Two-Stage, Hybrid Flowshop Scheduling Problem

The NP-Completeness theory states that exact and efficient algorithms are unlikely to exist for the class of NP-difficult problems. One way to deal with NP hardness is to relax the optimality requirement and look for solutions instead that are close to the optimum. This is the main idea behind the approximation algorithms, which are called heuristic or metaheuristic. The problem of motion estimation is a process with a high degree of computational complexity, it requires sufficient memory space and execution time. It represents the cost of static, dynamic and video image sequence coding. The main task is to minimize the distortion rate and improve visual quality. This makes research in the field of coding, image compression and video focus on finding efficient algorithms to carry out the estimation of movement in a reasonable time. If a list of images of n elements is analyzed, there are feasible solutions. So, an exhaustive search is too slow, even for small values of the solution space. Therefore, from a practical point of view, it is crucial to have efficient and fast heuristic algorithms that avoid thorough search. In this investigation we design and implement heuristic algorithms, based on the frequency domain, which are applied on the coefficients of the discrete transform of the cosine and wavelets. Also, we propose temporal domain algorithms such as block-matching algorithms, which focus your search on the maximum coincidence of the current image with the reference one. The algorithms used during the implementation of this research work were written with the mathematical programming language MATLAB. In addition, we review the basic concepts of image processing, video, compression algorithms and motion estimation frequently used. The evaluation of the algorithms was carried out with a set of images provided by a previous acquisition system. We show the improvement of visual quality, the amount of compressed or reconstructed information and the behavior of the methods in the search for similarities between pixels or images. Finally, we contribute to the dissemination of new lines of scientific research that lead to the expansion and improvement of the study, the generation of new knowledge, since it is a young area within the Education discipline of Nicaragua.

Both network function virtualization (NFV) and edge computing (EC), especially the latter, are attracting more and more attention in recent years. A growing number of network service providers are migrating their services from the cloud to the edge for better QoS services, while the recent researches on NFV also concentrate on deploying NFV services in edge computing networks. However, NFV deployment in edge networks is a troublesome challenge and is fairly alien from conventional NFV deployment problems in data centres. Edge network differs from the data center network in the following two aspects: firstly, edge nodes are constrained in computing capacity, and secondly, the network connections between edge nodes are unstable and dynamic, which may show large variance over time. This means edge computing should be designed for high-efficient use of physical edge nodes’ resources. To address the challenges above, we investigate a new NFV Service Chain Placement problem in edge computing environments (NSCP-EC) in this paper. We first prove that the NSCP-EC problem is NP-complete. Then we propose a new metric which can better measure the capacity utilization rate of physical resources, and analyze its advantages with details. Based on the new metric, we propose two heuristic but efficient algorithms called MINI and MINI-tree. To confirm the performance of the two algorithms, we conduct simulations. The result demonstrates that MINI gains an advantage over genetic algorithm (GA) and MINI-tree orevails over MINI in tree topology conditions in the aspects of physical resource utilization, acceptance rate and running time. Both theoretical analysis and simulation results confirm the feasibility of the algorithms.

Multi-objective green scheduling of integrated flexible job shop and automated guided vehicles

A new efficient algorithm is derived for the minimal period retiming by formal manipulation. Contrary to all previous algorithms, which used fixed period feasibility checking to binary-search a candidate range, the derived algorithm checks the optimality of a feasible period directly. It is much simpler and more efficient than previous algorithms. Experimental results showed that it is even faster than ASTRA, an efficient heuristic algorithm. Since the derived algorithm is incremental by nature, it also opens the opportunity to be combined with other optimization techniques.

Hardware/software (HW/SW) partitioning and scheduling are the crucial steps during HW/SW co-design. It has been shown that they are classical combinatorial optimization problems. Due to the possible sequential or concurrent execution of the tasks, HW/SW partitioning and scheduling has become more difficult to solve optimally. In this paper more efficient heuristic algorithms are proposed for the HW/SW partitioning and scheduling. The proposed algorithm partitions a task graph by iteratively moving the task with highest benefit-to-area ratio in higher priority. The benefit-to-area ratio is updated in each iteration step to cater for the task concurrence. The proposed algorithm for task scheduling executes the task lying in hardware-only critical path in higher priority to enhance the task forecast. A large body of experimental results conclusively shows that the proposed heuristic algorithm for partitioning is superior to the latest efficient combinatorial algorithm (Tabu search) cited in this paper. Moreover, the Tabu search for partitioning has been further improved by utilizing the proposed heuristic solution as its initial solution. In addition, the proposed scheduling algorithm obtains the improvements over the most widely used approaches by up to 10% without large increase in running time.

We discuss how the idea of unicast 1+1 protection can be efficiently extended to protect multicast connections in optical backbone networks. Particularly, we show how to achieve instantaneous failure recovery and cost efficiency by allowing intermediate nodes to merge their incoming flows by a simple network code, i.e., logical OR operation. Under simple network coding, the problem of minimum cost multicast 1+1 protection is formulated as a 2-connectivity problem. In order to solve this problem, an optimal ILP and three efficient heuristic algorithms are proposed. Simulation results on real-world networks show that the average cost of our best heuristic algorithm is only 2.6 % higher compared to the optimal ILP solution.

