Cost Criterion Research Articles

A real-time energy and cost efficient vehicle route assignment neural recommender system

This paper presents a neural network recommender system algorithm for assigning vehicles to routes based on energy and cost criteria. In this work, we applied this new approach to efficiently identify the most cost-effective medium and heavy duty truck (MDHDT) powertrain technology, from a total cost of ownership (TCO) perspective, for given trips. We employ a machine learning based approach to efficiently estimate the energy consumption of various candidate vehicles over given routes, defined as sequences of links (road segments), with little information known about internal dynamics, i.e. using high level macroscopic route information. A complete recommendation logic is then developed to allow for real-time optimum assignment for each route, subject to the operational constraints of the fleet. We show how this framework can be used to (1) efficiently provide a single trip recommendation with a top-k vehicles star ranking system, and (2) engage in more general assignment problems where n vehicles need to be deployed over m(m≤n) trips. This new assignment system has been deployed and integrated into the POLARIS.11POLARIS is an Argonne-based high-performance, open-source agent-based modeling framework for simulating large-scale transportation systems. Transportation System Simulation Tool for use in research conducted by the Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium (SMART, 2024).

Randomized iterative feedback tuning for fast MIMO feedback design of a mechatronic system

Iterative feedback tuning (IFT) enables the tuning of feedback controllers using only measured data to obtain the gradient of a cost criterion. The aim of this paper is to reduce the required number of experiments for MIMO IFT. It is shown that, through a randomization technique, an unbiased gradient estimate can be obtained from a single dedicated experiment, regardless of the size of the MIMO system. The gradient estimate is used in a stochastic gradient descent algorithm. The approach is experimentally validated on a mechatronic system, showing a significantly reduced number of experiments compared to standard IFT.

A customer-driven quality function deployment approach for intelligent product design: A case of automatic-dishwasher

Quality Function Deployment (QFD) is one of the very effective customer-driven product development tools. With automatic dishwashers entering the Chinese market, manufacturers can use QFD to develop products that meet the unique Chinese customer requirements and explore the Chinese market. To help manufacturers maximize profits by developing automatic dishwashers to China’s market situation and competitive environment, this paper proposes an improved QFD model considering competitive learning and cost analysis based on market information analysis with Product Life Cycle (PLC) theory. On the one hand, the improved QFD model determines the life cycle stage of the product by analyzing the characteristics of the market. The improvement rate of customer requirements (CRs) determined by competitive learning in different stages has different influence on the importance weights of CRs, so that the new products developed based on design requirements (DRs) converted from CRs can better adapt to the current market environment. On the other hand, the cost strategy will also change according to different PLC stages. In cost analysis, different cost criteria weights will be determined in different life cycle stages to deal with product development risks in different market environments and help enterprises achieve better sustainable development. Finally, we take Chinese HE company’s automatic-dishwasher design as an example to show the specific application of the proposed model. The proposed model identifies the important DRs and their relative importance ranking for the product development. Comparison with existing QFD models shows the ranking obtained through the model is better integrated with market information, enabling the development of products that are better suited to the market, and helping companies to achieve sustainable development.

Simultaneous optimization of electric production, azo dye, and yeast wastewater treatment in microbial fuel cell: Selection of the most suitable optimal conditions by PROMETHEE

