Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling

doi:10.1016/j.eswa.2013.10.026

Expert Systems with Applications

Volume 41, Issue 6, May 2014, Pages 2947-2956

https://doi.org/10.1016/j.eswa.2013.10.026 Get rights and content

Highlights

•
GSMOGA combines the advantages of the greedy search method and MOGA.
•
GSMOGA is able to quickly generate a number of feasible solutions.
•
GSMOGA arranges schedules (feasible solutions) based on multi-depot and multidemand.
•
GSMOGA reveals what resources are required and acquired at demand and supply point.
•
GSMOGA is capable of arranging routing schedules for each form of transport.

Abstract

To enable the immediate and efficient dispatch of relief to victims of disaster, this study proposes a greedy-search-based, multi-objective, genetic algorithm capable of regulating the distribution of available resources and automatically generating a variety of feasible emergency logistics schedules for decision-makers. The proposed algorithm dynamically adjusts distribution schedules from various supply points according to the requirements at demand points in order to minimize unsatisfied demand for resources, time to delivery, and transportation costs. The proposed algorithm was applied to the case of the Chi–Chi earthquake in Taiwan to verify its performance. Simulation results demonstrate that under conditions of a limited/unlimited number of available vehicles, the proposed algorithm outperforms the MOGA and standard greedy algorithm in ‘time to delivery’ by an average of 63.57% and 46.15%, respectively, based on 10,000 iterations.

Introduction

The emergency logistics scheduling problem (ELSP) deals with the need to identify, inventory, dispatch, mobilize, transport, recover, demobilize, and accurately track human and material resources in the event of a disaster. Rapid response is the most critical concern in emergency logistics. Despite the wealth of research in this area, relief efforts in the northeast area of Japan devastated by the 311 earthquake were delayed by 10 days. The main problem was a lack of planning in accordance with existing road conditions and the distribution of available resources.

Most previous studies have adopted integer and linear programming to solve such problems, using a single objective or a weighted sum of objectives. However, this approach tends to be limited in real-world situations, which typically presents multiple conflicting objectives. Furthermore, this approach tends not to provide detailed routing schedules. This paper proposes a novel greedy-search-based multi-objective genetic algorithm (GSMOGA) focusing on two requirements: (1) dispatching relief resources sufficient to satisfy the requirements at all demand points; (2) the transport of relief resources with minimum delay and transportation costs. GSMOGA is far more effective than typical local search methods, such as Tabu, thanks to its incorporation of a greedy search protocol followed by the encoding sequence of chromosomes using MOGA. The resulting output is a diverse selection of routing schedules without the need for a time-consuming local search process. GSMOGA is a hybrid method combining the advantages of greedy search with those of MOGA.

The remainder of the paper is organized as follows. In Section 2, we review the literature related to emergency logistics planning. The definition of the ELSP and its essential objective functions is described in Section 3. In Section 4, we propose the algorithm used to map out optimal transport routes and maximize the delivery of resources in the minimum time at the minimum cost. Experimental results depicting two scenarios are presented in Section 5. Conclusions and considerations for future work are presented in Section 6.

Section snippets

Literature review

Solutions to the ELSP differ according to environment, population distribution, transportation networks, relief requirements, and geographic situation. In addition, the services required for the provision of food, facilities, human resources, and transportation must be taken into consideration in the design of a dispatch schedule. A number of algorithms have been proposed to overcome the difficulties associated with ELSP: Dynamic integer linear programming (Sheu, 2007), goal programming (

Problem definition

Section 3.1 presents definitions of the presented notation. The formulation of the problem is outlined in Section 3.2 (see Fig. 1).

The proposed algorithm

The proposed GSMOGA integrates the speed of greedy search with the diversity of MOGA. A flowchart of the proposed algorithm is presented in Fig. 2.

Simulation

To verify the efficacy of the proposed GSMOGA algorithm, we designed a number of simulations based on the 921 (Chi–Chi) earthquake in Taiwan, using the benchmark school29. Simulations were performed on an Intel Xeon E5620 2.4 GHz processor with 3.87 GB of RAM running on Windows XP.

