یک روش تصمیم گیری ترتیبی گروهی برای برنامه ریزی استراتژیک جهت توسعه سیستم های عملیاتی وسایل نقلیه تجاری
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|27504||2002||16 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Transportation Research Part A: Policy and Practice, Volume 36, Issue 4, May 2002, Pages 335–350
This paper explores a new sequential decision methodology which integrates a generalized sequential probability ratio testing approach with a strategy-value matrix analytical tool to determine the developmental priorities of commercial vehicle operations (CVO) technical packages for CVO time-based strategic planning. The proposed method executes a sequential decision algorithm utilizing the strategic elements of strategy-value matrices which are estimated on the basis of the data collected from the survey respondents. In the process of sequential decision making, the identification of a specific CVO value-added technology package can be made once the condition of the minimum group decision-making cost is met. In addition to methodology development, a real case study together with a nation-wide mail survey to aid the estimation of the strategy-value matrix samples which were used as inputs to the proposed sequential decision algorithm was conducted in Taiwan to demonstrate the feasibility of the proposed method. Utilizing the proposed method, we determined efficiently the developmental priorities of CVO technology packages for short-term, mid-term, and long-term strategic plans, respectively. Our analyses results indicated that the CVO package used for fleet management appears to be the most urgently needed in the short-term CVO strategic plan; value-added technology packages including: (1) data warehousing, (2) information technology, (3) integration with the supply chain management (SCM) platform, (4) freight mobility, (5) integration with advanced traffic management systems (ATMS), and (6) extension for intermodal operations are assigned to the mid-term CVO strategic plan; and others including: (1) freight administration, (2) HAZMAT management, (3) on-board safety monitoring, and (4) roadside safety inspections are involved in the long-term CVO strategic plan. We expect that this study can make available the proposed decision-making support method with benefits not only for planning CVO development strategies, but also for re-examining the role of commercial vehicle operations in a comprehensive extent.
The increasing application of advanced electronic and communication technologies exerts a profound influence on transportation as well as logistics, and commercial vehicle operations (CVO) is one of the most impressive examples. CVO has been regarded as one of the fundamental subsystems in the architecture of intelligent transportation systems (ITS). Related technologies such as automatic vehicle location (AVL) systems, mobile communication systems, on-board computers (OBCs), dynamic routing and dispatching software have made commercial vehicles more technologically sophisticated and operationally flexible over the past decade. As a result, private sectors including shippers and carriers may obtain economic benefits by using CVO-related technologies. The development of CVO-related technologies has contributed much to enhancing distribution efficiency in logistical operations because it can control the flow of goods better. However, these benefits can be realized only when the potential of CVO-related technologies coincides with the strategic distribution objectives of logistics. In spite of the potential benefits provided by ITS-CVO, further efforts are needed to clarify the role of CVO in logistical systems. First, transportation cost does not correspond to logistical cost though it represents a significant item in the logistical cost. It is worth noting that physical distribution dealing with freight transportation is merely one of the activities in logistics rather than an entire process executed in the conventional field of freight transportation. In addition to minimum transportation cost, the operations of an integrated logistical system should seek to achieve other objectives such as minimum inventory cost, continuous quality improvement, and life-cycle support (Bowersox and Closs, 1996). Second, the potential of CVO-related technologies has not been fully utilized by either motor carriers or logistical operators although the adoption of CVO in ITS is rapidly growing. Results of a previous survey (Regan and Golob, 1999) indicated that almost 60% of the major carrier fleets in California are equipped with advanced location-communication devices such as AVL, electronic data interchange (EDI), and mobile communication devices; however, only a few of them are aware of utilizing these technologies efficiently to support decision-making for fleet management. Third, scant effort has been paid on examining the contribution of advanced transportation technologies to the improvement of logistical competence in the field of logistics. On the other hand, there are numerous studies devoted to exploring new operational strategies such as time-based control (e.g., just-in-time (JIT) and quick response (QR)) and system integration (e.g., the supply-chain management, and integration of supply chains with demand chains) for effective logistical management. A strategic plan designed systematically for the application of advanced CVO-related technologies to logistical management as well as freight transportation appears urgently needed. It should be noted that a critical issue on the development of CVO strategies is the difficulty in exploring the priority with respect to implementing specific CVO value-added technology packages based on the opinions of the extensive CVO-related groups. Therefore, CVO-related strategic plans proposed earlier (Federal Highway Administration, 1996 and Federal Highway Administration, 1999; Middleton et al., 1998) are worth re-examining because these proposed strategic plans were built on the basis of a comprehensive ITS architecture aiming merely to achieve the pre-specified objectives of ITS in the field of transportation. However, a framework constructed for freight transportation does not correspond to a framework built for logistical operations. Thus, it remains doubtful whether the published CVO-related strategic plans can also meet the increasing demands on distribution performance for efficient logistical management. In view of the aforementioned issues on the CVO development as well as the necessity of a systematically designed strategic plan, a sequential group-decision-making approach is proposed not only for specifying the priority in implementing CVO-related value-added technical packages but also for re-examining the role of ITS-CVO from a comprehensive point of view. The proposed method is primarily developed from the modified generalized sequential probability ratio tests (M-GSPRT) and the strategy-value matrix analytical methods. Compared with the conventional analytical methods used for CVO strategic planning, the proposed method exhibits several distinctive features summarized as follows: 1. In the study, time-based CVO strategic planning is treated as a group decision-making procedure for determining the CVO technical packages for the short-term, mid-term, and long-term strategic plans via recognizing the specific patterns of relative significance associated with the pre-specified CVO technical packages which are most commonly agreeable among the members of the group. 