Scheduling Methodology Research Articles

A data-based scheduling methodology for constructing hydrogen refueling stations

A data-based scheduling methodology for constructing hydrogen refueling stations

Enhancing Accessibility to Data in Data-Intensive Web Applications by Using Intelligent Web Prefetching Methodologies

Data-intensive Web Applications built using client–server architectures usually provide prefetching mechanisms to enhance data accessibility. Prefetching is a strategy of retrieving data before it is requested so that it can be ready when the user requests it. Prefetching reduces the load on the web server by making data available before the user requests it. Prefetching can be used for static content, such as images and web pages, as well as dynamic content, such as search results. Prefetching can also be used to improve the performance of web applications, as the data is available quickly. There are several scheduling methods, such as time-based scheduling, event-based scheduling and priority-based scheduling, for prefetching to ensure that essential data is always ready when the user requests it. In this study, we focus on time-based scheduling for prefetching. We introduce time-based scheduling methodologies using sequential pattern mining techniques and long-term short memory-based deep learning strategies. To show the usefulness of these strategies, we develop a prototype application. We conduct an extensive experimental study to evaluate the performance of the proposed time-based scheduling methodologies using both performance and accuracy metrics. Based on the computed metrics, using proposed prefetching methods provided a promising cache hit rate when using the optimal cache size. The results show that the proposed prefetching methodologies are useful in data-intensive web applications for enhancing data accessibility. Work remains to investigate the use of attention-based sequence-to-sequence models in the web prefetching domain.

Freshwater minimization in multistage multiproduct batch processes through intentional waiting

The growing demand for industrial water has motivated techniques for water reuse within the ambit of production scheduling. In batch processes, these seek to minimize freshwater usage together with an economic objective, such as makespan. It may, however, be possible to increase reuse potential by compromising on the makespan objective and intentionally delaying the start of stages so as to enable better time synchronization. This work seeks to develop a scheduling methodology for batch processes that reduce freshwater requirements by intentionally waiting between stages. A genetic algorithm-based formulation is proposed to develop such a production schedule where delays up to a maximum allowed waiting duration are introduced if it enables water reuse. The proposed scheduling strategy is demonstrated using a textile industry case study. Our results show that, on average, a 20% decrease in freshwater usage can be obtained for a single-batch campaign while for a multi-batch campaign, the savings in freshwater is nearly 59% for a 19% increase in makespan.

Continuous monitoring scheduling for moving targets by Earth observation satellites

The studies of earth observation satellite (EOS) scheduling for stationary targets have been increasing rapidly in recent years. However, these studies ignore EOS scheduling for moving targets (EOSSMT), which is urgently needed in many situations. EOSSMT is more complicated due to two factors, 1) location prediction of the moving targets and 2) algorithm design of EOSSMT optimization model for highly-nonlinear characteristics. In this article, we present a novel continuous monitoring scheduling methodology for moving targets by EOSs. Firstly, in order to predict the location of moving targets, a prediction-capture method, which can calculate the capture probability (CP) of the targets, is proposed. Then, based on the concept CP, we regard the EOSSMT as a stochastic integer nonlinear programming problem. Aiming to maximize observation times and duration, two objective functions with low complexity are proposed correspondingly. Finally, to solve our highly-nonlinear optimization model more efficiently, a dispersion-based heuristic (DBH) is proposed. In the computational experiments, a number of instances (scenarios), in which moving targets are successfully observed, verify the correctness and efficiency of our novel methodology for EOSSMT. Computational experiments also show that DBH can achieve better results than the Genetic Algorithm and Greedy Algorithm, with significantly less algorithm complexity.

Versatile Resource Management for Millimeter-Wave Cellular Network: Near Interference-Free Scheduling Methodology

In millimeter-wave (mmWave) cellular network, the highly directional beam generated by large-scale antenna array considerably depresses the inter-cell/intra-cell interferences. To fully exploit this feature in resource management, we introduce the concept of near interference-free (NIF) scheduling, representing a well-designed user scheduling scheme which avoids the possible strong interferences in mmWave network. In this paper, a generalized NIF scheduling methodology is proposed to jointly design user scheduling, beam scheduling and power allocation. The proposed methodology exhibits versatility to handle various optimization objectives, including the maximization of (weighted) sum rate, the maximization of minimum user rate, and the minimization of total transmit power. We take into account 3-dimensional user scheduling (over time, frequency and spatial domain) under the constraints induced by hybrid analog-digital beamforming. The directional beam feature is utilized to simplify the problem, and leads to linearity/convexity in the optimization of resource allocation. For each of the studied optimization objectives, simulation results validate that our proposed NIF scheduling methodology outperforms the state-of-the-art user scheduling schemes.

