Multi-objective Mathematical Model Research Articles

ASSESSMENT OF TCIP COMPULSORY EARTHQUAKE INSURANCE EVALUATIONS BY LOSS ADJUSTERS UNDER FUZZY LOGIC

Due to the geological, topographic and climatic characteristics of Turkey, natural disasters are frequently experienced. 96% of Turkey is in an earthquake zone with different degrees of danger zones. This situation clearly reveals the importance of having TCIP (Turkish Natural Catastrophe Insurance Pool) earthquake insurance in Turkey. Earthquake insurance certificate is an assurance that financial losses are compensated immediately after an earthquake. In the study, 53 number of experts (DM) working in the insurance sector and experts in TCIP insurance, earthquake & buildings’ structural mechanics, who are managers, engineers, insurance experts and instructors in public and private sectors, were interviewed. Criteria and sub-criteria of the study are formed as a result of the interviews and the established criteria and related sub-criteria (21 main criteria/75 sub-criteria) are evaluated. In the study, fuzzy multi-objective mathematical model (FMOM: by using Fuzzy AHP (Analytical Hierarchy Process), Fuzzy TOPSIS and Fuzzy VIKOR methods) are used to evaluate/calculate the damages within the scope of TCIP insurance after the earthquake by insurance experts. "Post-earthquake damage assessment form" is formed by using multi-criteria decision making (MCDM) method which is Fuzzy AHP (Analytical Hierarchy Process), Fuzzy TOPSIS and Fuzzy VIKOR methods. In the study, the criteria were weighted by using Fuzzy AHP method and among the criteria, “Building’s Structural System Type” which has the highest importance weight value, therefore it was the most important criterion. The second criterion is “Status of Column Damages/Fractures”, the third criterion is “The Intensity of the Earthquake”. By using Fuzzy TOPSIS and Fuzzy VIKOR methods, 5 number of insurance experts who evaluate/make TCIP insurance are evaluated and the most suitable/experienced insurance expert is selected.

MULTI-CRITERIA OPTIMIZATION FOR KNOWLEDGE EXTENSIONISTS IN INTERNAL KNOWLEDGE TRANSFER OF HIGHER EDUCATION IN NIGERIA

The demand for high education in an environment of an insufficient number of experts requires efficient knowledge management. The introduction of knowledge extensionists in higher learning institutions remains relevant to bridge the gap created by this imbalance. In this work, we developed a new multi-objective mathematical model for optimizing the knowledge transfer capability of extensionists and knowledge management in higher learning institutions in Nigeria. The Model provides the information needed for a detailed analysis of the design trade-off between conflicting objectives that are most interesting to a decision-maker. The binary Integer MultiObjective Model is a suitable model for a population of potential solutions. The Model focused on practical factors for effective knowledge transfer. Several indices were considered, such as number of task types, number of clusters (groups), number of employees, etc., as the knowledge transfer (KT) assessment factors. Some parameters are adequate to recognize the KT potential in an employee, such as whether task type can be handled by an employee, whether the employee is interested in cooperating with another employee, used the minimum size of the cluster in terms of the number of employees. The concept of “interest to work with each other” is a novel aspect considered in the recommended algorithm. Therefore, for illustration, an intelligent algorithm based on matrix and clustering concepts to evaluate data collected via a questionnaire from Waziri Umaru Federal Polytechnic Birnin Kebbi, Nigeria was developed. The model is suitable for any given set of data to be applied elsewhere

Integrated configuration design and capacity planning in a dynamic cloud manufacturing system

A cloud manufacturing (CMfg) system is presented as a novel service- and customer-oriented manufacturing paradigm that integrates the distributed manufacturing enterprises to share their manufacturing capabilities or resources and collaborate as an interconnected system in a dynamic environment. Since the high performance of this system depends on the formation of a suitable group of manufacturing service providers, this paper develops an integrated c onfiguration design and capacity planning problem for the CMfg system by considering the dynamic environment of this system. In this regard, dynamic service providers and dynamic demand are considered as two aspects of the dynamic nature of this system. A multi-period multi-objective mathematical model is proposed by maximising the utilities of all three stakeholders of the system. Moreover, three extensions of a discrete multi-objective grey wolf optimiser (DMOGWO) algorithm are devised to solve the medium- and large-scale instances. A comprehensive computational experiment is conducted to assess the performance of the developed meta-heuristic algorithms. Furthermore, by carrying out a sensitivity analysis, some managerial insight is suggested for the managers.

