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Dr. Umar Modibbo
ALIGARH MUSLIM UNIVERSITY, ALIGARH, INDIA

Basic Info


Research Keywords & Expertise

0 Inventory Control
0 Mathematical Programming
0 applied statistics
0 Reliability Optimization
0 Optimization theory and its applications

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Short Biography

Umar Muhammad Modibbo is a Lecturer at the Modibbo Adama University of Technology, Yola, Nigeria. He is working currently as a research scholar at the Aligarh Muslim University, Aligarh, India. He obtained his Master of Technology (M.Tech) and Bachelor of Technology (B.Tech) degrees in Operations Research at the Federal University of Technology, Yola, Nigeria (Now the Modibbo Adama University of Technology) in 2016 and 2010 respectively. His research areas include Mathematical Programming and its Applications, Reliability Optimization and Inventory & Supply Chain Management. He is an Associate-Fellow, Institute for Operations Research of Nigerian [INFORN] Membership No. AF18017, a lifetime Member, African Federation of Operations Research Societies [AFROS], International Federation of Operational Research Societies [IFORS] and Operational Research Society of India. He has published more than 15 research articles in journals of national and international repute. He is a reviewer of many journals.

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Journal article
Published: 23 July 2021 in Sustainability
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The demand for cost-efficient and clean power energy cannot be overemphasised, especially in a developing nation like India. COVID-19 has adversely affected many nations, power sector inclusive, and resiliency is imperative via flexible and sustainable power generation sources. Renewable energy sources are the primary focus of electricity production in the world. This study examined and assessed the optimal cost system of electricity generation for the socio-economic sustainability of India. A sustainable and flexible electricity generation model is developed using the concept of flexible fuzzy goal programming. This study is carried out with the aim of achieving the government’s intended nationally determined contribution goals of reducing emission levels, increasing the capacity of renewable sources and the must-run status of hydro and nuclear, and technical and financial parameters. The result shows an optimal cost solution and flexibility in how increased electricity demand would be achieved and sustained via shifting to renewable sources such as solar, wind and hydro.

ACS Style

Mohammad Khan; Asif Pervez; Umar Modibbo; Jahangir Chauhan; Irfan Ali. Flexible Fuzzy Goal Programming Approach in Optimal Mix of Power Generation for Socio-Economic Sustainability: A Case Study. Sustainability 2021, 13, 8256 .

AMA Style

Mohammad Khan, Asif Pervez, Umar Modibbo, Jahangir Chauhan, Irfan Ali. Flexible Fuzzy Goal Programming Approach in Optimal Mix of Power Generation for Socio-Economic Sustainability: A Case Study. Sustainability. 2021; 13 (15):8256.

Chicago/Turabian Style

Mohammad Khan; Asif Pervez; Umar Modibbo; Jahangir Chauhan; Irfan Ali. 2021. "Flexible Fuzzy Goal Programming Approach in Optimal Mix of Power Generation for Socio-Economic Sustainability: A Case Study." Sustainability 13, no. 15: 8256.

Journal article
Published: 05 June 2021 in Alexandria Engineering Journal
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The ever increasing pressure to conserve the environment from global warming cannot be overemphasized. Emission from the inventory and production process contributes immensely to global warming and hence, the need to device a sustainable green inventory by the operational managers. In this paper, a multi-item multi-objective inventory model with back-ordered quantity incorporating green investment in order to save the environment is proposed. The model is formulated as a multi-objective fractional programming problem with four objectives: maximizing profit ratio to total back-ordered quantity, minimizing the holding cost in the system, minimizing total waste produced by the inventory system per cycle and minimizing the total penalty cost due to green investment. The constraints are included with budget limitation, space restrictions, a constraint on cost of ordering each item, environmental waste disposal restriction, cost of pollution control, electricity consumption cost during production and cost of greenhouse gas emission in the production process. The model effectiveness is illustrated numerically, and the solution obtained to give a useful suggestion to the decision-markers in the manufacturing sectors.

ACS Style

Abdullah Ali H. Ahmadini; Umar Muhammad Modibbo; Ali Akbar Shaikh; Irfan Ali. Multi-objective optimization modelling of sustainable green supply chain in inventory and production management. Alexandria Engineering Journal 2021, 60, 5129 -5146.

AMA Style

Abdullah Ali H. Ahmadini, Umar Muhammad Modibbo, Ali Akbar Shaikh, Irfan Ali. Multi-objective optimization modelling of sustainable green supply chain in inventory and production management. Alexandria Engineering Journal. 2021; 60 (6):5129-5146.

