How can computational models be used to solve real-world problems?

Running Head: RESEARCH PAPER 1


Using customer-related data to enhance e-grocery home delivery

With the innovation of E-grocery home delivery services a lot of benefits has been realized whereby customers are able to order grocery products online and receive them at their doorsteps. Sellers have also managed to make a lot of profits as a result of this innovative technology. However, despite the efforts of sellers to offer reliable and timely delivery services to their customers’ many deliveries were becoming ineffective due to customers’ absence, and this was causing them to incur substantial operating losses especially on perishable products (Pan, & Qiao, 2017). Due to these challenges, various scholars have embarked on performing investigations in a bid to solve this real-world problem. Since the major problem, in this case, is operational challenges related to unsuccessful deliveries of products to customers, this study will review one computational model that has been created to solve this real-world problem and then review the literature of related surveys which will act as a guide in this research. Below, I have illustrated the computational model that was designed to solve this problem.

Delivery optimizing model for Vehicle Routing Problem

Min ∑i+ ∑Vdij x ijs.t.:

jxij= 1iV

ixih − ∑jxhj= 0,hV

Qiui Q,iV

Ui – uj Qxij− qj,i,jV

Ti + (tij+si)xijN(1−xij)⩽tj,i,jV




V = the number of customers

i∈V = each customer

qi = customers with known demand of any i∈V

tij>0 = travel time

dij>0 = distance

si = service time

ti = start time

Q = vehicle capacity

ui = variable showing accumulative total customer’s demand

N = route with a larger number of customers

How the model will help in solving the real world problem

This model will be helpful to e-grocery companies in several ways. They will be able to identify the customers who purchase their products more often. By identifying the regular customers, they will ensure that the products ordered by those customers are always available and that their trucks will continue delivering the products on time to maintain the regular customers loyalty (Giannikas, E. 2017). Also, the computational model will help the organizations to know the probability of finding a customer while delivering the grocery products. If the customers’ home attendance is high, then they will direct their delivery trucks towards that direction since the probability of meeting the customer is high and thus a business transaction will be realized


With innovating this computational model, the authors were hoping to reduce the logistics challenge that existed in the delivery of e-grocery products. E-grocery delivery companies had been experiencing a lot of operational challenges when they deliver grocery products ordered by customers to their premises and then find that the customer is not present (Giannikas, E. 2017). This problem used to cost them a lot of operational expenses which they could have avoided if they did a computational analysis to assess the probability of meeting the customer while making the deliveries. Also, by developing this model, the researchers hoped to minimize the fuel costs the e-grocery home delivery companies used to incur when they make unsuccessful deliveries.

Besides, this model is not only helpful to e-grocery home delivery companies, but it’s also helpful to other organizations that major in supply chain logistics. Related organizations experiencing challenges related to the ones highlighted in this study can apply this computational model in enhancing efficient and effective logistics management. Additionally, this computational model will assist in limiting the vehicle routing problems which limit effective deliveries and consequently, organizations majoring in supply chain logistics will be able to realize their objectives. Nonetheless, the major in performing this research is solving the crisis facing e-grocery home delivery companies by developing a delivery optimizing model for vehicle routing which will assist the companies in determining the probability of finding customers in their residential areas while making the home deliveries.

Research questions

The following research questions were formulated to assist in the execution of the established goals:

• How can computational models be used to solve real-world problems?

• Is there an already existing imperfect model regarding this study?

• What are the major challenges associated with the model?

• What arguments do previous surveys provide concerning this topic?

Literature review

Due to increased development in global trade and technology, there has been a lot of competition between organizations on providing better services to their customers. These competitions have caused organizations to seek innovative ways of maximizing their profits in order to maintain their existence in the market or to outdo their competitors. Organizations that deal with logistics usually undergo a lot of operational expenses while managing their supply chains and most of them have been quitting the industry due to massive losses. After realizing this problem, scholars have invested in investigating the significant operational challenges causing these organizations to leave the supply chain industry. The major challenge these scholars unearthed to be facing these organizations was fuel costs which used to accelerate when the logistics companies delivered products to customers and then find that they are not present. Also, the organizations were facing challenges of many routes whereby they had to keep going around a large area while delivering products to customers and this was increasing the fuel cost, and the input-output relationships were defective. Many scholars thus embarked on this investigation in an effort to ensure that goods are delivered to customers on time and that the organizations are able to secure their profits.