Modelling and analysis of online ride-sharing platforms – A sustainability perspective

By directly connecting passengers and private transportation service providers, online ride-sharing platforms reduce the number of intermediaries and improve the utilization of transportation resources. In addition to economic benefits, ride-sharing could provide environmental and social advantages. This paper studies a sustainability-oriented operational model for online ride-sharing platforms. We develop a holistic multi-objective mathematical model considering important indicators of the triple-bottom-line and examine its effects on the dispatching strategy. An effective method has been introduced to solve the model, as well as an approach to efficiently generate the Pareto front. Moreover, we validate the model and the solution methods through an extensive case study with different scenarios based on large-scale real-world data. We study the correlation among the objectives and analyse their variation trends when adjusting the influential factors. Finally, in this paper, useful managerial insights are provided for decision-makers of the platforms, such as how to balance the triple-bottom-line in a smart way.

Investigating the Optimal Design Variables of Concrete Slabs with Impact Resistance by Development a Multi-Objective Model to Save Cost-Time of Construction

Globally, construction industry plays a key role in development of an economy of any country. On the other side, construction projects suffer extensive delays and cost overruns exceeding by that the budgeted money and the estimated time. Therefore, adopting techniques like optimization technique in construction media became a necessity for preventing the actual cost and execution time of projects from exceeding the planned cost and estimated time, respectively. Since, concrete is the most essential construction and the most used material in construction projects construction where it influences extremely gross domestic products of numerous nations in the world, a multi-objective mathematical model with mixed linear and binary constraints is developed to specify the optimal solution of differently designed concrete slabs to impact loads. Eight concrete slabs had various design parameters, such as concrete thickness, steel fiber ratio, reinforcement ratio, and steel stiffener thickness are used in this study. The mathematical model comprised two objective functions: minimization cost and time, and had a number of constraints like concrete ingredients, load, deflection, and weight. The outputs of the developed model revealed a variety of slab design combinations with different cost, time, and deadloads depending on the limitations of serviceability loads.

A multi-objective mathematical model of financial flows in Omni-Channel distribution systems

Nowadays, companies and financial institutions have different strategies and priorities aligned with their financial goals and digital maturity; however, they rapidly make efforts to meet customers’ ever-changing expectations. One of the technologies that can meet the needs and expectations of customers faster and more accurately is the Omni-channel distribution system. Within this system, a set of distribution channels is defined. In addition to increasing the level of service and customer satisfaction, the amount of product demand and sales increases, resulting in more revenue and profit. The present study aimed to design a mathematical multi-objective Mixed Integer Linear Programming (MOMILP) model to investigate the relationship between different components of an Omni-Channel system and an innovative retail business model in the form of a financial approach for the optimization of profitability. In order to confirm the accuracy of the proposed mathematical model, numerical experiments were carried out using near-reality data based on an integration of Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Red Deer Algorithm (RDA). To solve the problem, the input parameters of this algorithm and mathematical model were provided in the form of problems with three sizes: small, medium, and large. The problem with the small size provided a fast and highly precise optimized response.

A global-responsive supply chain considering sustainability and resiliency: Application in the medical devices industry

Given the requirements of international businesses, this research addresses the supply chain network design problem in the real-world situation by considering four critical features: sustainability, resiliency, responsiveness, and globalization. For this purpose, a multi-objective mathematical model is proposed that minimizes the environmental impacts and the total costs and maximizes the social impacts while considering the resilience and responsiveness of the global supply chain. Then, the modified fuzzy robust stochastic method is employed to tackle the uncertainty. This study selects one of the most important medical devices during the pandemic (COVID-19) namely the blood bank refrigerator as a case study. Afterwards, the proposed multi-objective model is solved by developing a novel method named as augmented lexicographic weighted Tchebycheff method. Based on the obtained results, an increase in the responsiveness level of the supply chain can lead to increasing the sustainability dimensions, including job opportunities, safety, carbon emission, and economic aspects. Moreover, an increase in demands harms the economic, environmental, and responsiveness targets. The demand has a pivotal role in selecting resilience strategy, as well.

