Name
Abstract | ||
|---|---|---|
There are a host of complex operational problems arising in transportation and logistics which are characterized by dynamic information processes, complex operational characteristics and decentralized control structures. Yet, they are also optimization problems. The optimization community has made outstanding progress in the solution of large optimization problems when information processes are static (we do not model the arrival of new information) and when the entire problem can be viewed as being part of a single control structure. Not surprisingly, this technology has been extremely successful in applications such as planning airline operations which meet these requirements. This paper has grown out of the challenges we faced modeling complex operational problems arising in freight transportation and logistics, which are characterized by highly dynamic information processes, complex operational characteristics and decentralized control structures. Whereas people solve more traditional problems have struggled with the development of effective algorithms, we have struggled with the more basic challenge of simply modeling the problem. We feel that our ability to solve these problems is limited by the languages that we use to express them. Classical mathematical paradigms do not provide an easy and natural way to represent the optimization of these problems in the presence of dynamic information processes, or to capture the complexities of large scale operations. In particular, models do not capture the organization and flow of information in large organizations, preferring instead to assume the presence of a single, all-knowing decision-maker. As a result, most dynamic models posed in the literature are myopic or deterministic. The characteristics of more complex operations has spawned an extensive literature presenting models that are unique to a particular industry. For example, we solve airline fleet assignment problems (Hane et al. [22]), railroad car distribution problems (Jordan and Turnquist [26], Mendiratta and Turnquist [27], Haghani [21], and Herren [23], for example), the load matching problem of truckload trucking (Powell [33], Powell [34], Schrijver [40]), routing and scheduling problems in less-than-truckload trucking (Powell [32], Crainic and Roy [12]), the flow management problem in air traffic control (Andreatta and Romanin-Jacur [2] and Odoni [29]) and the management of ocean containers (Crainic, Gendreau and Dejax [11]). Even within an industry, rail car distribution is dif |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1023/A:1013111608059 | Annals of Operations Research |
Keywords | Field | DocType |
Dynamic Resource,Transformation Problem,Resource Transformation | Information flow (information theory),Mathematical optimization,Decentralised system,Scheduling (computing),Air traffic control,Computer science,Operations research,Transformation problem,Dynamic models,Airline operations,Optimization problem | Journal |
Volume | Issue | ISSN |
104 | 1-4 | 1572-9338 |
Citations | PageRank | References |
18 | 1.27 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Warren B. Powell | 1 | 1614 | 151.46 |
| Joel A. Shapiro | 2 | 316 | 29.93 |
| Hugo Simão | 3 | 106 | 8.38 |