GRASP-Tabu Search Algorithms for the Route Planning Problem in Spatial Crowdsourcing

With the speedy progress of mobile devices, a lot of commercial enterprises have exploited crowdsourcing as a useful approach to gather information to develop their services. Thus, spatial crowdsourcing has appeared as a new platform in e-commerce and which implies procedures of requesters and workers. A requester submits spatial tasks request to the workers who choose and achieve them during a limited time. Thereafter, the requester pays only the worker for the well accomplished the task. In spatial crowdsourcing, each worker is required to physically move to the place to accomplish the spatial task and each task is linked with location and time. The objective of this article is to find an optimal route to the worker through maximizing her rewards with respecting some constraint, using an approach based on GRASP with Tabu. The proposed algorithm is used in the literature for benchmark instances. Computational results indicate that the proposed and the developed algorithm is competitive with other solution approaches.

Path Selection for the Inspection Robot by m-Generalized q-Neutrosophic PROMETHEE Approach

Path planning can be considered the most vital task of the autonomous robot. In this task, selecting an optimal route from the starting to the target position becomes an important problem that must be addressed when multiple competing optimization priorities are considered. Thus, a novel route assessment strategy based on a multi-criteria decision-making approach is proposed. The m-generalized q-neutrosophic PROMETHEE (PROMETHEE-mGqNS) method is applied to aggregate the competing route assessment requirements and choose an optimal route. A case study is investigated to explain the proposed strategy for path planning in a typical environment and indicates the method stability when incomplete input data characteristics are present.

WeRoute: Route Optimization Web-Based System and Driver Mobile Application

This paper intends to conceptualise an optimisation solution for vehicle routing that can get the best routing result and release the most optimal route to the driver, namely WeRoute. The objectives of the paper are to manage the data efficiently, save time, reduce cost, enhance customer satisfaction, and decrease the emission of carbon. Moreover, this is also known as the vehicle routing problem, which deals with a range of variables, including drivers, stops, roads, and customers. The method, Genetic algorithm, was developed to improve the efficiency of generating feasible routes for a project. A team of drivers and several stops are needed to generate the solution of optimising the vehicle routing. It can be said that the more drivers or stops, the more complicated the problem becomes, such as cost controls and vehicle limitations. Thus, a route optimisation tool slowly becomes the key to ensuring the delivery business as efficiently as possible.

DEVELOPMENT OF A MODEL FOR OPTIMIZING MUNICIPAL SOLID WASTE STREAMS AT THE ROUTE LEVEL

All over the world, a huge amount of waste is generated every minute. The organization of an effective waste management system is very topical. The economic efficiency and costs of the users of the system depend on a properly organized system of accumulation, transportation, processing or disposal. The article discusses the process of working with waste in the cities of Russia. The technique and methods of working with waste in large cities at the first stage of removal are described. The problem of optimization of the territorial scheme for the disposal of solid municipal waste has been formulated. The technological process of operation of the first leg garbage truck on the route is described and considered. The criteria and factors influencing the choice of the optimal route are formulated. The necessary initial data for calculating the optimal scheme of waste gravitation are described. The function of total route costs has been built.

Personalized Route Planning System Based on Driver Preference

At present, most popular route navigation systems only use a few sensed or measured attributes to recommend a route. Yet the optimal route considered by drivers needs be based on multiple objectives and multiple attributes. As a result, these existing systems based on a single or few attributes may fail to meet such drivers’ needs. This work proposes a driver preference-based route planning (DPRP) model. It can recommend an optimal route by considering driver preference. We collect drivers’ preferences, and then provide a set of routes for their choice when they need. Next, we present an integrated algorithm to solve DPRP, which speeds up the search process for recommending the best routes. Its computation cost can be reduced by simplifying a road network and removing invalid sub-routes. Experimental results demonstrate its effectiveness.

Method of Fuzzy Parametric Selection for Making a Reasonable Decision by an Intelligent Training Module

Selection of an optimal route within the intelligent approach provides for the possibility of applying soft models and computing in estimation of the trainee presence in the knowledge space. Among numerous ways of representation and processing of information of this type, the special place is held by those able to adapt to the maximal number of NO factors, characterizing the actual training situations, their measurable data and actual methods and ways of their processing that have ambiguities, uncertainties and incompleteness of the respective models and methods. In this paper, we suggest to extend the certain well-proven best practices in data analysis and transformation in the training environment information space to solving the actual training management problems. In addition, the paper demonstrates approaches to the use of Pareto-optimal approaches for fuzzy and underdetermined situations in actual training processes. Formally, this problem is solved with a fuzzy systemic graph. Variants of calculating procedures, allowing the use of the available apparatus of soft models and computing with the purpose to eliminate uncertainties when forming grounded decisions, are given. Methods and criteria of route options selection with regard of vaguely defined functional specification requirements have been developed pursuant to the study.