Microbial fuel cell is a clean technology in which waste treatment and energy production continue simultaneously. Process performance depends on the independent variables and their interaction with each other specific to the waste to be treated. In this study, Microbial Fuel Cell (MFC), in which yeast production process wastewater is oxidized in the anolyte, and azo dye is reduced in the catholyte under abiotic conditions, was optimized using the Response Surface Methodology for maximum waste treatment and energy production. The independent variables were wastewater type, electrode array, and amount of sulfate-reducing inhibitor, while the dependent variables were azo dye removal, maximum power density, chemical oxygen demand (COD) removal, and coulomb efficiency. The effect of variables on all responses was determined using Analysis of Variance (ANOVA) and 3-dimensional (3D) graphs. The suitability of the models was tested by verification experiments under the determined optimum conditions (OPT 1, 2 and 3). Optimum experiments were ranked with the PROMETHEE approach according to azo dye removal (mg/L), maximum power density (mW/m2), COD removal (g/L), coulombic efficiency (%), and electrode cost (€/m2) criteria. The preference order of the optimum experiments was determined as OPT 1>OPT 2>OPT 3. With OPT 1, azo dye removal of 16.8 mg/L, maximum power density of 95.34 mW/m2, COD removal of 5.8 g/L and coulombic efficiency of 0.23 % were achieved. With a 1000 Ω external resistor, the maximum power density and coulombic efficiency increased to 147 mW/m2 and 1.5 %, respectively. In addition, the determined optimum experiments were examined using polarization curve and Electrochemical Impedance Spectroscopy.

Penentuan Faktor Prioritas dalam Pemilihan Moda Transportasi Penumpang Rute Medan-Pematangsiantar dengan Menggunakan Metode Fuzzy-AHP dan Topsis

The aim of this research is to help Unimed students determine priority modes of passenger transportation that suit the needs and capabilities of the community for the Medan-Pematangsiantar route. This research uses the Fuzzy-AHP and Topsis methods. The research results reveal that cost criteria have the greatest influence in choosing a mode of transportation, while safety criteria have the smallest influence. Apart from that, the most popular mode of transportation is buses, while the least popular is travel agents.

Modelling and optimization of a distributed flow shop group scheduling problem with heterogeneous factories

In this paper, we solve a distributed flow shop group scheduling problem with heterogeneous factories, which we call the distributed heterogeneous flow shop group scheduling problem (DHFGSP). The objective is to minimize the energy consumption cost of the critical factory (the factory with the highest energy consumption cost among all factories). Although the DHFGSP is very meaningful for today’s production situation, it has not captured attention so far. Firstly, in this paper, a mixed integer linear model is developed. Secondly, four heuristic methods are presented based on specific scheduling rules and energy consumption cost criteria features. Thirdly, an effective iterative greedy algorithm based on Q-learning (Q_PIG) is proposed. In the Q_PIG, a family ordering rule is proposed to exchange with families in other factories to explore better quality solutions during the local search. Moreover, a Q-learning algorithm is embedded to select operations (family operations or job operations) for the current solution. The embedding of Q-learning enables the current solution to execute high-quality operations. Comprehensive experiments show that the Q_PIG is very effective for the problem solved.

تحسين جدول صيانة المعدات الدوارة باستخدام معيار التكلفة المتوقعة الكلية ومعيار تباين التكلفة

Inspection and maintenance of rotating equipment have gained credit in petrochemicals, refineries and oil and gas industries over the past decades due to the hazard failures resulting from overpressures and fluctuated temperatures surrounding the industrial zone. Therefore, any equipment operating under the harsh operational conditions should take the cost and time of inspection and maintenance into account. Maintenance of rotating equipment is an expensive event in terms of the cost and time, which are the driving forces to improve the maintenance performance. The paper aimed at optimizing the maintenance schedule for the grouped pumps in the refinery plant using the expected cost criterion and expected cost-variance criterion. This is characterized by the application of techniques to prolong equipment life, minimize downtimes, and enhance all aspects of reliability, availability and maintainability. The results showed that the cost and time of maintenance could optimize based on the rotating equipment, which could implement according to the preventive and corrective maintenance policy. The results analysis has revealed that applying optimization of maintenance schedule could help practitioners in decision-making to estimate the optimal cost and time, and reduce planning efforts in the future. Keywords: Pumps equipment, Maintenance policy, Cost and time of maintenance.

Partially observable optimal control using exponential cost criterion

We consider a partially observable optimal control model in which the system and observation noises are correlated, and the cost criterion is in the form of an exponential of an integral. A new minimum principle criterion is derived and applied to compute the explicit solution of linear exponential Gaussian optimisation problem with correlated noises.

