网络团购(on-line group shopping)作为一种新兴和重要的商业模式B2T (Business to Team)近年来发展迅速,同时又呈现出一些不确定性。这一商业模式的价值主张或价值体现(value proposition)在于通过集合订单使消费者获取最佳的价格(find...网络团购(on-line group shopping)作为一种新兴和重要的商业模式B2T (Business to Team)近年来发展迅速,同时又呈现出一些不确定性。这一商业模式的价值主张或价值体现(value proposition)在于通过集合订单使消费者获取最佳的价格(find the best price),同时为消费者带来了特殊的消费和服务体验,其本质是一种折扣交易(discount deals)。但国内这一模式的发展普遍存在同质化、易于模仿和缺乏可持续的盈利模式等问题而遭遇发展瓶颈。文章采用e3-value系统分析方法和工具,对网络团购商业模式的价值本体与价值活动进行了结构化分析;针对其缺乏行业协同性问题,基于价值网这一价值创造和协同的“软集成”模式,提出了一种新的商业模式——团购价值网;进一步的价值分析表明,这一新的商业模式不仅能通过构建共生共赢的价值网生态系统形成新的价值共同体,还有利于提高各参与方的收益。最后从管理的角度,提出了一些对策与建议,以实现网络团购商业模式的重组与优化,提升这一行业的竞争力与生命力。展开更多
Seeking shortest travel times through smart algorithms may not only optimize the travel times but also reduce carbon emissions, such as CO2, CO and Hydro-Carbons. It can also result in reduced driver frustrations and ...Seeking shortest travel times through smart algorithms may not only optimize the travel times but also reduce carbon emissions, such as CO2, CO and Hydro-Carbons. It can also result in reduced driver frustrations and can increase passenger expectations of consistent travel times, which in turn points to benefits in overall planning of day schedules. Fuel consumption savings are another benefit from the same. However, attempts to elect the shortest path as an assumption of quick travel times, often work counter to the very objective intended and come with the risk of creating a “Braess Paradox” which is about congestion resulting when several drivers attempt to elect the same shortest route. The situation that arises has been referred to as the price of anarchy! We propose algorithms that find multiple shortest paths between an origin and a destination. It must be appreciated that these will not yield the exact number of Kilometers travelled, but favourable weights in terms of travel times so that a reasonable allowable time difference between the multiple shortest paths is attained when the same Origin and Destinations are considered and favourable responsive routes are determined as variables of traffic levels and time of day. These routes are selected on the paradigm of route balancing, re-routing algorithms and traffic light intelligence all coming together to result in optimized consistent travel times whose benefits are evenly spread to all motorist, unlike the Entropy balanced k shortest paths (EBkSP) method which favours some motorists on the basis of urgency. This paper proposes a Fully Balanced Multiple-Candidate shortest path (FBMkP) by which we model in SUMO to overcome the computational overhead of assigning priority differently to each travelling vehicle using intelligence at intersections and other points on the vehicular network. The FBMkP opens up traffic by fully balancing the whole network so as to benefit every motorist. Whereas the EBkSP reserves some routes for cars on high priority, our algorithm distributes the benefits of smart routing to all vehicles on the network and serves the road side units such as induction loops and detectors from having to remember the urgency of each vehicle. Instead, detectors and induction loops simply have to poll the destination of the vehicle and not any urgency factor. The minimal data being processed significantly reduce computational times and the benefits all vehicles. The multiple-candidate shortest paths selected on the basis of current traffic status on each possible route increase the efficiency. Routes are fewer than vehicles so possessing weights of routes is smarter than processing individual vehicle weights. This is a multi-objective function project where improving one factor such as travel times improves many more cost, social and environmental factors.展开更多
文摘网络团购(on-line group shopping)作为一种新兴和重要的商业模式B2T (Business to Team)近年来发展迅速,同时又呈现出一些不确定性。这一商业模式的价值主张或价值体现(value proposition)在于通过集合订单使消费者获取最佳的价格(find the best price),同时为消费者带来了特殊的消费和服务体验,其本质是一种折扣交易(discount deals)。但国内这一模式的发展普遍存在同质化、易于模仿和缺乏可持续的盈利模式等问题而遭遇发展瓶颈。文章采用e3-value系统分析方法和工具,对网络团购商业模式的价值本体与价值活动进行了结构化分析;针对其缺乏行业协同性问题,基于价值网这一价值创造和协同的“软集成”模式,提出了一种新的商业模式——团购价值网;进一步的价值分析表明,这一新的商业模式不仅能通过构建共生共赢的价值网生态系统形成新的价值共同体,还有利于提高各参与方的收益。最后从管理的角度,提出了一些对策与建议,以实现网络团购商业模式的重组与优化,提升这一行业的竞争力与生命力。
文摘Seeking shortest travel times through smart algorithms may not only optimize the travel times but also reduce carbon emissions, such as CO2, CO and Hydro-Carbons. It can also result in reduced driver frustrations and can increase passenger expectations of consistent travel times, which in turn points to benefits in overall planning of day schedules. Fuel consumption savings are another benefit from the same. However, attempts to elect the shortest path as an assumption of quick travel times, often work counter to the very objective intended and come with the risk of creating a “Braess Paradox” which is about congestion resulting when several drivers attempt to elect the same shortest route. The situation that arises has been referred to as the price of anarchy! We propose algorithms that find multiple shortest paths between an origin and a destination. It must be appreciated that these will not yield the exact number of Kilometers travelled, but favourable weights in terms of travel times so that a reasonable allowable time difference between the multiple shortest paths is attained when the same Origin and Destinations are considered and favourable responsive routes are determined as variables of traffic levels and time of day. These routes are selected on the paradigm of route balancing, re-routing algorithms and traffic light intelligence all coming together to result in optimized consistent travel times whose benefits are evenly spread to all motorist, unlike the Entropy balanced k shortest paths (EBkSP) method which favours some motorists on the basis of urgency. This paper proposes a Fully Balanced Multiple-Candidate shortest path (FBMkP) by which we model in SUMO to overcome the computational overhead of assigning priority differently to each travelling vehicle using intelligence at intersections and other points on the vehicular network. The FBMkP opens up traffic by fully balancing the whole network so as to benefit every motorist. Whereas the EBkSP reserves some routes for cars on high priority, our algorithm distributes the benefits of smart routing to all vehicles on the network and serves the road side units such as induction loops and detectors from having to remember the urgency of each vehicle. Instead, detectors and induction loops simply have to poll the destination of the vehicle and not any urgency factor. The minimal data being processed significantly reduce computational times and the benefits all vehicles. The multiple-candidate shortest paths selected on the basis of current traffic status on each possible route increase the efficiency. Routes are fewer than vehicles so possessing weights of routes is smarter than processing individual vehicle weights. This is a multi-objective function project where improving one factor such as travel times improves many more cost, social and environmental factors.