Conclusion

This study proposed a novel algorithm (GSMOGA), which integrates the MOGA and the greedy algorithm with a focus on three objectives: minimizing unsatisfied demand for resources, time to delivery, and transportation costs. GSMOGA generates several feasible solutions instead of a single optimal solution as provided by existing methods. In addition, GSMOGA combines the greedy search and MOGA to overcome the disadvantages of both methods. Furthermore, GSMOGA can arrange routing schedules for each

References (21)

M.-S. Chang et al.
A scenario planning approach for the flood emergency logistics preparation problem under uncertainty
Transportation Research Part E: Logistics and Transportation Review
(2007)
Z.-H. Hu
A container multimodal transportation scheduling approach based on immune affinity model for emergency relief
Expert Systems with Applications
(2011)
F. Liberatore et al.
A hierarchical compromise model for the joint optimization of recovery operations and distribution of emergency goods in humanitarian logistics
Computers & Operations Research
(2014)
L. Özdamar et al.
A hierarchical clustering and routing procedure for large scale disaster relief logistics planning
Transportation Research Part E: Logistics and Transportation Review
(2012)
P.M. Reyes
Logistics networks: A game theory application for solving the transshipment problem
Applied Mathematics and Computation
(2005)
J.-B. Sheu
An emergency logistics distribution approach for quick response to urgent relief demand in disasters
Transportation Research Part E: Logistics and Transportation Review
(2007)
J.-B. Sheu
Dynamic relief-demand management for emergency logistics operations under large-scale disasters
Transportation Research Part E: Logistics and Transportation Review
(2010)
S. Yan et al.
Optimal scheduling of emergency roadway repair and subsequent relief distribution
Computers & Operations Research
(2009)
L. Yang et al.
A fuzzy multi-objective programming for optimization of fire station locations through genetic algorithms
European Journal of Operational Research
(2007)
W. Yi et al.
Ant colony optimization for disaster relief operations
Transportation Research Part E: Logistics and Transportation Review
(2007)

There are more references available in the full text version of this article.

Cited by (152)

