INDUSTRIAL ENGINEERING APPLICATIONS IN TRANSPORTATION:THE PARAMETERS AND FUNCTIONS ASSOCIATED WITH TRANSPORTATION

By -

THE PARAMETERS AND FUNCTIONS ASSOCIATED WITH TRANSPORTATION

There are two basic parameters that affect the design of freight transportation processes: the territory to be covered and the frequency with which the transportation events occur. These parameters are not static. Demand (e.g., number of shipments per week or day) fluctuations make these parameters dynamic in nature. Demand in transportation is often seasonal. The Thanksgiving holiday in the United States is one of the highest demand days for air travel. The Saturday prior to Mother’s Day usually presents a substantial increase in package delivery volume compared to other Saturdays. Demand can vary by time of day or day of the week. Traffic volume in a major city varies greatly between Monday morning at 7:30 am and Thursday morning at 3:00 am. The transportation system must be designed to handle these variations in demand effectively.

It is important to note that what drives demand in the private freight transportation sector is cost and quality of service. The design (planning), execution, and measurement of the transportation system have an impact on the cost and quality of the transportation process. The better the costs and quality of service, the greater the demand and therefore, the greater the need to alter the service territory and the frequency of service.

Transportation planning, execution, and measurement are the fundamental functions associated with transportation and are integral to the success of the transportation system.

Planning is needed across all areas of the transportation system. Planning the overall distribution network, regional (e.g., certain geography) planning, and site (e.g., local) planning are crucial. Asset planning, including buildings and facilities, vehicles, aircraft, trailers, and materials-handling equip- ment, is also required. Demand variations drive decisions for asset quantity and capacity. The use of owned vehicles supplemented by leased vehicles during peak (high-demand) times is an example of the decisions that need to be made during the planning process. The impact of demand on scheduling, vehicle routing, and dispatching drives labor and asset needs. Labor can often be the largest com- ponent of cost in the transportation industry. The transportation planning activity is charged with developing plans that, while minimizing costs, meet all service requirements.

The execution of the plan is as important as the planning of the transportation process itself. An excellent plan properly executed results in lower costs and higher quality of service. This in turn drives demand and therefore the need for new plans.

Finally, the proper measurement of the effectiveness of the transportation system, in real time, offers a mechanism by which transportation managers, supply chain specialists, and industrial engi- neers can constantly reduce costs and improve service. With new technologies such as wireless communication, global positioning systems (GPS), and performance-monitoring technology, mea- surement systems allow the users to improve transportation processes as needed.

Comments

Post a Comment

Popular posts from this blog

DUALITY THEORY:THE ESSENCE OF DUALITY THEORY

By -
Image
THE ESSENCE OF DUALITY THEORY Given our standard form for the primal problem at the left (perhaps after conversion from another form), its dual problem has the form shown to the right. Thus, with the primal problem in maximizatio n form, the dual problem is in minimization form instead. Furthermore, the dual problem uses exactly the same pa r amete r s as the primal problem, but in different locations, as summarized below. 1. The coefficients in the objective function of the primal problem are the right-hand sides of the functional constraints in the dual problem. 2. The right-hand sides of the functional constraints in the primal problem are the coef- ficients in the objective function of the dual problem. 3. The coefficients of a variable in the functional constraints of the primal problem are the coefficients in a functional constraint of the dual problem. To highlight the comparison, now look at these same two problems in matrix notation (as introduced at the beginn
Read more

NETWORK OPTIMIZATION MODELS:THE MINIMUM SPANNING TREE PROBLEM

By -
Image
THE MINIMUM SPANNING TREE PROBLEM The minimum spanning tree problem bears some similarities to the main version of the shortest-path problem presented in the preceding section. In both cases, an undi r ecte d and connected network is being considered, where the given information includes some mea- sure of the positive length (distance, cost, time, etc.) associated with each link. Both problems also involve choosing a set of links that have the shortest total length among all sets of links that satisfy a certain property. For the shortest-path problem, this property is that the chosen links must provide a path between the origin and the destination. For the minimum spanning tree problem, the required property is that the chosen links must provide a path between ea c h pair of nodes. The minimum spanning tree problem can be summarized as follows: 1. You are given the nodes of a network but not the links. Instead, you are given the po- tential links and the positive length for each
Read more

NETWORK OPTIMIZATION MODELS:THE SHORTEST-PATH PROBLEM

By -
Image
THE SHORTEST-PATH PROBLEM Although several other versions of the shortest-path problem (including some for directed networks) are mentioned at the end of the section, we shall focus on the following simple version. Consider an undi r ecte d and connected network with two special nodes called the origin and the destination. Associated with each of the links (undirected arcs) is a non- negative distance. The objective is to find the shortest path (the path with the minimum total distance) from the origin to the destination. A relatively straightforward algorithm is available for this problem. The essence of this procedure is that it fans out from the origin, successively identifying the shortest path to each of the nodes of the network in the ascending order of their (shortest) distances from the origin, thereby solving the problem when the destination node is reached. We shall first outline the method and then illustrate it by solving the shortest-path problem encountered by the See
Read more