E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Spatial and Spatio-Temporal Planning for Urban Health and Sustainability"

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 30 March 2018

Special Issue Editors

Guest Editor
Dr. Hung Chak Ho

Department of Land Surveying and Geo-informatics, Hong Kong Polytechnic University
E-Mail
Guest Editor
Dr. Ta-Chien Chan

Research Center for Humanities and Social Sciences, Academia Sinica
E-Mail
Guest Editor
Dr. Man Sing Wong

Department of Land Surveying and Geo-informatics, Hong Kong Polytechnic University
Website | E-Mail
Interests: remote sensing; geographical information system

Special Issue Information

Dear Colleagues,

Population increase and urbanization are two factors influencing urban health and sustainability. Previous studies have demonstrated that sustainable policy and planning protocols can mitigate urban issues related to health and sustainability. Recent studies have found that using spatial or spatio-temporal approaches, such as geospatial modelling, may be able to enhance planning assessment. However, most studies of spatial planning only apply simple applications such as a spatial overlaying technique to improve the assessment of sustainable planning, while the advantages and limitations of using such spatial or spatio-temporal techniques for planning have not yet been discussed. This Special Issue aims to be the first platform to comprehensively address the questions above.

Specific topics of this special issue include (but are not limited to):

1) innovative techniques of spatial and spatio-temporal planning for urban health mitigation;

2) advanced modelling for spatial/spatio-temporal planning and urban sustainability;

3) literature reviews of advantages and limitations of spatial and spatio-temporal planning for urban health/sustainability;

4)  projection of spatial or spatio-temporal data for forecasting and future planning;

5) linkage between spatial/spatio-temporal planning and urban sustainability policy; and

6) open topics related to spatio-temporal modelling and urban health/sustainability

Dr. Hung Chak  Ho
Dr. Ta-Chien Chan
Dr. Man Sing Wong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • spatial analytics
  • urban planning
  • wellbeing
  • urban health
  • urban sustainability
  • spatio-temporal modelling

Published Papers (1 paper)

View options order results:
result details:
Displaying articles 1-1
Export citation of selected articles as:

Research

Open AccessArticle Solving a More Flexible Home Health Care Scheduling and Routing Problem with Joint Patient and Nursing Staff Selection
Sustainability 2018, 10(1), 148; doi:10.3390/su10010148
Received: 27 November 2017 / Revised: 2 January 2018 / Accepted: 4 January 2018 / Published: 9 January 2018
PDF Full-text (851 KB) | HTML Full-text | XML Full-text
Abstract
Development of an efficient and effective home health care (HHC) service system is a quite recent and challenging task for the HHC firms. This paper aims to develop an HHC service system in the perspective of long-term economic sustainability as well as operational
[...] Read more.
Development of an efficient and effective home health care (HHC) service system is a quite recent and challenging task for the HHC firms. This paper aims to develop an HHC service system in the perspective of long-term economic sustainability as well as operational efficiency. A more flexible mixed-integer linear programming (MILP) model is formulated by incorporating the dynamic arrival and departure of patients along with the selection of new patients and nursing staff. An integrated model is proposed that jointly addresses: (i) patient selection; (ii) nurse hiring; (iii) nurse to patient assignment; and (iv) scheduling and routing decisions in a daily HHC planning problem. The proposed model extends the HHC problem from conventional scheduling and routing issues to demand and capacity management aspects. It enables an HHC firm to solve the daily scheduling and routing problem considering existing patients and nursing staff in combination with the simultaneous selection of new patients and nurses, and optimizing the existing routes by including new patients and nurses. The model considers planning issues related to compatibility, time restrictions, contract durations, idle time and workload balance. Two heuristic methods are proposed to solve the model by exploiting the variable neighborhood search (VNS) approach. Results obtained from the heuristic methods are compared with a CPLEX based solution. Numerical experiments performed on different data sets, show the efficiency and effectiveness of the solution methods to handle the considered problem. Full article
Figures

Figure 1

Back to Top