2. Employing the specified strategy-value matrices, the relative significance of a given CVO technical package is measurable, and used as the input to the proposed method for sequential decision making. 3. The maximum sample size used in the proposed sequential group-decision-making algorithm is controllable, and accordingly, it can be pre-determined on the basis of the total number of valid samples collected from survey respondents to address the issues on the limitation of data acquisition. 4. The thresholds specified for decision making in the proposed decision algorithm vary with the number of samples input to the algorithm, and converge at the same point where the maximum sample size is researched. Such a feature helps to make a final decision for the time-based CVO strategic planning. Besides the aforementioned distinctive features of the proposed method, there are several reasons that may help to explain our adoption of the M-GSPRT approach in formulating the time-based CVO strategic planning as a problem of sequential group decision making in the study. In the study, we emphasize that a proper procedure of group decision making must rely primarily on the accuracy in identifying the patterns of the collected data existing commonly among the members of the decision making group rather than merely on the statistics of the collected limited data. The above insistence of ours is significant particularly under the condition that the nature of data itself is difficult to quantify, and exhibits, to a great extent, the characteristics of a random variable. Otherwise, the proposed method may face similar issues that remain in some statistical methods such as traditional regression analytical tools. Moreover, according to our experiences in successfully utilizing another related technique, MSPRT, for pattern recognition in some earlier studies (Sheu, 1997; Sheu and Ritchie, 1998), we found that the SPRT-based sequential analytical technologies appear strikingly applicable to multi-pattern classification based on specific group decision-making criteria. Supposedly, a feasible CVO strategic plan is defined as a strategic plan that can meet the needs of most of the members of the CVO society. The critical procedure of strategic planning is then to identify the optimum combination of the CVO-related value-added technology packages that can satisfy the CVO society with the group-based maximum strategy value. This is also our motivation of conducting a study with a method developed from M-GSPRT technologies. The rest of the paper is organized as follows. The fundamentals of M-GSPRT and matrix-based analytical tools are introduced in Section 2. Section 3 presents the specification of a strategy-value matrix. Section 4 depicts a mail survey conducted in the study and data processing for further analysis. Section 5 describes the primary procedures of the sequential decision-making algorithm as well as the results generated by the proposed method for the CVO strategic planning. Finally, concluding remarks are summarized in Section 6.
نتیجه گیری انگلیسی
This paper has presented a sequential decision approach to analyze as well as establish the framework of the time-based CVO strategic plans. The proposed method is composed of four primary procedures including: (1) specifying the dimensions of the strategy-value matrix, (2) survey design based on the pre-specified framework of the strategy-value matrix, (3) estimating the samples of the strategy-value matrix, and (4) sequential group decision-making for strategic planning. In addition, a nation-wide mail survey was conducted to demonstrate the feasibility of the proposed sequential decision algorithm for formulating and analyzing strategic plans in the study. Three time-based CVO strategic plans established for the short-term, mid-term, and long-term CVO development were identified and analyzed via the proposed method. Our analyses results have also revealed the significance of fleet management as well as IT in the CVO strategic plans, and thus, their value-added technology packages will be in a great demand in the near future. This study differs from the published CVO strategic plans in two aspects. First, the role played by CVO in either intelligent transportation systems or in contemporary logistical management, and their relationships are clarified before conducting the CVO strategic planing. Second, in addition to identifying the time-based strategic plans for the CVO development, the proposed method permits analyzing, qualitatively and quantitatively, the established CVO strategic plans employing the estimated strategy-value matrix. It is expected that this study can make available the proposed strategic planning method with benefits not only for the development of CVO-related technical packages, but also for clarifying the role played by ITS-CVO and identifying its significance in the operations of traditional freight transportation and logistics. It is also worth noting that the study has provided a striking demonstration of using sequential analytical technologies in research related to group decision-making. On the basis of the present results, our further research will aim at developing advanced CVO-related technologies for extending CVO applications to other systems such as supply chain management and intelligent intermodal transportation systems (IITS). Moreover, qualitative and quantitative evaluation on strategies developed via different alternatives warrants more research. Using the same database as the input, further research concerning the comparison of the output generated from the proposed method with that of other alternatives is also suggested for implementing these packages in practice.