Decision Making in Manufacturing and Services
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ISSN 1896-8325
e-ISSN: 2300-7087
Call number
Volume
Vol. 15
Date
2021
Description
Journal
Decision Making in Manufacturing and Services
AGH University of Science and Technology Press (2007-)
ISSN: 1896-8325 e-ISSN: 2300-7087
ISSN: 1896-8325 e-ISSN: 2300-7087
journal.volume.project
Contains
Journal Issues
Articles
Analysis of Six Sigma Tools Utilization in Phases of DMAIC Cycle
(Wydawnictwa AGH, 2021) Soliński, Bartosz
Six Sigma has been developed and successfully used in many organizations for many years. The use of Six Sigma in process-improvement requires the systematic and disciplined use of specific tools and techniques in the DMAIC cycle. The DMAIC cycle includes five phases: Define, Measure, Analyze, Improve, and Control this is one of the most popular Six Sigma improvement cycles that are used in improving existing processes. When improving processes in accordance with the DMAIC cycle, it is important to have knowledge of the tools and techniques that are used and the ability to select them for the specifics of a project and the appropriate phases of the cycle. This article critically reviews the literature on the use of individual tools in the appropriate phases of the DMAIC cycle and uses a semi-structured interview method with specialists in the field of using Six Sigma. The obtained results of the analyses can contribute to the study of the validity of using individual tools and techniques for the effective use of Six Sigma and provide a useful comparative review for theoreticians and practitioners who want to use the appropriate tools in the DMAIC cycle.
Deadhead Minimization Problem in Multi-Depot Public Transport System
(Wydawnictwa AGH, 2021) Szczyrbak, Robert
This paper addresses a vehicle scheduling problem in the public transport system of Krakow, Poland. The primary objective is to develop and evaluate a mathematical model for assigning bus schedules to depots in a way that minimizes non-revenue (deadhead) kilometers. The proposed model, referred to as the Deadhead Minimization Problem in Multi-Depot Public Transport System (DMPMDPTS), seeks to reduce the total distance that is traveled by vehicles from their home depots to the starting points of their first scheduled routes and from the final terminals back to their depots. The model assumes fixed-route structures and known deadhead distances between terminals. Real-world data that was based on the Krakow Municipal Transport (KKM) was used to validate and verify the model. The optimization model was implemented in AMPL and solved using the GLPK Integer Optimizer (v4.43). Computational experiments were conducted across multiple cases that differed in their constraints and parameters in order to assess the model’s flexibility and performance. In all of the cases, optimal solutions were obtained in brief computation times. Compared to the existing operational schedules, the model consistently reduced deadhead kilometers. Case 1 achieved improvements without altering the numbers of vehicles per depot, while Case 2 led to further reductions of the costs of redistributing vehicles among depots, resulting in a less-balanced load structure. These findings demonstrated the model’s potential for supporting decision-making in depot allocation within public transport operations.
Adoption of Electromobility in Urban Transport in Poland – Cost-Benefit Trade-Offs and Decision-Making Challenges
(Wydawnictwa AGH, 2021) Wiśniowski, Wojciech
Electromobility is increasingly recognized as a cornerstone of sustainable urban transport strategies. This paper presents an analysis of selected economic, environmental, and infrastructural implications of transitioning from internal combustion engine vehicles to electric vehicles (EVs) in urban settings. Through a cost-benefit analysis, the study compares the purchase and operating costs of EVs and conventional cars across mini, compact, and premium market segments, accounting for factors such as energy consumption, fuel and electricity prices, annual mileage, and carbon emissions. The development and expansion of charging infrastructure, along with the integration of smart grid solutions and energy storage capabilities, are examined in the context of meeting the growing demand from a rising fleet of EVs. Additionally, the paper analyzes changes in urban mobility behaviors, highlighting the shift toward shared mobility and ecomobility, and discusses how these trends can reshape urban transportation to improve quality of life and reduce environmental impacts. Drawing on current trends, national electromobility development plans in Poland, and international best practices, the study identifies challenges and enablers for policymakers and decision-makers in the transportation and energy sectors, highlighting the need for coordinated planning and policy support to ensure the long-term viability and sustainability of electromobility in urban environments.
How to Interpret AHP/ANP Application Results in a Really Meaningful Manner?
(Wydawnictwa AGH, 2021) Ginda, Grzegorz
Final decision recommendations rely heavily on ranking Decision-Making Units (DMUs), often achieved using Saaty’s Analytic Hierarchy/Network Process (AHP/ANP). AHP/ANP provides precise overall priority scores which decision-makers commonly treat as definitive for ranking purposes. This reliance means that even minimal numerical differences between DMUs are used to determine the final selection. However, this strict adherence to tiny numerical distinctions – disregarding the actual degree of difference – is problematic. Practically, it risks rejecting DMUs whose performance is only slightly inferior, methodologically, it contradicts the qualitative nature of the input (pairwise comparisons) with the quantitative output. This tension raises the question of achieving an adequate qualitative interpretation of the quantitative rankings. To resolve this, the paper proposes clustering approaches to help decision-makers reliably group and discriminate among similar DMUs. These methods aim to justify more informed choices by avoiding spurious precision. The approaches were tested using two diverse decision cases. The results are promising and indicate that these clustering techniques can be useful under certain specific circumstances.
Operations Research in Municipal Solid Waste Management: Decision-Making Problems, Applications, and Research Gaps
(Wydawnictwa AGH, 2021) Gdowska, Katarzyna
Municipal Solid Waste Management (MSWM) represents a complex, multi-level decision domain that involves strategic, tactical, and operational planning under economic, environmental, and social constraints. This paper reviews the state of Operations Research (OR) applications to MSWM. The analysis encompasses optimization, simulation, metaheuristic, and hybrid approaches that address decision problems ranging from facility siting and capacity expansion to routing and scheduling. The study classifies OR contributions across decision levels, identifying methodological patterns and dominant model types such as mixed-integer programming, metaheuristics, and simulation-optimization frameworks. Despite significant progress in optimization and the integration of sustainability, critical gaps remain in uncertainty modeling, system-wide integration, and data-driven decision support. Deterministic formulations prevail at the strategic and tactical levels, while uncertainty is mainly explored in operational routing. Cross-level coordination among infrastructure planning, fleet design, and daily operations remains underdeveloped. Furthermore, persistent data scarcity and the limited incorporation of behavioral factors constrain the practical applicability of OR models. The review concludes with a research agenda that advocates for multi-level, uncertainty-aware, and dynamic optimization frameworks, supported by standardized data infrastructures and behavioral insights.