Abstract
In this consultant proposal, we wish to address the transportation efficiency problems encountered at your waste container transport company due to traffic congestion and other factors. We seek to utilize a minimal cost network flow model to form the basis for your decision-support transportation and shipping schedule system. The solution can be implemented in a very succinct five-step process, and the constraints are few.
Introduction
This consultant proposal is meant to help your transport company improve your specific issue with transport operations. A waste container transport company needs a prototype decision-support system in order to address various factors that are inhibiting efficient operation. Currently, their problems involve addressing sites not being visited due to truck breakdowns or traffic congestion; not dealing with these issues adequately is leading to less efficient truck runs, higher costs, and fewer satisfied customers. With this in mind, a new system is required to make routing of trucks and delivery services more efficient. The following proposal will describe this process in detail, as well as the models that will be used to create said system.
Data Collection
This data can be collected in a number of ways, and through many avenues. The company can provide me with the vast majority of this data; location of sites, fleet, inventory, and their normal method of operations can all come from them. However, I could also research routes from either atlases, maps or the Department of Transportation, to determine the best routes for each site, or create the potential for numerous alternate routes in case of traffic congestion. Other relevant literature could be used to determine precedent and types of models for creating this decision-support system.
Literature Review
Tamta et al. (2011) have a very strong user-friendly algorithm that applies to all types of transportation problems, including weight, demand at destination node, transportation cost, etc. Their algorithm involves weight being the highest priority for being solved; their algorithm is Weight (source and destination) = Demand / Cost (source and destination). By using this algorithm, one can determine the most important sites (or destination nodes) that must be focused on.
Model
In order to address the issues found in this particular situation, a minimum-cost network flow model (MCNF) will be utilized. In an MCNF, conservation of flow through a node is guaranteed; each edge is provided with a flow, and possesses a capacity, which cannot be exceeded by the flow.
Implementation
For the company to utilize the MCNF model, a database would have to be created based on a) site, b) type of waste, c) type of preferred container, d) time windows for sites and waste processing facilities; e) driver availability and driving times, and so on. Shipments with the highest weight (as determined by the Tamta model) will be given top priority for scheduling and route mapping. Routes would be determined that would allow a driver to take one type of waste from a site, deliver it to a waste processing facility, and replace the appropriate container at said site. The model would be implemented gradually, and would be used to create efficient, timely schedules based on shifts to create maximum flow from facility to site to processing plant. Full integration of model would take place within three months.
Constraints
There are but a few constraints to the MCNP model that would have to be considered. First, the model itself would have to be tweaked somewhat to accommodate the sheer number of factors (cost, etc.) that must be considered. Also, issues such as traffic congestion, etc., are not an exact science, and certain mitigating circumstances could still lead to shipping inefficiencies - we are still working with an imperfect system, to a great extent. However, given these few constraints, using an MCNP model to structure your decision-support system should be an appropriate solution.
Conclusion
With the implementation of an MCNF model for your new decision-support system, the maximum amount of efficient productivity could be reached, utilizing the maximum number of hours for the drivers, possessing multiple redundancies in terms of routes and replacement drivers to address missed pickup times, and more. This is the most efficient means by which waste containers can be transported back and forth between sites and waste processing facilities with a minimum of traffic congestion and fewer dissatisfied customers. We sincerely hope that you find our suggestions agreeable, and will work hard with you to implement these changes as quickly and efficiently as needed. Thank you for your time.
References
Cattryse D, Geeroms K, Proost A and Van der Heyde C, 1996, Container transport - a case study,
Logistics Information Management, vol. 9, no. 6, pp. 15-23.
Tamta P, Pande B, Dhami HS, 2011, Development of an algorithm for all type of transportation
problems, International Journal of Computer Applications vol. 30, no. 6, p. 24.
