Freight Operations Modelling for Urban Delivery and Pickup with Flexible Routing: Cluster Transport Modelling Incorporating Discrete-Event Simulation and GIS

Round 1

Reviewer 1 Report

The authors have pointed out some limitations of the model and opportunities for further development. However, the model does not take into account some factors that would be useful to include: cargo measurement, cargo capacity, cost parameters, delivery windows, driver schedules.

What are the main objectives of the paper? To create a DES model for realistic freight routing? What for? In my view modelling is not a goal in itself. As a rule, in the situations under consideration, the objective is to optimise costs. In this context, a significant omission is the lack of an economic evaluation of the results obtained. 

 

Author Response

Point 1: The authors have pointed out some limitations of the model and opportunities for further development. However, the model does not take into account some factors that would be useful to include: cargo measurement, cargo capacity, cost parameters, delivery windows, driver schedules.

Response 1: To create a more complete logistics model, it would be necessary to include additional operational processes. One of the key operational constraints is consolidation. This is where the dispatcher at the dock allocates the incoming consignments to individual drivers. This is a complex task because of the need to consider total cargo weight and volume relative to the truck capacity. It is further complicated by the high daily variability of consignments, at least for general freight firms. The dispatcher also needs to ensure delivery windows and customer service measures are met, and this is especially so with time-critical consignments. Furthermore, any consignments not delivered on the day of arrival at the dock will accumulate to the next day, and this is problematic because of limited storage capacity on the dock – the system operates in a lean manner. Variability in driver availability is another consideration, although in practice perhaps not as severe as might be thought as such firms tend to assign drivers to overlapping suburbs, and have floater vehicles available. There is also pick-up and line-haul to consider for a more complete model. 

 

Point 2: What are the main objectives of the paper? To create a DES model for realistic freight routing? What for? In my view modelling is not a goal in itself. As a rule, in the situations under consideration, the objective is to optimise costs. In this context, a significant omission is the lack of an economic evaluation of the results obtained.

Response 2: The main objective of the paper is to find a method to create a PUD model with considerations of historic data and driver’s decisions. This paper presents the method to develop the model and validation of results. The optimisation of costs is not a part of this paper. In discussion with the industry it was identified that the main objective function from an operational perspective was time to complete the delivery tour, as this affects cost and service quality.  In this paper we do not seek to optimize delivery time, but rather find ways to create models that could be used for such purposes in a general freight situation of high daily variability of consignments.

Reviewer 2 Report

This study presents a model for realistic freight routing, which considers the driver’s routing decisions.

No need to mention objective, approach, etc in the abstract. 

Better to combine sections 1 & 2 and clearly mention the research gap and what is the focus of this study.

Provide details related to data. Method section needs elobration. 

The results section is compressive in terms of presenting findings of the study how the ever critical discussion with compare to existing method is missing 

Author Response

Point 1: No need to mention objective, approach, etc in the abstract.

Response 1: Structured abstract headings are now removed.

 

Point 2: Better to combine sections 1 & 2 and clearly mention the research gap and what is the focus of this study.

Response 2: We would prefer to keep these sections separate if possible, but are happy for them to be merged in copy editing if needed.

 

Point 3: Provide details related to data. Method section needs elobration.

Response 3: Method section has been elaborated.

 

Point 4: The results section is compressive in terms of presenting findings of the study how the ever critical discussion with compare to existing method is missing.

Response 4: Table 7 in the DISCUSSION has been elaborated to provide a more holistic summary of the body of knowledge.

Reviewer 3 Report

The reviewer comments as follows:

1.The paper not yet clearly definition the simple cluster suburb model, and intersection based model the objection function, decision variables, input variables, and output variables?

2.This paper proposed five PUD model to compare their travel time and travel distance. But their outcomes cannot express the performance of saving travel cost, such fuel fee, or delivery delay time.

3.The research correlation coefficient between travel speed and travel time is -0.996. The research outcomes show the more travel speed, the more travel time. This result can’t convince me is make sense. 

4.The delivery sequence for 5 cases models of Figure 8 and Figure 9 are the same, therefore, their travel distances differences less. Why the authors are not applied the TSP algorithm to compare with five models by the costs of shortest delivery sequence?

