Minimum Completion Time Research Articles

Profit and Satisfaction Aware Order Assignment for Online Food Delivery Systems Exploiting Water Wave Optimization

Online Food delivery, a specialized application of mobile crowdsourcing, has accelerated its popularity due to rushed urban lifestyle in recent times. The food delivery order assignment to workers that maximizes the service qualities, such as maximization of workers’ profit and minimization of order completion time to enhance the customer satisfaction at the same time, is a challenging problem. Existing works in the literature are limited either by focusing solely on minimizing order completion time or reduction of cost incurred in delivery of orders. In this paper, we develop a framework for optimal assignment of food delivery orders to workers as a multi-objective linear programming (MOLP) problem that makes a trade-off in between the worker profit and customer satisfaction. Due to NP-hardness of the above MOLP, a polynomial time solution of the food delivery problem has been developed, namely WWOFooD, exploiting Water Wave Optimization, a meta-heuristic algorithm. The WWOFooD not only explores ways of enhancing service quality of the customers, but also ensures giving additional incentives to the workers providing faster and reliable food delivery services. The results of simulation experiments depict that the WWOFooD offers competitive workers’ profit as well as significantly enhances customer satisfaction compared to the state-of-the-art works.

Completion Time Minimization for UAV-Enabled Surveillance Over Multiple Restricted Regions

This work examines a UAV-enabled surveillance mission over multiple restricted regions and aims to determine the optimal UAV trajectory that minimizes the mission completion time. The UAV is prohibited from entering the restricted regions due to government regulations or adversarial concerns. However, during the surveillance of a region, the UAV can move along the region's boundary to reduce its distance to the next region once the local task is completed. To exploit this advantage, we propose a minimum completion time (MinTime) algorithm that first determines the visiting order of the regions by employing an approximate solution of the traveling salesman problem (TSP) and then optimizes the UAV trajectory over the sequence of restricted regions using dynamic programming. In the presence of obstacles, we further propose an obstacle-aware MinTime (OA-MinTime) algorithm that treats each obstacle as an additional restricted region with zero surveillance duration, allowing the UAV to avoid the obstacles in a more efficient manner. A modified TSP solution is also proposed by taking into consideration the additional distance required to circumvent the obstacles on each inter-POI path. Simulation results show that the proposed MinTime and OA-MinTime algorithms can significantly reduce the total completion time compared to conventional minimum-distance approaches.

Adaptive Genetic Algorithm Based on Individual Similarity to Solve Multi-Objective Flexible Job-Shop Scheduling Problem

Aiming at the coupling of energy consumption and completion time in flexible job-shop scheduling, this paper took makespan and energy consumption as the optimization objectives, established a scheduling model, and proposed a scheduling strategy based on improved genetic algorithm. Firstly, a hybrid initialization method based on global minimum completion time selection and global minimum workload selection is introduced to generate the initial population, and the scale of the initial population is expanded to increase the diversity of the population; Secondly, the generation method of offspring individuals is improved, grouped according to the non-dominated ranking level and crowding degree of individuals in the population, and the self-contained individuals are generated by performing crossover and mutation, neighborhood search simulated annealing and reverse learning crossover mutation operations respectively. Finally, an improved adaptive crossover and mutation operation based on individual similarity is proposed, which is applied to the algorithm to improve the search ability of the algorithm. Relevant experimental results show that the proposed adaptive genetic algorithm based on individual similarity is feasible and effective in flexible job-shop scheduling.

Minimizing Total Weighted Completion Times for Semi-online Single Machine Scheduling

This paper addresses the semi-online scheduling problem for total weighted completion time minimization on a single machine, where a combination of information on jobs’ release dates and processing times is considered. In this study, jobs can only arrive at known future times and a lower bound on jobs’ processing times is known in advance. A new semi-online algorithm, called MCSWPT (Modified Critical Shortest Weighted Processing Time), that makes use of the available pieces of information in order to produce better schedules compared to its online peers is presented. A numerical analysis is presented, showing the impact of having the considered partial information.

