Space mission risk, sustainability and supply chain: review, multi-objective optimization model and practical approach

This paper investigates the convergence of risk, sustainability, and supply chain in space missions, including a review of fundamental concepts, the introduction of a multi-objective conceptual optimization model, and the presentation of a practical approach. Risks associated with space missions include technical, human, launch, space environment, mission design, budgetary, and political risks. Sustainability considerations must be incorporated into mission planning and execution to ensure the long-term viability of space exploration. The study emphasizes the importance of considering environmental sustainability, resource use, ethical concerns, long-term planning, international collaboration, and public outreach in space missions. It emphasizes the significance of reducing negative environmental consequences, increasing resource use efficiency, and making responsible and ethical actions. The paper offers a multi-objective optimization conceptual model that may be used to evaluate and choose sustainable space mission tactics. This approach considers a variety of elements, including environmental effects, resource utilization, mission cost, and advantages for society. It provides a systematic decision-making approach that examines trade-offs between different criteria and identifies optimal conceptual model solutions that balance risk, sustainability, and supply chain objectives. A practical approach is also offered to demonstrate the use of the multi-criteria optimization conceptual model in a space mission scenario. The practical approach demonstrates how the model can aid in the development of mission strategies that minimize risks, maximize resource consumption, and fit with sustainability goals. Overall, this paper delivers a multi-criteria optimization conceptual model and provides a space mission planning practical approach, as well as an overview of the interaction between risk, sustainability, and supply chain in space mission organization, planning, and execution.This research was partially supported by the AGH University of Science and Technology, Kraków, Poland (16.16.200.396) and the financial aid of the Polish Ministry of Science and Higher Education (MNISW) grants (N N519 405934; 6459/B/T02/2011/40) and the Polish National Science Centre (NCN) research grant (DEC-2013/11/B/ST8/04458). Moreover, I appreciate the support of the Spanish Ministry of Science, Innovation, and Universities (RED2018-102642-T; RED2022-134703-T; PID2019-111100RB-C22/AEI/10.13039/501100011033). Additionally, I acknowledge the support from the Public University of Navarra, Pamplona, Spain and the University of California at Berkeley, USA. The research was also partially supported by the European Union Horizon 2020 research and innovation program under Marie-Skłodowska Curie, No: 101034285

A Reference Point Approach to Bi-Objective Dynamic Portfolio Optimization

The portfolio selection problem presented in this paper is formulated as a biobjective mixed integer program. The portfolio selection problem considered is based on a dynamic model of investment, in which the investor buys and sells securities in successive investment periods. The problem objective is to dynamically allocate the wealth on different securities to optimize by reference point method the portfolio expected return and the probability that the return is not less than a required level. In computational experiments the dataset of daily quotations from the Warsaw Stock Exchange were used

A Reference Point Method to Triple-Objective Assignment of Supporting Services in a Healthcare Institution

This paper presents an application of mixed integer programming model for op- timal allocation of workers among supporting services in a hospital. The services include logistics, inventory management, financial management, operations management, medical analysis, etc. The optimality criterion of the problem is to minimize operational costs of supporting services subject to some specific constraints. The constraints represent specific conditions for resource allocation in a hospital. The overall problem is formulated as a triple- objective assignment model, where the decision variables represent the assignment of people to various jobs. A reference point approach with the Chebyshev metric is applied for the problem solution. The results of computational experiments modeled on a real data from a hospital in Lesser Poland are reported

A Bi-Objective Portfolio Optimization with Conditional Value-at-Risk

This paper presents a bi-objective portfolio model with the expected return as a performance measure and the expected worst-case return as a risk measure. The problems are formulated as a bi-objective linear program. Numerical examples based on 1000, 3500 and 4020 historical daily input data from the Warsaw Stock Exchange are presented and selected computational results are provided. The computational experiments prove that the proposed linear programming approach provides the decision maker with a simple tool for evaluating the relationship between the expected and the worst-case portfolio return

