A novel multi-dimensional modeling approach to integrated municipal solid waste management
Journal of cleaner production · Nov 10, 2017
Abstract: An integrated system for waste reduction, collection, composting, recycling, and disposal is required to tackle the growing challenge of municipal solid waste management. Integrated Solid Waste Management (ISWM) involves many executive, operational and managerial decisions such as the siting of waste processing and disposal units, selection of waste-treatment technologies and allocation of waste flow to processing facilities and landfills. This study aims to satisfy the sustainability requirements for designing an ISWM system by taking economic, environmental and social factors into account. The present study utilized Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in the Geographic Information System (GIS) environment and developed a reliable Suitability Indicator for siting the ISWM system's components. A new model is developed to minimize the fixed cost, minimize the transportation cost and maximize the suitability of the entire system. Considering the real-world constraints, the proposed model is proven to be practical, efficient and case-independent for designing an ISWM system and supporting the existing systems in both developing and developed countries. To realize the model effectively, the augmented version of ε-constraint is employed so that the model is implemented in the General Algebraic Modeling System (GAMS) software. The proposed approach is successfully applied to the existing ISWM system of Tehran, Iran, and the results show that 12%, 22% and 17% improvement can be achieved in terms of the fixed cost, transportation cost and total suitability of the system, respectively.
Annals of Operations Research · Feb 1, 2019
Abstract: Integrated solid waste management (ISWM) comprises activities and processes to collect, transport, treat, recycle and dispose municipal solid wastes. This paper addresses the ISWM location-routing problem in which different types of municipal solid wastes are factored concurrently into an integrated system with all interrelated facilities. To support a cost-effective ISWM system, the number of locations of the system’s components (i.e. transfer stations; recycling, treatment and disposal centres) and truck routing within the system’s components need to be optimized. A mixed-integer linear programming (MILP) model is presented to minimise the total cost of the ISWM system including transportation costs and facility establishment costs. To tackle the non-deterministic polynomial-time hardness of the problem, a stepwise heuristic method is proposed within the frames of two meta-heuristic approaches: (i) variable neighbourhood search (VNS) and (ii) a hybrid VNS and simulated annealing algorithm (VNS + SA). A real-life case study from an existing ISWM system in Tehran, Iran is utilized to apply the proposed model and algorithms. Then the presented MILP model is implemented in CPLEX environment to evaluate the effectiveness of the proposed algorithms for multiple test problems in different scales. The results show that, while both proposed algorithms can effectively solve the problem within practical computing time, the proposed hybrid method efficiently has produced near-optimal solutions with gaps of < 4%, compared to the exact results. In comparison with the current cost of the existing ISWM system in the study area, the presented MILP model and proposed heuristic methods effectively reduce the total costs by 20–22%.
Variable fleet size and mix VRP with fleet heterogeneity in integrated solid waste management
Journal of Cleaner Production · Sep 1, 2019
Abstract: The Integrated Solid Waste Management (ISWM) is a recent effective tool to manage with the growing challenge of Municipal Solid Waste (MSW). The ISWM integrates all the system components (i.e. transfer, treatment, recycling and disposal of wastes) to enhance the sustainable waste management whilst reducing operational costs. In this paper, we investigate an integrated framework of the Fleet Size and Mix Vehicle Routing Problem (VRP) to optimize the cost-effective ISWM system. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is developed to concurrently minimize the transportation cost in the entire waste management system and total deviation from the fair load allocation to transfer stations. A complete ISWM system with all interdependent facilities and multiple technologies, is developed to address a tri-echelon Fleet Size and Mix VRP with a heterogeneous fleet of vehicles under multiple technologies and waste compatibility constraints. The model was solved for both the Preemptive and Non-Preemptive conditions using Lexicographic and Goal Programming optimization approaches. The model was tested on a case of ISWM in the Southern part of Tehran, Iran.
Journal of Environmental Planning and Management · Nov 9, 2020
Abstract: This paper undertakes a critical review of the solution approaches, methods and techniques applied to model municipal solid waste (MSW) management systems over the last decade (2007-2018). Sustainability and integration concepts are explored to evaluate the effectiveness of advanced models in achieving sustainable MSW management practices. Papers published are categorized into three main classes with respect to the methods applied to evaluate the operational efficiency and performance of IMSW systems. These include System Assessment (SA), Multi-Criteria Decision Making (MCDM) and Operation Research (OR) techniques. Each class is then analyzed by reviewing the key studies as the representatives of the class and potential improvements are suggested to achieve a sustainable Integrated Solid Waste Management (ISWM) system.