Life cycle assessment-based optimization approaches for sustainable disposal of municipal solid waste

Increasing municipal solid waste (MSW) has caused severe environmental pollution and social problems, which require effective treatment and resource utilization. Therefore, combined with advanced sustainable gasification technology, a classified MSW collection, transportation, and treatment process was established in this study. To optimize MSW management, a mathematical multi-objective model was established to optimize the revenue from the value-added energy electricity products and control greenhouse gas (GHG) emission. Life cycle assessment (LCA) was introduced into the objective evaluation to quantitatively evaluate the environmental greenhouse effect caused by gasification technology. Then, to further explore regional variations in gasification implementation, four practical case studies were presented to demonstrate the applicability and efficiency of the proposed model in selecting the disposal processes, determining volumes, generating income, and controlling GHG emissions. The sensitivity analysis of the gasification ratio (GR) in different cases shows that an increase in GR will improve economic benefits and reduce GHG emissions. In addition, the optimal GR varies in countries; for example, it was 0.6–0.7 in China, 0.9 in the USA, 0.6–0.8 in the UK and Japan. Therefore, a practical and effective management method for MSW gasification based on LCA is proposed, which can assist in the development of targeted, valid emission reduction measures for MSW gasification to reduce the environmental impact and provide practical guidance for the MSW gasification implementation. • A multi-objective model was built to optimize sustainable MSW gasification system. • Life cycle assessment is validly applied to the normalized evaluation of environmental objective. • The balance between economic and environmental benefits was achieved. • Different technical configurations can be set up to adapt to more real and universal situations. • Practical guidance is provided in sustainable MSW management from real life case.

A multiobjective mathematical model to form the best team at sports clubs: team harmony and player performance objectives

PurposeTeam coaches of sports clubs are highly concerned when forming the best team to win the upcoming match at the stage before that particular game. Even if a team squad is comprising of a limited number of players, the combination of them makes a complicated problem with a huge number of possible line-ups. This study aims to build a mathematical model to solve this problem with the objectives of maximum player performance and team harmony.Design/methodology/approachThis paper proposes a novel approach of a multiobjective mathematical model on team harmony and player performance. Two objectives are chosen as these are the most important perspectives that define the best team. The model outputs are nondominated solutions of these two objectives.FindingsThese solutions are displayed to the team coach to decide the best team according to strategical, psychological and conditional preferences of him/her. A real-life example is demonstrated to show the model validity and interpretation of the results by using the technique for order preference by similarity to an ideal solution on a volleyball team formation problem.Originality/valueThis paper proposes a multiobjective mathematical model on team harmony and player performance to solve the team coach’s hard and complicated problem.

Robust design of a green-responsive closed-loop supply chain network for the ventilator device.

This study aims to investigate the closed-loop supply chain network design problem considering the environmental and responsiveness features. For this purpose, a multi-objective mathematical model is suggested that minimizes the carbon emissions and the total costs and maximizes the responsiveness of the system. Due to the dynamic space of the business environment, uncertainty is an integral part of the supply chain problem. Therefore, this research applies the robust possibilistic programming method to cope with uncertainty. Afterwards, since the research problem has a high level of the complexity, a hybrid solution approach based on a heuristic method and the meta-goal programming method is developed to solve the research problem in a reasonable time. Then, due to the importance of the ventilator device during the recent pandemic (COVID-19), this study considers this product as a case study. The main contribution of the current study is to design a green-responsive closed-loop supply chain network under uncertainty using a multi-objective robust possibilistic programming model, for the first time in the literature, especially in the medical devices industry. On the other side, the other contribution of this study is to develop an efficient hybrid solution method. The achieved results demonstrate the efficiency of the offered model and the developed hybrid method. Eventually, by carrying out sensitivity analysis, the impact of some of the critical parameters on the model is investigated. Based on the obtained results, an increase in the demand sizes leads to increasing the environmental damages and the total costs while reducing the responsiveness level. On the other side, an increase in the rate of return leads to an increase in all of the objective functions. Also, the achieved results show that when the capacity parameter is increased, the total costs are decreased, but the responsiveness and environmental impacts are increased.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-022-19105-1.

Spatially explicit multi-objective mathematical model for invasive species management

Spatially explicit multi-objective mathematical model for invasive species management

Increasing the sustainability of a fresh vegetables supply chain through the optimization of funding programs: A multi-objective mathematical programming approach

Purpose: This research develops a model to improve the quality and freshness of sold vegetables through a funding program between farmers and retailers. Through this program, retailers who are interested in the distribution of first quality vegetables provide funds to farmers to increase their production this type of vegetables through the acquisition of new machinery, technology, or training.Design/methodology/approach: The problem is solved through a multi-objective mathematical programming model that simultaneously optimizes the supply chain profits, the waste of vegetables, the economic unfairness among farmers, the unfairness in the distribution of funds, and the freshness of sold vegetables. The ?-constraint method is used to obtain several non-dominated solutions to the problem after linearizing the non-lineal equations related to the unfairness objectives.Findings: Results show that it is possible to improve the indicators related to the vegetable waste, the economic unfairness, the unfairness in the distribution of funds and the freshness of vegetables while maintaining similar to optimal profits for the supply chain. Interesting trade-offs between the five objectives are identified, which can be used by supply chain members to select the most appropriate solution to be implemented in the real supply chain.Originality/value: This research models aspects relevant to the agri-food sector that have not been previously modelled for the problem under study. The main novelties of this paper are the consideration of the limited shelf life of the vegetables as well as the requirement of ensuring a minimum freshness at the moment of their sale, the price dependence on the quality and freshness of vegetables, the optimization of vegetable waste and the freshness of vegetables sold, as well as the joint optimization of the five previously defined objectives.

