An artificial bee colony-based hybrid approach for waste collection problem with midway disposal pattern

Qu, Wenyu; Guo, Zhaoxia; Lau, Hoong Chuin; He, Zhenggang

doi:10.1016/j.asoc.2018.12.033

Cited by 26 publications

(19 citation statements)

References 46 publications

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“…Zhang et al [31] consider multi-vehicle allocation while considering the single objective of minimizing route distance. Wei et al [32] use the Artificial Bee Colony algorithm to determine garbage collection routes resulting in the minimum emission of CO 2 . Another optimization objective in route optimization for waste management has been the minimization of the total travel time, such a target is described by the authors of [33][34][35][36] who aim to reduce travel time while considering emptying of waste bins.…”

Section: Smart City Servicesmentioning

confidence: 99%

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Syed

Sierra-Sosa

Kumar

et al. 2022

Sensors

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One of the prime aims of smart cities has been to optimally manage the available resources and systems that are used in the city. With an increase in urban population that is set to grow even faster in the future, smart city development has been the main goal for governments worldwide. In this regard, while the useage of Artificial Intelligence (AI) techniques covering the areas of Machine and Deep Learning have garnered much attention for Smart Cities, less attention has focused towards the use of combinatorial optimization schemes. To help with this, the current review presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things (IoT). A mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. This review will help researchers by providing them a consolidated starting point for research in the domain of smart city application optimization.

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Section: Smart City Servicesmentioning

confidence: 99%

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Syed

Sierra-Sosa

Kumar

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…ree objectives including operational cost and social costs are considered and optimised. Wei et al [17] develop an artificial bee colony and variable neighbourhood descend-based hybrid approach to investigate the waste collection problem. e socalled midway disposal pattern, which refers that the waste loaded in vehicles could be dumped to the nearest disposal facility dynamically, contributes to significant carbon emission reduction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraints ( 15) and (16) set the working time limit on routes. Constraints (17) and (18) denote the start time of collection tasks on all the facilities. Constraints ( 19)-( 22) define all decision variables.…”

Section: Mathematical Model Constructionmentioning

confidence: 99%

Transportation Risk Control of Waste Disposal in the Healthcare System with Two-Echelon Waste Collection Network

Lyu

et al. 2021

Mathematical Problems in Engineering

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This paper investigates a vehicle routing problem arising in the waste collection of the healthcare system with the concern of transportation risk. Three types of facilities abstracted from the health system are investigated in this paper, namely, facilities with collection points, facilities without collection points, and small facilities. Two-echelon collection mode is applied in which the waste generated by small facilities is first collected by collection points, and then transferred to the recycling centre. To solve this problem, we propose a mixed-integer linear programming model considering time windows and vehicle capacity, and we use particle swarm optimisation (PSO) algorithm for solving large-scale problems. Numerical experiments show the capability of the proposed algorithm. Sensitivity analysis is conducted to investigate the influence of facilities with collection points and the collection routes. This research can provide a decision support tool for the routing of waste collection in the healthcare system.

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“…ey proposed a fuzzy multiobjective planning model to model the supply chain for the processing of waste generated after the disaster. Wei et al [26] developed a hybrid algorithm based on an artificial bee colony algorithm to solve the waste collection problem in the form of a CVRP problem considering intermediary disposal.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization Model for Integrated Municipal Solid Waste System Using Stochastic Chance-Constraint Programming under Uncertainty: A Case Study in Qazvin, Iran

Zaeimi

Rassafi

2021

Journal of Advanced Transportation

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Municipal solid waste management (MSW) is a factor that affects environmental pollution and the spread of diseases in cities. Therefore, an efficient MSW management system results in reducing the cost of environmental impact by tackling the processes of waste collection, recycling, and disposal. In this study, a biobjective optimization model is developed which aims to minimize the costs of facility location and transportation planning and the emission of environmental pollutants. Furthermore, to consider the uncertain nature of the problem, demand or the volume of the generated waste is considered as a random parameter. As a result, a stochastic mathematical programming model with probable constraints is developed. To solve and validate the model, the ε-constraint approach has been employed. Moreover, for a real-world application of the proposed model, a case study is implemented in Qazvin, Iran. Finally, various problems are solved for different levels of reliability and an efficient MSW system is designed for each of them. Results show that the proposed method was able to achieve Pareto solutions where managers can decide to choose one of them based on their priorities in comparison with the current status. Moreover, results revealed cost and emission would be reduced by increasing confidence level. Finally, a comparison is made between our proposed ε-constraint method and one of the recently used solution approaches.

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An artificial bee colony-based hybrid approach for waste collection problem with midway disposal pattern

Cited by 26 publications

References 46 publications

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Transportation Risk Control of Waste Disposal in the Healthcare System with Two-Echelon Waste Collection Network

Optimization Model for Integrated Municipal Solid Waste System Using Stochastic Chance-Constraint Programming under Uncertainty: A Case Study in Qazvin, Iran

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