Dissecting American Fuzzy Lop: A FuzzBench Evaluation

AFL is one of the most used and extended fuzzers, adopted by industry and academic researchers alike. Although the community agrees on AFL’s effectiveness at discovering new vulnerabilities and its outstanding usability, many of its internal design choices remain untested to date. Security practitioners often clone the project “as-is” and use it as a starting point to develop new techniques, usually taking everything under the hood for granted. Instead, we believe that a careful analysis of the different parameters could help modern fuzzers improve their performance and explain how each choice can affect the outcome of security testing, either negatively or positively. The goal of this work is to provide a comprehensive understanding of the internal mechanisms of AFL by performing experiments and by comparing different metrics used to evaluate fuzzers. This can help to show the effectiveness of some techniques and to clarify which aspects are instead outdated. To perform our study, we performed nine unique experiments that we carried out on the popular Fuzzbench platform. Each test focuses on a different aspect of AFL, ranging from its mutation approach to the feedback encoding scheme and its scheduling methodologies. Our findings show that each design choice affects different factors of AFL. Some of these are positively correlated with the number of detected bugs or the coverage of the target application, whereas other features are related to usability and reliability. Most important, we believe that the outcome of our experiments indicates which parts of AFL we should preserve in the design of modern fuzzers.

Trio-lattice scheduling for effective resource utilisation of solar energy to cater plead irrigation

In actual practice, the most demanded agricultural loads (MDAL) are not being taken care of due to the scheduled power supply methodology practised by the present power distribution companies. Consequently, seasonal cultivations are affected due to insufficient availability of power supply. In this paper, the furnished result of a walk survey performed within an agricultural predominant area located in the vicinity of 33 kV/11 kV 5 MVA substation sited at lat. 13.62°N, long. 79.24°E and alt. 256 m confirms the above statement, i.e. about 62.12% of farming is affected due to insufficient availability of power supply. To address the concerns of the non-consideration of MDAL within the aforesaid expanse, a solar power generator of appropriate size, 103kWp derived based on the level of mismatch power attained by using ETAP software is proposed to erect near the utility grid and feed MDAL. Also, as a contribution, trio-lattice load scheduling methodology is introduced and applied to effectively utilise the generated electricity by scheduling the agricultural load points countered following the possible solar energy generation. Performance assessment indices: ADI, ERoS and ARoS are introduced to reconcile the effective utilisation of generated electricity by the proposed load scheduling methodology. The derived results and arrived performance assessment indices confirm that the proposed scheduling methodology could effectively utilise the solar energy resource and the concern system caters to the necessity of pleading irrigation.

A novel clustering and optimal resource scheduling in vehicular cloud networks using MKMA and the CM‐CSO algorithm

SummaryVarious opportunities aimed at the employment of delay‐sensitive applications in the vehicular environment are presented by the vehicular cloud (VC). Contrary to diverse wireless networks, VC networks possess exceptional features among others, namely, shorter transmission time along with a higher dynamic topology. Although integration with the cloud offers higher storage along with computation capabilities, it as well entails restricted resource availability. The restrictions on the number of resources serve as a challenge in servicing the applications with their necessary quality of service (QoS) guarantees as the number of service requests for applications keeps on augmenting with diverse circumstances. Thus, the need for an effective scheduling methodology arises to decide the sequence of servicing application requests and successful utilization of a broadcast medium, along with data transmission. To do efficient resource scheduling on VC networks, an optimization algorithm, namely, the crossover and mutation (CM)‐centered chicken swarm optimization (CSO) is proposed and implemented with the help of a publicly available dataset. Initially, the VC infrastructure is initialized and some vehicle information is extracted as features. Next, the Brownian motion‐centered bacteria foraging optimization (BM‐BFO) algorithm chooses the essential features. Centered on the chosen features, the vehicles are clustered using the modified K‐means algorithm. Next, as for the cloud server's virtual machines (VMs), the resource information is extracted. Lastly, the CM‐CSO algorithm carries out the optimal scheduling in the VC by means of the clustered features of vehicles and features of the VM. The proposed techniques' findings are scrutinized and analogized to the other prevailing methodologies to confirm that the proposed work performs effectively and gives optimal resource allocation (RA) to the VC.