Sustainable Cities and Societies · Aug 10, 2019
Abstract: The growing challenge of Municipal Solid Waste Management in mega-cities calls for development of practical decision-making support tools to assist authorities in City Logistics and Urban Planning. This study aims to optimize the logistics network and transportation system of ISWM where the complete chain of MSW/residue is formulated as a tri-echelon ISWM logistics network. Assuming various levels of complexity of a realistic ISWM system, a Mixed-Integer Linear Programming (MILP) model is developed to formulate the ISWM system in the framework of the Fleet Size and Mix Vehicle Routing Problem with Time Windows. Addressing uncertainty in ratios of MSW generation, a two-stage stochastic optimization approach is proposed to effectively support the cost-effective ISWM transportation system by determining the optimal fleet size and decomposition, vehicles routes and capacity-allocation to the system components. The proposed approach successfully was applied to a real case of ISWM in southern Tehran, Iran. The numerical experiments showed the usefulness of the approach to minimize the economic costs of the system under uncertainty. Furthermore, the results verified the strength and effectiveness of the method when experiencing larger deviations between the estimated and actual amounts of the uncertain parameter and in cases of unplanned disruptions in the system network.
Australasian Journal of Environmental Management · Dec 1, 2020
Abstract: Appropriate planning and strategies in Municipal Solid Waste (MSW) management could significantly decrease environmental and economic costs imposing on both management authorities and local communities. MSW landfill siting has been a growing challenge since unsuitability of landfills could cause destructive impacts on the environment and human health in addition to economic costs. In this study, Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are utilised in an integrated decision-making framework to develop an efficient Suitability Index (SI) to assess the suitability of landfills of New South Wales, Australia, with respect to a comprehensive set of evaluation criteria. A preliminary factors map and a concluding composite SI map are developed in the geographic information system environment, and a solid ranking scheme is proposed to assess the landfills. A wide range of statistical analysis is conducted to obtain practical and specific results for different divisions in three different managerial scenarios. Comprehensiveness of the applied decision-making model, efficiency of the developed SI and adequacy of the performed statistical analysis provide a competent supporting tool for the authorities for ongoing landfills replacement plans, rehabilitation plans and future landfill siting programs.
Australasian Journal of Information Systems · Sep 22, 2015
Abstract: Municipal solid waste management is one of the challenging issues in mega cities due to various interrelated factors such as operational costs and environmental concerns. Cost as one of the most significant constraints of municipal solid waste management can be effectively economized by efficient planning approaches. Considering diverse waste types in an integrated municipal solid waste system, a mathematical model of the location-routing problem is formulated and solved in this study in order to minimize the total cost of transportation and facility establishment.
Adaptation of simulated annealing to an integrated municipal solid waste location-routing problem
International Journal of Logistics Systems and Management · Sep 8, 2017
Abstract: This paper aims to propose an integrated municipal solid waste management network covering multiple types of wastes concurrently and utilise a location-routing problem framework to minimise the establishment cost of interrelated facilities (i.e., transfer stations; treatment, recycling and disposal centres) in the network and the transportation cost of wastes in the entire network. The defined problem consists of the concurrent site selection of the locations of the system's all facilities among the candidate locations and the determination of routes and amount of shipments among the selected facilities to minimise the total cost of transportation and facility establishment. As the addressed problem exhibits the non-deterministic polynomial-time hardness (NP-hardness), an adaptation of the simulated annealing algorithm is proposed in this paper. The experiment results, when compared with the exact solutions obtained by mixed-integer programming in terms of solution fitness and computing time, imply that the employed algorithm works effectively and efficiently.
Modular recycling supply chain under uncertainty: a robust optimisation approach
The International Journal of Advanced Manufacturing Technology · Apr 1, 2018
Abstract: It is estimated that recycling can avert approximately 50% annual landfill cost, while simultaneously recovering lost materials valued at 4 to 9.5% of the total logistics network cost. This study proposes a robust integrated reverse logistics supply chain planning model with a modular product design at different quality levels. A mixed-integer programming (MIP) model is formulated to maximise the profit by considering the collection of returned products, the recovery of modules and the proportion of the product mix at different quality levels. This paper proposes the collection of returnable items (end-of-life, defective and under-warranty products) through retail outlets and the appropriate recovery of modules to manage these using a network of recovery service providers. The modular product design approach is adopted to create design criteria that provide an improved recovery process at a lower cost. This robust model seeks solutions close to the mathematically optimal solutions for a set of alternative scenarios identified by a decision-maker. The efficacy of the proposed model is evaluated by a given set of variously sized numerical expressions and sensitivity analyses. A robust solution is found that appraises the impact of two major sources of uncertainty, demand rate and the volume of returned products of a key recycled material.
