Decision Making Advances

ABSTRACT


Introduction
The increase in online shopping, especially in urban areas, resulted in a rapid increase in the number of delivered parcels.Consequently, the number of delivery vehicles on the streets increased rapidly [1].The necessity for sustainable development in urban areas calls for the adoption of more environmentally friendly delivery transport modes [2−3].
Cargo bikes becoming a very popular and sustainable solution when it comes to parcel distribution in urban areas [4].A cargo bike is defined as a modified bicycle with a cargo box, enabling it to transport a variety of goods and loads [5].One of the possible ways to achieve sustainable urban freight transport is by considering cargo bikes [6].Parcel distribution by cargo bikes becomes an easier and ecologically friendly solution.With the minimal level of noise, zero-emission pollution, and a solid luggage capacity, postal and logistics companies are trying to move away from traditional delivery modes (gas and diesel-powered vehicles) to this more sustainable way [7].Zhang et al. [8] and Llorca & Moeckel [1] stated that replacing vans with cargo bikes could reduce costs and emissions derived from parcel delivery by 28 % and 22 %, respectively.Organizing a business process involves decision-making [9].
This paper considers an investment decision on cargo bike delivery concept selection using the extended Alternative Ranking Order Method Accounting Two-Step Normalization (AROMAN) multicriteria decision-making (MCDM) approach.The authors of this paper investigated a better solution in terms of whether the company to perform the delivery by investing in its fleet of cargo bikes or renting cargo bikes from third-party.The third solution is the combination of the two types.Several essential criteria have been considered in this case: cargo bike procurement cost (C1), cargo bike maintenance cost (C2), return on investment (C3), and financial profitability (C4).For this research, three experts working for an e-commerce company in the Czech Republic participated in evaluating the alternatives.The names of the experts and company name are not mentioned due to their internal business policy.The top management of the company considers the possibility of investing in cargo bikes to perform parcel distribution to the final customers.On the other hand, there is also a possibility of renting cargo bikes from third parties.
The main motivation for this research appeared during the discussion with its top management where the authors offered the solution according to the expert's evaluation from the e-commerce company.To resolve this issue, the authors decided to apply the extended AROMAN approach.To compare the results, the MARCOS and ARAS methods were applied.The results of the comparative analysis indicate that the best solution for the e-commerce company is to rent cargo bikes from a third-party contractor.
The structure of the paper is organized as follows: Section 2 is the literature review.Section 3 is the description of the extended AROMAN method.Section 4 is the case study, while Section 5 gives the concluding remarks.

Literature Review
This section reviews the literature regarding cargo bikes as a sustainable last-mile delivery mode.The primary research sources were the Web of Science and Google Scholar databases.By reviewing the literature, it can be noticed that cargo bikes draw huge attention among scientists all around the world.Various issues regarding cargo bikes have been tackled.
For instance, Fikar et al. [10] proposed a decision support system to investigate dynamic last-mile distribution facilitating cargo bikes.Assmann et al. [11] provided an impact assessment model for the implementation of cargo bike transshipment points in urban districts.Büttgen et al. [12] evaluated the distribution costs and CO2 emissions of a two-stage distribution system with cargo bikes in Innsbruck City.Dybdalen & Ryeng [5] addressed the issue of cargo bike insurance while operating on winter roads.Llorca & Moeckel [1] assessed the potential of cargo bikes and electrification for lastmile parcel delivery by simulating the urban freight flows.Naumov & Pawlus [13] addressed the optimal packing and routing problem to improve LMD using cargo bikes.Niels et al. [14] investigated the design and operation of an urban electric courier cargo bike system in Munich, Germany.Nürnberg [15] analyzed the usage of cargo bikes in urban logistics on the example of Stargard.Rudolph et al. [16] solved the localization of urban micro consolidation centers for LMD based on real demand data and city characteristics.Vasiutina et al. [17] considered the environmental impact of using cargo bikes in cities through a comprehensive review of the existing approaches.The research is summarized in Table 1.

Our Study Cargo Bike Delivery Concept Selection
Based on the literature survey, the investment decision on cargo bike delivery concept selection has not been previously addressed.To fulfill the research gap, the authors of this paper address the investment decision on the cargo bike delivery concept selection issue.

An Extended AROMAN method
In this paper, an extended AROMAN method is applied.This method combines the normalized data from two-step normalization and obtains an average matrix from normalized data [18].The extended AROMAN method can be described in the following steps: Step 1. Determine the initial decision-making matrix with the input data.
Before the decision-making procedure starts, it is necessary to define the initial decision-making matrix with the input data.Depending on the problem, the input data are mostly gathered in advance, regarding the alternatives and criteria.Therefore, let us suppose we have a decision matrix Xmxn with the input data  11 ,…,  2 , … ,   as follows: ,  = , , … , ,  = , , … , . ( Step 2. Normalize the input data. After the decision-making matrix with the input data is defined, the second step is to normalize the input data.This means that the input data should be structured in intervals between 0 and 1.There are two types of normalization, as defined in Eqs.(2)−(3).
Step 2.1 Normalization 1 (Linear): Step 2.2 Normalization 2 (Vector): where the normalization techniques in Step 2 are used for both criterion types (min and max).
The aggregated averaged normalization is done by applying the following equation: where    denotes the aggregated averaged normalization and  is a weighting factor varying from 0 to 1.In our case, we considered  to be 0.5.

