如何进一步优化“哑铃式”生产工艺

煤炭科研院所转制后的企业大多采用"哑铃式"生产模式,即设计、组装(生产的两头)由自身控制,中间加工环节放在各外委厂家加工,以避免科研院所自身加工生产条件不足的缺陷,在转制起始阶段能迅速组织生产,形成较为快速的经济增长。但随着产品品种逐渐增多、产品生产数量日益增加,采用本模式的生产工艺逐步暴露出一定的缺陷,如:产品质量的不稳定、产品库存的积压、产品成本的增加等等一系列问题。因此本文将通过对多种新产生问题的分析,提出进一步优化"哑铃式"生产工艺的方法。     

基于粒子群算法下的交叉口信号配时优化

为了合理地优化交叉口信号配时,通过分析交叉口的评价指标,建立了以车辆的平均延误、停车次数最小、交叉口整体通行能力最大、各相位有效绿灯时间和交叉口周期时长作为约束条件的数学模型。并通过改进前人研究基础上的动态加权系数,将多目标的非线性优化问题转化为单一目标的非线性规划问题,为了得出更科学稳定的解,提出了改善粒子群算法系统稳定性的2种方法,并将其与粒子群算法结合起来。然后以Matlab为求解工具,结合临邑市某一交叉口实例进行求解分析。最后的结果表明,在使用改进后的粒子群算法进行优化后交叉口通行能力较之现状提升了9%,延误下降了28%,停车次数下降了9%,且各项优化结果均优于Webster,改进后的算法在程序中运行300代,到216代才开始收敛,而未改进的算法稳定性较差,优化结果和收敛曲线则随着实验次数的变化而变化,最后的结论证明了该算法和模型的可靠性。     

Islamic and conventional portfolios optimization under investor sentiment states: Bayesian vs Markowitz portfolio analysis

This paper investigates the portfolio optimization under investor’s sentiment states of Hidden Markov model and over a different time horizon during the period 2004–2016. To compare the efficient portfolios of the Islamic and the conventional stock indexes, we have employed two approaches: the Bayesian and Markowitz mean-variance. Our findings reveal that the Bayesian efficient frontier of Islamic and conventional stock portfolios is affected by the investor’s sentiment state and the time horizon. Our findings also indicate that the investor’s sentiment regimes change the Islamic and the conventional optimal diversified portfolios.Moreover, the results show that the potential diversification benefits seem to be more important when using the Bayesian approach than when applying the Markowitz approach. This finding is valid for the bearish, depressed, bullish and calm states in Islamic stock markets. However, the diversification of potential portfolios is significant only for the bullish and the bubble states in the conventional financial markets.The findings of the study provided additional evidence for investors to exploit googling investor sentiment states to evaluate the portfolio performance and make an optimal portfolio allocation.     

Electrification of a city bus network—An optimization model for cost-effective placing of charging infrastructure and battery sizing of fast-charging electric bus systems

The deployment of battery-powered electric bus systems within the public transportation sector plays an important role in increasing energy efficiency and abating emissions. Rising attention is given to bus systems using fast charging technology. This concept requires a comprehensive infrastructure to equip bus routes with charging stations. The combination of charging infrastructure and bus batteries needs a reliable energy supply to maintain a stable bus operation even under demanding conditions. An efficient layout of the charging infrastructure and an appropriate dimensioning of battery capacity are crucial to minimize the total cost of ownership and to enable an energetically feasible bus operation. In this work, the central issue of jointly optimizing the charging infrastructure and battery capacity is described by a capacitated set covering problem. A mixed-integer linear optimization model is developed to determine the minimum number and location of required charging stations for a bus network as well as the adequate battery capacity for each bus line. The bus energy consumption for each route segment is determined based on individual route, bus type, traffic, and other information. Different scenarios are examined in order to assess the influence of charging power, climate, and changing operating conditions. The findings reveal significant differences in terms of required infrastructure. Moreover, the results highlight a trade-off between battery capacity and charging infrastructure under different operational and infrastructure conditions. This paper addresses upcoming challenges for transport authorities during the electrification process of the bus fleets and sharpens the focus on infrastructural issues related to the fast charging concept.     

Robust grain supply chain design considering post-harvest loss and harvest timing equilibrium

This paper presents a bi-level robust optimization model, where a food company maximizes its profit and minimizes post-harvest loss by optimally deploying grain processing/storage facilities and determining grain purchase price, while a group of spatially distributed non-cooperative farmers determine harvest time, shipment, storage, and market decisions under yield uncertainty and market equilibrium. The non-cooperative behavior of the food company and the farmers is represented by a bi-level Stackelberg leader follower’s game model with mixed-integer decision variables. The proposed model and solution approach are applied to case studies for Illinois and Brazil.     

Measuring the perceived container leasing prices in liner shipping network design with empty container repositioning

This paper aims to measure the perceived container leasing prices at different ports by presenting a two-stage optimization method. In stage I, we propose a practical liner shipping network design problem with empty container repositioning. The proposed problem further considers the use of foldable containers and allows the mutual substitution between empty containers to decrease the number of empty containers to be repositioned. In stage II, the inverse optimization technique is used to determine the perceived container leasing prices at different ports, based on the solution obtained in stage I. Based on a set of candidate liner shipping service routes, a mixed-integer nonlinear programming model is built for the proposed problem in stage I. The nonlinear terms are linearized by introducing the auxiliary variables. Numerical experiments based on a realistic Asia-Europe-Oceania liner shipping network are carried out to account for the effectiveness of our two-stage optimization method.     

A multi-objective sustainable load planning model for intermodal transportation networks with a real-life application

Growing importance of intermodal transportation necessitates modeling and solving load planning problems by taking into account various complex decisions simultaneously like transportation mode/service type selection, load allocation, and outsourcing. This paper presents a mixed-integer mathematical programming model for a multi-objective, multi-mode and multi-period sustainable load planning problem by considering import/export load flows to satisfy transport demands of customers and many other related issues. Several multiple objective optimization procedures are utilized in order to handle conflicting objectives simultaneously under crisp and fuzzy decision making environments. A real-life case study is also performed to present application and usefulness of the proposed model.     

A profit-maximization location-capacity model for designing a service system with risk of service interruptions

This paper considers the design of an immobile service system in which each facility’s service process is subject to the risk of interruptions. The location-capacity decisions and allocations are simultaneously made to maximize the difference between the service provider’s profit and the sum of customers’ transportation and waiting costs. An efficient Lagrangian-based solution algorithm is developed, which solves large-sized instances with up to 50 service facilities and 500 customers in a few seconds. Several sensitivity analyses and managerial insights are presented. The model is also applied to a case study on a logistics network design problem in the zinc mining industry.     

关于优化流量经营管理的思考

本文将以中国移动为研究对象,针对流量经营管理作出分析思考。     

A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting

Air transport demand forecasting is receiving increasing attention, especially because of intrinsic difficulties and practical applications. Total passengers are used as a proxy for air transport demand. However, the air passenger time series usually has a complex behavior due to their irregularity, high volatility and seasonality. This paper proposes a new hybrid approach, combining singular spectrum analysis (SSA), adaptive-network-based fuzzy inference system (ANFIS) and improved particle swarm optimization (IPSO), for short-term air passenger traffic prediction. The SSA is used for identifying and extracting the trend and seasonality of air transport demand and the artificial intelligence technologies, including ANFIS and IPSO, are utilized to deal with the irregularity and volatility of the demand. The HK air passenger data are collected to establish and validate the forecasting model. Empirical results clearly points to the enormous potential that the proposed approach possesses in air transport demand forecasting and can be considered as a viable alternative.