Chicago/Turabian Style

Abdullah Ali H. Ahmadini; Umar Muhammad Modibbo; Ali Akbar Shaikh; Irfan Ali. 2021. "Multi-objective optimization modelling of sustainable green supply chain in inventory and production management." Alexandria Engineering Journal 60, no. 6: 5129-5146.

Journal article
Published: 20 May 2021 in Journal of King Saud University - Science
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Achieving sustainable development goals agenda 2030 is the aspiration of all the United Nation’s member countries. Countries have an uneven distribution of natural resources, economic strength, and capacity building. Many studies analyze these goals using various models and considering different aspects of the kingdom of Saudi Arabia's (KSA) economy. However, none of the studies modelled the key indicators mathematically to quantify the achievement level towards vision 2030; hence aims to bridge the existing literature gap In this paper, the sustainable development goals (SDGs) of KSA are considered, a mathematical model is formulated in light of fuzzy and weighted goal programming using membership function. The model comprises three goals related to the gross domestic product, sustainable energy consumption and employment capacity of the economy's contributing sectors. The analytic hierarchy process integrated to compute the goals’ weights using row geometric mean method. The study established the goals's satisfaction level with 57% overall achievement of the vision 2030. Individually, economic growth goal is 45% achievable. The clean energy consumption-related goal is 67% realizable. The employment-related goal is 78% attainable within the time frame of the vision 2030. The study suggests that KSA diversify its energy sector by concentrating and investing more resources in alternative energy sources, including renewable energy such as solar, wind, biomass, and nuclear energy. Also, there is a need to involve more vibrant and talented youths in the critical decision-making process. The model is simple and can be replicable in a similar country with slight modifications.

ACS Style

Ali AlArjani; Umar Muhammad Modibbo; Irfan Ali; Biswajit Sarkar. A new framework for the sustainable development goals of Saudi Arabia. Journal of King Saud University - Science 2021, 33, 101477 .

AMA Style

Ali AlArjani, Umar Muhammad Modibbo, Irfan Ali, Biswajit Sarkar. A new framework for the sustainable development goals of Saudi Arabia. Journal of King Saud University - Science. 2021; 33 (6):101477.

Chicago/Turabian Style

Ali AlArjani; Umar Muhammad Modibbo; Irfan Ali; Biswajit Sarkar. 2021. "A new framework for the sustainable development goals of Saudi Arabia." Journal of King Saud University - Science 33, no. 6: 101477.

Journal article
Published: 18 March 2021 in Reliability Engineering & System Safety
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Designing an optimal reliability system with a minimum cost is one of the challenging problems in engineering field over the years. Estimating the subsystem reliability and determining the optimal number of component allocation will give an insight into the adequate performance of the entire system under study. In some scenarios, if a subsystem fails, then the whole system will fail, thus posing a severe threat to the safety of engineers and personnel involved. Hence, the need to optimally determine and allocate the required number of components as well as estimating their reliability so that the system’s safety would be guaranteed. In this paper, two procedures for estimating system reliability function are proposed using the maximum likelihood estimators and uniformly minimum variance unbiased estimators. The reliability functions of some selected lifetime distributions are estimated using the proposed procedures via a simulation study. The study presents a hybrid concept of estimation and optimization theory in reliability allocation problem. The work illustrated through a case study, and an optimization technique used to determine the optimal number of components at a minimum cost. The study would be useful for decision-makers in heavy industries/complex systems to help estimate the reliability of system components, optimize the allocation and manufacturer selection problems.

ACS Style

Umar Muhammad Modibbo; Mohd. Arshad; Omer Abdalghani; Irfan Ali. Optimization and estimation in system reliability allocation problem. Reliability Engineering & System Safety 2021, 212, 107620 .

AMA Style

Umar Muhammad Modibbo, Mohd. Arshad, Omer Abdalghani, Irfan Ali. Optimization and estimation in system reliability allocation problem. Reliability Engineering & System Safety. 2021; 212 ():107620.

Chicago/Turabian Style

Umar Muhammad Modibbo; Mohd. Arshad; Omer Abdalghani; Irfan Ali. 2021. "Optimization and estimation in system reliability allocation problem." Reliability Engineering & System Safety 212, no. : 107620.

