Research on Circular Area Search algorithm of multi-robot service based on SOA cloud platform

Considering autonomous mobile robots with a variety of specific functions as a kind of service, when there are many types and quantities of services and the density of regional services is large, proposing an algorithm of Circular Area Search (CAS) because of the problem of multi-robot service scheduling in various areas. Firstly, Django is used as the web framework to build the Service-Oriented Architecture (SOA) multi-robot service cloud platform, which is the basic platform for multi-service combination. Then, the service type, the latitude and longitude and the scoring parameters of the service are selected as the service search metrics to design the CAS algorithm that based on the existing service information registered in MySQL and the Gaode Map for screening optimal service, and then providing the service applicant with the best service. Finally, the service applicant applies for the self-driving tour service as an example to perform performance simulation test on the proposed CAS algorithm. The results show that the CAS algorithm of the multi-robot service cloud platform proposed in this paper is practical compared to the global search. And compared with the Greedy Algorithm experiment, the service search time is reduced about 58% compared with the Greedy Algorithm, which verifies the efficiency of CAS algorithm.     

Quantum computation based bundling optimization for combinatorial auction in freight service procurements

Combinatorial auction is a useful trade manner for transportation service procurements in e-marketplaces. To enhance the competition of combinatorial auction, a novel auction mechanism of two-round bidding with bundling optimization is proposed. As the recommended the auction mechanism, the shipper/auctioneer integrates the objects into several bundles based on the bidding results of first round auction. Then, carriers/bidders bid for the object bundles in second round. The bundling optimization is described as a multi-objective model with two criteria on price complementation and combination consistency. A Quantum Evolutionary Algorithm (QEA) with β-based rotation gate and the encoding scheme based on non-zero elements in complementary coefficient matrix is developed for the model solution. Comparing with a Contrast Genetic Algorithm, QEA can achieve better computational performances for small and middle size problems.     

Auxiliary error and probability density function based neuro-fuzzy model and its application in batch processes

This paper focuses on resolving the identification problemof a neuro-fuzzymodel (NFM) applied in batch processes. A hybrid learning algorithm is introduced to identify the proposed NFM with the idea of auxiliary error model and the identification principle based on the probability density function (PDF). The main contribution is that the NFM parameter updating approach is transformed into the shape control for the PDF ofmodeling error.More specifically, a virtual adaptive control systemis constructed with the aid of the auxiliary errormodel and then the PDF shape control idea is used to tune NFM parameters so that the PDF of modeling error is controlled to follow a targeted PDF, which is in Gaussian or uniform distribution. Examples are used to validate the applicability of the proposed method and comparisons are made with the minimum mean square error based approaches.     

Modeling trade-offs among ecosystem services in agricultural production systems

Although agricultural ecosystems can provide humans with a wide set of benefits agricultural production system management is mainly driven by food production. As a consequence, a need to ensure food security globally has been accompanied by a significant decline in the state of ecosystems. In order to reduce negative trade-offs and identify potential synergies it is necessary to improve our understanding of the relationships between various ecosystem services (ES) as well as the impacts of farm management on ES provision. We present a spatially explicit application that captures and quantifies ES trade-offs in the crop systems of Llanada Alavesa in the Basque Country. Our analysis presents a quantitative assessment of selected ES including crop yield, water supply and quality, climate regulation and air quality. The study is conducted using semantic meta-modeling, a technique that enables flexible integration of models to overcome the service-by-service modeling approach applied traditionally in ES assessment.     

A generalized method for the inherent energy performance modeling of machine tools

Machine tools (MTs), as the key equipment of manufacturing systems, have enormous quantities and consume a great amount of energy. However, the diversity of both machines and their energy consumption properties make it difficult to transfer the energy-saving knowledge and services among different MT. To facilitate the initialization configuration of energy-saving services, the inherent energy performance (IEP) is investigated to describe the differences in energy consumption among MTs, and a generalized method for modeling the IEP of MT and its electrical subsystems is proposed. Three key enablers, including generalized experimental design rules, automatic coding, and data processing algorithms, are presented and integrated into a supporting system to reduce the modeling efforts and knowledge requirements. Case studies of an offline manufacturing scenario and an Internet of Things (IoT)-enabled manufacturing scenario were carried out to verify the effectiveness and convenience of the proposed method. The results show that the proposed method can provide essential modeling support for large-scale energy-saving service configurations and energy-efficient MT development.     