Cross-Layer Network Codes for Completion Time Minimization in Device-to-Device Networks

In this paper, we joinlty optimize cross-layer network coding (CLNC) and device-to-device (D2D) communications to facilitate the recovery of popular common files that are missed at multiple user devices (UDs). In the envisioned system, a group of near-by UDs cooperate with each other and use CLNC to combine their received files, such that the UDs can recover their missing files using D2D communications. The proposed CLNC mechanism enables the cloud base-station (CBS) to select the coding decisions, adapt the rate allocation, and adjust the transmit power of the UDs to reduce the interference. Specifically, such a CLNC configuration brings a new trade-off among selecting the transmitting UDs and their coding decisions, and scheduling the transmission rate and power. To this end, we formulate an optimization problem to minimize the completion time required for recovering the missing files at the UDs. The proposed optimization problem is shown to be intractable because an optimal solution depends on the future coding decisions and UDs’ heterogeneous rates. To overcome such intractiblity, we propose an online solution that is updated at each transmission slot. In particular, we first design a graph called herein the D2D rate-aware instantely decodable NC (RA-IDNC), where its vertices have weights that judiciously balance between the rate of the transmitting UDs and the number of their scheduled UDs. Subsequently, we propose an innovative and efficient CLNC solution that iteratively selects a set of transmitting UDs and optimizes their powers using a function evaluation (FE) method. In each iteration, a new transmitting UD is selected provided that the resultant interference does not significantly degrade the completion time performance. Simulation results show that the proposed CLNC can significantly reduces the completion time compared to the benchmark schemes.

Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems

Mobile edge computing (MEC) provides effective cloud services and functionality at the edge device, to improve the quality of service (QoS) of end users by offloading the high computation tasks. Currently, the introduction of deep learning (DL) and hardware technologies paves a method in detecting the current traffic status, data offloading, and cyberattacks in MEC. This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks (SNN) to determine the traffic status in the MEC system. Also, an adaptive sampling cross entropy (ASCE) technique is utilized for data offloading in MEC systems. Moreover, the modified salp swarm algorithm (MSSA) with extreme gradient boosting (XGBoost) technique was implemented to identification and classification of cyberattack that exist in the MEC systems. For examining the enhanced outcomes of the AIMDO-SMEC technique, a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDO-SMEC technique with the minimal completion time of tasks (CTT) of 0.680.

Minimization of completion time variance in flowshops using genetic algorithms

The majority of the flowshop scheduling literature focuses on regular performance measures like makespan, flowtime etc. In this paper a flowshop scheduling problem is addressed where the objective is to minimize completion time variance (CTV). CTV is a non-regular performance measure that is closely related to just-in-time philosophy. A Microsoft Excel spreadsheet-based genetic algorithm (GA) is proposed to solve the problem. The proposed GA methodology is domain-independent and general purpose. The flowshop model is developed in the spreadsheet environment using the built-in formulae and function. Addition of jobs and machines can be catered for without the change in the basic GA routine and minimal change to the spreadsheet model. The proposed methodology offers an easy-to-handle framework whereby the practitioners can implement a heuristic-based optimization tool with the need for advanced programming tools. The performance of the proposed methodology is compared to previous studies for benchmark problems taken from the literature. Simulation experiments demonstrate that the proposed methodology solves the benchmark problems efficiently and effectively with a reasonable accuracy. The solutions are comparable to previous studies both in terms of computational time and solution quality.

Energy-efficient project scheduling with supplier selection in manufacturing projects

In make-to-order or engineer-to-order systems, the overarching process of producing complex and highly customized products along with managing multiple stakeholders (including suppliers) can be considered to be a project scheduling problem. Yet, despite the need for an advanced project scheduling plan for manufacturing, there is little research in the literature considering both project scheduling and supplier selection in a manufacturing context. Due to the scarcity of resources in manufacturing, this project scheduling problem resembles the well-known resource constrained project scheduling problem (RCPSP). Additionally, with the increasing awareness of the environment and the need to minimize energy consumption and noise pollution, and continuing concern with worker safety, there is a need for innovative methods to improve green factors (energy, noise, and safety) in manufacturing projects. This paper, therefore, proposes for manufacturing projects an energy-efficient resource constrained project scheduling plan embedded with a supplier selection strategy called the green RCPSP for manufacturing (or GRCPSPM). This proposed GRCPSPM is designed as a bi-objective problem with the conjoint objectives of minimization of project completion time and green project indicators (GPI). To solve that bi-objective problem, a genetic algorithm-based memetic algorithm (MA) is proposed and experimental results show that the proposed MA outperforms the well-known non-dominated sorting genetic algorithm-II (NSGA-II) approach and evolutionary programming (EP) algorithm for a number of self-generated project scheduling instances, in terms of both solution quality and computational efficiency. The obvious outcome of this study is to support the selection of an appropriate supplier based on resource processing speeds and resource GPI, thereby ensuring green manufacturing project scheduling with minimum completion time and minimum energy usage.