After qualitative investigations, the VRP Model was discovered in the year 1959 by Ramser and Dantzig, and it has been tested in various surveys and proved to be effective thereafter Fernández-Delgado & Amorim, 2014). In the VRP model illustrated above, E-grocery home delivery organizations were experiencing a lot of logistics, but those who managed to apply this model were able to realize their profit margins. The researchers in this study realized that in order to optimize deliveries of grocery products by the e-grocery home delivery companies the organizations needed to implement this strategy and after executing the VRP computational model in this scenario, they were able to determine the probability of delivery being successful after analyzing the customer’s home characteristics. This computational model has been applied in various research studies, and most of them proved its practicability.

WANG & CHEN, (2008) did qualitative research on the VRP model in a bid to evaluate whether it can be applied to solve the problem of multi-depot supply systems. The researchers embarked on this research after realizing that multi-depot supply companies were undergoing a lot of operational expenses as their trucks traveled into various customers to deliver the ordered products. These companies would experience a lot of challenges when they delivered the products to customers only to find that the customer had already sourced the product from other suppliers after the multi-depot company delays in delivering the product. This delay was a factor that used to cost the organizations a lot of expenses since they had to take the liability of the undelivered products. in an effort to disentangle this challenge these researchers integrated the VRP model and the GUI-type programming. They wanted to test the hypothesis “the logistics costs of a group of customers can be minimized without overfilling the delivery trucks and increasing the travel time” (WANG & CHEN, 2008).

This study suggested that this problem can be solved using a three-step strategy which will change the multi-supply center into a sole supply center, and this strategy will make problem-solving easier. The single supply center will also optimize vehicle routing problems as well as minimize the costs that customers used to incur due to delayed deliveries. Throughout the research, the investigators were able to support the hypothesis, and still, the conclusion they made was in line with the main topic. Their findings proposed that the VRP Model was very useful in solving operational problems associated with supply chain complications.

Liao & Wu, (2017), also did an investigation on a computational model for Vehicle Routing Problem. They were interested in innovating a model that would help to facilitate easy supply of emergency facilities to minimize the inconveniences that arise in times of emergencies thus hindering effective rescues. Usually, emergencies are unpredictable, and their occurrence leads to significant challenges due to unpreparedness. Emergency departments more often experience this challenge because emergencies happen at an unexpected time and location and rescue teams as well as rescue facilities ought to be availed within a specified time (Liao & Wu, 2017). This inconvenience causes a significant challenge to the emergency organizations since at times they may have difficulties in describing the location to their rescue trucks leading to routing problems as the vehicles struggle to locate the emergency scenes.

The researchers applied heuristics algorithm to determine the effectiveness of the dispatch plan. According to the findings they made, many unsuccessful deliveries of emergency facilities were happening due to vehicle routing problems, and thus the heuristic algorithm proved to be a useful computational model that can be applied in reducing the inconveniences which used to occur in a time of emergencies (Liao & Wu, 2017). Basing on their findings, the researchers concluded that this problem could be solved by the utilization of a heuristic algorithm in decision making. The model would help the organizations in designing a fleet dispatch plan that would aid in the fast delivery of emergency facilities to reduce the inconveniences that used to occur when emergencies happened.

Gies, T. (2018), also did a study on the same topic whereby he aimed at developing an optimization model for vehicle routing problem in multi-product frozen food delivery. The demand for fresh, cold food was increasing in the market with advancement in technology and awareness of healthy living. These products were mostly demanded in the urban centers, but their availability was limited due to delays in deliveries associated with the long distances the delivery trucks had to travel. Unlike in other supply chains, the supply of these products required refrigerated facilities that will enhance the shelf-life of these products by the time they reach the market (Gies, T. (2018). However, most of the organizations involved in these logistics lacked enough finances to comply with the strict refrigeration requirements of these products. The demand and supply statistics didn’t match since the demands were exceeding supply, and most of the suppliers could not manage to supply quality products due to lack of refrigeration facilities which are capital intensive. It is after noting this market disequilibrium that this researcher saw a need to create an optimization model for vehicle routing problem towards enhancing efficient and effective multi-product frozen food delivery (Gies, T. 2018).

