مسیریابی وسیله نقلیه پویا برای تحویل آنلاین B2C
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|23980||2005||13 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Omega, Volume 33, Issue 1, February 2005, Pages 33–45
Electronic commerce (EC) is increasingly popular in today's businesses. The business-to-consumer EC environment has voluminous, unpredictable, and dynamically changing customer orders. A major part of the delivery system of this environment is the dynamic vehicle routing (DVR) system. This study investigates several algorithms suitable for solving the DVR problem in business-to-consumer (B2C) EC environment. It designs the solution process into three phases: initial-routes formation, inter-routes improvement, and intra-route improvement. A computer program is created to demonstrate a system simulating vehicle routing process under the online B2C environment. The simulated system collects data for system performance indexes such as simulation time, travel distance, delivery time, and delay time. The results show that when orders are placed through the Internet in an online B2C environment, the Nearest algorithms can be used to find satisfactory routes during the first phase of a DVR delivery system. The three-phase solution process is proven to be significantly better in travel distance and delivery time than the conventional single-phase solution process.
Delivering goods to customers is a critical activity in any business. On-time delivery relies heavily on effective vehicle routing once the merchandise is out the supplier's door and on its way to the customer. The problem of vehicle routing is much more complicated in an electronic commerce (EC) environment where the process of buying, selling, or exchanging products, services, and information is done through the Internet. In business-to-business (B2B) EC environment, the buyers and sellers are all business units. The main concern is how to maintain the efficiency of the supply chain partnership, which coordinates the order generation, the order taking, and the order fulfilment and distribution. In this environment, the buyers purchase products and services from the sellers with or without an intermediary. The two business partners integrate Just-in-Time (JIT) manufacturing and JIT inventory policy with JIT delivery. In fact, a JIT delivery service could be provided by either a buyer's, a seller's, or a third-party's deliverer (such as FedEx, UPS, or DHL). It is a coordinated effort of the deliverer and the seller or the suppliers of the seller. It is normal that these business partners have long-term relationships. The orders are normally planned, repeated, and reliable. Contrary to B2B environment, the delivery policy of business-to-consumer (B2C) EC environment is different. The orders in online B2C environment are small in size, instantaneous, ever changing, and placed by numerous consumers. These customers are normally bargain seekers and care less about loyalty to the sellers. The coordination between the buyers and the sellers for JIT delivery is extremely difficult, if not impossible. When a customer places an order through the Internet, the best practice for the seller is to ship the goods from an adjacent distribution center or partnering supplier. However, the availability of on-hand inventory (or safety stock) is very limited under JIT production setting and the goods will very likely be shipped from a distance depot. Therefore, in the B2C environment the need of having a quick-response vehicle dispatching system that handles dynamic demands of consumers is much greater than that in the B2B. This calls for an effective routing of delivery vehicles in order to minimize the travel distance and the delivery time. There are various vehicle routing algorithms in the literature. However, none of them alone is useful in the online B2C environment. These algorithms work best only when customer orders are planned and can be predicted by the delivery system. This study adopts the heuristic approach of the existing dynamic vehicle routing technique to solve the delivery problem in the online B2C environment. The solution process is divided into three phases. The purposes of this study are: (1) to demonstrate a system simulating vehicle routing process under the online B2C environment, (2) to verify that the three-phase solution process performs significantly better than the single-phase solution process, and (3) to identify the optimal algorithms and improvement strategies for vehicle routing in the online B2C environment. The remaining paper is organized as follows. Section 2 reviews the existing literature on vehicle routing. Section 3 describes a three-stage dynamic vehicle routing process. Section 4 presents the architecture of the prototype of a dynamic vehicle routing system. Section 5 demonstrates the prototype system by running a simulation experiment. During the simulation, several route improvement strategies are tested using combinations of different existing algorithms. The performance indexes such as travel distance, service time, system time, and delay time are collected to analyze the simulation scenarios. Based on the simulation results, conclusions and recommendations about the delivery performance of these route improvement strategies are presented in Section 6.
نتیجه گیری انگلیسی
A computer program demonstrating the dynamic vehicle routing system in an online B2C environment was created to collect data such as simulation time, vehicle travel distance, delivery time, and delay time in this study. These indexes enable us to select the best combination of existing algorithms for routing vehicles efficiently and effectively under different conditions in the online B2C environment. The result of simulation reveals that if there are no orders in the system initially, the First-Fit-Nearest algorithm has the smallest simulation time when it is combined with Model 5. It can find the smallest vehicle travel distance, averaged delivery time, averaged delay time, and new-order averaged delivery time, when combined with Model 8. On the other hand, if the system does have initial orders, the best combination is the First-Fit-Nearest algorithm with Model 5 for finding the smallest simulation time, averaged delay time, and new-order averaged delivery time. Combining the First-Fit-Nearest algorithm with Model 6 yields the smallest vehicle travel distance and averaged delivery time. Regardless of whether or not the system has initial orders, the worst combination is the First-In-First-Serve algorithm with Model 1 and the single-phase solution process (Model 1) has much longer averaged travel distance and delivery time than the two-phase (Models 2, 3, 4, 7) or three-phase (Models 5, 6, 8, 9) solution process. Furthermore, we can draw the following conclusions and recommendations. (1) The algorithm that diminishes the impact of the delivery time caused by new orders cost (i.e., the smallest vehicle travel distance) is a combination of Best-Fit-Nearest for initial-routes formation, Insertion for inter-routes improvement, and or-OPT for intra-route improvement (Model 5). (2) Customer satisfaction is highest (i.e., the smallest averaged delivery time and averaged delay time) if the First-Fit-Nearest algorithm is used for initial-routes formation together with either or-OPT or 2-Swap for intra-route improvement. In this case, the inter-routes improvement algorithm should use 2-Exchange when the system has no initial order or use Insertion when the system has initial orders. (3) The best algorithm to perform initial-routes formation for new orders is First-Fit-Nearest or Best-Fit-Nearest. The data reveal that or-OPT, 2-Swap, Insertion, and 2-Exchange do not significantly improve the new orders. (4) Regarding the satisfaction of both existing consumers and new consumers (the smallest averaged delivery time, averaged delay time, and new-order averaged delivery time), the best combination is using the First-Fit-Nearest or Best-Fit-Nearest algorithm for initial-routes formation; Insertion for inter-routes improvement; and either or-OPT or 2-Swap for intra-route improvement.