An efficiency-based interactive dynamic technique with interval-valued hesitant fuzzy constraint cone for rescue route planning
2023, Expert Systems with Applications
This paper develops an efficiency-based interactive dynamic technique to design the post-disaster rescue route by combining the data envelopment analysis (DEA), traveling salesman problem (TSP) and interval-valued hesitant fuzzy constraint cone (IVHFCC). The traditional DEA potentially presumes that the weights of the input (output) criteria are unrestricted, resulting in its inapplicability to some complex decision problems. To restrict the weights of input (output) criteria legitimately, this paper constructs an IVHFCC based on expert’s subjective preferences in the form of interval-valued hesitant fuzzy sets. This paper defines the additive consistent normal interval-valued hesitant fuzzy preference relation (NIVHFPR) and analyses its desirable properties. Based on these properties, three linear programming models are proposed to repair the incomplete NIVHFPR, judge the additive consistency of the repaired NIVHFPR and generate the additive consistent NIVHFPR. By incorporating the TSP, DEA and the constructed IVHFCC, an efficiency-based interactive dynamic model is originally proposed to generate the optimal rescue route for the rescue team. Some desirable properties of this efficiency-based interactive dynamic model are analyzed. The validity of this model is demonstrated with a real post-disaster rescue route planning problem of Wenchuan earthquake. Some comparative analyses are provided to show the superiorities of the proposed technique.
Humanitarian logistics optimization models: An investigation of decision-maker involvement and directions to promote implementation
2023, Socio-Economic Planning Sciences
Reports of successful implementation of humanitarian optimization models in the field are scarce. Incorporating real conditions and the perspective of decision-makers in the analysis is crucial to enhance the practical value and managerial implications. Although it is known that implementation can be hindered by the lack of practitioner input in the structure of the model, its priorities, and the practicality of solution times, the way these aspects have been introduced in humanitarian optimization models has not been investigated. This study looks at the way research has involved practitioners in different aspects of the design of optimization models to promote implementation. It investigates the aspects affecting the implementation of the models and opportunities to guide future optimization contributions. The article introduces a systematic literature review of 105 articles to answer the research questions. The results are contrasted with a multi-criteria decision analysis using responses from Mexican practitioners. The study found that only 10% of the articles involved practitioners for modelling decisions, which was confirmed by a major gap between the objectives used in the literature and the priorities of Mexican practitioners. In terms of swift decision-making, fewer than 22% of the articles surveyed introduced new solution methods to deliver results in a sensible time. The study also identified very limited inclusion of environmental concerns in the objective functions even though these are a priority in the global agenda. These findings are discussed to propose research directions and suggest best practices for future contributions to promote the implementation of humanitarian logistics models.
Optimization models and solving approaches in relief distribution concerning victims’ satisfaction: A review
2023, Applied Soft Computing
Relief distribution is one of the most widely studied topics in the domain of emergency logistics. Optimization models and solving approaches have become one of the most powerful tools for tackling relief distribution problems. In this context, victims’ satisfaction should be considered as one significant indicator to evaluate relief distribution operations. Therefore, this survey addresses some of the most representative publications working with optimization models and solving approaches in relief distribution concerning victims’ satisfaction. Firstly, collected models are discussed from the commonly used objectives for describing victims’ satisfaction: the shortest distribution time, the lowest unmet demand, and the maximum fairness. Second, gathered solving approaches are analyzed from exact algorithms, heuristic algorithms, and machine learning algorithms respectively. Heuristic algorithms are further studied into four groups: genetic algorithm, ant colony optimization, particle swarm optimization, and others. Finally, development trends of models and approaches in relief distribution concerning victims’ satisfaction and potential interest in the cross-disciplinary are showcased.
Emergency logistics network optimization with time window assignment
2023, Expert Systems with Applications
During natural disasters or accidents, an emergency logistics network aims to ensure the distribution of relief supplies to victims in time and efficiently. When the coronavirus disease 2019 (COVID-19) emerged, the government closed the outbreak areas to control the risk of transmission. The closed areas were divided into high-risk and middle-/low-risk areas, and travel restrictions were enforced in the different risk areas. The distribution of daily essential supplies to residents in the closed areas became a major challenge for the government. This study introduces a new variant of the vehicle routing problem with travel restrictions in closed areas called the two-echelon emergency vehicle routing problem with time window assignment (2E-EVRPTWA). 2E-EVRPTWA involves transporting goods from distribution centers (DCs) to satellites in high-risk areas in the first echelon and delivering goods from DCs or satellites to customers in the second echelon. Vehicle sharing and time window assignment (TWA) strategies are applied to optimize the transportation resource configuration and improve the operational efficiency of the emergency logistics network. A tri-objective mathematical model for 2E-EVRPTWA is also constructed to minimize the total operating cost, total delivery time, and number of vehicles. A multi-objective adaptive large neighborhood search with split algorithm (MOALNS-SA) is proposed to obtain the Pareto optimal solution for 2E-EVRPTWA. The split algorithm (SA) calculates the objective values associated with each solution and assigns multiple trips to shared vehicles. A non-dominated sorting strategy is used to retain the optimal labels obtained with the SA algorithm and evaluate the quality of the multi-objective solution. The TWA strategy embedded in MOALNS-SA assigns appropriate candidate time windows to customers. The proposed MOALNS-SA produces results that are comparable with the CPLEX solver and those of the self-learning non-dominated sorting genetic algorithm-II, multi-objective ant colony algorithm, and multi-objective particle swarm optimization algorithm for 2E-EVRPTWA. A real-world COVID-19 case study from Chongqing City, China, is performed to test the performance of the proposed model and algorithm. This study helps the government and logistics enterprises design an efficient, collaborative, emergency logistics network, and promote the healthy and sustainable development of cities.
