Scale Integration (VLSI) design. Timing analysis eliminates the occurrence of non-functional path. In this work, path tracing and clustering algorithms are proposed to optimize the critical path. The path elimination technique based on clustering is tested on some combinational benchmark circuits.

Abstract:
Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environment. A problem in traditional CAPP system is that the multiple planning tasks are treated in a linear approach. This leads to an overconstrained overall solution space, and the final solution is normally far from optimal or even nonfeasible. A single sequence of operations may not be the best for all the situations in a changing production environment with multiple objectives such as minimizing number of setups, maximizing machine utilization, and minimizing number of tool changes. In general, the problem has combinatorial characteristics and complex precedence relations, which makes the problem more difficult to solve. The main contribution of this work is to develop an intelligent CAPP system for shop-floor use that can be used by an average operator and to produce globally optimized results. In this paper, the feasible sequences of operations are generated based on the precedence cost matrix (PCM) and reward-penalty matrix (REPMAX) using superhybrid genetic algorithms-simulated annealing technique (S-GENSAT), a hybrid metaheuristic. Also, solution space reduction methodology based on PCM and REPMAX upgrades the procedure to superhybridization. In this work, a number of benchmark case studies are considered to demonstrate the feasibility and robustness of the proposed super-hybrid algorithm. This algorithm performs well on all the test problems, exceeding or matching the solution quality of the results reported in the literature. The main contribution of this work focuses on reducing the optimal cost with a lesser computational time along with generation of more alternate optimal feasible sequences. Also, the proposed S-GENSAT integrates solution space reduction, hybridization, trapping out of local minima, robustness, and convergence; it consistently outperformed both a conventional genetic algorithm and a conventional simulated annealing algorithm. 1. Introduction This section presents a brief overview of the CAPP and importance of sequencing, a short description of the complexity of this class of problem, and the need for global search techniques to efficiently solve it. Process planning is defined as the activity of deciding which manufacturing processes and machines should be used to perform the various operations necessary to produce a component, and the sequence that the processes should follow. Alternatively, process planning is the systematic determination of

Abstract:
Grid computing is based on large scale resources sharing in a widely connected network. Grid scheduling is defined as the process of making scheduling decisions involving allocating jobs to resources over multiple administrative domains. Scheduling is the one of the key issues in the research. Matchmaking is a key aspect in the grid environment. Matching a job with available suitable resources has to satisfy certain constraints. Resource discovery is one of the key issues for job scheduling in the grid environment. The proposed Bee optimization algorithm is to analyze Quality of Service (QoS) metrics such as service class, job type in the heterogeneous grid environment. QoS parameters play a major role in selecting grid resources and optimizing resources effectively and efficiently. The output of the proposed algorithm is compared with max-min and min-min algorithm.

Abstract:
Simulation is essential when studying manufacturing processes or designing production systems. This project was a real case study which involved a job shop with five similar CNC milling machines. A total of six jobs were performed and each of them consisted of a different set of operations. The sequence of the six jobs to enter the system was determined by the sequencing rules including shortest setup time (SST), shortest processing time (SPT), shortest processing and setup time (SPST), earliest due date (EDD), least process (LP), and lowest volume (LV). The setup time was taken into consideration to make the results more realistic. Due to the complexity of the model, WITNESS was used to simulate all the sequencing rules. The best approach was then determined by comparing the results of each rule. By doing this, the case company would be able to make a better decision on which job should be processed first instead of selecting it randomly among the jobs.

Abstract:
The conventional optimization methods were generally based on a deterministic approach, since their purpose is to find out an accurate solution. However, when the solution space is extremely narrowed as a result of setting many inequality constraints, an ingenious scheme based on experience may be needed. Similarly, parameters must be adjusted with solution search algorithms when nonlinearity of the problem is strong, because the risk of falling into local solution is high. Thus, we here propose a new method in which the optimization problem is replaced with stochastic process based on path integral techniques used in quantum mechanics and an approximate value of optimal solution is calculated as an expected value instead of accurate value. It was checked through some optimization problems that this method using stochastic process is effective. We call this new optimization method “stochastic process optimization technique (SPOT)”. It is expected that this method will enable efficient optimization by avoiding the above difficulties. In this report, a new optimization method based on a stochastic process is formulated, and several calculation examples are shown to prove its effectiveness as a method to obtain approximate solution for optimization problems.