5.The TSP model was not applied to discuss and compare the performance with proposed five models, to achieve this paper can solve the actual delivery routes problems.

6.The advantage and disadvantage of proposed models please put on the literature reviews or methods, not lay on the final discussion.

Comments for author File: Comments.pdf

Author Response

Point 1: The paper not yet clearly definition the simple cluster suburb model, and intersection based model the objection function, decision variables, input variables, and output variables?

Response 1:

The overall design of the DES model included an operations part and a transport part. The operations part includes a DES model for consignment arrival and freight consolidation, which takes into account weight and volume relative to truck capacity. These results are not reported here.  For the transport network, which is the focus of the current paper, the problem includes freight loading at the dock via forklift, travel to destination, unloading at destination, and regular backhaul pickup. Two DES models were devised for the transportation, with slightly different architectures. The first is called the Simple Suburb Model. It has the defining feature that suburbs are directly connected to nearby suburbs by distance parameters. The second model is called the Intersection Based Model. It is more complex as it includes major road intersections. This gives greater ability to model the route specifics. In both cases, the overall architecture of the model consists of geographic centres of the suburbs represented as nodes in the model. Distances between suburbs are included, but the two models differ in how they accomplish this. The objective function was time to complete the delivery tour, and total distance taken. Of these parameters, the time was more important from the client’s operational perspective, because of the need to complete all deliveries within the day. However from the perspective of emissions, e.g. tonne.km, the distance is the more important. Input variables are speeds, route selection, distances between clusters/intersections, destination and number of consignments in the load.  Average speeds for different types of roads were calculated based on GPS data. These speeds were applied to networks.

[Text to this effect has been added to 3.2 Approach.]

 

Point 2: This paper proposed five PUD model to compare their travel time and travel distance. But their outcomes cannot express the performance of saving travel cost, such fuel fee, or delivery delay time.

Response 2: The intention of this paper is to find a method for developing PUD models by DES method.  Saving travel cost, such fuel fee, or delivery delay time are not included in this paper since these are initial models, and they are related to travel time and distance.

 

Point 3: The research correlation coefficient between travel speed and travel time is -0.996. The research outcomes show the more travel speed, the more travel time. This result can’t convince me is make sense.

Response 3: Table 6 should not be used to determine correlations between time and distance, as the data represent different competing methods.

 

 

Point 4: The delivery sequence for 5 cases models of Figure 8 and Figure 9 are the same, therefore, their travel distances differences less. Why the authors are not applied the TSP algorithm to compare with five models by the costs of shortest delivery sequence?

Response 4: The route was determined from discussion with dispatchers and drivers, not via TSP. The reason TSP was not applicable here, was because the real operations includes a regular backhaul whereby the truck must take a specific last segment of the return route. In contrast TSP requires freedom to select all segments of the route.

 

Point 5: The TSP model was not applied to discuss and compare the performance with proposed five models, to achieve this paper can solve the actual delivery routes problems.

Response 5: As identified above, the real operations require a regular backhaul segment, which is incompatible with the TSP algorithm. Nonetheless, we applied TSP by both time and distance objective functions, with the results shown in Figure 8 (a) and (b) respectively. The (a) and (b) results give similar sequence of deliveries to the imposed sequence. However both TSP methods place the backhaul pickup in an operationally impractical sequence. For this particular industrial operation it is important to place the pickup last, once the truck has been emptied of deliveries, but TPS does not respect this constraint.

Text to this effect added to section 5.1.1.

 

Point 6: The advantage and disadvantage of proposed models please put on the literature reviews or methods, not lay on the final discussion.

Response 6: Consolidating our findings with those of the literature suggests that the advantages and disadvantages of GIS models, vs. combined GIS + DES models, may be summarized as in Table 7. 