Multi-Criteria Optimization for Fleet Size with Environmental Aspects

[EN] This research concerns multi-criteria vehicle routing problems. Mathematical models are formulated with mixed-integer programming. We consider maximization of capacity of truck vs. minimization of utilization of fuel, carbon emission and production of noise. The problems deal with green logistics for routes crossing the Western Pyrenees in Navarre, Basque Country and La Rioja, Spain. We consider heterogeneous fleet of trucks. Different types of trucks have not only different capacities, but also require different amounts of fuel for operations. Consequently, the amount of carbon emission and noise vary as well. Companies planning delivery routes must consider the trade-off between the financial and environmental aspects of transportation. Efficiency of delivery routes is impacted by truck size and the possibility of dividing long delivery routes into smaller ones. The results of computational experiments modeled after real data from a Spanish food distribution company are reported. Computational results based on formulated optimization models show some balance between fleet size, truck types, utilization of fuel, carbon emission and production of noise. As a result, the company could consider a mixture of trucks sizes and divided routes for smaller trucks. Analyses of obtained results could help logistics managers lead the initiative in environmental conservation by saving fuel and consequently minimizing pollution.This work has been partially supported by the National Research Center (NCN), Poland (DEC2013/11/B/ST8/04458), by AGH, and by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180- C3-P and TRA2015-71883-REDT), and the Ibero-American Program for Science and Technology for Development (CYTED2014-515RT0489). Likewise, we want to acknowledge the support received by the CAN Foundation in Navarre, Spain (Grants CAN2014-3758 and CAN2015-70473). The authors are grateful to anonymous reviewers for their comments.Sawik, B.; Faulin, J.; Pérez-Bernabeu, E. (2017). Multi-Criteria Optimization for Fleet Size with Environmental Aspects. Transportation Research Procedia. 27:61-68. https://doi.org/10.1016/j.trpro.2017.12.05661682

A SPC strategy for decision making in manufacturing processes

Tapping is an extensively employed manufacturing process by which a multi-teeth tool, known as tap, cuts a mating thread when driven into a hole. When taps are new or slightly worn the process is usually in control and the geometry of the resulting threads on the work piece is correct. But as the tap wear increases the thread geometry deviates progressively from the correct one and eventually the screw threads become unacceptable.The aim of this paper consists on a development of statistical process control strategy for decision making according to data coming from the current signal of the tap spindle for assessing thread quality. It could operate on line and indicates when the tap wear is so critical that, if the process were continued, it would result in unacceptable screw threads. The system would be very cost-effective since the tapping process could be run without any operator intervention

The Effect of Environmental Criteria on Locating a Biorefinery: A Green Facility Location Problem

Underestimating facility location decisions may penalize business performance over the time. Those penalties usually have been studied from the economic point of view analyzing its impact on profitability. Additionally, the concern about the obtaining of sustainability is gaining importance leading to seek for renewable energy sources to reduce greenhouse gas emissions. However, little attention has been paid on choosing a location considering environmental criteria. Thus, this work aims at determining a biorefinery location considering its impacts on natural resources. Therefore, a mixed integer linear programming (MILP) model is developed taking into account the crop location and the biomass production seasonality to obtain an apposite location that minimizes environmental impact. The initial version of this paper was presented at ICIL 2016 Conference.

A Reference Point Method to Triple-Objective Assignment of Supporting Services in a Healthcare Institution

Abstract. This paper presents an application of mixed integer programming model for optimal allocation of workers among supporting services in a hospital. The services include logistics, inventory management, financial management, operations management, medical analysis, etc. The optimality criterion of the problem is to minimize operational costs of supporting services subject to some specific constraints. The constraints represent specific conditions for resource allocation in a hospital. The overall problem is formulated as a tripleobjective assignment model, where the decision variables represent the assignment of people to various jobs. A reference point approach with the Chebyshev metric is applied for the problem solution. The results of computational experiments modeled on a real data from a hospital in Lesser Poland are reported. Keywords: reference point method, assignment problem, mixed integer programming, services operations management, healthcare planning. Mathematics Subject Classification: 90B50 -management decision making, 90B80 -discrete location and assignment, 90C11 -mixed integer programming, 90C90 -applications of mathematical programming

A reference point method to triple-objective assignment of supporting services in a healthcare institution

Tyt. z nagłówka.Bibliogr. s. 46.Dostępny również w formie drukowanej.ABSTRACT: This paper presents an application of mixed integer programming model for optimal allocation of workers among supporting services in a hospital. The services include logistics, inventory management, financial management, operations management, medical analysis, etc. The optimality criterion of the problem is to minimize operational costs of supporting services subject to some specific constraints. The constraints represent specific conditions for resource allocation in a hospital. The overall problem is formulated as a triple-objective assignment model, where the decision variables represent the assignment of people to various jobs. A reference point approach with the Chebyshev metric is applied for the problem solution. The results of computational experiments modeled on a real data from a hospital in Lesser Poland are reported. KEYWORDS: reference point method, assignment problem, mixed integer programming, services operations management, healthcare planning