Abstract:
We show that memristive networks-namely networks of resistors with memory-can efficiently solve shortest-path optimization problems. Indeed, the presence of memory (time non-locality) promotes self organization of the network into the shortest possible path(s). We introduce a network entropy function to characterize the self-organized evolution, show the solution of the shortest-path problem and demonstrate the healing property of the solution path. Finally, we provide an algorithm to solve the traveling salesman problem. Similar considerations apply to networks of memcapacitors and meminductors, and networks with memory in various dimensions.

Abstract:
Advancements in technology have led to a paradigm shift fromtraditional to personalized learning methods with varied implementationstrategies. Presenting an optimal personalized learning path in aneducational hypermedia system is one of the strategies that is important inorder to increase the effectiveness of a learning session for each student.However, this task requires much effort and cost particularly in definingrules for the adaptation of learning materials. This research focuses onthe adaptive course sequencing method that uses soft computingtechniques as an alternative to a rule-based adaptation for an adaptivelearning system. The ability of soft computing technique in handlinguncertainty and incompleteness of a problem is exploited in the study. Inthis paper we present recent work concerning concept-based classificationof learning object using artificial neural network (ANN). Self OrganizingMap (SOM) and Back Propagation (BP) algorithm were employed todiscover the connection between the domain concepts contained in thelearning object and the learner’s learning need. The experiment resultshows that this approach is assuring in determining a suitable learningobject for a particular student in an adaptive and dynamic learningenvironment.

Abstract:
An optimization scheme for minimizing makespan of Gari processing jobs using improved initial population Genetic Algorithm (GA) is proposed. GA with initial population improved by using job sequencing and dispatching rules of First Come First Served (FCFS), Shortest Processing Time (SPT), Longest Processing Time (LPT), and Modified Johnson’s Algorithm for -machines in order to obtain better schedules than is affordable by GA with freely generated initial population and by individual traditional sequencing and dispatching rules was used. The traditional GA crossover and mutation operators as well as a custom-made remedial operator were used together with a hybrid of elitism and roulette wheel algorithms in the selection process based on job completion times. A test problem of 20 jobs with specified job processing and arrival times was simulated through the integral 5-process Gari production routine using the sequencing and dispatching rules, GA with freely generated initial population, and the improved GA. Comparisons based on performance measures such as optimal makespan, mean makespan, execution time, and solution improvement rate established the superiority of the improved initial population GA over the traditional sequencing and dispatching rules and freely generated initial population GA. 1. Introduction One of the most important products obtained from the processing of cassava is “Gari.” Gari is a creamy-white, granular flour with a slightly fermented flavor and a slightly sour taste made from fermented, gelatinized fresh cassava tubers. Gari processing industries occupy a substantial portion of small and medium enterprises (SMEs) in Nigeria. In the past few decades, research on Gari production has yielded tremendous gain particularly in the areas of developing Gari processing machine and improving on its quality. However, little or no attention has been given to scheduling of customers’ orders in a way that would improve delivery performance and inventory management and reduce production cycle times and overall cost associated with the production process. Hence, operational bottlenecks are often experienced in the day to day activities while the desire for appropriate processor becomes imperative. Job scheduling in a Gari processing firm is analogous to a flow shop in which a set of -jobs have to be processed with identical flow patterns on -machines. In their works [1–3] developed scheduling models on the following assumptions.(i)Each of the -jobs has the same ordering of machines for its process sequence.(ii)At a time, every job is processed on

This paper presents an
optimization problem about terminal distribution network path, defining the
research problem through its distribution operation process, next to the
terminal distribution route optimization. To begin with, dynamic optimization
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in the most appropriate distribution route to the minimum distribution distance
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algorithm.