Analytic hierarchy process application in selecting the mode of transport for a logistics company

Abstract

#nofulltext#As a multi-criteria decision-making (MCDM) method, the analytic hierarchy process (AHP) has been used considerably to solve hierarchical or network-based decision problems in socio-economic fields. Following an in-depth explanation of the transport function in logistics and an overview of the MCDM methods, the AHP model is employed in the paper for a logistics company in selecting the most suitable way of transportation between two given locations in Turkey. The criteria used in the selection of transportation modes are identified as the cost, speed, safety, accessibility, reliability, environmental friendliness, and flexibility. Several cost parameters (transportation, storage, handling, bosphorus crossover) are incorporated into the decision-making process. The application is carried out in instructional character. The results of the study indicate that the railway transportation, which is not widely used in Turkey, is also an alternative and suitable means of transportation

Decision-Support Framework for Selecting the Optimal Road Toll Collection System

“…The application of MCDM results in the ranking of alternatives, from the most to the least favorable, thus allowing comparison of alternatives. Some common methods for MCDA in transport applications include the technique for order of preference by similarity to ideal solutions (TOPSIS) [19,20], analytic hierarchy process (AHP) [21][22][23], grey evaluation method (GE) [24,25], simple additive weighting (SAW) [26], and elimination and choice expressing reality ELECTRA [27]. In particular, MCDM methods are often used for formulating DSFs for optimal technological system selection [28][29][30][31][32][33][34][35][36].…”

AFS Theory based Integrated Multiple Criteria Decision Making Model for Transportation Mode Selection

“…The simple additive weighting (SAW), also known as weighted sum model, is one of the most popular and simple multicriteria decision analysis technique for evaluating a set of alternatives in terms of a set of criteria. In a multiple criteria decision problem with alternatives and criteria, the performance score of each alternative can be derived using following equation: * = ∑ =1 (12) where (for = 1, 2, … , and = 1, 2, … , ) represents the normalized rating of alternative with respect to criterion and represents the normalized weight of criterion . The alternative with maximum performance score ( * ) is considered as best alternative.…”

“…This phase begins with forming a team of 5 experts responsible for identifying a set of selection criteria important to determine best mode of transportation. A list of selection criteria was prepared based on expert's opinion and literature survey ( [20], [15], [12], [4], [16], [8], [7]) and a calibration process was applied to narrow down the list to include only those criteria the travelers' feel pertinent to select a transportation mode. This process resulted in a set of eight criteria -1 : safety, 2 : speed, 3 : travel cost, 4 : interchange, 5 : accessibility, 6 : comfort, 7 : environment friendliness, and 8 : capacity.…”

“…Similarly, the weights of all transportation modes over each criterion : = 1, 2, … , 8 are obtained and a normalised (since the weights determined using AHP are already normalised, therefore no further normalisation is required) fuzzy decision matrix is obtained in Table 6. In this last phase, a weighted normalised decision matrix is established (Table 7) and the performance of each transportation mode (rank) is evaluated using Equation (12). The graphical representation of overall performance scores of transportation modes is shown in Figure 1.…”

Inventory Management of Railway Logistics Park Based on Artificial Neural Network

“…From the perspectives of logistics safety, greenness, and social benefits, Binder et al [18] identified the factors affecting RLP's operational benefits, constructed a reasonable evaluation index system (EIS), and provided the intelligent park management center with the top and bottom design techniques for operation management platform. Kumru and Kumru [19] planned and designed the technical and functional architectures for intelligent RLP inventory management, which involves the goods dispatching command center, goods sales and distribution center, comprehensive management center, and logistics information interaction center. Sharp et al [20] provided new ideas for RLP intermodal transport and inventory management, with the aid of space-time positioning through crowd sensing, image recognition by multiple visual sensors, and adaptive selfoptimization by artificial intelligence (AI).…”