Facility location optimization model for emergency humanitarian logistics
TL;DR: In this paper, a survey on the facility location problems that are related to emergency humanitarian logistics based on both data modeling types and problem types is presented, where the authors examine the pre-and post-disaster situations with respect to facility location, such as the location of distribution centers, warehouses, shelters, debris removal sites and medical centers.
Abstract: Since the 1950s, the number of natural and man-made disasters has increased exponentially and the facility location problem has become the preferred approach for dealing with emergency humanitarian logistical problems. To deal with this challenge, an exact algorithm and a heuristic algorithm have been combined as the main approach to solving this problem. Owing to the importance that an exact algorithm holds with regard to enhancing emergency humanitarian logistical facility location problems, this paper aims to conduct a survey on the facility location problems that are related to emergency humanitarian logistics based on both data modeling types and problem types and to examine the pre- and post-disaster situations with respect to facility location, such as the location of distribution centers, warehouses, shelters, debris removal sites and medical centers. The survey will examine the four main problems highlighted in the literature review: deterministic facility location problems, dynamic facility location problems, stochastic facility location problems, and robust facility location problems. For each problem, facility location type, data modeling type, disaster type, decisions, objectives, constraints, and solution methods will be evaluated and real-world applications and case studies will then be presented. Finally, research gaps will be identified and be addressed in further research studies to develop more effective disaster relief operations.
Citations
More filters
[...]
TL;DR: To study the operational behaviour of λ-terms, this work will use the denotational (mathematical) approach to choose a space of semantics values, or denotations, where terms are to be interpreted.
Abstract: To study the operational behaviour of λ-terms, we will use the denotational (mathematical) approach. A denotational semantics for a language is based on the choice of a space of semantics values, or denotations, where terms are to be interpreted. Choosing a space with nice mathematical properties can help in proving the semantic properties of terms, since to this aim standard mathematical techniques can be used.
880 citations
TL;DR: A novel multi-objective multi-period mixed integer program for reverse logistics network design in epidemic outbreaks, which aims at determining the best locations of temporary facilities and the transportation strategies for effective management of the exponentially increased medical waste within a very short period is proposed.
Abstract: The outbreak of an epidemic disease may pose significant treats to human beings and may further lead to a global crisis. In order to control the spread of an epidemic, the effective management of rapidly increased medical waste through establishing a temporary reverse logistics system is of vital importance. However, no research has been conducted with the focus on the design of an epidemic reverse logistics network for dealing with medical waste during epidemic outbreaks, which, if improperly treated, may accelerate disease spread and pose a significant risk for both medical staffs and patients. Therefore, this paper proposes a novel multi-objective multi-period mixed integer program for reverse logistics network design in epidemic outbreaks, which aims at determining the best locations of temporary facilities and the transportation strategies for effective management of the exponentially increased medical waste within a very short period. The application of the model is illustrated with a case study based on the outbreak of the coronavirus disease 2019 (COVID-19) in Wuhan, China. Even though the uncertainty of the future COVID-19 spread tendency is very high at the time of this research, several general policy recommendations can still be obtained based on computational experiments and quantitative analyses. Among other insights, the results suggest installing temporary incinerators may be an effective solution for managing the tremendous increase of medical waste during the COVID-19 outbreak in Wuhan, but the location selection of these temporary incinerators is of significant importance. Due to the limitation on available data and knowledge at present stage, more real-world information are needed to assess the effectiveness of the current solution.
238 citations
Cites background or result from "Facility location optimization mode..."
...[1] compared the performance of several network design models in different stages of decision-making....
[...]
...The number and impact of both natural and human-related catastrophes have been increasing since the 1950s [1]....
[...]
...From a general perspective, the epidemic logistics problem belongs to emergency or disaster logistics management that focuses on four consecutive decision-makings associated with risk mitigation, preparation, response activities, and post-disaster recovery [1,14]....
[...]
TL;DR: An extensive review of extant literature published in operations and supply chain management journals as well as popular interdisciplinary journals is undertaken, which draws a roadmap from reviewing earlier review papers to performance evaluation of HOSCM related studies.
Abstract: The field of supply chain management has been extensively studied, while its role in disaster relief operations has received limited contributions. Recent dedicated special issues on Humanitarian Operations and Supply Chain Management (HOSCM) and a dedicated Journal focusing on humanitarian logistics and supply chain management clearly indicate the growing popularity of the HOSCM literature. The purpose of our current study is to undertake an extensive review of extant literature published in operations and supply chain management journals as well as popular interdisciplinary journals. A review of 362 papers published between 2011 and 2017 provides a thematic outline of the study. The study pivots around nine key themes, which have gained prominent attention from HOSCM scholars, and draws a roadmap from reviewing earlier review papers to performance evaluation of HOSCM related studies. Some of the key themes include humanitarian logistics, theory focused research, case studies, mathematical models, humanitarian supply chain properties and resources needed for efficient and effective management of humanitarian operations. Finally, our study offers several research directions which may take the existing debates to a next level.
152 citations
TL;DR: A bi-objective robust optimization model is presented that is resilient to disaster scenarios and aims to minimize the time and cost of delivering products to customers after the occurrence of a disaster, while it considers the possibility of multiple disruptions in facilities and routes.
