1、中文 3700 字 ,2050 单词 外文翻译 原文 Pareto-Improving Contracts for Express Package Delivery Services Material Source: University of California Author: Alexandra M. Newman Abstract: We address the problem of an express package delivery company in structuring a long-term customer contract whose terms may inclu
2、de prices that differ by day-of-week and by speed-of-service. The company traditionally offered speed-of-service pricing to its customers, but without day-of-week differentiation,resulting in customer demands with considerable day-of-week seasonality. The package delivery company hoped that using da
3、y-of-week and speed-of-service price differentiation for contract customers would induce these customers to adjust their demands to become counter-cyclical to the non-contract demand. Although this usually cannot be achieved by pricing alone, we devise an approach that utilizes day-of-week and speed
4、-of-service pricing as an element of a Pareto-improving contract. The contract provides the lowest-cost arrangement for the package delivery company while ensuring that the customer is at least as well off as he would have been under the existing pricing structure. The contract pricing smoothes the
5、package delivery companys demand and reduces peak requirements for transport capacity. The latter helps to decrease capital costs, which may allow a further price reduction for the customer. We formulate the pricing problem as a biconvex optimization model, and present a methodology for designing th
6、e contract and numerical examples that illustrate the achievable savings. Keywords:transportation contracts； contract pricing； speed-of-service pricing； time-of-use pricing； day-of-week pricing 1. INTRODUCTION Most package delivery companies PDCs charge a premium for faster delivery, but the practic
7、e of pricing by day of week is very limited. In the absence of this type of price differentiation, shipment volumes exhibit strong day-of-week patterns, especially in the express package delivery market. Although the schedules of various ground transport vehicles often can be adjusted to account for
8、 this day-of-week seasonality, express package delivery companies rely heavily on aircraft, for which it is not possible to match shipping capacity to demand very well. Consequently, excess shipping capacity varies by day of week and by route.When negotiating with potential high-volume contract cust
9、omers, it may be advantageous to offer the customer an incentive to release packages countercyclically to the overall demand pattern. Such a counter-cyclical release pattern would improve the profit of the PDC in two ways. First, revenue is generated using available excess capacity for which the inc
10、remental operating costs are quite small. Second, by smoothing the overall demand pattern, requirements for additional transport capacity typically provided by commercial carriers at premium prices are minimized, and the PDC is able to provide more reliable service to all customers because the reduc
11、ed peak loads pose less strain on pickup, delivery, and sortation resources. Because the incremental cost of servicing a contract customer with a counter-cyclical demand pattern may be small, the PDC may be able to pass on the savings to its customers by charging lower average prices. Our research w
12、as motivated by a PDC whose management had hoped to induce the companys contract customers to behave in the desired way via day-ofweek and speed-of-service pricing alone. As we explain in more detail later, this is usually not possible. For this reason, we seek to develop a methodology for structuri
13、ng contracts?which may include day-ofweek and speed-of-service pricing as one element? that achieves the highest total profit for the PDC while ensuring that the customer is at least as well off as he would be under an existing contract or under any arbitrary reference price structure. We examine th
14、is problem assuming that the PDC is negotiating with one major customer at a time. The most promising opportunities for improving the PDCs profit via more complex contract pricing arrangements occur in situations in which several customers sharing an aircraft route have similar day-ofweek seasonalit
15、y. This phenomenon occurs frequently due to weekly procurement cycles. For example, automobile assembly plants request deliveries of many parts on Monday morning to supply the assembly line for the week. Although this may not be optimal,typical material requirements planning systems operate on a wee
16、kly schedule, and the procurement process follows suit. Component suppliers in the same vicinity that provide parts to a given assembly plant therefore ship on the same day, usually Friday. The PDC would like all of these customers to modify their shipment plans, but it usually faces the problem of
17、negotiating with them one at a time. When negotiating with a given customer, the PDC could consider likely outcomes of later negotiations with other customers, but this is obviously difficult to do because of the uncertainty involved. In our approach, various problem data can be specified to account
18、 for any capacity availability profiles induced by non-contract customers and other contract customers that the PDC wishes to consider. In this paper, we focus on the flow of a class of homogeneous or nearly homogeneous packages from a single shipper typically a manufacturer that provides vendor-man