Original article
Published: 20 January 2021 in Complex & Intelligent Systems
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Selective maintenance problem plays an essential role in reliability optimization decision-making problems. Systems are a configuration of several components, and there are situations the system needs small intervals or break for maintenance actions, during the intervals expert carried out the maintenance actions to replace or repair the deteriorated components of the systems. Because of the uncertainty associated with the component’s operational time, failure, and next mission duration create a new challenge in determining optimal components allocation and evaluating future missions successfully. In this paper, a multi-objective selective maintenance allocation problem is formulated with fuzzy parameters under neutrosophic environment. A new defuzzification technique is introduced based on beta distribution to convert fuzzy parameters into crisp values. The neutrosophic goal programming technique is used to determine the compromise allocation of replaceable and repairable components based on the system reliability optimization. A numerical illustration is used to validate the model and ascertain its effectiveness. The result is compared with two other approaches and found to be better. The method is flexible and straightforward and can be solved using any available commercial packages. The extension of the concept can be useful to other complex system reliability optimization.

ACS Style

Murshid Kamal; Umar Muhammad Modibbo; Ali AlArjani; Irfan Ali. Neutrosophic fuzzy goal programming approach in selective maintenance allocation of system reliability. Complex & Intelligent Systems 2021, 7, 1045 -1059.

AMA Style

Murshid Kamal, Umar Muhammad Modibbo, Ali AlArjani, Irfan Ali. Neutrosophic fuzzy goal programming approach in selective maintenance allocation of system reliability. Complex & Intelligent Systems. 2021; 7 (2):1045-1059.

Chicago/Turabian Style

Murshid Kamal; Umar Muhammad Modibbo; Ali AlArjani; Irfan Ali. 2021. "Neutrosophic fuzzy goal programming approach in selective maintenance allocation of system reliability." Complex & Intelligent Systems 7, no. 2: 1045-1059.

Case study
Published: 06 October 2020 in Environment, Development and Sustainability
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Sustainability of developmental goals remains the significant challenges for every nation in the world. In an environment of flexibility coupled with multiple and conflicting objectives on social economic, energy and environmental issues, a fuzzy goal programming approach proves to be suitable in helping policymakers for attaining their aspirational goals as it takes imprecise information into account. In this study, we proposed a multi-objective goal programming model to analyse the socio-economic, environmental and energy sector of Nigeria. The sectors relating to the gross domestic product, electricity consumption, employment and greenhouse gases emissions are regarded as the key sustainable development goals (SDGs) for the year 2030, upon which all other goals depend on directly or indirectly. We considered four scenarios based on decision-makers' value judgments using the analytic hierarchy process (AHP). Moreover, we have developed a weighted fuzzy goal programming model for the SDGs incorporating the AHP. The model reveals that individual goals are achievable at 81, 83, and 89 per cents, respectively, while the overall achievement function indicated only 73 per cent achievement is possible. The optimal employment for the various economic sectors is also computed at various tolerance values and presented alongside Pareto objective values. The results yielded an economic justification mathematically and provided insight for critical and future strategic planning towards achieving sustainable economic policies. The models can be replicated in the context of other nations in addressing long-term socio-economic planning and indispensable requirements for renewable sources inclusion to satisfy future energy demand economically without environmental damages.

ACS Style

Umar Muhammad Modibbo; Irfan Ali; Aquil Ahmed. Multi-objective optimization modelling for analysing sustainable development goals of Nigeria: Agenda 2030. Environment, Development and Sustainability 2020, 23, 9529 -9563.

AMA Style

Umar Muhammad Modibbo, Irfan Ali, Aquil Ahmed. Multi-objective optimization modelling for analysing sustainable development goals of Nigeria: Agenda 2030. Environment, Development and Sustainability. 2020; 23 (6):9529-9563.

Chicago/Turabian Style

Umar Muhammad Modibbo; Irfan Ali; Aquil Ahmed. 2020. "Multi-objective optimization modelling for analysing sustainable development goals of Nigeria: Agenda 2030." Environment, Development and Sustainability 23, no. 6: 9529-9563.

Journal article
Published: 19 September 2020 in Mathematics
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Vendor selection is an established problem in supply chain management. It is regarded as a strategic resource by manufacturers, which must be managed efficiently. Any inappropriate selection of the vendors may lead to severe issues in the supply chain network. Hence, the desire to develop a model that minimizes the combination of transportation, deliveries, and ordering costs under uncertainty situation. In this paper, a multi-objective vendor selection problem under fuzzy environment is solved using a fuzzy goal programming approach. The vendor selection problem was modeled as a multi-objective problem, including three primary objectives of minimizing the transportation cost; the late deliveries; and the net ordering cost subject to constraints related to aggregate demand; vendor capacity; budget allocation; purchasing value; vendors’ quota; and quantity rejected. The proposed model input parameters are considered to be LR fuzzy numbers. The effectiveness of the model is illustrated with simulated data using R statistical package based on a real-life case study which was analyzed using LINGO 16.0 optimization software. The decision on the vendor’s quota allocation and selection under different degree of vagueness in the information was provided. The proposed model can address realistic vendor selection problem in the fuzzy environment and can serve as a useful tool for multi-criteria decision-making in supply chain management.

