Multi-objective sustainable capacitated location routing problem formulation in sustainable supply-chain management

2021 ◽  
pp. 1-16
Author(s):  
Luis Fernando Galindres ◽  
Frederico Gadelha Guimarães ◽  
Ramón Alfonso Gallego-Rendón
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Problem Formulation ◽  
Sustainable Supply Chain ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Chain Management ◽  
Multi Objective ◽  
Location Routing ◽  
Capacitated Location

scholarly journals A multi-objective model for the green capacitated location-routing problem considering environmental impact

2017 ◽  
Vol 110 ◽  
pp. 114-125 ◽  
Author(s):  
Eliana M. Toro ◽  
John F. Franco ◽  
Mauricio Granada Echeverri ◽  
Frederico Gadelha Guimarães
Keyword(s):  
Environmental Impact ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Multi Objective ◽  
Location Routing ◽  
Objective Model ◽  
Capacitated Location

Fuzzy multi-objective location and routing problem

2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi
Keyword(s):  
Firefly Algorithm ◽  
High Reliability ◽  
Research Area ◽  
Nsga Ii ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Allocation ◽  
Multi Objective ◽  
Location Routing ◽  
The Cost

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.

Sustainable supply chain management: continuing evolution and future directions

2019 ◽  
Vol 50 (1) ◽  
pp. 122-146 ◽  
Author(s):  
Craig R. Carter ◽  
Marc R. Hatton ◽  
Chao Wu ◽  
Xiangjing Chen
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Theory Development ◽  
Future Research ◽  
Sustainable Supply Chain ◽  
Unit Of Analysis ◽  
Chain Management ◽  
Content Type ◽  
Empirical Validity ◽  
Sustainable Supply Chain Management

Purpose The purpose of this paper is to update the work of Carter and Easton (2011), by conducting a systematic review of the sustainable supply chain management (SSCM) literature in the primary logistics and supply chain management journals, during the 2010–2018 timeframe. Design/methodology/approach The authors use a systematic literature review (SLR) methodology which follows the methodology employed by Carter and Easton (2011). An evaluation of this methodology, using the Modified AMSTAR criteria, demonstrates a high level of empirical validity. Findings The field of SSCM continues to evolve with changes in substantive focus, theoretical lenses, unit of analysis, methodology and type of analysis. However, there are still abundant future research opportunities, including investigating under-researched topics such as diversity and human rights/working conditions, employing the group as the unit of analysis and better addressing empirical validity and social desirability bias. Research limitations/implications The findings result in prescriptions and a broad agenda to guide future research in the SSCM arena. The final section of the paper provides additional avenues for future research surrounding theory development and decision making. Originality/value This SLR provides a rigorous, methodologically valid review of the continuing evolution of empirical SSCM research over a 28-year time period.

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