Improvement in sealing assembly process using project management techniques

Managing the assembly process is crucial in ensuring that organization objectives are met. An assembly process involves integration of various subassemblies which in turn is obtained by combining various other subassemblies or individual items. Due to such complexities, the need for managing arises since integration involves lot of dependency. Dependency can be in various form such as completion time of various subassemblies, unavailability of items or having a complex precedence relationship. To manage such subassemblies a sound approach is needed. The usage of project management techniques such as critical path method (CPM), Project evaluation and review technique (PERT) have been found wide applicability in small and large scale project wherein achieving minimal completion time becomes a priority. This particular work focusses on applying these techniques to identify the critical path in a sealing assembly process and thereby allocating suitable resources to ensure faster completion is achieved. It is seen that the project completion time is reduced considerably by allocating suitable resources on the critical path.

Mathematical model bounds for maximizing the minimum completion time problem

Journal of Applied Mathematics and Computational Mechanics, Prace Naukowe Instytutu Matematyki i Informatyki, Politechnika Częstochowska, Scientific Research of the Institute of Mathematics and Computer Science, Czestochowa University of Technology

Automatic generation of iterated greedy algorithms for the non-permutation flow shop scheduling problem with total completion time minimization

In this paper we address the non-permutation flow shop scheduling problem, a more general variant of the flow shop problem in which the machines can have different sequences of jobs. We aim to minimize the total completion time. We propose a template to generate iterated greedy algorithms, and use an automatic algorithm configuration to obtain efficient methods. This is the first automated approach in the literature for the non-permutation flow shop scheduling problem. The algorithms start by building a high-quality permutation solution, which is then improved during a second phase that generates non-permutation solutions by changing the job order on some machines. The obtained algorithms are evaluated against two well-known benchmarks from the literature. The results show that they can find better schedules than the state-of-the-art methods for both the permutation and non-permutation flow shop scheduling problems in comparable experimental conditions, as evidenced by comprehensive computational and statistical testing. We conclude that using non-permutation schedules is a viable alternative to reduce the total completion time that production managers should consider.

A Comprehensive Fuzzy Decision-Making Method for Minimizing Completion Time in Manufacturing Process in Supply Chains

In manufacturing firms, there are many factors that can affect product completion time in production lines. However, in a real production environment, such factors are uncertain and increase the adverse effects on product completion time. This research focuses on the role of internal factors in small- and medium-scale supply chains in developing countries, enhancing product completion time during the manufacturing process in fuzzy conditions. In the first step of this research, a list of factors was found clustered into six main groups: technology, human resources, machinery, material, facility design, and social factors. In the next step, fuzzy weights of each group factor were determined by a fuzzy inference system to reflect the uncertainty of the factors in utilizing product completion time. Then, a hybrid fuzzy–TOPSIS-based heuristic is proposed to generate and select the best production alternative. The outcomes showed that the proposed method could generate and select the alternative with a 10.13% lower product completion time. The findings also indicated that using the proposed fuzzy method will cause less minimum variance compared to the crisp mode.

Completion Time Minimization for UAV-Assisted Mobile-Edge Computing Systems

The explosive computation demands in the Internet of Things (IoT) have triggered the research interests on unmanned aerial vehicle (UAV) assisted mobile-edge computing (MEC) systems even though there are still many challenges, such as computing delay requirement, multi-UAV cooperation, and resource management. This letter focuses on the computing delay issue in MEC systems assisted by multiple UAVs with the goal of task completion time minimization. In particular, both the partial offloading and binary offloading modes are considered by jointly optimizing time slot size, terminal devices scheduling, computation resource allocation, and UAVs’ trajectories. Particularly, an non-LoS channel model is adopted for UAV-ground communication. To handle the formulated problems, we develop alternating optimization algorithms by invoking the successive convex approximation method, Karush-Kuhn-Tucker conditions and penalized method. Numerical results show that the completion time is significantly decreased by the proposed algorithms.

