FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3164297125> ?p ?o ?g. }

• W3164297125 endingPage "1453" @default.
• W3164297125 startingPage "1427" @default.
• W3164297125 abstract "Purpose The purpose of this study is to investigate the optimization of the scheduling of production and transportation systems while considering delay time (DT) and environmental pollution (EP) concurrently. To this, an integrated multi-site manufacturing process using a cumulative transportation system is investigated. Additionally, a novel multi-society genetic algorithm is developed to reach the best answers. Design/methodology/approach A bi-objective model is proposed to optimize the production and transportation process with the objectives of minimizing DT and EP. This is solved by a social dynamic genetic algorithm (SDGA), which is a novel multi-society genetic algorithm, in scenarios of equal and unequal impacts of each objective. The impacts of each objective are calculated by the analytical hierarchical process (AHP) using experts’ opinions. Results are compared by dynamic genetic algorithm and optimum solution results. Findings Results clearly depict the efficiency of the proposed algorithm and model in the scheduling of production and transportation systems with the objectives of minimizing DT and EP concurrently. Although SDGA’s performance is acceptable in all cases, in comparison to other genetic algorithms, it needs more process time which is the cost of reaching better answers. Additionally, SDGA had better performance in variable weights of objectives in comparison to itself and other genetic algorithms. Research limitations/implications This research is an improvement which allows both society and industry to elevate the levels of their satisfaction while their social responsibilities have been glorified through assuaging the concerns of customers on distribution networks’ emission, competing more efficient and effective in the global market and having the ability to make deliberate decisions far from bias. Additionally, implications of the developed genetic algorithm help directly to the organizations engaged with intelligent production and/or transportation planning which society will be merited indirectly from their outcomes. It also could be utilitarian for organizations that are engaged with small, medium and big data analysis in their processes and want to use more effective and more efficient tools. Originality/value Optimization of EP and DT are considered simultaneously in both model and algorithm in this study. Besides, a novel genetic algorithm, SDGA, is proposed. In this multi-society algorithm, each society is focused on a particular objective; however, in one society all the feasible answers will have been integrated and optimization will have been continued." @default.
• W3164297125 created "2021-06-07" @default.
• W3164297125 creator A5016601567 @default.
• W3164297125 creator A5029689913 @default.
• W3164297125 date "2021-05-26" @default.
• W3164297125 modified "2023-10-13" @default.
• W3164297125 title "Optimization of delay time and environmental pollution in scheduling of production and transportation system: a novel multi-society genetic algorithm approach" @default.
• W3164297125 cites W1483815747 @default.
• W3164297125 cites W1787253685 @default.
• W3164297125 cites W1967536980 @default.
• W3164297125 cites W1969029115 @default.
• W3164297125 cites W1999056306 @default.
• W3164297125 cites W1999341779 @default.
• W3164297125 cites W2005216095 @default.
• W3164297125 cites W2007989648 @default.
• W3164297125 cites W2017051349 @default.
• W3164297125 cites W2023572015 @default.
• W3164297125 cites W2027670672 @default.
• W3164297125 cites W2035145232 @default.
• W3164297125 cites W2038150365 @default.
• W3164297125 cites W2043459322 @default.
• W3164297125 cites W2047489154 @default.
• W3164297125 cites W2049465129 @default.
• W3164297125 cites W2049614971 @default.
• W3164297125 cites W2055446335 @default.
• W3164297125 cites W2057559819 @default.
• W3164297125 cites W2064644384 @default.
• W3164297125 cites W2071178252 @default.
• W3164297125 cites W2072139095 @default.
• W3164297125 cites W2076632180 @default.
• W3164297125 cites W2077946512 @default.
• W3164297125 cites W2080644754 @default.
• W3164297125 cites W2081291675 @default.
• W3164297125 cites W2083888906 @default.
• W3164297125 cites W2084389164 @default.
• W3164297125 cites W2101401210 @default.
• W3164297125 cites W2136394661 @default.
• W3164297125 cites W2143086378 @default.
• W3164297125 cites W2144566232 @default.
• W3164297125 cites W2154798037 @default.
• W3164297125 cites W2157846217 @default.
• W3164297125 cites W2203891779 @default.
• W3164297125 cites W2313986230 @default.
• W3164297125 cites W2571937602 @default.
• W3164297125 cites W2597318035 @default.
• W3164297125 cites W2770918695 @default.
• W3164297125 cites W2784141244 @default.
• W3164297125 cites W2793758713 @default.
• W3164297125 cites W2811242098 @default.
• W3164297125 cites W2908815041 @default.
• W3164297125 doi "https://doi.org/10.1108/mrr-04-2020-0203" @default.
• W3164297125 hasPublicationYear "2021" @default.
• W3164297125 type Work @default.
• W3164297125 sameAs 3164297125 @default.
• W3164297125 citedByCount "4" @default.
• W3164297125 countsByYear W31642971252021 @default.
• W3164297125 countsByYear W31642971252022 @default.
• W3164297125 countsByYear W31642971252023 @default.
• W3164297125 crossrefType "journal-article" @default.
• W3164297125 hasAuthorship W3164297125A5016601567 @default.
• W3164297125 hasAuthorship W3164297125A5029689913 @default.
• W3164297125 hasConcept C111919701 @default.
• W3164297125 hasConcept C119857082 @default.
• W3164297125 hasConcept C126255220 @default.
• W3164297125 hasConcept C127413603 @default.
• W3164297125 hasConcept C139719470 @default.
• W3164297125 hasConcept C162324750 @default.
• W3164297125 hasConcept C206729178 @default.
• W3164297125 hasConcept C2778348673 @default.
• W3164297125 hasConcept C2909468537 @default.
• W3164297125 hasConcept C31258907 @default.
• W3164297125 hasConcept C33923547 @default.
• W3164297125 hasConcept C39432304 @default.
• W3164297125 hasConcept C41008148 @default.
• W3164297125 hasConcept C42475967 @default.
• W3164297125 hasConcept C526734887 @default.
• W3164297125 hasConcept C55416958 @default.
• W3164297125 hasConcept C74172769 @default.
• W3164297125 hasConcept C87345402 @default.
• W3164297125 hasConcept C8880873 @default.
• W3164297125 hasConcept C98045186 @default.
• W3164297125 hasConceptScore W3164297125C111919701 @default.
• W3164297125 hasConceptScore W3164297125C119857082 @default.
• W3164297125 hasConceptScore W3164297125C126255220 @default.
• W3164297125 hasConceptScore W3164297125C127413603 @default.
• W3164297125 hasConceptScore W3164297125C139719470 @default.
• W3164297125 hasConceptScore W3164297125C162324750 @default.
• W3164297125 hasConceptScore W3164297125C206729178 @default.
• W3164297125 hasConceptScore W3164297125C2778348673 @default.
• W3164297125 hasConceptScore W3164297125C2909468537 @default.
• W3164297125 hasConceptScore W3164297125C31258907 @default.
• W3164297125 hasConceptScore W3164297125C33923547 @default.
• W3164297125 hasConceptScore W3164297125C39432304 @default.
• W3164297125 hasConceptScore W3164297125C41008148 @default.
• W3164297125 hasConceptScore W3164297125C42475967 @default.
• W3164297125 hasConceptScore W3164297125C526734887 @default.
• W3164297125 hasConceptScore W3164297125C55416958 @default.
• W3164297125 hasConceptScore W3164297125C74172769 @default.

• next