A Novel Design Optimization For Reliable Networking Is Investigated In Ethernet Ring Mesh Networks With The Itu-T G.8032 Ethernet Ring Protection Recommendation. Designing An Ethernet Ring Mesh Consisting Of Multiple Rings To Achieve The Maximum Network Availability Is An Np-Complete Problem. We Show That A Graph With Vertex Connectivity Equal To Or Greater Than Two Is A Sufficient Condition For Designing An Ethernet Ring Mesh. A Novel Design Rule With An Efficient Heuristic Algorithm Is Proposed To Find A Design Close To An Optimal Ethernet Ring Mesh In Terms Of Maximum Network Availability. Compared With An Optimal Network Design Algorithm With An Exponential Enumeration Search Cost, The Heuristic Algorithm Achieves Network Availability Nearly As High As That Of An Optimal Design At A Polynomial Cost.

Automatic layout of tables is required in online applications because of the need to tailor the layout to the viewport width, choice of font, and dynamic content. However, if the table contains text, minimizing the height of the table for a fixed maximum width is NP-hard. Thus, more efficient heuristic algorithms are required. We evaluate the HTML table layout recommendation and find that while it generally produces quite compact layout it is brittle and can lead to quite uncompact layout. We present an alternate heuristic algorithm. It uses a greedy strategy that starts from the widest reasonable layout and repeatedly chooses to narrow the column for which narrowing leads to the least increase in table height. The algorithm is simple, fast enough to be used in online applications, and gives significantly more compact layout than is obtained with HTML’s recommended table layout algorithm.

Scheduling of meetings is one of the basic functions demanded in office automation. With respect to the automation of this function, the Meeting Timetable Rearrangement problem (MTR) to update a current meeting timetable for an additional meeting has been formulated. However, it has already been shown that MTR is NP‐hard. Consequently, we should develop an approximation algorithm or a heuristic algorithm for MTR. Such an algorithm is required to find a feasible solution which satisfies all constraints, but not necessarily an optimal solution. This paper discusses a problem to determine whether or not there exists a feasible solution for a given instance of MTR. First, we show that this problem is NP‐complete. That is, there is no polynomial‐time approximation algorithm for MTR unless P = NP. This implies that we should develop a heuristic algorithm for MTR. Second, we present a polynomial‐time algorithm which finds a feasible solution for a subproblem of MTR. By combining this algorithm and a hill‐climbing approach, an efficient heuristic algorithm can be developed.

Modeling and design of global logistics systems: A review of integrated strategic and tactical models and design algorithms

The paper deals with the problem of selection of the most informative features. A new effective and efficient heuristic possibilistic clustering algorithm for feature selection is proposed. First, a brief description of basic concepts of the heuristic approach to possibilistic clustering is provided. A technique of initial data pre- processing is described and a fuzzy correlation measure is considered. The new algorithm is described and then illustrated on the well-known Iris data set benchmark and the results obtained are compared with those by us- ing the conventional, well-known and widely employed method of principal component analysis (PCA). Conclu- sions and suggestions for future research are given.

A heuristic algorithm is developed for the classical scheduling problem in which independent jobs are nonpreemptively scheduled on identical parallel machines with the objective of minimizing the makespan with load balancing. The algorithm uses iteratively the LPT procedure to be successful computational tools in many hard Combinatorial Optimization problems. We present an approximation algorithm based on balance the Hopfild with binary decision variables which are a kind of assignment variables for the classical deterministic scheduling problem of minimizing the makespan on identical machines. These problems are characterized by their complexity, known as NP-hard in most cases. Computational results indicate that the proposed algorithm is very competitive with respect to well-known constructive algorithms for a large number of benchmark.

Algorithmic aspects for multiple-choice hardware/software partitioning

This study addresses the problem of minimizing the makespan for scheduling parallel batch machines, where jobs are processed in batches and each machine has the same capacity. The processing time for each batch includes setup, preheating, and heat preservation stages, introducing both serial and parallel batching characteristics. Jobs differ in size, weight, and ready time. A mixed-integer linear programming model is formulated and its correctness is verified using small-scale instances. As the problem is NP-hard, a novel heuristic algorithm is proposed, which utilizes a dynamic scheduling strategy to effectively reduce machine idle waiting time between batches. The time complexities of the algorithm are analyzed, and the worst-case performance of the new constructive heuristic is also evaluated. For small-scale problems, the performance of the heuristics is compared with the optimal solutions obtained from the MILP model, while for large-scale problems, comparisons are made with lower bounds. Three heuristic algorithms and two improved genetic algorithms are used as benchmark algorithms. The experimental results demonstrate that the new constructive heuristic outperforms these five algorithms in terms of scheduling performance.

The capacitated \(p\)-median problem (CPMP) is one of the well-known facility-location problems. The objective of the problem is to minimize total cost of locating a set of capacitated service points and allocating a set of demand points to the located service points, while the total allocated demand for each service point is not be greater than its capacity limit. This paper presents an efficient heuristic algorithm based on the local branching and relaxation induced neighborhood search methods for the CPMP. The proposed algorithm is a heuristic technique that utilizes a general mixed integer programming solver to explore neighborhoods. The parameters of the proposed algorithm are tuned by design of experiments. The proposed method is tested on a large set of benchmark instances. The results show that the method outperforms the best method found in the literature.