Logistical management system

1. A computer-implemented method, comprising:

receiving a request to transport a first good for a first job from a first location to a second location;

identifying a plurality of eligible transporters for transporting the first good from the first location to the second location, wherein the plurality of eligible transporters comprises a first transporter and a second transporter, and wherein the second transporter was assigned a second job to transport a second good before the request to transport the first good was received;

determining, by a transporter acceptance chance machine-learning model, a likelihood of the first transporter accepting the first job, wherein the transporter acceptance chance machine-learning model is trained based on historical data of the first transporter accepting previous jobs;

determining an estimated transporter acceptance time for the first transporter based on the likelihood of the first transporter accepting the first job, wherein the estimated transporter acceptance time relates to an estimated amount of time for the first transporter to accept a solicitation to transport the first good;

deriving an estimated completion time (ECT) for the first transporter to complete the first job based on the estimated transporter acceptance time for the first transporter;

determining a plurality of sequences for completing the first job and the second job by the second transporter, the plurality of sequences comprising:

a first sequence comprising the second transporter picking up the first good before picking up the second good and dropping off the second good before dropping off the first good; and

a second sequence comprising the second transporter picking up the second good before picking up the first good and dropping off the first good before dropping off the second good;

deriving, based on a trained transporter model, a plurality of ECTs for the plurality of sequences by the second transporter, the plurality of ECTs comprising a first ECT for the first sequence and a second ECT for the second sequence;

selecting a shortest ECT of the plurality of ECTs as a representative ECT for the second transporter;

comparing the ECT for the first transporter with the representative ECT for the second transporter;

in response to the ECT for the first transporter being shorter than the representative ECT for the second transporter, selecting the first transporter to transport the first good; and

sending a request to the first transporter to accept the first job.

2. The computer-implemented method of claim 1 , wherein determining the estimated transporter acceptance time for the first transporter comprises dividing a predetermined solicit time by the likelihood of the first transporter accepting the first job.

3. The computer-implemented method of claim 2 , wherein the predetermined solicit time is 90 seconds.

4. The computer-implemented method of claim 1 , the identifying of the eligible transporters comprising:

identifying a plurality of available transporters within a searchable distance from the first location; and

identifying the plurality of eligible transporters from the plurality of available transporters based on the first good,

wherein the plurality of eligible transporters comprise one or more of a certification for transporting the first good and a transportation carrier suitable for transporting the first good.

5. The computer-implemented method of claim 1 , further comprising:

determining that the plurality of eligible transporters meet or exceed a predetermined minimum number of eligible transporters,

wherein the deriving of the ECT for the first transporter and the deriving of the plurality of ECTs for the plurality of segments are executed in response to the plurality of eligible transporters meeting or exceeding the predetermined minimum number of eligible transporters.

6. The computer-implemented method of claim 1 wherein:

the plurality of sequences further comprises a third sequence comprising the second transporter picking up the first good before picking up the second good and dropping of the first good before dropping off the second good, and

the plurality of ECTs comprises a third ECT for the third sequence.

7. The computer-implemented method of claim 1 , further comprising:

determining a failure of the first transporter to respond to the request to accept the first job within a predetermined period of time,

wherein the identifying of the plurality of eligible transporters is performed again based on the failure of the first transporter to respond to the request to accept the first job.

8. The computer-implemented method of claim 1 , wherein the ECT for the first transporter is further based on a tender time to move the first good from a vehicle that has arrived at the second location to a tender location of the second location, wherein the vehicle is operated by the first transporter.

9. The computer-implemented method of claim 1 , wherein the first transporter is identified as one of the plurality of eligible transporters after being assigned a third job.

10. The computer-implemented method of claim 7 , wherein after the failure of the first transporter to respond to the request to accept the first job, the first transporter is considered again as one of the plurality of eligible transporters.