Bibliometric analysis and system review of vehicle routing optimization for emergency material distribution
2022, Journal of Traffic and Transportation Engineering (English Edition)
Determining the optimal vehicle routing of emergency material distribution (VREMD) is one of the core issues of emergency management, which is strategically important to improve the effectiveness of emergency response and thus reduce the negative impact of large-scale emergency events. To summarize the latest research progress, we collected 511 VREMD-related articles published from 2010 to the present from the Scopus database and conducted a bibliometric analysis using VOSviewer software. Subsequently, we cautiously selected 49 articles from these publications for system review; sorted out the latest research progress in model construction and solution algorithms; and summarized the evolution trend of keywords, research gaps, and future works. The results show that domestic scholars and research organizations held an unqualified advantage regarding the number of published papers. However, these organizations with the most publications performed poorly regarding the number of literature citations. China and the US have contributed the vast majority of the literature, and there are close collaborations between researchers from both countries. The optimization model of VREMD can be divided into single-, multi-, and joint-objective models. The shortest travel time is the most common optimization objective in the single-objective optimization model. Several scholars focus on multiobjective optimization models to consider conflicting objectives simultaneously. In recent literature, scholars have focused on the impact of uncertainty and special events (e.g., COVID-19) on VREMD. Moreover, some scholars focus on joint optimization models to optimize vehicle routes and central locations (or material allocation) simultaneously. Solution algorithms can be divided into two primary categories, i.e., mathematical planning methods and intelligent evolutionary algorithms. The branch and bound algorithm is the most dominant mathematical planning algorithm, while genetic algorithms and their enhancements are the most commonly used intelligent evolutionary algorithms. It is shown that the nondominated sorting genetic algorithm II (NSGA-II) can effectively solve the multiobjective model of VREMD. To further improve the algorithm's performance, researchers have proposed improved hybrid intelligent algorithms that combine the advantages of NSGA-II and certain other algorithms. Scholars have also proposed a series of optimization algorithms for specific scenarios. With the development of new technologies and computation methods, it will be exciting to construct optimization models that consider uncertainty, heterogeneity, and temporality for large-scale real-world issues and develop generalized solution approaches rather than those applicable to specific scenarios.
A multi-objective evolutionary optimisation model for heterogeneous vehicles routing and relief items scheduling in humanitarian crises
2022, Decision Analytics Journal
In a disaster scenario, relief items distribution is required as early as possible for the disaster victims to reduce the associated risks. For the distribution tasks, an effective and efficient relief items distribution model is essential to generate relief items distribution schedules to minimise the impact of disaster to the disaster victims. However, developing efficient distribution schedules is challenging as the relief items distribution problem has multiple objectives to look after where the objectives are mostly contradictorily creating a barrier to simultaneous optimisation of each objective. Also, the relief items distribution model has added complexity with the consideration of multiple supply points having heterogeneous and limited vehicles with varying capacity, cost and time. In this paper, multi-objective evolutionary optimisation with the greedy heuristic search has been applied for the generation of relief items distribution schedules under heterogeneous vehicles condition at supply points. The evolutionary algorithm generates the disaster region distribution sequence by applying a global greedy heuristic search along with a local search that finds the efficient assignment of heterogeneous vehicles for the distribution. This multi-objective evolutionary approach provides Pareto optimal solutions that decision-makers can apply to generate effective distribution schedules to optimise the distribution time and vehicles’ operational cost. In addition, this optimisation process also incorporated the minimisation of unmet relief items demand at the disaster regions. The optimised distribution schedules with the proposed approach are compared with the single-objective optimisation, weighted single-objective optimisation and greedy multi-objective optimisation approaches. The comparative results showed that the proposed multi-objective evolutionary approach is an efficient alternative for finding the distribution schedules with optimisation of distribution time and operational cost for the relief items distribution with heterogeneous vehicles in humanitarian crisis.

View all citing articles on Scopus

View full text

Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling

Highlights

Abstract

Introduction

Section snippets

Literature review

Problem definition

The proposed algorithm

Simulation

Conclusion

Transportation Research Part E: Logistics and Transportation Review

Expert Systems with Applications

Computers & Operations Research

Transportation Research Part E: Logistics and Transportation Review

Applied Mathematics and Computation

Transportation Research Part E: Logistics and Transportation Review

Transportation Research Part E: Logistics and Transportation Review

Computers & Operations Research

European Journal of Operational Research

Transportation Research Part E: Logistics and Transportation Review