A bi-objective home healthcare routing and scheduling problem considering patients’ satisfaction in a fuzzy environment
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TLDR
This study proposes a bi-objective optimization methodology to model a multi-period and multi-depot home healthcare routing and scheduling problem in a fuzzy environment and develops a new modified multi-objectives version of SEO by using an adaptive memory strategy, so-called AMSEO.About:
This article is published in Applied Soft Computing.The article was published on 2020-08-01 and is currently open access. It has received 106 citations till now. The article focuses on the topics: Job shop scheduling & Optimization problem.read more
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Artificial neural network model to predict the compressive strength of eco-friendly geopolymer concrete incorporating silica fume and natural zeolite
Amir Ali Shahmansouri,Maziar Yazdani,Saeed Ghanbari,Habib Akbarzadeh Bengar,Abouzar Jafari,Hamid Farrokh Ghatte +5 more
TL;DR: In this paper, an Artificial Neural Network (ANN) was proposed to predict the compressive strength of pozzolanic GPC based on ground granulated blast-furnace slag.
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An integrated sustainable medical supply chain network during COVID-19
TL;DR: In this article, the authors proposed three hybrid meta-heuristic algorithms, namely, ant colony optimization, fish swarm algorithm, and firefly algorithm, hybridized with variable neighborhood search to solve the sustainable medical supply chain network model.
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Sustainable vehicle routing problem for coordinated solid waste management
TL;DR: The main findings are the practical solutions with the use of sustainability goals for the CSWM and further application and development of the adaptive memory social engineering optimizer in the routing optimization.
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Blockchain in supply chain management: a review, bibliometric, and network analysis
TL;DR: In this paper, the authors conducted a systematic review through bibliometric and network analysis to identify how blockchain can contribute to supply chain management, and determined the key authors, significant studies, and the collaboration patterns that were not considered by the previous publications on this angel of supply-chain management.
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Sustainable distributed permutation flow-shop scheduling model based on a triple bottom line concept
TL;DR: A novel multi-objective mixed integer linear model is developed, which aims not only to minimize the total energy consumption related to production, but also, to maximize, for the first time, the social factors linked to job opportunities and lost working days.
References
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No free lunch theorems for optimization
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Response surface methodology
TL;DR: In this article, the Response Surface Methodology (RSM) is used for scheduling and scheduling in response surface methodologies, and it is shown that it can be used in a variety of scenarios.
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Simultaneous Optimization of Several Response Variables
TL;DR: In this article, the authors present a set of conditions that will result in a product with a desirable combination of properties, which is a problem facing the product development community in general.
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Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case
TL;DR: The results of a simulation study that opens some new research tensions on the impact of COVID-19 (SARS-CoV-2) on the global SCs are presented and an analysis for observing and predicting both short-term and long-term impacts of epidemic outbreaks on the SCs along with managerial insights are offered.
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An interactive possibilistic programming approach for multiple objective supply chain master planning
Seyed Ali Torabi,Elkafi Hassini +1 more
TL;DR: In this article, a multi-objective possibilistic mixed integer linear programming model (MOPMILP) is proposed for integrating procurement, production and distribution planning considering various conflicting objectives simultaneously as well as the imprecise nature of some critical parameters such as market demands, cost/time coefficients and capacity levels.