ACS Style

Irfan Ali; Armin Fügenschuh; Srikant Gupta; Umar Muhammad Modibbo. The LR-Type Fuzzy Multi-Objective Vendor Selection Problem in Supply Chain Management. Mathematics 2020, 8, 1621 .

AMA Style

Irfan Ali, Armin Fügenschuh, Srikant Gupta, Umar Muhammad Modibbo. The LR-Type Fuzzy Multi-Objective Vendor Selection Problem in Supply Chain Management. Mathematics. 2020; 8 (9):1621.

Chicago/Turabian Style

Irfan Ali; Armin Fügenschuh; Srikant Gupta; Umar Muhammad Modibbo. 2020. "The LR-Type Fuzzy Multi-Objective Vendor Selection Problem in Supply Chain Management." Mathematics 8, no. 9: 1621.

Journal article
Published: 22 July 2020 in NIGERIAN ANNALS OF PURE AND APPLIED SCIENCES
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Investment in various types of assets is an exciting choice of most successful business entrepreneurs. Investors have no option than to make a holistic decision regarding the position of their wealth within the context of the portfolio. In this paper, a Dynamic Programming (DP) algorithm and Modern Portfolio Theory (MPT) were used to determine the optimal returns of investments and the risks involved. Also, the correlation between expected returns and risk of investments were analyzed. Data of four securities were collected from the Nigeria Stock Exchange, Yola, for the sample period of 2016. Dynamic programming was found to be a more efficient algorithm for determining how much to invest in each investment portfolio. Through the analysis of the investments, OANDO PLC and Nigeria Breweries were respectively selected with high optimal returns of N426000. That is, investing N5 million in OANDO PLC yields a return of N269000 and N4 million in Nigeria Breweries yields N160000. One observation about these investments is that they all have a high risk of investment.

ACS Style

Umar Modibbo. Optimal Selection of Portfolio in Nigerian Stock Exchange using Dynamic Programming. NIGERIAN ANNALS OF PURE AND APPLIED SCIENCES 2020, 3, 179 -185.

AMA Style

Umar Modibbo. Optimal Selection of Portfolio in Nigerian Stock Exchange using Dynamic Programming. NIGERIAN ANNALS OF PURE AND APPLIED SCIENCES. 2020; 3 (2):179-185.

Chicago/Turabian Style

Umar Modibbo. 2020. "Optimal Selection of Portfolio in Nigerian Stock Exchange using Dynamic Programming." NIGERIAN ANNALS OF PURE AND APPLIED SCIENCES 3, no. 2: 179-185.

Article
Published: 30 April 2020 in Environment, Development and Sustainability
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Modelling for long-term goals involving multiple factors and criteria often require incorporating decision-makers preferences to realize optimum satisfaction. Goal programming (GP) is an operational research technique that is relevant to analysing decision-making problems with multiple competing and conflicting objectives. Multi-objective goal programming approach takes advantage of striking the trade-off between the overachievement and underachievement of the decision-makers future aspirations. The concept of GP with a satisfaction function integrates the preference of the decision-makers explicitly. In this paper, we proposed a multi-objective optimization model integrating economic growth, electricity consumption, greenhouse gas emission and the number of employees across the primary, secondary and tertiary sectors of Indian economy using the concept of GP with a satisfaction function. The model validated with data from the three economic sectors, and the results provided a quantitative justification for achieving economic growth, electricity consumption, with optimal employment strength across the sectors, for the sustainable development goals of India vision 2030. Also, a strong suggestion for improvement and encouragement in the use of renewable energies such as wind and solar and reduction in fossil fuels utilization to arrest the high emission tendencies shortly was evidence by the solution.

ACS Style

Irfan Ali; Umar Muhammad Modibbo; Jahangir Chauhan; Maryam Meraj. An integrated multi-objective optimization modelling for sustainable development goals of India. Environment, Development and Sustainability 2020, 23, 3811 -3831.

AMA Style

Irfan Ali, Umar Muhammad Modibbo, Jahangir Chauhan, Maryam Meraj. An integrated multi-objective optimization modelling for sustainable development goals of India. Environment, Development and Sustainability. 2020; 23 (3):3811-3831.

Chicago/Turabian Style

Irfan Ali; Umar Muhammad Modibbo; Jahangir Chauhan; Maryam Meraj. 2020. "An integrated multi-objective optimization modelling for sustainable development goals of India." Environment, Development and Sustainability 23, no. 3: 3811-3831.