Targeting DNN Inference Via Efficient Utilization of Heterogeneous Precision DNN Accelerators

Modern applications rely more and more on the simultaneous execution of multiple DNNs, and Heterogeneous DNN Accelerators (HDAs) prevail as a solution to this trend. In this work, we propose, implement, and evaluate low precision Neural Processing Units (NPUs) which serve as building blocks to construct HDAs, to address the efficient deployment of multi-DNN workloads. Moreover, we design and evaluate HDA designs that increase the overall throughput, while reducing the energy consumption during NN inference. At the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">design time</i> , we implement HDAs inspired by the big.LITTLE computing paradigm, consisting of 8-bit NPUs together with lower precision bit-width NPUs. Additionally, an NN-to-NPU scheduling methodology is implemented to decide at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">run-time</i> how to map the executed NN to the suitable NPU based on an accuracy drop threshold value. Our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hardware/software co-design</i> reduces the energy and response time of NNs by 29% and 10% respectively when compared to state-of-the-art homogeneous architectures. This comes with a negligible accuracy drop of merely 0.5%. Similar to the traditional CPU big.LITTLE, our asymmetric NPU design can open new doors for designing novel DNN accelerator architectures, due to their profound role in increasing the efficiency of DNNs with minimal losses in accuracy.

Scheduling Optimization Using an Adapted Genetic Algorithm with Due Regard for Random Project Interruptions

Current socio-economic conditions impose certain requirements on construction and renovation projects that need new methods making evaluations of construction work performance schedules more reliable. Towards this end, the authors propose a consolidated methodology of construction work scheduling based on the interval estimation technique. The boundaries of the interval, as well as determining minimum and maximum construction time, are obtained by minimizing and maximizing the term of construction work performance by introducing random interruptions into successions of critical and subcritical works. Such reasons for interruptions as the failure of key construction machines, unavailability of labor resources, and accidental man-induced or natural impacts are considered. Risk calculations are employed to devise an approach to evaluating the reliability of construction schedules, including minor schedules designated for single-facility projects and major schedules developed for projects that encompass the construction of groups of buildings and structures. Projects on construction of monolithic reinforced concrete frames of buildings were used to verify the efficiency of the proposed approaches to work performance scheduling.

Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm

During the last decades, intelligent mobile robots have been recognized as one of the most promising and emerging solutions used for fulfilling material transport demands in intelligent manufacturing systems. One of the most significant characteristics of those demands is their multi-objectivity, where identified objectives might usually conflict. Therefore, obtaining the optimally scheduled robotic-based material transport system that is simultaneously facing several conflicting objectives is a highly challenging task. To address such a challenge, this paper proposes a novel multi-objective Grey Wolf Optimizer (MOGWO) methodology to efficiently schedule material transport systems based on an intelligent single mobile robot. The proposed optimization methodology includes the comprehensive analysis and the mathematical formulation of 13 novel fitness functions combined to form a Pareto front of the multi-objective optimization problem and a novel strategy for optimal exploration of multi-objective search space. Moreover, four metrics, i.e., Generational Distance (GD), Inverted Generational Distance (IGD), Spacing (SP), and Maximum Spread (MS), are employed to quantitively evaluate and compare the effectiveness of the proposed enhanced MOGWO algorithm with three state-of-the-art metaheuristic methods (MOGA, MOAOA, and MOPSO) on 25 benchmark problems. The results achieved through two experimental scenarios indicate that the enhanced MOGWO algorithm outperforms other algorithms in terms of convergence, coverage, and the robust optimal Pareto solution. Finally, transportation paths based on obtained scheduling plans are experimentally corroborated by the mobile robot RAICO (Robot with Artificial Intelligence based Cognition) within a physical model of the intelligent manufacturing environment. The achieved experimental results successfully demonstrate the efficiency of the proposed methodology for optimal multi-objective scheduling of material transport tasks based on a single mobile robotic system.

