TASC-MADM: Task Assignment in Spatial Crowdsourcing Based on Multiattribute Decision-Making

The methodology, formulating a reasonable task assignment to find the most suitable workers for a task and achieving the desired objectives, is the most fundamental challenge in spatial crowdsourcing. Many task assignment approaches have been proposed to improve the quality of crowdsourcing results and the number of task assignment and to limit the budget and the travel cost. However, these approaches have two shortcomings: (1) these approaches are commonly based on the attributes influencing the result of task assignment. However, different tasks may have different preferences for individual attributes; (2) the performance and efficiency of these approaches are expected to be improved further. To address the above issues, we proposed a task assignment approach in spatial crowdsourcing based on multiattribute decision-making (TASC-MADM), with the dual objectives of improving the performance as well as the efficiency. Specifically, the proposed approach jointly considers the attributes on the quality of the worker and the distance between the worker and the task, as well as the influence differences caused by the task’s attribute preference. Furthermore, it can be extended flexibly to scenarios with more attributes. We tested the proposed approach in a real-world dataset and a synthetic dataset. The proposed TASC-MADM approach was compared with the RB-TPSC and the Budget-TASC algorithm using the real dataset and the synthetic dataset; the TASC-MADM approach yields better performance than the other two algorithms in the task assignment rate and the CPU cost.

Pooling and Comparing Noise Annoyance Scores and “High Annoyance” (HA) Responses on the 5-Point and 11-Point Scales: Principles and Practical Advice

The use of different noise annoyance scales across studies and socio-acoustic surveys, in particular the popular 5-point verbal and 11-point numerical scales, has made the evaluation, comparison, and pooling of noise annoyance responses among studies a taxing issue. This is particularly the case when “high annoyance” (HA) responses need to be compared and when the original studies used different scales; thus, there are different so-called cutoff points that define the part of the scale that indicates the HA status. This paper provides practical guidance on pooling and comparing the respective annoyance data in both the linear and logistic regression context in a statistically adequate manner. It caters to researchers who want to carry out pooled analyses on annoyance data that have been collected on different scales or need to compare exposure–HA relationships between the 5-point and 11-point scales. The necessary simulation of a cutoff point non-native to an original scale can be achieved with a random assignment approach, which is exemplified in the paper using original response data from a range of recent noise annoyance surveys. A code example in the R language is provided for easy implementation of the pertinent procedures with one’s own survey data. Lastly, the not insignificant limitations of combining and/or comparing responses from different noise annoyance scales are discussed.

A Genetic Algorithm for Subcontractors Selection and Allocation in Multiple Building Projects

In construction industry general contractors (GCs) need to manage and conduct numerous projects simultaneously. To this aim, they usually have subcontractors conduct available tasks and projects. So, subcontractor management is becoming a major challenge. To deal with this challenge, GCs aim to reduce total costs of projects including employment /subcontracting costs, indirect costs, tardiness penalties, and the money which must be paid for movement of workforce from one project to another. The aim of current research study is to propose a model for selecting subcontractors and assigning available tasks in the project to them in order to reduce the costs of the GC. Then, a genetic algorithm (GA) is proposed to solve a real problem. The proposed algorithm is innovative from three points of view: (1) generation of initial population; (2) subcontractor assignment approach and (3) the objective function. The problem was also solved by means of an exact method. Then, the results of proposed algorithm were compared to the outcomes of the exact method. This comparison shows that the proposed algorithm can efficiently help GCs select subcontractors and assign available tasks to each subcontractor when several projects must be carried out simultaneously.

Optimum Energy Control of a Robotic Electric Vehicle at Time with Improved Control Assignment Approaches

Vehicles have made extraordinary commitment to the development of current culture by fulfilling the requirements for more prominent portability in everyday life. The development of Internal Combustion Engine has contributed a ton to the car area. In any case, a lot of harmful discharges as carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), unburned hydrocarbons (HCs, etc have been messing contamination up, a dangerous atmospheric devation, and annihilation of the ozone layer. As the current pattern proposes, this method of transport is probably going to supplant inside burning motor (ICE) vehicles soon. Every one of the primary EV segments has various innovations that are at present being used or can get conspicuous later on. Improved control assignment approach strategies are fit for securing ideal force taking care of, obliging framework errors, and fitting ongoing applications can fundamentally improve the powertrain productivity at various working conditions. Rule-based techniques are just organized and effectively implementable continuously; however, a restricted optimality in force dealing with choices can be achieved. Enhancement based strategies are more fit for achieving this optimality at the cost of expanded computational burden. Over the most recent couple of years, these improvement based strategies have been being worked on to suit continuous application utilizing more prescient, recognitive and man-made reasoning apparatuses. This paper presents a conversation about these new patterns continuously improved control assignment approach. Consequently HEVs give better fuel economy contrasted with ICE based vehicles/regular vehicle. Energy management techniques are the calculations that choose the force split among motor constantly to improve the fuel economy and advance the presentation of HEVs.

A competency-driven staff assignment approach to improving employee scheduling robustness

Presented paper concerns the competency-driven staff assignment and scheduling approach to the management of project portfolios subject to perturbations caused by employee absences and/or unexpected arrival of high priority jobs. Proactive strategy is considered, which exploits the concept of employee substitutability to improve the robustness of personnel allocation in the case of occurrence of specific types of disruptions. Solutions obtained using the model of a constraint satisfaction problem developed in this study are validated in series quantitative and qualitative experiments. With a view to future implementation in a Decision Support Systems dedicated to prototyping of proactive personnel allocation, a methodology employing the concept of a competency framework-based robustness measure is proposed. Implemented in a declarative framework, the proposed approach allows one to find a redundant competency framework robust to a given set of disruptions.