The researcher aimed at developing a model that would reduce the time windows constraints and the difficulty of overloading the trucks which often decreased the shelf-life of the products. Also, he aimed at developing a model that would help in shortening the route that the trucks used to follow to reduce unsuccessful deliveries or when the products reached the customers in lousy condition. In a bid to come up with a long-lasting solution, he suggested a Genetic Algorithm for the model (Gies, T. (2018). He thus did computational tests with real data in an effort to establish the justification of the model. According to the findings he got, he proposed that multi-temperature linkages could be adopted to minimize the initial installation costs of the refrigeration facilities.

By adopting the multi-temperature linkages, the organizations would be able enable to pack more products and consequently they will be able to manage to meet the rising demand as they park more than one type of frozen food product in the delivery trucks. He also found that most of the trucks used to overload the packing boxes and this factor was limiting freshening causing the products to reach the market in low quality. The suggestion he made on this constraint was controlled packing to avoid pressing the products. Also, he concluded that computational models are very significant in solving real-world problems hence their importance should not be underrated.

According to Juliandri & Mawengkang, (2018) computational models have been very useful in solving real-life problems. They supported this proposition with qualitative research which involved developing a discreet optimization model for Vehicle routing problem with planning side restraints. The aim of performing this research was to attempt to answer the problematic question of how scheduling restraints could be controlled to minimize operation costs by organizations dealing with logistics. The researchers realized that a lot of unplanned challenges used to occur in logistics and thus the researchers aimed at developing a computational model that would minimize vehicle routing problems and consequently reduce the organization’s operational costs. The researchers thus embarked on qualitative research whereby they used a linear integer program which was based on a mixture of heuristics (Juliandri & Mawengkang, 2018).

According, to the findings they got, a significant variance existed on the scheduling of the trucks and this was the major contributor to unplanned constraints during deliveries which not only derailed customers’ delivery but they also caused the substantial operational losses on the organizations. Basing on the statistics, they got from the computational model; the researchers proposed that this challenge could be solved through proper analysis of vehicle routing statistics which will guide in making of better decisions with regards to the scheduling of the delivery trucks and implementation of routing policies (Juliandri & Mawengkang, 2018). They concluded the research by stating that computational models can be used in solving a variety of real-life problems in which theoretical frameworks may have failed. The information provided by these articles has been very essential and it will guide in furthering the research on how computational models can be used to solve real-life problems to mitigate human constraints.


Cheng, A., & Yu, D. (2013). Genetic algorithm for vehicle routing problem. In ICTE 2013: Safety, Speediness, Intelligence, Low-Carbon, Innovation (pp. 2876-2881).

Fernández-Delgado, M., Cernadas, E., Barro, S., & Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems? The Journal of Machine Learning Research15(1), 3133-3181.

Giannikas, E. (2017). Using customer-related data to enhance e-grocery home delivery.

Gies, T. (2018). An Optimization Model for the Vehicle Routing Problem in Multi-product Frozen Food Delivery: A case study. Learned Publishing31(1), 69-76. doi: 10.1002/leap.1142

Juliandri & Mawengkang, (2018) Discrete Optimization Model for Vehicle Routing Problem with Scheduling Side Cosntraints: (2018). Retrieved from

Liao, T., Hu, T., & Wu, Y. (2017). A Time-dependent Vehicle Routing Algorithms for Medical Supplies Distribution Under Emergency. Operations And Supply Chain Management: An International Journal, 161. doi: 10.31387/oscm0280188

Pan, S., Giannikas, V., Han, Y., Grover-Silva, E., & Qiao, B. (2017). Using customer-related data to enhance e-grocery home delivery. Industrial Management & Data Systems117(9), 1917-1933.

WANG, S. X., GAO, L., CUI, X. G., & CHEN, X. M. (2008). Study on multi-depots vehicle routing problem and its ant colony optimization [J]. Systems Engineering-Theory & Practice2, 020.

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