Step 4. Separately summarize the normalized weighted values of the criteria type min (𝑳 𝒊 ) and the normalized weighted values of the max type (𝑨 𝒊 ).
This can be calculated by applying Eqs. ( 6)−( 7) as follows: where  represents the coefficient degree of the criterion type.Since we included both criterion types, we considered parameter  to be 0.5.However, there is a possibility to make variations of the parameter λ when considering the criteria type.For example, if the decision-making problem has two criteria of type min and 1 criterion of type max, this means that the coefficient λ can be 2/3.This logic can be followed to obtain the preference among the considered alternatives as well.
Step 6. Calculate the difference between the values   ^and   ^and apply the final ranking: where   denotes the final rank

Case Study
This section presents the case study where the extended AROMAN method found its application.The extended AROMAN method is applied to the cargo bike delivery concept selection.In other words, the authors of this paper investigated a better solution in terms of whether the company to perform the delivery by investing in its own fleet of cargo bikes or renting cargo bikes from thirdparty.The third solution is to combine those two alternatives.Four criteria have been considered in this case: cargo bike procurement cost (C1), cargo bike maintenance cost (C2), return on investment (C3), and financial profitability (C4).Besides, three experts participated in the alternative assessment procedure.All the experts belong to the e-commerce company in the Czech Republic.The alternatives are compared to the established criteria and assessed on a one-to-five-point scale.The criteria weights are also determined by the experts.The illustration of the problem is presented in Figure 1.The assessment of each expert is presented in Tables 2-4.

Table 2
Expert's assessment of the first alternative  Based on the expert assessment, the input-data matrix is formulated and presented in Table 5.The linear and vector normalizations are calculated and presented in Tables 6 and 7, respectively.Since λ is considered as 0.5,   ^ and   ^ values are calculated by applying Eq. ( 8) and Eq. ( 9), respectively.The final rank is calculated by applying Eq. ( 10) and the results are presented in Table 10.To compare the results obtained by the extended AROMAN method, MARCOS [19] and ARAS [20] are applied to the same case.The results of the comparative analysis are depicted in Figure 3.The results of the comparative analysis reveal that delivery concept 2 is the highest-ranked alternative in both cases.Therefore, it is strongly suggested to the e-commerce company to apply the cargo bike distribution concept 2 (i.e., to contact a third party and rent the fleet of cargo bikes to perform the delivery).

Conclusion
This paper addressed the investment decision on the cargo bike delivery concept selection problem in the e-commerce company.The extended AROMAN method was applied to the problem to rank the cargo bike delivery concepts.Three alternatives were considered: the delivery concept by using the own fleet of cargo bikes, the delivery concept by contracting a third-party provider, and the delivery concept by using a combination of the first two alternatives.The extended method ranked alternative 2 (the delivery concept by contracting a third-party provider) as the best solution for the e-commerce company.The second-best alternative was alternative 3, while the worst-ranked alternative was alternative 1 (the delivery concept by using the own fleet of cargo bikes).The obtained results are in line with other state-of-the-art research studies that investigate sustainable delivery modes [21−23].
To compare the results, the MARCOS and ARAS methods were applied, and the results show the same ranking order as the extended AROMAN method.The solution presented in this paper is limited only to the typical example of the e-commerce company since its experts participated in the decisionmaking process and evaluated the alternatives according to the company's perspective.However, the extended AROMAN method is general and can be used in such cases by various companies considering the cargo bike investment decision or any other decision-making problem.
The future direction of this paper should be to apply some other MCDM methods to evaluate and select the best third-party company from whom the e-commerce company should rent the cargo bikes to perform the delivery.
routing problem using cargo bikes Niels et al.[14] Design and operation of an urban electric courier cargo bike system Nürnberg [15] Usage of cargo bikes in urban logistics Rudolph et al. [16] Localization of urban micro consolidation centers for LMD Vasiutina et al. [17] Environmental impact of using cargo bikes in cities -review (−)  *  ,  = , , … , ;  = , , … , .

( 1 )
Delivery concept by own fleet of cargo bikes

Table 3
Expert's assessment of the second alternative

Table 5
An initial decision-making matrix

Table 6
Linear normalization of the initial decision-making matrix

Table 9
Aggregated averaged weighted normalized matrix with summarized criterion types