An agent-based simulation modeling framework for Mobility-as-a-Service (MaaS)

This study develops an agent-based intelligent Mobility-as-a-Service (MaaS) simulation model consisting of three types of agents (i.e., MaaS fleet unit, travelers, and central intelligent mobility assignment module) to assess mobility service assignment processes balancing conflicting entities (e.g., demand and supply) within the MaaS ecosystem. The study follows a two-level optimization method (i.e., lower, and upper levels). It employs artificial intelligence and multicriteria decision making to solve two-dimensional mobility assignment problems (demand vs. supply). The novelty of this assignment process is that it implements an intelligence module accounting for the past system performance to make a future assignment decision in favor of both sides of the operation. This study processes 24-hour trip requests extracted from Nova Scotia Travel Activity (NovaTRAC) survey data in real-time. Two scenarios (1-D and 2-D assignments) are compared using the cost criteria, such as total waiting time, empty time, and idle time. The 1-D scenario refers to mobility assignment that emphasizes the demand side only, and the 2-D scenario formulates mobility assignment by balancing the demand and supply sides of the MaaS ecosystem. Experimental results indicate that a MaaS fleet of 350 units is the most balanced fleet in both scenarios. However, the 2-D optimization method reduces the overall supply cost by 25%. Moreover, 2-D operation demonstrates a higher fleet utilization over a considerable period, whereas the 1-D design guarantees a higher fleet utilization only during the peak period. Results of this study provide us with a benchmark for assessing more complex MaaS operation scenarios which will further aid in advancing the operational MaaS ecosystem. Our findings can help policymakers implement cost-effective MaaS solutions supporting sustainable urban mobility and SDG 13: Climate Action.

MODEL OF SPARE PARTS INVENTORY MANAGEMENT IN THE SUPPLY SYSTEM: THE CASE OF THE ‘OLL TRAK PARTS’ COMPANY

The article identifies the relevance of the problem of optimisation of material reserves of a company and their effective management, as the state of existing reserves has a decisive impact on the competitiveness of the company, its financial condition, and the corresponding performance results. It allows the formulation of the research objective in this development. It is a given that inventory management technology is an integral part of the management system of any manufacturing and trading company. The efficiency of these companies depends on such critical criteria as the number of costs incurred for inventory management. Traditional indicators, such as inventory volume, resource turnover, and continuity of supply, cannot be used in isolation to accurately measure the extent to which an inventory management system is improving since they are part of the overall cost criterion. The article examines scientific developments of theoretical foundations for implementing rational solutions in the supply chain, where the authors use modern research methods and approaches. After analysing these studies, we identify the main problematic issues: determining a rational way of servicing orders by the core production warehouse systems, organising an efficient product supply form, and developing rational inventory management technologies throughout the entire product supply chain. We create a structural model of interaction between the warehouse system of the ‘OLL TRAK PARTS’ company and suppliers and customers, formed with the participation of certain parties in all relevant operations. The structure of stocks accounts for the customer’s needs in a multi-nomenclature offer of spare parts for trucks, cars, and agricultural machinery supplied by ‘OLL TRAK PARTS’. The authors build a mathematical model of spare parts inventory management under limited demand conditions, which considers the following parameters of influence: the period for ordering spare parts, the cost of supplying ordered spare parts, and the cost of storing one unit of spare parts inventory. To achieve maximum measurement accuracy with a minimum number of trials and to maintain the statistical reliability of the results, we develop a full-factor experiment plan for three input parameters with three levels of values, consisting of 27 series of trials. The experiment allowed us to model the change in minimum costs with different order volumes of spare parts. Based on the results of the regression analysis of the obtained values of the estimated indicator, we determined the type of regression model – a linear form with a non-zero coefficient. Keywords: model, warehouse, system, logistics, interaction.

Optimal Service Time Distribution for an M/G/1 Waiting Queue

Suppose that in an M/G/1 waiting queue, the server can choose between two service time distributions. We look for the choice that enables us to minimize the expected value of a cost criterion that takes into account the cost incurred by working faster and the time needed to empty the waiting line. The random final time is the first time there is no customer waiting for service. When the service times are exponential random variables, we can appeal to dynamic programming to obtain the optimal solution. In the general case, conditional probability is used. Particular problems in which the capacity of the system is finite are solved explicitly.