Optimized Software Tools to Generate Large Spatio-Temporal Data Using the Datacubes Concept: Application to Crop Classification in Cap Bon, Tunisia

In the context of a changing climate, monitoring agricultural systems is becoming increasingly important. Remote sensing products provide essential information for the crop classification application, which is used to produce thematic maps. High-resolution and regional-scale maps of agricultural land are required to develop better adapted future strategies. Nevertheless, the performance of crop classification using large spatio-temporal data remains challenging due to the difficulties in handling huge amounts of input data (different spatial and temporal resolutions). This paper proposes an innovative approach of remote sensing data management that was used to prepare the input data for the crop classification application. This classification was carried out in the Cap Bon region, Tunisia, to classify citrus groves among two other crop classes (olive groves and open field) using multi-temporal remote sensing data from Sentinel- 1 and Sentinel-2 satellite platforms. Thus, we described the new QGIS plugin “Model Management Tool (MMT)”. This plugin was designed to manage large Earth observation (EO) data. This tool is based on the combination of two concepts: (i) the local nested grid (LNG) called Tuplekeys and (ii) Datacubes. Tuplekeys or special spatial regions were created within a LNG to allow a proper integration between the data of both sensors. The Datacubes concept allows to provide an arranged array of time-series multi-dimensional stacks (space, time and data) of gridded data. Two different classification processes were performed based on the selection of the input feature (the obtained time-series as input data: NDVI and NDVI + VV + VH) and on the most accurate algorithm for each scenario (22 tested classifiers). The obtained results revealed that the best classification performance and highest accuracy were obtained with the scenario using only optical-based information (NDVI), with an overall accuracy OA = 0.76. This result was obtained by support vector machine (SVM). As for the scenario relying on the combination of optical and SAR data (NDVI + VV + VH), it presented an OA = 0.58. Our results demonstrate the usefulness of the new data management tool in organizing the input classification data. Additionally, our results highlight the importance of optical data to provide acceptable classification performance especially for a complex landscape such as that of the Cap Bon. The information obtained from this work will allow the estimation of the water requirements of citrus orchards and the improvement of irrigation scheduling methodologies. Likewise, many future methodologies will certainly rely on the combination of Tuplekeys and Datacubes concepts which have been tested within the MMT tool.

Scheduling and Securing Drone Charging System Using Particle Swarm Optimization and Blockchain Technology

Unmanned aerial vehicles (UAVs) have emerged as a powerful technology for introducing untraditional solutions to many challenges in non-military fields and industrial applications in the next few years. However, the limitations of a drone’s battery and the available optimal charging techniques represent a significant challenge in using UAVs on a large scale. This problem means UAVs are unable to fly for a long time; hence, drones’ services fail dramatically. Due to this challenge, optimizing the scheduling of drone charging may be an unusual solution to drones’ battery problems. Moreover, authenticating drones and verifying their charging transactions with charging stations is an essential associated problem. This paper proposes a scheduling and secure drone charging system in response to these challenges. The proposed system was simulated on a generated dataset consisting of 300 drones and 50 charging station points to evaluate its performance. The optimization of the proposed scheduling methodology was based on the particle swarm optimization (PSO) algorithm and game theory-based auction model. In addition, authenticating and verifying drone charging transactions were executed using a proposed blockchain protocol. The optimization and scheduling results showed the PSO algorithm’s efficiency in optimizing drone routes and preventing drone collisions during charging flights with low error rates with an MAE = 0.0017 and an MSE = 0.0159. Moreover, the investigation to authenticate and verify the drone charging transactions showed the efficiency of the proposed blockchain protocol while simulating the proposed system on the Ethereum platform. The obtained results clarified the efficiency of the proposed blockchain protocol in executing drone charging transactions within a short time and low latency within an average of 0.34 s based on blockchain performance metrics. Moreover, the proposed scheduling methodology achieved a 96.8% success rate of drone charging cases, while only 3.2% of drones failed to charge after three scheduling rounds.