Behavior and mechanism of in-situ synthesis of auxiliary electrode for electrochemical sulfur sensor by calcium aluminate system

The behavior and mechanism of in-situ synthesis of the auxiliary electrode for sulfur sensor were investigated in this work, aiming for better application of calcium aluminate system in synthesizing the auxiliary electrode used for sulfur sensor. The in-situ reaction experiment was developed. In addition, the thermodynamic and kinetic calculations were adopted to further study the in-situ reaction possibility and the reaction rate. The results indicated that the value of lg(a_[S]/a_[O]) should be greater than a particular value to ensure the occurrence of the in-situ reaction. After immersion into the molten iron, the CaS phase was synthesized in the calcium aluminate system. The relationship between the reaction rate and reaction time was exponential, and the initial reaction rate was affected by the CaO content incorporated in the calcium aluminate system and the sulfur content in the molten iron. The initial in-situ reaction rate greatly increased with the increase of the CaO content and sulfur content. For example, the initial reaction rate was as high as 14.87 s^-1 when the calcium aluminate system containing 60 wt% of CaO and for a sulfur content of 0.077 wt% in the molten iron. Moreover, the reason that the sulfur sensor fabricated by the ZrO₂(MgO) tube with the calcium aluminate coating with different components had the same response time when measuring the different sulfur contents in the molten iron was further explained.     

Sustainability of ride-hailing services in China’s mobility market: A simulation model of socio-technical system transition

As a symbol of sharing economy, ride-hailing services have spread to Asia, where various forms of services have been established according to the existing socio-technical regimes, including legal, policy, and environmental concerns. China, in particular, has accepted ride-hailing services by revising existing institutions. This study analyzes China’s acceptance of ride-hailing services in the socio-technical system context set up for legacy services. Simulation modeling, combined with transition theory and an agent-based model, is used to analyze the data. This study calculates consumer disutility based on mobility market share, reflecting consumer preference, and predicts the sustainability of ride-hailing services. The simulation results conclude that legalization and socio-technical context are of importance for sustainable mobility.     

Bayesian changepoint and time-varying parameter learning in regime switching volatility models

This paper proposes a combined state and piecewise time-varying parameter learning technique in regime switching volatility models using multiple changepoint detection. This approach is a Sequential Monte Carlo method for estimating GARCH & EGARCH based volatility models with an unknown number of changepoints. Modern auxiliary particle filtering techniques are used to calculate the posterior densities and online forecasts. This approach also automatically deals with the common ancestral path dependence problem faced in these type volatility models. The model is tested on Borsa Istanbul (BIST) formerly known as Istanbul Stock Exchange (ISE) market data using daily log returns. A full structural changepoint specification is defined in which all parameters of the conditional variance of the volatility models are dynamic. Finally, it is shown with simulation experiments that the proposed approach partitions the series into several regimes and learns the parameters of each regime's volatility model in parallel with the multiple changepoint detection process.     

Liner services network design and fleet deployment with empty container repositioning

Transportation demand of shipping container fluctuates due to the seasonality of international trade, thus, every 3–6 months, the liner company has to alter its current liner shipping service network, redeploy ships and design cargo routes with the objective of minimizing the total cost. To solve the problem, the paper presents a mixed integer linear program model. The proposed model incorporates several relevant constraints, such as weekly frequency, the transshipment of cargo between two or more service routes, and transport time. Extensive numerical experiments based on realistic date of Asia–Europe–Oceania shipping operations show that the proposed model can solve real-case problems efficiently by CPLEX. The results demonstrate that the model can reduce ship’s capacity consumption and raise ships’ capacity utilization.     

Resource allocation for multi-class services in multipath networks

In multipath networks, multiple paths are available for each pair of source and destination and can be used to carry data packets parallelly. It has been recognized that using multipath could promote the transmission reliability and fault tolerance, and improve the performance of increasingly bandwidth-hungry multi-media applications. In this paper we propose the resource allocation model for multi-class services in multipath networks with the objective of utility maximization, which is an intrinsically difficult problem of nonconvex optimization. We firstly analyze the model for only elastic services and obtain the optimal rate allocation for them. Then we also discuss the model for inelastic services with nonconcave (sigmoidal or discontinuous) utilities which share common links with elastic ones, and obtain some sufficient conditions under which the global optimum for both elastic and inelastic services can be obtained. For the nonconvex optimization problem, we present a heuristic algorithm using Particle Swarm Optimization (PSO), which can lead to improved solutions over existing approaches. Finally, some numerical examples are given to verify the results obtained.