Self-Adaptive Risk-Based Inspection Planning in Petrochemical industry by evolutionary algorithms

This research proposes a multi-objective mathematical model for Self-Adaptive Risk-Based Inspection Planning (SARBIP). Risk management is essential to organizations to ensure that a proper maintenance strategy is in place. An inspection plan is required to improve the overall safety and achieve optimal configurations for the investment, repair, and maintenance, in order to minimize the total expenses. To this end, a SARBIP model is proposed to obtain optimal solutions. The proposed model serves a dual purpose by reducing both the expenses and risk level. The proposed algorithm, which is used as the model's solution, is validated by being applied to a real case study in the Iranian petrochemical industry. The model is executed through two algorithms, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Multi-Objective Particle Swarm Optimization (MOPSO). The outcomes indicate that MOPSO outperforms other algorithms in large-scale problems.

Designing a Cost-Time-Quality-Efficient Grinding Process Using MODM Methods

In this paper, a multi-objective mathematical model is developed to optimize the grinding parameters Such as grinding time, cost, and related surface quality metrics such as workpiece speed, depth of cut and wheel speed. The mathematical model consists of three conflicting objective functions subject to wheel wear and production rate constraints. Exact methods cannot solve the NLP model in few seconds, therefore using Meta-heuristic algorithms that provide near-optimal solutions is not suitable. Considering this, five multi-objective decision-making (MODM) have been used to solve the multi-objective mathematical model using general algebraic modelling system (GAMS) software to achieve the optimal parameters of the grinding process. The MODM methods provide different effective solutions where the decision-maker (DM) can choose each solution in different situations. Different criteria have been considered to evaluate the performance of the five MODM methods. Also, a technique for order of preference by similarity to ideal solution (TOPSIS) has been used to obtain the priority of each method and determine which MODM method performs better considering all criteria simultaneously. The results indicated that the weighted sum method (WSM) and goal programming method (GP) are the best MODM methods, as both of them provide competitive solutions. In addition, these methods obtained solutions that have minimum grinding time, cost, and surface roughness among other MODM methods.

6σ uncertainty optimisation strategy for USV navigation performance based on reliability analysis

This study presents a Design for Six Sigma (DFSS) optimisation strategy to improve the robustness and reliability of uncertainty parameters, focusing on the problem of navigation performance failure for unmanned surface vehicles (USVs) when uncertainty exists. The three aspects of USV performance are speedability, manoeuvrability, and seakeeping, and its multi-objective mathematical model is accurately constructed through validation. A design of experiment (DOE) technique is conducted to assign the weights using normalised processing. Based on this, a sensitivity analysis is implemented via the optimal Latin hypercube (OLH) method to reveal the correlation relationships between the variables and determine the main factors. A deterministic optimisation design (DOD) is performed using different exploratory optimisation techniques to obtain optimal certainty results. However, the design variables tend to leap across the boundary constraints once they experience a slight interference. Therefore, uncertainty optimisation is conducted to restrict the responses within the constraints. A six sigma (6σ) optimisation based on DFSS is adopted here to improve the reliability of the USV performance parameters. The DFSS results show a high credibility compared to the DOD results, indicating that the proposed 6σ optimisation strategy has a great degree of immunity to uncertainty in actual situations.

Research on Optimal Access Point and Capacity of Multiscenario Wind Turbine Based on Voltage Sag

Aiming at reducing the impact on sensitive loads and the economic losses caused by voltage sags in the distribution network containing wind turbines, a multiobjective mathematical model of the optimal access point and access capacity is established from the three perspectives of the basic costs of wind turbines, the economic losses of voltage sags, and the economic losses of the power grid. A scene generation method based on the similarity of wind curve changes and a scene reduction algorithm based on DPC & k-means are proposed to solve the mathematical problem of wind turbine uncertainty. Based on the analysis of the voltage sag of the distribution network containing wind turbines, the evaluation indicator of the voltage sag depth of the bus and the evaluation indicator of the economic loss of the voltage sag of the sensitive load are proposed. The simulation results of the IEEE 33-bus system show that the reasonable access of wind turbines can help reduce the power loss and voltage sag of the distribution network, thereby ensuring the safe and economical operation of the system.