Reviewer 4 Report

I want to congratulate the authors on an exciting paper. The article presented falls within the area of interest of the journal Infrastructures. However, some clarifications and editorial corrections are required before publication:

1. Table 1 - please prepare the tables by the requirements of the journal
2. Line 180: Google maps® was used to explore road conditions.   Could you please elaborate on how Google Maps was used? It is widely known that the data provided directly in the application differs a lot from the actual data on the route.
3. GENERALLY COMMENT - please add a source of Figures and data in Tables
4. Lines 171 -174 and 229-234 - please explain the inconsistency of the assumptions made. 171-174 - one-year shipment data from before the COVID-19 pandemic is used, while 229-234 uses data from the pandemic period. The inconsistency is that the COVID-19 pandemic completely changed how we approach transportation planning and execution and consumer habits.
5. The Conclusions section does not contain any conclusions from the research. It is a summary of the content of the article and the issues contained therein. In the presented form, it should instead be called Summary. Please, put the most important conclusions from the presented research in the chapter. 
6. References - please fill in the dates of access to the materials in case of Internet sources.

Author Response

Point 1: Table 1 - please prepare the tables by the requirements of the journal.

Response 1: Tables have been edited to Journal style.

 

Point 2: Line 180: Google maps® was used to explore road conditions.   Could you please elaborate on how Google Maps was used? It is widely known that the data provided directly in the application differs a lot from the actual data on the route.

Response 2: The use of Google Maps has been clarified in the Method.

The street view of Google maps® was used to identify road details, intersections, and identification of customer locations. In contrast, average speeds for truck routes were calculated by using GPS data, and GIS was used to determine TPS routes. Route selection and delivery sequence were validated with the industry client.

 

Point 3: GENERALLY COMMENT - please add a source of Figures and data in Tables.

Response 3: Sources have been added to Tables 2, 3, Figure 2, 4.

 

 

Point 4: Lines 171 -174 and 229-234 - please explain the inconsistency of the assumptions made. 171-174 - one-year shipment data from before the COVID-19 pandemic is used, while 229-234 uses data from the pandemic period. The inconsistency is that the COVID-19 pandemic completely changed how we approach transportation planning and execution and consumer habits.

Response 4: Consignment data were used from 2019, and used to analyse customer clusters. GPS data were unavailable for 2019, and instead used 2020 data. The GPS data were only used to investigate trucks routes and calculate speeds. Although 2020 represented COVID-19 operating conditions, it is believed that the overall road conditions, speeds and routes were representative. This is because GPS data was used for the whole year, and while there were COVID-19 lockdowns in New Zealand they were of short duration. Furthermore the truck route was validated by checking with the driver and the dispatcher.

This clarification has been added to the METHOD.

 

Point 5: The Conclusions section does not contain any conclusions from the research. It is a summary of the content of the article and the issues contained therein. In the presented form, it should instead be called Summary. Please, put the most important conclusions from the presented research in the chapter.

 

Response 5: Thank you. This has now been attended to, with the addition of the following text.

 

In terms of travel time, the predictions of the simple suburb model (70 min) and intersection model (78 min) showed reasonable agreement with the ground truth from GPS (73 min), whereas the GIS estimated times (47-50 min) were unrealistic. The issue with GIS is that it did not represent the slow speeds at intersections and congestion, nor the unloading  times. The intersection based model was the most accurate, but its more detailed network takes more effort to construct in DES. The simple suburb model had acceptable accuracy from the perspective of the industry client, and a much simpler structure to programme into DES.

Regarding  travel distances, the discrepancies were much smaller: both the simple suburb and intersection based predicted about 36 km, compared to 39 km for the GPS ground truth. In this metric the GIS models performed well, at 40 km and 39km.

We conclude that when the purpose of simulation is to quantify the time taken -  which is typically the paramount operational need – then the simple suburb model provides a good balance between accuracy and ease of simulation. When the purpose is only to quantify distance travelled, then the GIS methods are the best. For those cases where both time and distance need to be simulated, as arises when considering emission metrics such as tonne.km, then the intersection based model appears to be the best. It has the advantage of being able to accommodate both time and distance within one model (rather than having to use GIS and DES), with reasonable accuracy.

 

 

 

Point 6: References - please fill in the dates of access to the materials in case of Internet sources.

Response 6: This has been checked.

Round 2

Reviewer 4 Report

After revisions are made to the text, the article is suitable for publication in the journal. Congratulations!