دانلود مقاله ISI انگلیسی شماره 556
عنوان فارسی مقاله

برون سپاری خدمات رانندگی برای به حداکثر رساندن سود

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
556 2009 11 صفحه PDF سفارش دهید 7240 کلمه
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عنوان انگلیسی
Ride service outsourcing for profit maximization
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Transportation Research Part E: Logistics and Transportation Review, Volume 45, Issue 1, January 2009, Pages 138–148

کلمات کلیدی
برون سپاری خدمات حمل و نقل - تخصیص ناوگان - برنامه ریزی راه انداز - نسل ستونی -
پیش نمایش مقاله
پیش نمایش مقاله برون سپاری خدمات رانندگی برای به حداکثر رساندن سود

چکیده انگلیسی

Transportation service subcontracting is becoming an effective means of business for many leading carriers to maintain their market dominance and profitability. This research is based on an outsourcing practice in one of the few largest limousine fleet companies in the US, whose (mostly advance) demand for services often exceeds its capacity. We develop a model for ride service outsourcing. A column generation method is proposed. The outsourcing decision is made simultaneously with pre-scheduling of in-house ride services as well as staffing. Test with services data indicates that the developed method dramatically improves the carrier’s outsourcing decision.

مقدمه انگلیسی

In today’s world of transportation services, it is typical that a large carrier has a number of affiliated smaller carriers such as owner–operators. They often operate in a collaborative relationship. Transportation services contracted to these large carriers are frequently subcontracted, or outsourced, to their affiliated smaller carriers at a reduced rate. This service outsourcing harnesses the complementarity of service capacities among carriers, and in the same time helps the large carrier maintain its market dominance and profitability. In this case, the subcontracting carrier serves as both a service provider (to directly serve the selected requests:) and a business intermediary (to bridge the affiliated carriers and the unmet requests). An important challenge to the large carrier in this context is to decide which service requests to subcontract out when its own capacity is in shortage. This study is based on a real application at BostonCoach Inc., a premier worldwide provider of executive sedan, limousine and event transportation services. The customers include corporate executives, celebrities and leisure travelers. Its primary business is in the large metropolitan areas in the United States such as New York, Boston, Washington DC, Philadelphia, Chicago, etc. The operations are independent of each other between service areas. Take the sub-fleet in Boston as an example. Boston is one of BostonCoach’s major service areas. Generally on a typical day, about 50% of the requests were booked three days advance, inclusively 70% two days advance and 85% one day advance. During the daily operation, new demand are called in for service (called spot demand later), which account for the remaining 15% services. Subcontracting decisions, according to the agreements between BostonCoach and its collaborating companies, have to be made one day (24 h) early. When BostonCoach projects to have demand exceeding its capacity the next day, it subcontracts some of its currently booked rides to those smaller affiliated fleets. The rides subcontracted are equivalently called vended rides later. In addition, if the number of subcontracts is not enough, denials of ride requests could take place. Request denial is costly in terms of loss of customer’s goodwill. BostonCoach experienced about 5% denials before our proposed new outsourcing system. When using out proposed method, the average denial rate dropped to below 1.5% in the benchmark tests. Experience shows that it is very important to have a good estimate of its service capability in relation to the future (projected) demand. An efficient scheduling system is such a means to determine the need for subcontracting and to make in-house ride pre-assignment.

نتیجه گیری انگلیسی

Demand forecasting remains an area that warrants careful study in the future. Ride demand forecast for assessment of service capacity need or for the purpose of outsourcing decisions must respect the three dimensional characteristic of the problem due to its combinatorial nature. In this paper, we introduce a simple and yet practical way of forecast. This paper studies a popular practical problem on service subcontracting between carriers by utilizing routing/scheduling techniques. The study problem renders readers a perspective to collaborations between transportation service providers. While there are multiple ways for collaboration between carriers, each with unique benefit (Song and Regan, 2001), this paper focuses on outsourcing, and presents an optimization based method for subcontracting in order to maximize the company’s profit. The main contribution of this paper remains in our application of this integrated scheduling for outsourcing to demonstrate the significant profit improvement potential. From the practical view point, our proposed algorithm successfully solves the outsourcing problem together with the in-house pre-scheduling. It is worth noting that in-house pre-scheduling is an integral part of the outsourcing decision due to the combinatorial nature of the three dimensional problem. Clearly, the assessment of in-house service capacity is key to this subcontracting decision making. In addition, worth noting but not a focus of this paper, our proposed column generation method directly solves the shortest path problem with resource constraints and significantly improves on the computational time of the previously developed scheduling system. Note that the capacity needed when the total demand unfold gradually over the course of a day could be slightly higher than when the total demand is given at the beginning. In the first case, the sedan sometimes might not know where to position for next ride after a service, resulting in time wasted in transition (i.e. waiting for next assignment). However, we believe that the second case, which we use in our problem, is a good conservative approximation. This is based on two facts. First, about 85% of the rides are known a priori; secondly, a significant buffer time between service calls to account for randomness in travel time is added in the proposed algorithm.

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