In today's competitive business landscape, organizations must streamline their planning and management processes for efficiency and profitability. Effective resource management, especially in the case of human res...
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In today's competitive business landscape, organizations must streamline their planning and management processes for efficiency and profitability. Effective resource management, especially in the case of human resources, is crucial. Scheduling challenges in human resource management become more complex when considering worker skills. Balancing fairness for workers and the company necessitates the inclusion of additional constraints and variables, making manual problem-solving more time-consuming and complex. This study proposes a goal programming method, a mathematical model that addresses both primary and goal constraints, to optimize solutions and minimize deviation variables. The case study of a real manufacturer's Banbury process department with 51 workers, 24 tasks, and two shifts has been analyzed. The mathematical model is utilized to allocate tasks, optimize worker skill utilization, and identify positions to close when worker availability falls short.
In this paper, we made an investigation about an uncertain interval supply chain network model with risk and visibility (UISCNMwRV). We consider the available budget for supply chain visibility, production capacity, c...
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In this paper, we made an investigation about an uncertain interval supply chain network model with risk and visibility (UISCNMwRV). We consider the available budget for supply chain visibility, production capacity, cost of reducing supply risk, cost of enhancing supply chain visibility, demand for each product in each period, the expected value of demand, minimum order quantity, purchase price, the impact of supply risk, supply risk, maximum allowable supply risk, supply chain visibility, minimum visibility are uncertain interval parameters for the effect of resenting pandemic COVID-19. To model the proposed UISCNMwRV, we have developed two different models namely the expected value model (EV model) and the chance-constrained model (CC model) using uncertain interval programming techniques. These two models are formulated under the framework of uncertain interval theory. Then, the equivalent deterministic transformations of these models are formulated and are solved in two different methods namely goal programming method and linear weighted method. Finally, a real-life example of the UISCNMwRV is included to show the effectiveness and usefulness of our proposed study.
In today's world, the role of supply chain management including transportation plays a vital role in the financial and economic growth of the country. The supply chain management is most seriously impacted by a ne...
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In today's world, the role of supply chain management including transportation plays a vital role in the financial and economic growth of the country. The supply chain management is most seriously impacted by a new global pandemic, known as coronavirus disease 2019 (COVID-19). This effect becomes more challenging for the manufacturers as well as suppliers due to the high demand for most essential items. The twin effects of uncertain interval supply chain visibility (SCV) and supply chain risk (SCR) on the efficiency of the supply chain management are explored in this paper. In this investigation, two different models namely the expected value model (EV model) and the chance-constrained model (CC model) using uncertain interval programming techniques have been developed. These models are converted into their corresponding deterministic forms using goal programming method and linear weighted method. Hence, our model incorporates the objectives of SCV maximization, SCR minimization, and cost minimization under the constraints of budget, demand, capacity, supply risk, supply availability, etc. The sensitivity analysis and managerial insight of the models are also discussed to examine the variation in the objective function for deciding the case of emergency environment like pandemic causes by COVID 19. Finally, a real-life example is given to exhibit the effectiveness and usefulness of our proposed study.
Building on previous research, this paper establishes a new multi-objective program scheduling model of Logistics service supply chain that considers the uncertainty of operation time for functional logistics service ...
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Building on previous research, this paper establishes a new multi-objective program scheduling model of Logistics service supply chain that considers the uncertainty of operation time for functional logistics service providers (FLSPs) in a mass customization service environment. This model aims to minimize total scheduling costs, minimize the difference between the scheduled and actual time of each service process, and maximize the average satisfaction of FLSPs. Considering the differences in target priority, the goal programming method is applied to solve the model hierarchically. In addition, the effects of scheduling parameters on scheduling objectives are provided after numerical analysis. (C) 2015 Elsevier Ltd. All rights reserved.
Thousands of victims and millions of affected people are consequences of natural disasters, every year. Therefore, it is essential to prepare a proper response program that considers early activities of disaster manag...
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Thousands of victims and millions of affected people are consequences of natural disasters, every year. Therefore, it is essential to prepare a proper response program that considers early activities of disaster management. In urban areas, local organizations and municipal authorities are responsible for disaster-related operations such as providing emergency shelters, proper equipment, and relief supplies in order to reduce sufferings of survivors. In this paper, a new multi-objective mixed-integer linear programming model is developed for preparation planning phase of disaster management. The proposed model is inspired from a real case study of an urban district in Iran, which considers both humanitarian- and cost-based objectives in a goal-programming approach. Proactive damage estimation result of Risk Assessment tool for Diagnosis of Urban Areas against Seismic Disaster software is used as an input in order to allocate affected people to local emergency management facilities that should be established. The location allocation model is solved for both current municipal subregional zoning and a virtual zoning approach that creates auxiliary cells. Mathematical results show that the second approach can reduce logistic costs and increase total coverage simultaneously. In other words, using virtual zones would help the authorities create a better collaboration between neighboring local areas and as a result, efficiency of the decisions improves.
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