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Cloud computing provides a wide variety of services, from small to big businesses, to individual consumers. Cloud computing's features entice users to migrate their operations from traditional platforms to cloud platforms. In comparison to traditional systems, cloud computing has an extremely powerful processing capacity. Requests for resources are considered tasks in the cloud, and appropriate resources are allocated depending on user needs. However, owing to high demand and volume of requests, cloud struggles to allocate resources. Task schedulers are employed in cloud computing to address these issues. Various task scheduling methods have been presented in several research publications, and the quest for a better scheduling model continues. In this paper, a task scheduling method based on a hybrid optimization algorithm is presented, which effectively schedules jobs with the least amount of waiting time. In addition to these, other parameters, such as the overall production time, execution time, waiting time, efficiency, and utilization are included in this study. The simulation results show that the proposed scheduling method is superior to conventional Ant Colony and Particle Swarm Optimization-based scheduling algorithms in terms of performance. [ABSTRACT FROM AUTHOR]

The multiverse optimizer (MVO) is one of the most trending algorithms used nowadays. The searching space in MVO is restricted by the best solution only, leading to a poor searching domain, therefore, a long searching time. This paper proposes an improved multiobjective multi-verse optimizer (IMOMVO) as a novel population optimization technique to solve task scheduling problems. The IMOMVO is introduced to overcome the drawbacks risen in the original MVO and its latest enhanced version mMVO. The proposed method solves the problem of the average positioning (AP) by dynamically enhancing the equation of updating the AP based on the best and the second-best available solutions. To evaluate The proposed IMOMVO, several datasets scenarios containing various tasks and virtual machines (Vms) were used to test the approach's capability. Standard evaluation metrics are used to validate the results of the proposed method; task execution time, throughput, and the Vms processing power. The proposed method obtained better results according to the evaluation measures than other state-of-the-art methods. The execution time achieves less time when compared to the mMVO as the proposed method achieved 186.33 s for executing 100 tasks and 934.92 for executing 600 tasks. The throughput results also achieved astonishing results as for 100 tasks, the throughput achieved 0.19, and the Vm processing power for the proposed method was 0.25 Kw for executing 100 tasks. [ABSTRACT FROM AUTHOR]

The reconfigurable manufacturing system is an advanced field of research that has surpassed the efficiency of other manufacturing systems due to its high throughput, cost-effectiveness, and ability to accommodate product variety. An important problem addressed in the field of reconfigurable manufacturing systems is process planning which assigns configurations to different operations. Process planning has been the focus of research for almost two decades; however, a comprehensive review is lacking to highlight the possible streams of future contributions to this field of research. To this end, this study presents a systematic review of the process planning in a reconfigurable manufacturing system with a focus on optimization efforts. This review is organized in two interconnected phases, i.e., a theoretical phase and an implementation phase. The theoretical phase reviews the concerned literature regarding the levels of analysis, reconfigurable manufacturing system (RMS) characteristics, different research themes, and change agents. On the other hand, the implementation phase reviews the literature regarding the use of different objective functions, constraints, solution approaches, nature of problem/solution, and the use of industrial applications. Several future research streams are provided which can guide, in a broader sense, the advancement of process planning literature. As a demonstration, quality, supply chain, and human/operator issues are highlighted to advance the scope of applicability of the concerned literature. A thorough analysis of the operator aspects in scheduling literature is presented which can benefit practitioners working in a changeable/reconfigurable manufacturing environment. Moreover, practical process planning approaches are offered to analyze the cost, time, and quality aspects. Finally, the conclusion of the study is provided. [ABSTRACT FROM AUTHOR]

