Optimizing order-picking systems (OPSs) while considering human factors and integrating key decisions is a major challenge for warehouse managers. This study presents a two-stage framework based on multi-attribute dec...
详细信息
Optimizing order-picking systems (OPSs) while considering human factors and integrating key decisions is a major challenge for warehouse managers. This study presents a two-stage framework based on multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) models to integrate decisions on picker selection, order batching, batch assignment, picker routing, and scheduling. In the first stage, the human factors affecting picker selection are considered as the problem's criteria and the available pickers are treated as alternatives. The fuzzy entropy method and fuzzy COmplex PRoportional ASsessment (COPRAS) are used to weight the factors and rank the pickers, respectively. In the second stage, a three-objective mathematical model is formulated to minimize makespan and the operating costs of picking while maximizing the total scores of the selected pickers. The improved augmented epsilon constraint method (AUGMECON2) and the non-dominated sorting genetic algorithm II (NSGA-II) are applied to solve the proposed model. The performance of the two methods is tested on well-known benchmark instances and a real-world case study. The NSGA-II algorithm can generate optimal results using only about 6.58% of the CPU time required by AUGMECON2 to solve the problem. Our computational experiments show that increasing the number of pickers from 2 to 8 and doubling their capacity reduces the makespan by 2.61% and 2.74%, respectively.
This paper focuses on implementing a decentralized rainwater harvesting (DRH) program for rural cities under stakeholder interests and purchasing power restrictions. The study is conducted by considering: (1) a no tax...
详细信息
This paper focuses on implementing a decentralized rainwater harvesting (DRH) program for rural cities under stakeholder interests and purchasing power restrictions. The study is conducted by considering: (1) a no tax incentive scheme (NTIS);(2) a flat rate tax incentive scheme (FTIS);and (3) a progressive tax incentive scheme (PTIS). A three-stage decision framework is proposed. In the first stage, a water balance simulation model is developed to estimate the amount of rainwater that can be collected at individual households using different capacity rain barrels and to determine the threshold rain barrel capacity. In the second stage, the water balance simulation model is run multiple times with varying input parameters to obtain the probability distributions for rainwater used and water demand for different households using different capacity rain barrels, which are then used to generate scenarios for the third stage optimization model. In the third stage, a multi-objective stochastic mixed integer linear programming (Mo-SMILP) model is proposed that aims to determine the household participation rate and cumulative city-wide DRH capacity considering tax incentive schemes, stakeholder interests, and purchasing power restrictions. The proposed Mo-SMILP model is solved by using an improved augmented epsilon constraint method (AUGMECON 2) in order to understand the trade-off between city-wide household reliability, city-wide household savings, and city government savings. A case study of a small rural city in the State of Texas in the US is used to illustrate the effectiveness of the proposed decision framework. The results shows a significant decrease in city-wide household reliability when stakeholder interests and purchasing power are considered. In addition, the PTIS is best at improving city-wide household reliability and savings. NTIS and FTIS are best at improving city government savings depending on the household preference for reliability and savings. (C) 201
暂无评论