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Unleashing Sustainable Success: Assessing Expert Criteria Weights for Last-Mile Logistics

Juan Nicolas Gonzalez (https://orcid.org/0000-0001-8589-803X)

Natalia Sobrino

Jose Manuel Vassallo

Introduction

Involving experts with diverse backgrounds in Last-Mile Logistics (LML) is crucial for bridging theoretical approaches, current design practices, and decision-making processes. Gühnemann et al. (2012) emphasized the importance of including decision-makers with relevant experience in determining the weights assigned to each sustainability criterion. To achieve this, Bueno and Vassallo (2015) proposed a four-step process that combines the DELPHI and REMBRANDT methods:

  1. Designing a questionnaire: Develop a questionnaire that includes pairwise comparisons among sustainability criteria.
  2. Expert selection: Choose a minimum of thirty participants who can provide trustworthy responses for the weighting exercise.
  3. Survey administration: Distribute the questionnaire to the selected experts and collect their preferences using the REMBRANDT technique described by Olson et al. (1995) to derive criteria weights.
  4. Consensus evaluation: Conduct a statistical test to assess the level of consensus among the experts. Utilize R software to perform a simulation-based cross-validation technique for estimating consensus.

It is important to note that experts are requested to provide their perspective irrespective of the project context, ensuring the analysis can be uniformly applied to various contexts.

Once the consensus values have been obtained, the next step is to estimate the convergence weights. These weights represent the relative importance or priority assigned to each criterion based on the experts' collective judgment. The convergence weights provide valuable insights into the significance of each criterion within the context of the study. By estimating the convergence weights, the study gains a quantitative understanding of the experts' perspectives. This information allows for a more informed decision-making process and provides a solid foundation for the subsequent analysis and evaluation of last-mile logistics sustainability criteria.Overall, the estimation of convergence weights is a crucial step in the research process, as it provides valuable insights into the relative importance of different criteria, aiding in the development of sustainable last-mile logistics strategies and decision-making.

Preliminary

# Load libraries
suppressMessages(
	{
		install.packages("openxlsx")
		library("openxlsx")
		library("knitr")
		library("ggplot2")
		library("tidyverse")
		library("reshape2")
		install.packages("ggthemes")
		library("ggthemes")
		library("stringr")
		install.packages("ggridges")
		library("ggridges")
		library("patchwork")
	}
)

Questionnaire description

In line with the methodology devised by Bueno and Vassallo (2015), experts were tasked with providing graded comparative judgments regarding various sustainable criteria pertinent to last-mile logistics. This was achieved through a pairwise comparison questionnaire, with no consideration of the urban context. The questionnaire is accessible as open data from Vassallo et al. (2023). The experts were requested to assess the relative importance of different sustainability criteria using the REMBRANDT scale (Karbassi Yazdi & Abdi, 2017; Olson et al., 1995), which ranges from -8 (indicating a strong preference for Criteria 1 over Criteria 2) to +8 (indicating a strong preference for Criteria 2 over Criteria 1). To effectively evaluate trade-offs, it is crucial to comprehensively grasp how the improvement of one criterion may counterbalance the deterioration of another.

# Load data description
dictionary <- read.xlsx("https://zenodo.org/api/files/7898a318-2163-407d-b64c-898200ef053f/LEAD_STAR_LOGISTICS_SURVEY_REPORT.xlsx", sheet = 1)
View(dictionary)

Survey Administration

The online survey was shared with the Community of Practice, comprising a diverse range of stakeholders engaged in urban freight transport, including public administrations, local authorities, universities, logistics research centers, mobility clusters, logistics service providers, and freight transporter associations, among others. The survey was distributed to all Living Labs associated with the projects and promoted through various media channels, with the collaborative effort of all project partners (LEAD Project, 2020).

Importing and Cleaning Data

# Load Data
df <- read.xlsx("https://zenodo.org/api/files/7898a318-2163-407d-b64c-898200ef053f/LEAD_STAR_LOGISTICS_SURVEY_REPORT.xlsx", sheet = 2)
View(head(df, 10))
# Create a function to change text attributes to the equivalent REMBRANDT Scale 
replacef <- function (xtext){
  if (xtext == "Very strong preference Criteria 1") {
    eq <- -8
  } else if (xtext == "Strong Preference Criteria 1") {
    eq <- -6
  } else if (xtext == "Definite Preference Criteria 1") {
    eq <- -4
  } else if (xtext == "Weak preference Criteria 1") {
    eq <- -2
  } else if (xtext == "Indifference") {
    eq <- 0
  } else if (xtext == "Very strong preference Criteria 2") {
    eq <- 8
  } else if (xtext == "Strong Preference Criteria 2") {
    eq <- 6
  } else if (xtext == "Definite Preference Criteria 2") {
    eq <- 4
  } else if (xtext == "Weak preference Criteria 2") {
    eq <- 2
  } 
}
# Apply function for all comparisons
for (i in 5:34){
	df[, i] <- mapply(df[, i], FUN = replacef)
}

Descriptive Analysis

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