Dynamic Scheduling, Operation Control and Their Integration in High-Speed Railways: A Review of Recent Research

Railway system performances depend on effective dynamic scheduling and train operation control. The fast expansion and increasing complexity of high-speed railway (HSR) networks raise new challenges in maintaining the punctuality and efficiency in daily operations, in particular, in the event of disruption. This paper aims to review the state-of-art in dynamic traffic scheduling, trains operation control, and their integration for safer, more punctuate, efficient, and resilient HSRs, whose origins may trace back to their counterparts in traditional railways. First, the existing two-tier hierarchy of scheduling and control in HSR’s daily operation is introduced. At the higher layer of scheduling, a general model of dynamic train scheduling is discussed, followed by reviewing the scheduling methodologies. At the lower layer of train operation control, recent progress in tracking control of high-speed trains is discussed, with focus on the latest advances in single train control and cooperative control for multiple trains. Then, as the trend of technological progress for future HSRs, the recent development of integrating dynamic scheduling and operation control is introduced, which is made possible by efficient information exchanges among the scheduling subsystem and the train control subsystem. A three-layer integration framework and associated co-optimization methodologies are presented by introducing a co-optimization layer that bridges the separated scheduling and control layers. Finally, this review is concluded with discussions on open questions and possible directions for future research.

Development of scheduling methodology in a multi-machine flexible manufacturing system without tool delay employing flower pollination algorithm

This paper addresses machines, automated guided vehicles (AGVs), tool transporter (TT), and tools concurrent scheduling in a multi-machine flexible manufacturing system (FMS) for makespan minimization. The fewest number of copies of each tool type is employed to prevent tool delays, and job and tool shift times between machines are taken into account. The tools are placed in a central tool magazine (CTM), which shares and serves them to many machines to cut down the price of duplicating the tools in each machine. This simultaneous scheduling problem is challenging to solve because it entails determining the fewest tool copies of each tool kind without tool delay, assigning AGVs and tool copies to job-operations (jb-ons), ordering jb-ons on machines, and related trip operations such as deadheading and loaded flight times for both TT and AGVs. This paper uses a mixed-integer nonlinear programming (MINLP) framework to present the problem, and a flower pollination algorithm (FPA) is employed to solve it. For verification, a manufacturing company’s industrial problem is employed. The results show that employing two copies each for two tool types and one copy each for the remaining tool types causes no tool delay, reduction in makespan and cost, and the FPA outperforms the Jaya algorithm.

Decision-Making Framework for Prioritizing the Improvement of Pedestrian Facilities in Urban Areas Using Integrated Delphi, AHP, and TOPSIS Approach

The existing pedestrian facilities in urban areas can be improved by prioritizing the areas based on their infrastructure performance, location, safety, mobility, and liveability aspects. This paper aims to present a combination of Delphi, analytic hierarchy process (AHP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for prioritizing the improvement of pedestrian facilities in an urban area. The approach has been broken into three stages. Firstly, the significant factor assessment was done by the Delphi method. Secondly, AHP was applied to estimate the relative weightage of each factor. Finally, the TOPSIS method was used to rank the alternatives in preparation for the final decision. For better understanding, the proposed method was demonstrated by applying the framework in a case study. The analyses revealed that the proposed method is effective for prioritizing the improvement of pedestrian facilities in an urban area. The method described in this paper is intended to assist decision makers in ranking different sidewalk stretches in the urban areas that need to be improved based on various attributes. This paper makes a major impact by incorporating a novel approach for the decision making of pedestrian facilities improvement, which considered both subjective and objective judgments of multiple decision makers in the procedure at various stages using different multi-criteria decision making techniques. This approach can be combined with a scheduling methodology and optimization technique to increase the efficiency of the resource allocation process.