Identification and Assessment of Cost and Effort Criteria for Prioritisation during Root Cause Analysis

Purpose: This study sought to provide a matrix by which to prioritise hypotheses in an Ishikawa diagram while considering a combination of the hypotheses' relation to the problem and the cost and effort to investigate because potential failure causes in an Ishikawa diagram are often prioritised for investigation using subjective methods, and not concrete criteria. Methodology/Approach: A survey was sent to organisations seeking to determine three levels of prioritisation for effort in hours to investigate a problem and costs to investigate. The results were then entered into a matrix that listed three levels of relationship between the hypothesis and the problem as well as three levels of costs and effort. A scenario was given to study participants in the industry to determine if participants with the hypothesis prioritisation matrix could find the correct hypotheses to investigate first, more often than participants without the matrix. Findings: The survey resulted in criteria for prioritisation of hypotheses to investigate. A study with employees in the industry found that participants with a hypothesis prioritisation matrix identified the correct hypotheses to investigate first more often than participants without the matrix. Research Limitation/implication: This paper provides concrete criteria for prioritising hypotheses to investigate during a root cause analysis. Originality/Value of paper: This paper provides concrete criteria for prioritising hypotheses.

An integrated approach for economic evaluation of environmental and health costs of occupational diseases in the oil industry

The health sector is one of the components of development, social welfare and economic growth. The purpose of this study was to develop an economic evaluation model of the environmental and health costs of occupational diseases by hybrid approach. To achieve the study goal, a taxonomy of economic evaluation model of the environmental and health costs of occupational diseases has been developed. The Delphi method was used to identify health and environmental criteria and the analytic network process (ANP) method was used to weigh the sub-criteria. Finally, health and environmental cost were estimated based on the available information. Naft Subspecialty Hospital in Tehran, Iran (NSHT), was selected as the place of case study. In this study, eight and eleven sub-criteria were identified in the health and environmental sector, respectively. The ANP results indicated that the medicine and medical equipment cost criteria with a weight of 0.312 in the Medical sector, and the special and infectious waste cost criteria with a weight of 0.085 in the environmental sector were the most significant cost criteria in NSHT. Furthermore, the parametric model findings indicated that 99.84 and 0.16% of the total costs are associated with health and environmental costs, respectively. The findings indicated that 61.3% of the costs of the health sector were associated with the two sectors of medicine and medical equipment and the cost-of-service personnel, and 91.7% of the costs of the environmental sector are associated with wastewater treatment and the cost of electricity consumption. This study tried to present a quantitative model of the health and environmental costs of NSHT. Implemention of this integrated model can be a practical and effective step in allocating resources and prioritize interventions.

Decision Support System Using the Multi-Objective Optimization Based on Ratio Analysis (MOORA) Method to Determine the World of Work Partners Competency Test at SMK Karnas Sindangwangi

Vocational High Schools (SMK) are designed to prepare students to enter the world of work or business with a focus on specific skills. The Skills Competency Test (UKK) is an effort by the government to ensure the quality of education in SMK. SMK Karya Nasional Sindangwangi organises UKK Mandiri using standardised instruments from the government and involving workplace partners. Currently, the selection of workforce partners in this school still needs to be done conventionally, making it challenging to determine relevant partners. This research aims to build a Decision Support System (DSS) to determine the optimal workplace partners for implementing UKK at SMK using the Multi-Objective Optimization based on Ratio Analysis (MOORA) method. This method calculates the final value converted into a matrix to obtain a value index (Yi) used for partner ranking. The alternative with the highest value from the benefit and cost criteria calculation is considered the best. The results showed PT Telkom Indonesia (Persero) as the best partner with a weight value of 0.3235, followed by PT Len Industri (Persero) with a value of 0.2656, PT Akur Pratama (Yogya Group) 0.2240, PT Arta Flash Sintesa Nusantara 0.1844, PT Seong Sin Tech 0.1553, and PT Indosat Ooredoo Tbk 0.1365. This research provides recommendations for optimal workforce partners for vocational schools.