Big data analytics in cloud environments introduces challenges such as real-time load balancing besides security, privacy, and energy efficiency. This paper proposes a novel load balancing algorithm in cloud environments that performs resource allocation and task scheduling efficiently. The proposed load balancer reduces the execution response time in big data applications performed on clouds. Scheduling, in general, is an NP-hard problem. Our proposed algorithm provides solutions to reduce the search area that leads to reduced complexity of the load balancing. We recommend two mathematical optimization models to perform dynamic resource allocation to virtual machines and task scheduling. The provided solution is based on the hill-climbing algorithm to minimize response time. We evaluate the performance of proposed algorithms in terms of response time, turnaround time, throughput metrics, and request distribution with some of the existing algorithms that show significant improvements. [ABSTRACT FROM AUTHOR]

Algorithms have been designed for robot motion planning with various adaptability to different problems. However, how to choose the most suitable planner in a scene has always been a problem worthy of research. This paper aims to find the most suitable motion planner for each query under three different scenes and six different queries. The work lies in optimization of sampling-based motion planning algorithms through motion planning pipeline and planning request adapter. The idea is to use the pre-processing of the planning request adapter, to run OMPL as a pre-processer for the optimized CHOMP or STOMP algorithm, and connect through the motion planning pipeline, to realize the optimization of the motion trajectory. The optimized trajectories are compared with original trajectories through benchmarking. The benchmarking determines the most suitable motion planning algorithm for different scenarios and different queries. Experimental results show that after optimization, the planning time of the algorithm is longer, but the efficiency is significantly improved. In the low-complexity scenes, STOMP optimizes the sampling algorithm very well, improves the trajectory quality greatly, and has a higher success rate. CHOMP also has a good optimization of the sampling algorithm, but it reduces the success rate of the original algorithm. However, in more complex scenes, optimization performance of the two optimization methods may not be as good as the original algorithm. In future work, we need to find better algorithms and better optimization algorithms to tackle with complex scenes. [ABSTRACT FROM AUTHOR]

Cloud computing utilizes the ideas of planning and loads adjusting to relocate the assignments to underutilize VMs for adequately sharing the assets. Assignment planning is one of the basic issues in this condition identified with it. Cloud errand booking, an NP-hard improvement issue, has been attempted to tackle utilizing numerous meta-heuristic calculations. The substance ahead has two meta-heuristic calculations are consolidated in particular Whale Optimization Algorithm and Harmony Search Algorithm. This paper proposes a cloud errand booking arrangement dependent on half and half Whale Harmony enhancement calculation. The principal commitment of this work is adjusting the framework stack while endeavoring to limit the makespan and cost of a given arrangement of assignments. The CloudSim toolbox bundle was utilized to reproduce the new scheduling technique. At last, this method directs an examination to exhibit the execution of the proposed calculation. The trial results demonstrate that the proposed calculation can accomplish the objective of load balance in an abnormal state with lower execution time, cost and energy consumption. [ABSTRACT FROM AUTHOR]

Presently, cloud computing has become a very popular platform in the computing world. It provides users with high efficient resources on demand. Nevertheless, due to the strong turnout to use the cloud in everything in the Information Technology (IT) field, cloud platforms have become very crowded with heavy loads. Therefore, providers need to use new techniques to manage resources allocation to users. One of the most important techniques, in managing cloud resources, is scheduling technique. Recently, many heuristic and meta-heuristic techniques are developed to solve the scheduling problem. However, each technique is efficient to solve a part of the problem but unable to solve the overall problem. This paper presents a new technique called Water Pressure Change Optimization (WPCO) to solve the scheduling problem in cloud computing. The new WPCO technique is inspired from the phenomenon of water density changing when increasing the pressure due to the changing in the physical characteristics of the water. The new technique is evaluated and compared with the most recent existing techniques. The results indicate that the WPCO can distribute any number of tasks on the available resources in low time complexity. In addition, it improves schedule length, load balancing, resources utilization, memory usage and throughput of the cloud system. [ABSTRACT FROM AUTHOR]