19、aged inventory VMI services to a single consignee a downstream user of the manufactured parts. In the concluding section, we explain how our approach can be generalized to multiple package types. Because of the VMI arrangement, the shipper owns the goods and therefore incurs inventory holding costs
20、until the consignee utilizes the goods. We emphasize that our approach is designed for situations in which the customer has considerable control over the timing of package releases which would usually entail changes in the production schedule, and thus our approach probably would not be suitable for
21、 an Internet retailer that is expected to fulfill orders soon after they arrive, often by a speed or mode of service chosen by the end-customer. The remainder of this paper is organized as follows: The next section contains a review of the literature. This is followed by formal statements of the PDC
22、s and customers decision problems. In Section 4, we formulate the PDCs and customers problems under a price-only contract and discuss the shortcomings of such a contract in our problem context, and this discussion provides a backdrop for our solution strategy. In Section 5, we present the details of
23、 our methodology for structuring Pareto-improving contracts. Section 6 provides numerical examples that illustrate our proposed method and its benefits. Section 7 closes the paper with a discussion of extensions and generalizations of our approach. 2. Literature Review In this section, we provide an
24、 overview of the separate literatures on time-of-service pricing, and on speedof-service and priority pricing. It is important to point out that, to the best of our knowledge, there is very little research that considers both simultaneously. We first discuss time-of-service pricing with an emphasis
25、on electricity, toll roads, and computer and telecommunication network services, which are the most common application areas. Later in the section, we discuss the literature on speed-of-service and priority pricing, which tends to be less application-specific. In the interest of brevity, our citatio
26、ns are limited. Our intent is to provide the reader a sense of the issues that have been explored. 2.1. Time-of-Service Pricing Vickrey 1971 provides a very lucid qualitative discussion of the benefits of what he calls “responsive pricing,” that is, pricing that varies according to the state of the
27、system. Responsive pricing includes such concepts as dynamic pricing based on instantaneous real-time congestion, time-of-service pricing based on forecasted not real-time demand or congestion patterns, and pricing schemes in the vein of currentday revenue management. Vickrey 1971 mentions applicati
28、on areas such as long-distance telephone service, airline reservations, and water and power delivery?the very same types of applications that motivate present-day research 2.1.1. Electricity. Electricity markets are the most common application domain for time-of-use pricing, which is commonly referr
29、ed to as peak load pricing in this industry. Here, peak prices have the effect of both reducing total demand and shifting some demand to off-peak periods. Most of the research can be classified into three broad areas: 1 the welfare economics of time-of-use pricing, 2 models of price elasticity for e
30、lectricity, and 3 methods for setting prices. Seminal papers on the welfare benefits of peak-load pricing include Boiteux 1960 and Williamson 1966. Although much of the discussion is posed in terms of peak versus off-peak prices, Panzar 1976 argues that capacity costs depend not only on peak loads b
31、ut also on the loads during non-peak periods. Eckel 1987 examines the question of pricing based on demandclass i.e., industrial, commercial, and residential consumers. The literature on models of price elasticity for electricity is too extensive to discuss here. For a recent article, see Kamerschen
32、and Porter 2004. These price elasticity models and estimates are widely used in pricing methods, where the emphasis is on setting prices during peak demand periods so as to attenuate demand and thereby reduce capacity requirements. Crew et al. 1995 provide a historical perspective on optimization-ba
33、sed time-of-use pricing approaches, focusing on non-storable goods such as electricity. Borenstein 2005 highlights several important issues in designing a pricing scheme for utility companies, including: 1 how often prices change, and 2 how long the delay between setting and realizing a price is. Th
34、e most extreme, yet most effective, pricing scheme is real-time pricing. Borenstein describes various implementations of real-time pricing, and the implications of each. He notes that technology plays a key role in the effectiveness of real-time pricing. 2.1.2. Transportation Although peak pricing i
35、s not yet widespread in transportation systems, researchers have been espousing the welfare gains and social benefits for years, citing the need to consider factors such as congestion externalities and environmental effects. See, e.g., an early paper by Vickrey 1963 and a more recent anthology edite
36、d by Button and Verhoef 1998. Wachs 2005 describes the current state of peak-load pricing on urban road networks, noting that only recently has technology enabled such pricing methods More recent research on time-of-day pricing for toll roads, bridges, tunnels, etc., has begun to consider the impact of traveler choices. Generally, these models assume that the traveler has the