A Comprehensive Review of Load Balancing Techniques in Cloud Computing and Their Simulation with CloudSim Plus

Background: The field of cloud computing has been evolving for over a decade now. Load balancing is an important component of cloud computing. Load balancing implies scheduling of cloudlets (tasks) on virtual machines. Since this is an NP-hard problem, various heuristics for load balancing have been proposed in the research literature. The heuristics have been categorized, simulated and benchmarked in various ways; however, the information is scattered across many review articles. Objective: This review aims to bring a broad range of load balancing heuristics found in the research literature under one umbrella. It includes a comprehensive list of heuristics, a holistic set of criteria for their classification, and some key performance metrics and simulation tools used for their benchmarking. An illustration of a fair and comprehensive comparison of heuristics is provided using CloudSim Plus, a recent and advanced simulation tool. Method: The simulations performed with CloudSim Plus employ a generic model of task and machine heterogeneity with Poisson arrival of cloudlets and exponential distribution of cloudlet length to emulate actual cloud-computing scenarios. The simulation results in terms of key performance metrics are used to compare four centralized load balancing heuristics including Join Shortest Queue (JSQ), Join Idle Queue (JIQ), Round Robin and Minimum Completion Time (MCT).

Hybrid Scheduling for Multi-Equipment at U-Shape Trafficked Automated Terminal Based on Chaos Particle Swarm Optimization

Aimed to improve the efficiency of port operations, Shanghai Zhenhua Heavy Industries Co., Ltd. (ZPMC) proposed a new U-shape trafficked automated terminal. The new U-shape trafficked automated terminal brings a new hybrid scheduling problem. A hybrid scheduling model for yard crane (YC), AGV and ET in the U-shape trafficked automated terminal yard is established to solve the problem. The AGV and ET yard lanes are assumed to be one-way lane. Take the YC, AGV and ET scheduling results (the container transportation sequences) as variables and the minimization of the maximum completion time as the objective function. A scheduling model architecture with hierarchical abstraction of scheduling objects is proposed to refine the problem. The total completion time is solved based on a static and dynamic mixed scheduling strategy. A chaotic particle swarm optimization algorithm with speed control (CCPSO) is proposed, which include a chaotic particle strategy, a particle iterative speed control strategy, and a particle mapping space for hybrid scheduling. The presented model and algorithm were applied to experiments with different numbers of containers and AGVs. The parameters of simulation part refer to Qinzhou Port. The simulation results show that CCPSO can obtain a near-optimal solution in a shorter time and find a better solution when the solution time is sufficient, comparing with the traditional particle swarm optimization algorithm, the adaptive particle swarm optimization algorithm and the random position particle swarm optimization algorithm.

A new solution to distributed permutation flow shop scheduling problem based on NASH Q-Learning

Aiming at Distributed Permutation Flow-shop Scheduling Problems (DPFSPs), this study took the minimization of the maximum completion time of the workpieces to be processed in all production tasks as the goal, and took the multi-agent Reinforcement Learning (RL) method as the main frame of the solution model, then, combining with the NASH equilibrium theory and the RL method, it proposed a NASH Q-Learning algorithm for Distributed Flow-shop Scheduling Problem (DFSP) based on Mean Field (MF). In the RL part, this study designed a two-layer online learning mode in which the sample collection and the training improvement proceed alternately, the outer layer collects samples, when the collected samples meet the requirement of batch size, it enters to the inner layer loop, which uses the Q-learning model-free batch processing mode to proceed, and adopts neural network to approximate the value function to adapt to large-scale problems. By comparing the Average Relative Percentage Deviation (ARPD) index of the benchmark test questions, the calculation results of the proposed algorithm outperformed other similar algorithms, which proved the feasibility and efficiency of the proposed algorithm.

