Projects Distribution Algorithms for Regional Development

This paper aims to find an efficient method to assign different projects to several regions seeking an equitable distribution of the expected revenue of projects. The solutions to this problem are discussed in this paper. This problem is NP-hard. For this work, the constraint is to suppose that all regions have the same socio-economic proprieties. Given a set of regions and a set of projects. Each project is expected to elaborate a fixed revenue. The goal of this paper is to minimize the summation of the total difference between the total revenues of each region and the minimum total revenue assigned to regions. An appropriate schedule of projects is the schedule that ensures an equitable distribution of the total revenues between regions. In this paper, we give a mathematical formulation of the objective function and propose several algorithms to solve the studied problem. An experimental result is presented to discuss the comparison between all implemented algorithms

An optimal solution for the budgets assignment problem

Municipalities are service organizations that have a major role in strategic planning and community development that consider the future changes and society developments, by implementing set of projects with pre-allocated budgets. Projects have standards, budgets and constraints that differ from one community to another and from one city to another. Fair distributing of different projects to municipalities, while ensuring the provision of various capabilities to reach developmental role is NP-Hard problem. Assuming that all municipalities have the same strategic characteristics. The problem is as follows: given a set of projects with different budgets, how to distribute all projects to all municipalities with a minimum budget gap between municipalities. To derive equity distribution between municipalities, this paper developed lower bounds and eleven heuristics to be utilized in the branch-and-bound algorithms. The performance of the developed heuristics, lower bounds and the exact solutions are presented in the experimental study

Mathematical model bounds for maximizing the minimum completion time problem

This paper focuses on the parallel machine scheduling problem related to maximizing the minimum completion time. This problem affects several industrial applications. The application of this problem in real life is very impressive. This paper is based on the development of new lower bounds for the exact solution of the studied problem. It is shown in the literature that the problem is strongly NP-hard. The first developed lower bound is obtained by utilizing the probabilistic method to generate several solutions for the lower bound. The second is based on the knapsack problem with the iterative method. These numerical methods give new, better lower bounds

Near-optimal solutions and tight lower bounds for the parallel machines scheduling problem with learning effect

In this paper, the parallel machines scheduling problem with Dejong’s learning effect is addressed. The considered problem has a practical interest since it models real-world situations. In addition, this problem is a challenging one because of its NP-Hardness. In this work, a set of heuristics are proposed. The developed heuristics are categorized into two types. The first category is based on the dispatching methods, with new enhancement variants. The second type is more sophisticated and requires solving NP-Hard problems. Furthermore, several lower bounds are developed in order to assess the performance of the proposed heuristics. These lower bounds are based on solving the problem of the determination of the minimum average load under taking into account some observations. Among these observations, the existence of a limit position that the jobs are not allowed to exceed in any optimal schedule. Finally, an extensive experimental study is conducted over benchmark test problems, with up to 1500 jobs and 5280 instances. The obtained results are outperforming those proposed in the literature

Algorithms for Investment Project Distribution on Regions

This paper proposes an optimization system for solving an NP-hard problem by using several new algorithms and application programs. This study aims to identify a suitable distribution of investment projects across several developed industrial regions. It is assumed that all industrial regions involved have the same economic and strategic characteristics. The problem involves a set of projects that are to be assigned across regions. Each project creates an estimated number of new jobs, and the distribution of projects can be guided by minimizing the maximum total number of newly created jobs. The problem is NP-hard one, and it is difficult to determine the most appropriate distribution. We apply scheduling algorithms in order to solve the analyzed problem. Severalheuristics are developedto obtain the appropriate distribution of newly created jobs across all regions. A branch-and-bound method is employed in order to obtain the exact solution. The performance of the algorithm is demonstrated by the experimental results for a total number of 1850 instances

Dispatching-Rule Variants Algorithms for Used Spaces of Storage Supports

The paper is regarding the fair distribution of several files having different sizes to several storage supports. With the existence of several storage supports and different files, we search for a method that makes an appropriate backup. The appropriate backup guarantees a fair distribution of the big data (files). Fairness is related to the used spaces of storage support distribution. The problem is how to find a fair method that stores all files on the available storage supports, where each file is characterized by its size. We propose in this paper some fairness methods that seek to minimize the gap between used spaces of all storage supports. In this paper, several algorithms are developed to solve the proposed problem, and the experimental study shows the performance of these developed algorithms

Efficient Storage Approach for Big Data Analytics: An Iterative-Probabilistic Method for Dynamic Resource Allocation of Big Satellite Images

Satellite images play a crucial role in ecology as they provide rich information about the Earth’s surface. The deep analysis of satellite images presents a vast challenge due to the sheer size of the data that needs to be managed. Sophisticated storage solutions are required to handle the ever-increasing velocity of incoming data and to deal with potential latency or data loss. Storage balancing ensures efficient allocation and distribution of storage capacity across a system, which involves monitoring, analyzing, and adjusting how data is stored to optimize performance, minimize downtime, and maximize cost savings. Additionally, storage balancing helps avoid data bottlenecks by automatically redistributing data across multiple resources. While many solutions have been proposed to balance storage, no polynomial solution is available. This paper addresses the issue of transmitting a considerable amount of satellite images across the network to various storage supports. The challenge is to find an effective way to schedule these satellite images to the storage supports that lead to equitable results in distribution. Many heuristics and enhancement methods are proposed to solve this problem. The effectiveness of the algorithms presented in this paper was tested and analyzed through extensive testing. The experimental study shows that the proposed heuristics outperform those developed in the literature. Indeed, in 73.8% of cases, the best-proposed algorithm, the best iterative-selection satellite images algorithm ( $BIS$ ), reached the best solution compared to the best algorithm in the literature and the other proposed algorithms. The $BIS$ algorithm obtained an average gap of 0.147 in an average running time of 1.0654 s

AN EXACT ALGORITHM MINIMIZING THE MAKESPAN FOR THE TWO-MACHINE FLOWSHOP SCHEDULING UNDER RELEASE DATES AND BLOCKING CONSTRAINTS

This paper describes the problem of two-machine permutation flowshop scheduling with release dates where blocking constraint is authorized. The objective is the minimization of the makespan. This problem is proved as an NP-hard problem. Four lower bounds were developed in this paper to test experimental results with different classes. An optimal solution is also proposed based on the mathematical formulation and solved using the Cplex program.

Real time read-frequency optimization for railway monitoring system

Trains have a key role in transporting people and goods with the option of moving from source to destinations by passing through several stations, with time-based features like date scheduling and known arrival times, which makes time a critical factor. The main challenge here, is to ensure that the train trip or train schedules are not affected or delayed in any way during the whole train trip; by giving the control unit in the railway system, the required time to process requests regarding all collected data. This an NP-hard problem with an optimal solution of handling all collected data and all service requests by the control unit of the railway system. Operational research will be used to solve this problem by developing many heuristics to deal with tasks of real-time systems, to produce a significant time optimization in the railway systems. To solve this problem, the proposed approach employs optimization by adapting 22 heuristics based on two categories of algorithms, the separated blocks category algorithm and the blocks interference category algorithm. The proposed approach receives data from many different sources at the same time, then collects the received data and save it to a data base in the railway system control unit. Experimental results showed the effectiveness of the developed heuristics, more over the proposed approach minimized the maximum completion time that was elapsed in handling the received requests