11. A system, comprising:

a memory; and

a processor in communication with the memory and configured to:

receive a request to transport a first good for a first job from a first location to a second location;

identify a plurality of eligible transporters for transporting the first good from the first location to the second location, wherein the plurality of eligible transporters comprises a first transporter and a second transporter, and wherein the second transporter was assigned a second job to transport a second good before the request to transport the first good was received;

determine, by a transporter acceptance chance machine-learning model, a likelihood of the first transporter accepting the first job, wherein the transporter acceptance chance machine-learning model is trained based on historical data of the first transporter accepting previous jobs;

determine an estimated transporter acceptance time for the first transporter based on the likelihood of the first transporter accepting the first job, wherein the estimated transporter acceptance time relates to an estimated amount of time for the first transporter to accept a solicitation to transport the first good;

derive an estimated completion time (ECT) for the first transporter to complete the first job based on the estimated transporter acceptance time for the first transporter;

determine a plurality of sequences for completing the first job and the second job by the second transporter, the plurality of sequences comprising:

a first sequence comprising the second transporter picking up the first good before picking up the second good and dropping off the second good before dropping off the first good; and

a second sequence comprising the second transporter picking up the second good before picking up the first good and dropping off the first good before dropping off the second good;

derive, based on a trained transporter model, a plurality of ECTs for the plurality of sequences by the second transporter, the plurality of ECTs comprising a first ECT for the first sequence and a second ECT for the second sequence;

select a shortest ECT of the plurality of ECTs as a representative ECT for the second transporter;

compare the ECT for the first transporter with the representative ECT for the second transporter;

in response to the ECT for the first transporter being shorter than the representative ECT for the second transporter, select the first transporter; and

send a request to the first transporter to accept the first job.

12. The system of claim 11 , wherein the processor determines the estimated transporter acceptance time for the first transporter by dividing a predetermined solicit time by the likelihood of the first transporter accepting the first job.

13. The system of claim 12 , wherein the predetermined solicit time is 90 seconds.

14. The system of claim 11 , wherein the ECT for the first transporter is further based on a tender time to move the first good from a vehicle that has arrived at the second location to a tender location of the second location, wherein the vehicle is operated by the first transporter.

15. A computer-readable storage device having instructions stored thereon, execution of which, by a processing device, causes the processing device to perform operations comprising:

receiving a request to transport a first good for a first job from a first location to a second location;

identifying a plurality of eligible transporters for transporting the first good from the first location to the second location, wherein the plurality of eligible transporters comprises a first transporter and a seconds transporter, and wherein the second transporter was assigned a second job to transport a second good before the request to transport the first good was received;

determining, by a transporter acceptance chance machine-learning model, a likelihood of the first transporter accepting the first job, wherein the transporter acceptance chance machine-learning model is trained based on historical data of the first transporter accepting previous jobs;

determining an estimated transporter acceptance time for the first transporter based on the likelihood of the first transporter accepting the first job, wherein the estimated transporter acceptance time relates to an estimated amount of time for the first transporter to accept a solicitation to transport the first good;

deriving an estimated completion time (ECT) for the first transporter to complete the first job based on the estimated transporter acceptance time for the first transporter;

determining a plurality of sequences for completing the first job and the second job by the second transporter, the plurality of sequences comprising:

a first sequence comprising the second transporter picking up the first good before picking up the second good and dropping off the second good before dropping off the first good; and

a second sequence comprising the second transporter picking up the second good before picking up the first good and dropping off the first good before dropping off the second good;

deriving, based on a trained transporter model, a plurality of ECTs for the plurality of sequences by the second transporter, the plurality of ECTs comprising a first ECT for the first sequence and a second ECT for the second sequence;

selecting a shortest ECT of the plurality of ECTs as a representative ECT for the second transporter;

comparing the ECT for the first transporter with the representative ECT for the second transporter;

in response to the ECT for the first transporter being shorter than the representative ECT for the second transporter, selecting the first transporter; and

sending a request to the first transporter to accept the first job.

16. The computer-readable storage device of claim 15 , wherein determining the estimated transporter acceptance time for the first transporter comprises dividing a predetermined solicit time by the likelihood of the first transporter accepting the first job.

17. The computer-readable storage device of claim 16 , wherein the predetermined solicit time is 90 seconds.

18. The computer-readable storage device of claim 15 , wherein the ECT for the first transporter is further based on a tender time to move the first good from a vehicle that has arrived at the second location to a tender location of the second location, wherein the vehicle is operated by the first transporter.