Deciding on the priority of process parameters using the analytical hierarchy process in electrochemical wastewater treatment and investigation of paint wastewater treatability

In planning electrochemical treatment processes, it is thought that decision-making tools should be used in the selection of the main parameters that are likely to affect pollutant removal. According to detailed research in the literature, no previous study has systematically prioritized process parameters in electrochemical wastewater treatment using the analytical hierarchy process (AHP). In this study, published articles in the literature on the subject were used to determine which parameters were used in the treatment processes and with what frequency. Using the obtained results, the priorities of the process parameters were estimated with the help of AHP within the scope of the removal efficiencies and treatment cost criteria. According to the AHP analysis results, it was determined that the most effective parameters for both electrocoagulation and electrooxidation processes were pH (0.241), current density (0.171), voltage (0.171), conductivity (0.107), concentration (0.105) and reaction time (0.094). Four of the independent parameters were selected and treatability studies were carried out using both electrocoagulation (with Al electrode pair and Fe electrode pair) and electro oxidation (with Graphite electrode pair and Ti electrode pair) methods for different levels of each parameter. The pH, current density, voltage, and reaction time were chosen as independent parameters based on decision making process. Unit costs for removal of chemical oxygen demand with Al, Fe, graphite, and Ti electrodes were obtained as 1.05, 2.56, 0.074, and 14 USD per m3 wastewater, respectively. Unit costs for removal of color with Al, Fe, graphite, and Ti electrodes were obtained as 0.68, 1.20, 0.074, and 14 USD per m3 of wastewater, respectively. In addition, nine batch kinetic studies were conducted for optimum conditions obtained from pre-treatment studies with Al and Fe electrodes. Removal of chemical oxygen demand was achieved at 64 % with aluminum and iron electrodes at 40 and 70 min, respectively. The results obtained from kinetic studies showed that adsorption of organic substances fit the 2nd-order kinetic model and the correlation coefficient was 0.95 and 0.96 for aluminum and iron electrodes, respectively.

Discrete-time stopping games with risk-sensitive discounted cost criterion

Discrete-time stopping games with risk-sensitive discounted cost criterion

Методы и информационная система автоматизированного подбора лица, принимающего решения

The effectiveness of management activities is largely determined by the degree of reasonableness and efficiency of selecting a decision-maker and delegating the necessary powers to him. To solve this problem, it is advisable to use mathematical methods and information technologies that ensure the specified selection in an automated mode. This approach involves the creation of a DSS using unpopulated decision-making technologies. This allows you to identify employees or groups of employees who are most suitable for the implementation of managerial functions in terms of certain quality indicators. The use of unpopulated decision-making technologies in the interests of economic entities makes it possible to reduce the time and cost of their adoption by automating the transit management level. The purpose of the study considered in this article is to solve the problem of organizing the effective selection of a decision maker (DPR), or a group of people, as well as transferring the task to be performed automatically. To achieve this goal, the mathematical modeling of the LPR selection algorithm (LPR group) was carried out by performing operations on sets containing significant indicators and solving a mathematical programming problem. The proposed mathematical apparatus makes it possible to create a subsystem of the DSS responsible for the selection of a DSS (group of DSS) capable of making a high-quality management decision under given constraints, and transferring tasks to it for execution. The specified restrictions contain the type of task to be solved, the possibility of its implementation with the help of unpopulated decision-making technologies, competence limitations of LPR (groups of LPR), time constraints. The DSS subsystem, created on the basis of the proposed mathematical apparatus containing the task of optimizing the selection of LPR (LPR group) according to the cost criterion, makes it possible to minimize the cost of decision-making.

Artificial intelligence and policy making; can small municipalities enable digital transformation?

