Evaluating construction suppliers in last-mile delivery using zero-one integer programming and ordinal priority approach: a case study

Purpose Nowadays, receiving construction materials and goods on time and at the exact location is a big challenge for construction organizations. Last-mile delivery focuses on the last step of delivery of the general goods to the end customer in a streamlined way. Whereas oversized items are the exception for general commodities, construction materials are often heavy and include a wide range of irregularly shaped items. Also, construction sites have limited storage space and strict safety regulations. These challenges make the last-mile delivery of the construction industry different from general goods delivery. This study proposes a novel framework for evaluating construction suppliers in last-mile delivery of the construction supply chain management. Design/methodology/approach First, the most relevant last-mile delivery criteria associated with the construction sites are identified. Then, a zero-one nonlinear programming model is proposed with the aid of Kendall's coefficient of concordance to formulate the optimistic and pessimistic scenarios for expert opinions and determine the required number of experts. Later, using the ordinal priority approach (OPA) to identify the qualified construction supplier for last-mile delivery. Findings Based on the results of the study, the number of required experts for the supplier selection problem is a decision variable and should be optimized using mathematical programming for every specific problem. Several factors, such as conflict among experts and the stability of final ranks, can affect the required number of experts directly. Research limitations/implications The current study's practical implication is helping executives evaluate suppliers based on their contributions to delivering the materials and services to the end customer. Originality/value In this study, a novel framework is proposed with the aid of the OPA and zero-one nonlinear programming to select the construction suppliers. The optimal number of experts ensures a particular consensus level in the evaluation process, decreasing future risks and problems. The proposed method addresses scholars’ concerns about engaging enough experts for gathering reliable input data in decision-making problems.