101 citations
TL;DR: This survey focuses on casualty management (CM), which is one of the actions taken in the response phase of a disaster, and categorizes the existing research papers and case studies in each of these steps to suggest future directions for academics and practitioners.
96 citations
References
More filters
TL;DR: It is shown that the optimum location of a switching center is always at a vertex of the communication network while the best location for the police station is not necessarily at an intersection.
Abstract: The concepts of the "center" and the "median vertex" of a graph are generalized to the "absolute center" and the "absolute median" of a weighted graph a graph with weights attached to its vertices as well as to its branches. These results are used to find the optimum location of a "switching center" in a communication network and to locate the best place to build a "police station" in a highway system. It is shown that the optimum location of a switching center is always at a vertex of the communication network while the best location for the police station is not necessarily at an intersection. Procedures for finding these locations are given.
2,224 citations
"Facility location optimization mode..." refers background or methods in this paper
...The formulation for this mathematical model is as follows [27,28]:...
[...]
...The formulation for this problem is as follows [27]: Decision variables (addition):...
[...]
...This problem, known as the P-median, was developed by Hakimi [27]....
[...]
01 Jan 1974
TL;DR: The use of a maximal service distance as a measure of the value of a given locational configuration has been discussed at length by Toregas and ReVelle 1 who show that it is an important surrogate measurement for the value.
Abstract: The belief that mathematical location modeling can identify "optimal" location patterns rests on the basis that some realistic objective can be identified and by some measure quantified. For example, in the area of private facilities location analysis, a reasonably accurate statement of the objective of locating warehouses is to minimize the costs of manufacturing and distribution. Since most cost elements included in the objectives of private facility location can be reasonably estimated, the models can picture with some degree of accuracy the real location problem they are designed to solve. Unlike private facility location analysis, the objectives of public facility location analysis are more difficult to embrace and to quantify. The difficulty in defining direct measures of public objectives has resulted in a search for some surrogate measure with which the decision maker may be comfortable. Two different surrogate measures which have received attention in location models are: (1) total weighted distance or time for travel to the facilities, and (2) the distance or time that the user most distant from a facility would have to travel to reach that facility, that is, the maximal service distance.* The use of a maximal service distance as a measure of the value of a given locational configuration has been discussed at length by Toregas and ReVelle 1 who show that it is an important surrogate measurement for the value of a given locational configuration. For a given location solution, the maximum distance which any user would have to travel to reach a facility would reflect the worst possible performance of the system. In the regional location of emergency facilities such as fire stations or ambulance dispatching stations, the concept of the maximal service distance is well established. ~,3 The maximal service distance concept appears in the Location Set Covering
2,080 citations
"Facility location optimization mode..." refers background in this paper
...Church and Velle [43] developed constraint (10), which was reformulated as the following Eq....
[...]
TL;DR: This paper surveys the primary research, both theoretical and applied, in the area of robust optimization (RO), focusing on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology.
Abstract: In this paper we survey the primary research, both theoretical and applied, in the area of robust optimization (RO). Our focus is on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying prominent theoretical results of RO, we also present some recent results linking RO to adaptable models for multistage decision-making problems. Finally, we highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.
1,863 citations
"Facility location optimization mode..." refers background in this paper
...[74] for a review of the theory and applications of robust optimization....
[...]
...[74] addressed the general robust optimization as follows:...
[...]
TL;DR: This paper characterize the desirable properties of a solution to models, when the problem data are described by a set of scenarios for their value, instead of using point estimates, and develops a general model formulation, called robust optimization RO, that explicitly incorporates the conflicting objectives of solution and model robustness.
Abstract: Mathematical programming models with noisy, erroneous, or incomplete data are common in operations research applications. Difficulties with such data are typically dealt with reactively-through sensitivity analysis-or proactively-through stochastic programming formulations. In this paper, we characterize the desirable properties of a solution to models, when the problem data are described by a set of scenarios for their value, instead of using point estimates. A solution to an optimization model is defined as: solution robust if it remains "close" to optimal for all scenarios of the input data, and model robust if it remains "almost" feasible for all data scenarios. We then develop a general model formulation, called robust optimization RO, that explicitly incorporates the conflicting objectives of solution and model robustness. Robust optimization is compared with the traditional approaches of sensitivity analysis and stochastic linear programming. The classical diet problem illustrates the issues. Robust optimization models are then developed for several real-world applications: power capacity expansion; matrix balancing and image reconstruction; air-force airline scheduling; scenario immunization for financial planning; and minimum weight structural design. We also comment on the suitability of parallel and distributed computer architectures for the solution of robust optimization models.
1,793 citations
"Facility location optimization mode..." refers background in this paper
...[75] first proposed robust optimization, and since that time, it has been seen as an effective approach for the optimal design and management of supply chains operating in uncertain environments....
[...]
Journal Article•
TL;DR: In this article, the authors survey the primary research, both theoretical and applied, in the area of robust optimization and highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.
Abstract: In this paper we survey the primary research, both theoretical and applied, in the area of Robust Optimization (RO). Our focus is on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying prominent theoretical results of RO, we also present some recent results linking RO to adaptable models for multi-stage decision-making problems. Finally, we highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.
1,633 citations