This work presents a modified version of the SCO (Supervisory Control and Optimization) methodology, proposed in Pena et al. (Inf Sci 329:491-502, 2016) to deal with planning problems in flexible manufacturing systems. Although having proved to be an alternative to deal with this class of problems, the SCO methodology is limited by the fact that it can only be applied to deal with small batches of products. Previous works show that when considering manufacturing systems of a moderate degree of complexity, this approach is only efficient to generate solutions for batches containing very few products, as for larger batches, the necessary computational time to process a solution is very high. It is obvious that, for the problems in the real world, this dimension of production is very small, which, at first, makes the application of SCO methodology quite limited. Therefore, this work proposes a complementary approach to SCO, here called SCO-Concat, developed to carry out the planning in larger batches of production. The proposed methodology was tested in a plant of moderate size, and the results obtained show that planning for batches as large as desired can be achieved in an efficient manner by SCO-Concat at a very reduced computational cost. [ABSTRACT FROM AUTHOR]

Coverage and lifetime are two paramount problems in wireless sensor networks (WSNs). In this paper, a method called Multiround Distributed Lifetime Coverage Optimization protocol (MuDiLCO) is proposed to maintain the coverage and to improve the lifetime in wireless sensor networks. The area of interest is first divided into subregions, and then the MuDiLCO protocol is distributed to the sensor nodes in each subregion. The proposed MuDiLCO protocol works in periods during which sets of sensor nodes are scheduled, with one set for each round of a period, to remain active during the sensing phase and thus ensure coverage so as to maximize the WSN lifetime. The decision process is carried out by a leader node, which solves an optimization problem to produce the best representative sets to be used during the rounds of the sensing phase. The optimization problem formulated as an integer program is solved to optimality through a Branch-and-Bound method for small instances. For larger instances, the best feasible solution found by the solver after a given time limit threshold is considered. Compared with some existing protocols, simulation results based on multiple criteria (energy consumption, coverage ratio, and so on) show that the proposed protocol can prolong efficiently the network lifetime and improve the coverage performance. [ABSTRACT FROM AUTHOR]

A hub-and-spoke railway system is an efficient way of handling freight transport by land. A modern rail-rail train yard consists of huge gantry cranes that move the containers between the trains. In this context, we consider a rail-rail transshipment yard scheduling problem (TYSP) where the containers arrive to the hub and need to be placed on a train that will deliver them to their destination. In the literature, the problem is decomposed hierarchically into five subproblems, which are solved separately. First, the trains have to be grouped into bundles in which they visit the yard. Next, the trains have to be assigned to tracks within these bundles, namely parking positions. Then the final positions for the containers on trains have to be determined. Next, the container moves that need to be performed are assigned to the cranes. Finally, these moves have to be sequenced for each crane for processing. In this paper, an integrated MILP model is proposed, which aims to solve the TYSP as a single optimization problem. The proposed formulation also enables us to define more robust and complex objective functions that include key characteristics from each of the above-mentioned subproblems. The strength of our proposed formulation is demonstrated via computational experiments using the data from the literature. Indeed, the results show that the TYSP can be solved without the use of decomposition techniques and more insight can be obtained from the same input data used to solve particular single decomposed subproblems. [ABSTRACT FROM AUTHOR]

The hardware restrictions of surface mount placement machines, such as height, pick and place restrictions, and simultaneous pickup are often in printed circuit board (PCB)-related studies. This study proposes an efficient hybrid genetic algorithm (HGA) for solving the nozzle assignment problem and the component pick and place sequence problem. First, the proposed method obtains the sequence of the automatic nozzle changer (ANC) with the maximum number of simultaneous pickups and the minimum number of picks as the solution of the nozzle setup problem. Then, the proposed method uses the nearest neighbor search (NNS), 2-optimization, and a genetic algorithm (GA) with the known ANC sequences to obtain the PCB assembly time with the optimal component pick and place sequence. Experiments are conducted on the PCB of the EVEST EM-780 surface mount placement machine. Results show that the proposed HGA gives the lowest total number of picks, the shortest total head movement distance, and the minimum total PCB assembly time compared to those of other methods. [ABSTRACT FROM AUTHOR]

