A multiobjective GRASP–VND algorithm to solve the waste collection problem

López-Sánchez, Ana Dolores; Hernández-Díaz, Alfredo G.; Gortázar, Francisco; Hinojosa, Miguel A.

doi:10.1111/itor.12452

Cited by 17 publications

(12 citation statements)

References 35 publications

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“…López-Sánchez et al [35] Variable neighborhood search algorithm for a multi-objective optimization model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The main motivation is that since, in some cases, the objective function value may violate the expected or desire level, a penalty should be taken into account in the model. This feature has not been studied by previous variants of GP techniques [35]. Accordingly, due to the high probability of an unforeseen amount of waste in medical centers, the IMCGP method is utilized to tackle our proposed multi-objective model.…”

Section: Improved Multi-choice Goal Programming (Imcgp)mentioning

confidence: 99%

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Multi-Objective Optimization for Healthcare Waste Management Network Design with Sustainability Perspective

2021

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Healthcare Waste Management (HWM) is considered as one of the important urban decision-making problems due to its potential environmental, economic, and social risks and damages. The network of the HWM system involves important decisions such as facility locating, inventory management, and transportation management. Moreover, with growing concerns towards sustainable development objectives, HWM systems should address its environmental and social aspects as well as its economic and technical characteristics. In this regard, this paper formulates a novel multi-objective optimization model to empower companies in making optimized decisions considering the economic, environmental, and social aspects. Within the proposed model, the first objective function aims to minimize the transportation costs, processing costs, and establishment costs. The second objective function aims to minimize environmental risks and emissions related to the transportation of waste between facilities. The third objective function aims to maximize job creation opportunities. Formulating these three functions, an Improved Multi-Choice Goal Programing (IMCGP) approach is proposed to solve the multi-objective optimization model, which is then compared with the Goal Attainment Method (GAM). Finally, to show the applicability and feasibility of the proposed model, an illustrative example of healthcare waste management is analyzed, and the results are discussed.

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“…López-Sánchez et al [35] Variable neighborhood search algorithm for a multi-objective optimization model.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Improved Multi-choice Goal Programming (Imcgp)mentioning

confidence: 99%

Multi-Objective Optimization for Healthcare Waste Management Network Design with Sustainability Perspective

2021

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“…ey evaluated their model in a hypothetical case and a realistic one in the Sichuan province of China. López-Sánchez et al [17] developed a multiobjective optimization algorithm based on the variable neighborhood descent (VND) method to solve the waste collection problem in a city in southern Spain. e goals were to minimize total travel costs and to balance the vehicle routes.…”

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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“…In addition, waste logistic networks evolved from a straightforward source-to-landfill scheme to a multi-echelon network in which waste generally goes through several operations before reaching a destination site. SWM involves waste collection (see Rogge and Jaeger [45], Huang and Lin [27], López-Sánchez et al [32]), waste treatment (see Münster et al [40]), waste routing (see Sahoo et al [47], Inghels et al [28]) and waste disposal (see Pérez-López et al [42]).…”

Section: Introductionmentioning

confidence: 99%