The paper presents the original procedure of solving a multiple criteria stochastic ranking problem consisting in the evaluation of different variants of the distribution system. The problem originates from the analysis and construction of the redesign scenarios of the existing distribution system. The authors develop a computational procedure being a combination of a traditional – deterministic multiple criteria ranking method (e.g. Electre III/IV) and a classification algorithm (e.g. Bayes classifier). The proposed method is composed of six steps, including: stochastic data collection, random selection of deterministic numbers using simulation technique, solving a multiple criteria ranking problem with an application of a deterministic multiple criteria decision aiding/making (MCDM/A) method, the classification of deterministic relations between redesign scenarios (variants) to predefined classes using classification algorithm, the construction of a final ranking of redesign scenarios with an application of a spreadsheet, the recommendation of the compromise solution based on stochastic final ranking of redesign scenarios. The proposed approach is verified on the real-world analysis of the distribution system of goods which operates at the Polish electro-technical market. The results of computational experiments, including: ranking generation, classification and sensitivity analysis are demonstrated. The analysts’ final recommendation of the compromise solution selection is presented. It is based on a comprehensive analysis of the current state of the system, the perspectives of its development, decision maker's preferences, results of the computational experiments and sensitivity analysis

The article deals with the problem of building stacked pallet load units consisting of at least two stackable pallet load units. Moreover, this article concerns the part of the flow of goods in distribution networks that is prepared at the place of initial assembly in the form of palletized loading units designed for the final receiver. Such a unit does not exceed the limits of permissible weight or height. The article proposes a single-criteria binary programming model in which the goal is to minimize the pallet spaces required to accommodate the constructed units. In addition to the classical parameters of acceptable weight and height of the units, the constraints also take into account the fragility of the goods placed on each unit, filling the top layer of each unit, and its height homogeneity. The model developed was verified on a test dataset, and the savings from the use of optimum construction of the stacked palletized cargo units were demonstrated through the conducted experiments.

A sandwich pallet loading problem represents a significant challenge in the logistics of fast-moving consumer goods (FMCG), requiring optimisation of load units (LUs) arrangements to minimise their number in transportation and warehousing processes, leading to an environmental responsibility of organisations. This study introduces an innovative approach combining Dominance-Based Rough Set Theory (DRST) with a rule-based expert system to improve the efficiency of the pallet loading and to provide sustainable development. Key criteria and attributes for the LU assessment, such as weight, height, and fragility, are defined. DRST is utilised to classify these units, leveraging its capability to handle imprecise and vague information. The rule-based system ensures an optimal arrangement of LUs by considering critical control parameters, thereby reducing LU numbers and mitigating the environmental impact of logistics operations, as measured by energy consumption. The proposed approach is validated using real-world data from the FMCG distribution company. Results demonstrate that integrating DRST with an expert system improves decision-making consistency and significantly reduces the number of LUs. This study shows a way to increase the level of environmental responsibility of the organisation by cutting energy consumption and delivering economic and social benefits through fewer shipments. For example, the approach reduces energy consumption for a customer order delivery by 40%, from 0.60 to 0.36 (kWh/pskm).

This paper presents an approach combining simulation and multi-criteria decision analysis (MCDA) to model and evaluate options for passenger service organisation at a terminal. The methodology is motivated by changes planned by the EU concerning the introduction of the Entry/Exit System (EES) for advanced border control of passengers crossing the Schengen border having an impact on a passenger flow at the Border Crossing Point (BCP). The primary outcome is the selection of a recommended process configuration, including the types and number of servers required to ensure an efficient passenger flow within the BCP, and satisfactory service levels from the passenger's perspective. The authors propose a methodology that relies on a multi-stage and multi-level graph structure of the BCP. It enables the implementation of alternative technological solutions supporting border control, i.e., Manual Border Control (MBC), and automated solutions such as e-Gates (e-Gs) and Self-Service Kiosks (SSKs) to create a complex BCP structure. Unlike traditional approach, in this research both static and dynamic phenomena of traffic flow modeling, allowing for comprehensive control of passenger movement at the BCPs, is proposed. The research integrates traffic control, the composition of technical resources, staffing considerations, and spatial analysis into a single evaluative framework, providing a methodology to find the compromise solution for the process design. It consists of six stages: 1) analysis of the current state, 2) design of process variants and formalisation of evaluation criteria, 3) simulation models development for variants, 4) simulation of the current state and process variants, and analysis of results, 5) selection and application of the decision aiding method to find the compromise variant, and 6) result analysis. The proposed methodology has been applied to redesign the border control process at an airport terminal in the context of new border control procedures. Assuming that 39% of passengers require 10–120% more processing time due to new procedures, the recommended process includes new equipment configuration, increasing the total number of units by two. At the same time, the number of border guards remains unchanged, and the space required for passengers waiting in the queues is reduced by 30%.

This paper deals with the problem of the design and configuration of a distribution system that supports the delivery of goods to customers using various commercial channels. In this sense, a modelled distribution system, called an omni-channel distribution system, should ensure the delivery of the same products in different ways while guaranteeing its highest efficiency. The authors propose a single-criterion mixed-integer programming model for the configuration of a complex distribution system of goods in an omni-channel environment. A minimized cost function, composed of key logistics elements including transport, warehouse location, and manipulation processes, is the objective function. The model was experimentally verified, and as a result, potential savings of around 5–8% of the omni-channel design distribution system costs versus the multi-channel distribution system costs were identified. In the paper, an extended sensitivity analysis is also performed to show how specific parameters of the model can change the final recommendation.

The paper is concentrated on improvement of a delivery process of parts and components for a vehicle production company based on just-in-time system. The authors have proposed a four-stage solution procedure. In the first stage the logistics process has been analysed and modelled using EPC notation in ARIS Business Architect. The major activities in the process, cause and effect relationships, key resources (personal and technical) and their assignment to the activities have been identified. In the second stage, multicriteria mathematical model has been constructed to evaluate the process performance. In the third stage, the simulation model has been built with an application of simulation tool ARIS Business Simulator, and the computational experiments have been carried out. Next, in the fourth stage the set of variants representing changes in the current logistics process have been created, equivalent changes have been introduced to the simulation model and computational experiments have been carried out. Based on the nondeterministic simulation results, in the next stage the variants have been ranked with an application of stochastic decision aiding method – stochastic ELECTRE III. Finally, the compromise variant of the logistics process improvement has been recommended