This study investigates digital transformation and the usability of emerging technologies in policymaking. Prior studies categorised digital transformation into three distinct phases of digitisation, digitalisation, and digital transformation. They mainly focus on the operational or functional levels, however, this study considers digital transformation at the strategic level. Previous studies confirmed that using new emerging AI-based technologies will enable organisations to use digital transformation to achieve higher efficiency. A novel methodological AI-based approach for policymaking was constructed into three phases through the lens of organisational learning theory. The proposed framework was validated using a case study in the transportation industry of a small municipality. In the selected case study, a confirmatory model was developed and tested utilising the Structural Equation Modelling with data collected from a survey of 494 local stakeholders. Artificial Neural Network was utilised to predict and then to identify the most appropriate policy according to cost, feasibility, and impact criteria amongst six policies extracted from the literature. The results from this research confirm that utilisation of the AI-based strategic decision-making through the proposed generative AI platform at strategic level outperforms human decision-making in terms of applicability, efficiency, and accuracy.

Selection of unmanned aerial vehicle systems for border monitoring using the MPSI-SPOTIS method

PurposeThe present paper assesses the decision problem of selecting Unmanned Aerial Vehicle Systems (SARP) by the hybrid MPSI-SPOTIS approach for deployment in border control and transborder illicit combat.Design/methodology/approachBy the hybrid MCDA MPSI-SPOTIS approach, and from the database available in Gettinger (2019), models were filtered by Endurance, Range, Maximum Take-Off Weight (MTOW), and Payload, fitting within the classification of Categories EB 0 and 2. Category EB 1 was not considered in this study due to the limited number of models in the data source.FindingsThe use of the Multi-Criteria Decision Analysis (MCDA) tool MPSI-SPOTIS allowed the determination of weights by stochastic criteria, applied in a ranking method resistant to reverse ordering. The application of the method identified the Raybird-3 (Cat EB 0) and Searcher (Mk3) (Cat EB 2) models as the best alternatives. From a proposed clustering, other selection possibilities with close performance in the evaluation were presented. The cost criterion was not taken into consideration due to the absence of information in the data source employed. Future studies are suggested to include criteria related to the life cycle and acquisition cost of the models.Research limitations/implicationsThe cost criterion was not taken into consideration due to the absence of information in the data source used. Future studies are suggested to include criteria related to the life cycle and acquisition cost of the models.Originality/valueThis paper aims to propose a technology selection method applied to complex defense acquisitions when multiple factors influence the decision makers and it is hard to obtain a major optimum solution in multitask and multi-mission platform.

Techno-economic analysis of water-based CO2 capture method based on adiabatic compressed air energy storage: Comparison with monoethanolamine-based CO2 capture method

Carbon dioxide (CO2) capture and storage is considered an effective measure to mitigate climate change, used to reduce CO2 emissions from industrial sectors, especially for coal-fired power plants. In our previous work, a novel water-based CO2 capture (WCC) method based on adiabatic compressed air energy storage (A-CAES) was developed. This method is simple and environmentally friendly. However, unlike the widely used monoethanolamine (MEA)-based CO2 capture (MCC) technology, which consumes low-grade thermal energy, the WCC technology consumes high-grade electrical energy. Therefore, in this work, a comprehensive techno-economic analysis of the WCC technology is conducted and compared with the MCC technology from the perspective of carbon emission reduction. A universal economic assessment criterion of CO2 capture cost based on electricity prices is proposed, and the techno-economic analysis is based on the cost in 2022 USD ($). The result shows that the CO2 capture cost of the WCC system ranges from 53.74 to 71.70 $/tonne CO2 due to the different factors considered in computational models, taking 63.57%–84.81% of the MCC system under the same capture rate (85%). The dynamic payback period of the WCC system ranges from 7.14 years to 20.52 years, while the MCC system failed to recover capital throughout the entire lifecycle. Moreover, the CO2 capture cost of the WCC system can be reduced to below carbon tax (43 $/tonne CO2) when the CO2 concentration in the feed flue gas is above 18 vol%, achieving positive benefits with no carbon trading required, which means the WCC system can achieve positive profitability for higher CO2 concentration emission sectors. The results provide significant guidance for developing and promoting the WCC technology.