Coordinated routing system for fire detection by patrolling trucks with drones

Vehicle routing problem with drones (VRPD) is an extension of classical vehicle routing, which involves the usage of trucks and drones. Nowadays, bushfires are taken into account as a major global challenging subject. Indeed, forests are very vulnerable to fires, and the classical patrol systems cannot patrol all areas of the forest and pastures due to the difficult crossing of some routes.The literature survey demonstrated that the mathematical models are an efficient approach for planning forest and rangeland patrols. Drones and trucks are used in cooperation for forest monitoring in this study, with drones acting as an efficient and accurate means of monitoring routes that are impassable to ground vehicles. However, this novel approach deals with several substantial challenges, including limitation in the drone's battery and the routing problem in an integrated truck-drone system based on considering hard-to-reach areas and creating a trade-off between time and cost of the patrol process. These issues make significant differences between the proposed novel approach and the classical vehicle routing problem. Thus, it is essential to develop a comprehensive approach to resolve these crucial issues. The present study develops a multi-objective mathematical programming model as mixed-integer programming for simultaneous patrolling of trucks and drones. The augmented epsilon constraint (AEC) method has been considered to solve the two-objective mathematical programming model. Besides, Benders' decomposition (BD) algorithm has been employed to evaluate the achieved results in large-size problems. The performance of the BD algorithm has been confirmed in this study. The findings indicated that the proposed model efficiently protected the forest via monitoring all areas, including impassable areas where ground patrolling is impossible (e.g., the lack of roads due to dense vegetation). Also, it was proved that this system was a novel, cost-effective patrol, which prevents uncontrollable fires in the shortest possible time.

Multi-objective collaborative agreements amongst shipping lines and marine terminal operators for sustainable and environmental-friendly ship schedule design

Oceangoing ships transport a significant amount of different goods across the globe. The seaborne trade is expected to continue its growth despite the COVID-19 pandemic. The International Maritime Organization considers the amount of emissions produced by oceangoing ships as substantial. Shipping lines have to explore innovative and effective alternatives to meet challenging emission reduction targets. This study proposes a new type of collaborative agreements amongst shipping lines and marine terminal operators, where the shipping line is not only able to select the marine terminal for the ship service at every port of the shipping route but also request the appropriate arrival time window and handling rate. A multi-objective mathematical model is developed to capture the proposed collaborative agreements. The first objective of the model minimizes the cost components that are mostly driven by the economic perspectives, while the second one directly captures the environmental perspectives as well. A novel customized exact multi-objective optimization method, inspired by the ε-constraint method and the goal programming method, is developed to address the problem. The computational experiments are conducted for the Europe Pakistan India Consortium 2 (EPIC-2) shipping route. The results show that the developed solution method is able to generate the Pareto Fronts with sufficient solution density. The experiments also demonstrate that the proposed multi-objective mathematical model could facilitate the analysis of trade-offs amongst the economic and environmental perspectives in the ship schedule design. Furthermore, the importance of effective collaborative agreements amongst shipping lines and marine terminal operators is showcased as well.

A multi-objective mathematical model and evolutionary algorithm for the dual-resource flexible job-shop scheduling problem with sequencing flexibility

A multi-objective mathematical model and evolutionary algorithm for the dual-resource flexible job-shop scheduling problem with sequencing flexibility

An intelligent UAV path planning optimization method for monitoring the risk of unattended offshore oil platforms

To ensure the safe operation of static and dynamic devices in a limited platform space and prevent security threats caused by accidents and external intruders, the unmanned aerial vehicle (UAV) is becoming an important tool to monitor and reduce the operational risk of unattended offshore oil platforms. However, UAVs may encounter location errors, adversely affecting patrol efficiency and accuracy. Therefore, the optimal UAV path planning is vital to ensure complete monitoring routes and reduce the risk presented by the marine environment, devices, and employees on unattended offshore platforms. This paper established a multi-objective mathematical model with the shortest flight path and minimum correction times for the intelligent UAV patrol of unattended offshore platforms. An intelligent algorithm with large-scale constraints for UAV path planning was proposed based on non-dominated sorting genetic algorithms. A flight path length of 8076.11 m and 25 correction times presented the optimal solution. The results indicated that the proposed algorithm could be used to plan an effective and accurate three-dimensional (3D) UAV flight path according to the size of the offshore oil platform. This is highly significant for the intelligent risk monitoring of unattended offshore platforms in practical engineering in the future.