A dynamic scheduling process and methodology using route deviations and synchronized passenger transfers for flexible feeder transit services

The general lack of first/last mile connectivity is one of the main challenges encountered by intercity transport systems. New transportation alternatives called flexible transit systems, which combine on-demand service adjustment capabilities to the regular route and master schedule characteristics of the conventional transit, are therefore increasingly proposed. Feeder transit services can run as one type of flexible transit services, connecting a service area to a major intercity transport network through a transfer hub. Considering the unstable and constantly changing characteristics of daily passenger demand, a dynamic scheduling methodology for feeder transit services is proposed to minimize passenger crowding in vehicles. Deviations in the base route are allowed for accommodating optional requests or visiting potential transfer locations, which further offer a new type of flexible scheduling operation called synchronized passenger transfer. Operators can dynamically expand the spatial coverage of vehicles out of their regular routes to meet each other at appointed locations and times. Then, passengers can transfer between vehicles to minimize or even avoid passenger crowding. In this study, a multi-commodity network flow optimization model with side constraints in a three-dimensional space–time–state network is proposed for this type of dynamic scheduling problem. Additionally, a coordinate descent-based solution framework consisting of three steps (augmentation, decomposition, and linearization) is developed. The proposed model and solution approach are investigated using an eight-node simple network and the Sioux-Falls network, and the Chicago sketch network was adopted to demonstrate the practicability of the proposed model.

Intelligent Scheduling Methodology for UAV Swarm Remote Sensing in Distributed Photovoltaic Array Maintenance.

In recent years, the unmanned aerial vehicle (UAV) remote sensing technology has been widely used in the planning, design and maintenance of urban distributed photovoltaic arrays (UDPA). However, the existing studies rarely concern the UAV swarm scheduling problem when applied to remoting sensing in UDPA maintenance. In this study, a novel scheduling model and algorithm for UAV swarm remote sensing in UDPA maintenance are developed. Firstly, the UAV swarm scheduling tasks in UDPA maintenance are described as a large-scale global optimization (LSGO) problem, in which the constraints are defined as penalty functions. Secondly, an adaptive multiple variable-grouping optimization strategy including adaptive random grouping, UAV grouping and task grouping is developed. Finally, a novel evolutionary algorithm, namely cooperatively coevolving particle swarm optimization with adaptive multiple variable-grouping and context vector crossover/mutation strategies (CCPSO-mg-cvcm), is developed in order to effectively optimize the aforementioned UAV swarm scheduling model. The results of the case study show that the developed CCPSO-mg-cvcm significantly outperforms the existing algorithms, and the UAV swarm remote sensing in large-scale UDPA maintenance can be optimally scheduled by the developed methodology.

Dynamic domain transformation resource scheduling approach: water irrigation scheduling for urban farming

&lt;p&gt;Scheduling resources under limited resources using tailored approaches can be done successfully. However, there are situations and problems that require a schedule to handle uncertainties dynamically. The changes in the environment could lead to a non-optimal schedule, which could lead to the wastage of resources. The infeasible schedule could also be an outcome of changes that would render the schedule obsolete, and a new schedule must be generated. The majority of the scheduling problems are solved by a heuristic approach that utilizes a random number generator, thus the outcome is not guaranteed to be optimal. Domain transformation approach (DTA) is a scheduling methodology that has confirmed its expressive power in producing feasible and good quality schedules through avoidance of randomness elements as highly used in heuristic approaches. DTA has been employed in this study to solve the water irrigation scheduling for urban farming. The proposed model was tested on three different datasets. It was observed that the costs obtained on all datasets without utilizing the dynamic DTA are higher in all instances, which indicates that the solution produced by DTA is of higher quality. Thus, dynamic DTA is a more effective way of scheduling resources with considering ad-hoc changes.&lt;/p&gt;

Production Scheduling Methodology, Taking into Account the Influence of the Selection of Production Resources

The overwhelming majority of methodologies for the flexible flow shop scheduling problem proposed so far have a common feature, which is the assumption of constant time and cost for the execution of individual technological operations (ignoring an optimal selecting combination of individual employees and tools). Even if the existence of the influence of the selection of production resources on the course of operations is signaled in the available works, the research so far has not focused on the measurable effect of such a solution that takes into account this phenomenon in scheduling. The proposed production scheduling methodology, including the influence of employees and tools, turned out to be more effective in terms of minimizing the maximum completion time and the cost of the production process compared to existing solutions. The efficiency of the new proposed scheduling methodology was assessed using examples of four technological processes. The research was carried out on the basis of a dedicated adaptation of the Monte Carlo optimization algorithm in order to determine the actual effect of the new solution. The algorithm itself is not an integral part of the proposed solution, and the universal methodology developed will ensure significant profit for any optimization algorithm correctly implemented.