We address one of the external factors of personnel inventory behavior, deployments. The configuration of persistent unit deployments has the ability to affect everything from individual perceptions of service palatability to operational effectiveness. There is little evidence to suggest any analytical underpinnings to the U.S. Army deployment scheduling and unit assignment patterns. This paper shows that the deployment scheduling and unit assignment problem can be formulated as an interval graph such that modifications to traditional graph coloring algorithms provide an efficient mechanism for dealing with multiple objectives. [ABSTRACT FROM AUTHOR]

Chemotherapy outpatient scheduling is a complex, dynamic, uncertain problem. Chemotherapy centres are facing increasing demands and they need to increase their efficiency; however there are very few studies looking at using optimization technology on the chemotherapy scheduling problem. We address dynamic uncertainty that arises from requests for appointments that arrive in real time and uncertainty due to last minute scheduling changes. We propose dynamic template scheduling, a novel technique that combines proactive and online optimization and we apply it to the chemotherapy outpatient scheduling problem. We create a proactive template of an expected day in the chemotherapy centre using a deterministic optimization model and a sample of appointments. As requests for appointments arrive, we use the template to schedule them. When a request arrives that does not fit the template, we update the template online using the optimization model and a revised set of appointments. To accommodate last minute additions and cancellations to the schedule, we propose a shuffling algorithm that moves appointment start times within a predefined time limit. We test the use of dynamic template scheduling against the optimal offline solution and the actual performance of the cancer centre. We find improvements in makespan of up to 20 % when using dynamic template scheduling compared to current practice. [ABSTRACT FROM AUTHOR]

This paper deals with a pick and place robotic system design problem. The objective is to present an efficient method which is able to optimize the performances of the robotic system. By defining the suitable combination of scheduling rules, our method allows each robot to perform the assigned pick and place operations in real time in order to maximize the throughput rate. For that, we have developed different resolution methods which define the scheduling rule for each robot in order to seize the products from the first side of the system and to place them on the second side. We suggest three metaheuristics which are the ant colony optimization, the particle swarm optimization and the genetic algorithm. Then, we try to select the best algorithm which is able to get the best solutions with the lowest execution times. This is the main advantage of our methods compared to exact methods. This fact represents a great interest taking in consideration that our methods must respect a strong industrial constraint regarding the functioning of a real industrial robotic system. This constraint states that the answer time to manage the seizing strategies of the robots must be less than 1 second. Numerical results show that the different algorithms perform optimally for the tested instances in a reasonable computational time. [ABSTRACT FROM AUTHOR]

This paper presents an efficient heuristic method to minimize total flow time in no-wait flow shop scheduling. It is based on the assumption that the priority of a job in the initial sequence is given by the sum of its processing times on the bottleneck machines. Empirical results demonstrate the superiority of the proposed method over the best-known heuristics in the literature, while remaining the same complexity order of O( n2). [ABSTRACT FROM AUTHOR]

Motivated by experiences of coalition military forces in Iraq and Afghanistan, we analyze the allocation of route clearance teams (RCTs) to search for and neutralize improvised explosive devices (IEDs) on roadways traveled by military convoys. We model the interaction of a single RCT and a single convoy operating over a given roadway. Our goal is to reduce IED risk by improving coordination between the RCT and the convoy. We treat the distribution of IEDs along the road prior to the passage of the RCT as a non-homogeneous Poisson process. The RCT finds and clears IEDs according to a Bernoulli process. Enemy forces may emplace (reseed) additional IEDs in the temporal gap between the RCT clearance sweep and the arrival of the convoy. IED risk is defined as the expected number of IEDs encountered by the convoy. We identify certain characteristics of optimal RCT schedules including: the shape of the IED intensity function and the speed of reseeding substantially dictate the RCT schedule that minimizes IED risk; the more rapid the IED reseeding, the more critical is conformance to the optimal RCT schedule; an RCT having inferior detection probability, by employing superior scheduling can sometimes reduce convoy risk more than a less well scheduled RCT with superior detection probability; a discretized highway version of the problem can be efficiently optimized. We also briefly discuss applications of this model to the more complex problem of allocating several RCTs to protect a number of convoys scheduled to travel on a network of highways. [ABSTRACT FROM AUTHOR]