User defined weight based budget and deadline constrained workflow scheduling in cloud

AbstractCloud computing is a technology that is being used by scientists for the execution of huge scale workflows as it has various features such as availability, faster, cheaper, elasticity, and high accessibility. In addition, the users are charged on a pay per use basis which makes it more efficient to be used by various clients. Scheduling workflows in the cloud is a difficult task as it takes into account the dependencies of workflows along with quality of service (QoS) demands. The problem becomes more challenging when there are multiple QoS objectives which are often disagreeing to each other. A lot of research has been done to minimize makespan and execution cost under deadline and budget constraints. This article presents a workflow scheduling algorithm based on Jaya to minimize them both and returns solutions according to the weights a user gives. The algorithm has been compared with Min‐Min, Max‐Min, Round Robin, heterogeneous earliest finish time first, first come first serve, minimum completion time, dynamic heterogeneous earliest finish time first in WorkflowSim. The results are compared based on both cost and time. In the end, a trade‐off between the objectives has been shown. Results show that Jaya performs much better than other algorithms when averaged over several iterations.

Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing

Workflow scheduling is one of the significant issues for scientific applications among virtual machine migration, database management, security, performance, fault tolerance, server consolidation, etc. In this paper, existing time-based scheduling algorithms, such as first come first serve (FCFS), min–min, max–min, and minimum completion time (MCT), along with dependency-based scheduling algorithm MaxChild have been considered. These time-based scheduling algorithms only compare the burst time of tasks. Based on the burst time, these schedulers, schedule the sub-tasks of the application on suitable virtual machines according to the scheduling criteria. During this process, not much attention was given to the proper utilization of the resources. A novel dependency and time-based scheduling algorithm is proposed that considers the parent to child (P2C) node dependencies, child to parent node dependencies, and the time of different tasks in the workflows. The proposed P2C algorithm emphasizes proper utilization of the resources and overcomes the limitations of these time-based schedulers. The scientific applications, such as CyberShake, Montage, Epigenomics, Inspiral, and SIPHT, are represented in terms of the workflow. The tasks can be represented as the nodes, and relationships between the tasks can be represented as the dependencies in the workflows. All the results have been validated by using the simulation-based environment created with the help of the WorkflowSim simulator for the cloud environment. It has been observed that the proposed approach outperforms the mentioned time and dependency-based scheduling algorithms in terms of the total execution time by efficiently utilizing the resources.

A Multi Objective Time-Cost-Quality Optimization Model fot Project Scheduling and Human Resource Assignment Considering Manpower Competency

Today, information and knowledge are so intertwined with human life that the present age has been called the information era. In this situation, the role of human in the organization and the way of looking at him/her will have a significant contribution to the success or failure of the organization. The role of human resources is becoming more and more prominent and it is now mentioned as the only factor in gaining a sustainable competitive advantage. Furthermore, due to dramatic developments in the field of human resource management, competency-based human resource planning has found a special place. In this study, a project scheduling problem considering multi-skilled resource and manpower competency is investigated. To do this end, an integrated multi-objective optimization model of project scheduling and human resource assignment including total completion time and execution cost minimization and project quality maximization is presented. To solve problem exactly, a mixed-integer programming model is developed and then solved by e-constraint method in GAMS software. As the proposed model is NP-hard, a non-dominated sorting genetic (NSGA-II) algorithm is developed to solve the large scale problem. Furthermore, in order to validate the proposed algorithm, its results compare with exact method for instance problem in small scale. Results indicate that the proposed algorithm is outperformed and it can be used for real case problems.

Uncertain Time-Resource-Cost Trade-Off Models for Construction Project Schedule

Construction project schedule is to bring the completion time and the total cost into balance by allocating resources. In order to shorten the completion time and decrease the total cost as far as possible, three time-resource-cost trade-off models for construction project under uncertain environment, including expected value (E-V) model, chance-constrained (C-C) model and chance maximization (C-M) model, were built by using uncertain theory. The duration time of activities, the allocation time of material resources and human resources are considered as uncertain variables in the three models. The E-V model was to shorten the expected completion time under the chance constraint of cost. The C-C model was to obtain the minimum completion time under the chance constraints of completion time and cost. The C-M model was to maximize the chance that the completion time is not lager than the due date under the chance constraint of cost. For the convenience of calculation, the three models were changed into crisp equivalent form. A practical case study was provided to show the application process of three models and the results can be obtained by using the genetic algorithm. Finally, some extended construction project scheduling models are introduced.