{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T05:17:10Z","timestamp":1777267030964,"version":"3.51.4"},"reference-count":49,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2019,4,8]],"date-time":"2019-04-08T00:00:00Z","timestamp":1554681600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IMDS"],"published-print":{"date-parts":[[2019,4,8]]},"abstract":"Purpose<\/jats:title>This paper studies green vehicle routing problems of cold chain logistics with the consideration of the full set of greenhouse gas (GHG) emissions and an optimization model of green vehicle routing for cold chain logistics (with an acronym of GVRPCCL) is developed. The purpose of this paper is to minimize the total costs, which include vehicle operating cost, quality loss cost, product freshness cost, penalty cost, energy cost and GHG emissions cost. In addition, this research also investigates the effect of changing the vehicle maximum load in relation to cost and GHG emissions.<\/jats:p><\/jats:sec>Design\/methodology\/approach<\/jats:title>This study develops a mathematical optimization model, considering the total cost and GHG emission. The standard particle swarm optimization and modified particle swarm optimization (MPSO), based on an intelligent optimization algorithm, are applied in this study to solve the routing problem of a real case.<\/jats:p><\/jats:sec>Findings<\/jats:title>The results of this study show the extend of the proposed MPSO performing better in achieving green-focussed vehicle routing and that considering the full set of GHG costs in the objective functions will reduce the total costs and environmental-diminishing emissions of GHG through the comparative analysis. The research outputs also evaluated the effect of different enterprises\u2019 conditions (e.g. customers\u2019 locations and demand patterns) for better distribution routes planning.<\/jats:p><\/jats:sec>Research limitations\/implications<\/jats:title>There are some limitations in the proposed model. This study assumes that the vehicle is at a constant speed and it does not consider uncertainties, such as weather conditions and road conditions.<\/jats:p><\/jats:sec>Originality\/value<\/jats:title>Prior studies, particularly in green cold chain logistics vehicle routing problem, are fairly limited. The prior works revolved around GHG emissions problem have not considered methane and nitrous oxides. This study takes into account the characteristics of cold chain logistics and the full set of GHGs.<\/jats:p><\/jats:sec>","DOI":"10.1108\/imds-07-2018-0314","type":"journal-article","created":{"date-parts":[[2018,10,8]],"date-time":"2018-10-08T05:17:46Z","timestamp":1538975866000},"page":"473-494","source":"Crossref","is-referenced-by-count":116,"title":["A green vehicle routing model based on modified particle swarm optimization for cold chain logistics"],"prefix":"10.1108","volume":"119","author":[{"given":"Yan","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0809-9431","authenticated-orcid":false,"given":"Ming K.","family":"Lim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming-Lang","family":"Tseng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"issue":"27","key":"key2020092405273873600_ref001","doi-asserted-by":"crossref","first-page":"21610","DOI":"10.1007\/s11356-017-9740-8","article-title":"Model and algorithm for bi-fuel vehicle routing problem to reduce GHG emissions","volume":"24","year":"2017","journal-title":"Environmental Science & Pollution Research"},{"issue":"3","key":"key2020092405273873600_ref300","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.trd.2008.01.005","article-title":"The effects of route choice decisions on vehicle energy consumption and emissions","volume":"13","year":"2008","journal-title":"Transportation Research Part D Transport & Environment"},{"issue":"C","key":"key2020092405273873600_ref002","first-page":"45","article-title":"GVNs for a real-world rich vehicle routing problem with time windows","volume":"42","year":"2015","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"15","key":"key2020092405273873600_ref003","first-page":"318","article-title":"Optimization of distribution route of fresh agricultural products cold chain logistics based on artificial bee colony algorithm","volume":"45","year":"2017","journal-title":"Jiangsu Agricultural Sciences"},{"issue":"4","key":"key2020092405273873600_ref004","first-page":"247","article-title":"Optimized path transmission selection routing algorithm for reducing energy consumption in opportunity networks","volume":"21","year":"2014","journal-title":"Dynamics of Continuous Discrete & Impulsive Systems"},{"key":"key2020092405273873600_ref005","doi-asserted-by":"crossref","first-page":"2444","DOI":"10.1016\/j.proeng.2011.08.459","article-title":"An improved particle swarm algorithm and its application","volume":"15","year":"2011","journal-title":"Procedia Engineering"},{"key":"key2020092405273873600_ref006","unstructured":"Golden, B.L. 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