In this article we describe the development of a tool that allows planners to efficiently and effectively plan space within valuable areas of a shipyard. Traditionally, space is considered as resource; however, it is difficult to accurately account for and plan its consumption with the currently available planning software’s. The spatial scheduling tool described in this article can be used by planners to manually or automatically reserve space within the shipyard for construction of large blocks over the entire erection period of the ship. The software is coupled with a heuristic optimization solver inspired by an algorithm used for "3D bin-packing problems." The result is the ability to efficiently generate and compare multiple space allocation alternatives in a reduced time with the ultimate goal of maintaining the critical ship erection schedule. A better solution than manual or semi-automatic allocation of blocks can be obtained through the optimization module. [ABSTRACT FROM AUTHOR]

A fuzzy-neural approach is presented in this study to optimize the performance of job dispatching in a wafer fabrication factory. The traditional optimization methods in this field have a few problems. To tackle these problems, we performed several treatments. First, we applied a more effective fuzzy-neural approach to estimate the remaining cycle time of a job. Then we established a systematic procedure to determine the optimal values of the parameters in the two-factor tailored nonlinear fluctuation smoothing rule for the mean cycle time, in order to optimize the scheduling performance. To assess the effectiveness of the proposed methodology, we conducted a production simulation. According to the experimental results, the proposed methodology is better than the existing approaches in optimizing the average cycle time. [ABSTRACT FROM AUTHOR]

Scheduling of casting processes is the problem of determining the number of products that will be manufactured in each casting shift so as to optimize a predetermined objective function. In this paper, we propose a linear programming (LP) model that maximizes the average efficiency of process time for casting in real foundries. The considered casting process is pressure die casting, the most prevalent permanent mold casting process, equipped with an automated die exchange device. In permanent mold casting, the use of a mold or die installed on a casting machine requires a significant process time for casting. The objective function of the proposed LP model is defined as the average ratio of the actual process time to the pre-assigned operating time in a shift. A solution that minimizes the objective function will provide the maximum surplus time, hence optimizing the efficiency of casting operations in terms of process time. Numerical examples are presented to illustrate the applicability and reliability of the proposed model. The results demonstrate that this model is an effective and conveniently implemented tool for solving real casting sequences in a simple and practical framework. [ABSTRACT FROM AUTHOR]

This paper presents a new constructive heuristic, based on the principle of job insertion, for minimizing makespan in no-wait permutation flow shop scheduling problems. Empirical results demonstrate the superiority of the proposed approach over four of the best-known methods in the literature. Analytical expressions for the total number of partial and complete sequences generated by the algorithms are derived. Statistical tests of significance substantiate the improvement in solution quality. [ABSTRACT FROM AUTHOR]

Operating rooms (ORs) are simultaneously the largest cost center and greatest source of revenues for most hospitals. Due to significant uncertainty in surgery durations, scheduling of ORs can be very challenging. Longer than average surgery durations result in late starts not only for the next surgery in the schedule, but potentially for the rest of the surgeries in the day as well. Late starts also result in direct costs associated with overtime staffing when the last surgery of the day finishes later than the scheduled shift end time. In this article we describe a stochastic optimization model and some practical heuristics for computing OR schedules that hedge against the uncertainty in surgery durations. We focus on the simultaneous effects of sequencing surgeries and scheduling start times. We show that a simple sequencing rule based on surgery duration variance can be used to generate substantial reductions in total surgeon and OR team waiting, OR idling, and overtime costs. We illustrate this with results of a case study that uses real data to compare actual schedules at a particular hospital to those recommended by our model. [ABSTRACT FROM AUTHOR]