Environmental Monitoring and Assessment · Nov 1, 2020
Abstract: Sanitary waste disposal and site selection for establishing landfills are challenging problems for environmental planners. This paper aims to take environmental, socio-economic, geological, geomorphological, hydrological and ecological factors into consideration to provide a decision support framework for landfill siting. Analytical hierarchy process (AHP) and Decision Making Trial and Evaluation Laboratory (DEMATEL) are coupled to develop an efficient multi-criteria decision-making method to be utilized in a Geographic Information System (GIS) environment for evaluating the suitability for landfill siting. As the first attempt to employ DEMATEL effectively in a landfill site selection problem, the proposed method is tested with landfill siting scenarios in New South Wales (NSW), Australia. Regional analysis is also performed to identify the potentially most suitable statistical divisions for landfill siting in NSW. The top two ranked zones covering 0.7% and 22% of the study area, respectively, are considered as the optimal areas for establishing landfills, while the bottom two ranked zones are not recommended for further consideration. Further detailed analysis is also conducted on the existing landfills, which shows that 1.0% and 37.0% of them are ranks 1 and 2, respectively. The scenario-based analysis implies that, among the contributing factors; geological and economic factors are highly important.
International Journal of Production Research · Dec 15, 2012
Abstract: In this paper, we contemplate the problem of scheduling a set of n jobs in a no-wait flexible flow shop manufacturing system with sequence dependent setup times to minimising the maximum completion time. With respect to NP-hardness of the considered problem, there seems to be no avoiding application of metaheuristic approaches to achieve near-optimal solutions for this problem. For this reason, three novel metaheuristic algorithms, namely population based simulated annealing (PBSA), adapted imperialist competitive algorithm (AICA) and hybridisation of adapted imperialist competitive algorithm and population based simulated annealing (AICA + PBSA), are developed to solve the addressed problem. Because of the sensitivity of our proposed algorithm to parameter's values, we employed the Taguchi method as an optimisation technique to extensively tune different parameters of our algorithm to enhance solutions accuracy. These proposed algorithms were coded and tested on randomly generated instances, then to validate the effectiveness of them computational results are examined in terms of relative percentage deviation. Moreover, some sensitive analyses are carried out for appraising the behaviour of algorithms versus different conditions. The computational evaluations manifestly support the high performance of our proposed novel hybrid algorithm against other algorithms which were applied in literature for related production scheduling problems.
A hybrid NSGA-II and VNS for solving a bi-objective no-wait flexible flowshop scheduling problem
The International Journal of Advanced Manufacturing Technology · Nov 1, 2014
Abstract: We address the no-wait k-stage flexible flowshop scheduling problem where there are m identical machines at each stage. The objectives are to schedule the available n jobs so that makespan and mean tardiness of n jobs are minimized. Sequence-dependent setup times are treated in this problem as one of the prominent practical assumptions. This problem is NP-hard, and therefore we present a new multiobjective approach for solving the mentioned problem. The proposed meta-heuristic is evaluated based on randomly generated data in comparison with two well-known multiobjective algorithm including NSGA-II and SPEA-II. Due to sensitivity of our proposed algorithm to parameter values, a new approach for tackling of this issue was designed. Our proposed method includes Taguchi method (TM) and multiobjective decision making (MODM). We have chosen six measures into two groups. Qualitative metrics including number of Pareto solutions (NPS), diversity metric (DM) as well as the spread of non-dominance solution (SNS) and quantitative metrics including the rate of achievement to two objectives simultaneously (RAS), mean ideal distance (MID) and quality metric (QM) to evaluate the performance of our proposed algorithms. Computational experiments and comparisons show that the proposed NSGA-II + VNS algorithm generates better or competitive results than the existing NSGA-II and SPEA-II for the no-wait flexible flow shop scheduling problem with sequence-dependent setup times to simultaneous minimizing the makespan and mean tardiness criterion.
International Journal of Computer Integrated Manufacturing · Sep 1, 2016
Abstract: This paper explores a no-wait hybrid flow shop scheduling problem (NWHFSSP) with realistic assumptions, including unrelated parallel machines at each stage, machine eligibility, sequence-dependent set-up times and different ready times, in order to minimise the mean tardiness. The largest position value rule is proposed to transmute continuous vectors of each solution into job permutations. Also, a novel biogeography-based optimisation (BBO) algorithm is developed to solve the aforementioned problem. To evaluate the effect of various parameters on the performance of the proposed BBO algorithm, response surface methodology (RSM) is employed. Production scenarios for small-scale and large-scale problems are created and tested for the validation purposes. Computational experiment results indicate that the proposed BBO outperforms all of the tested algorithms in terms of four measures, namely, mean relative percentage deviation (RPD), standard deviation of RPD, best RPD and worst RPD. It is shown that BBO produces the best solutions among the tested algorithms in terms of not only the four RPD measures but also computation time.
Scientia Iranica · Jun 1, 2013
Abstract: This paper focuses on solving the bi-objective problem of no-wait two-stage flexible flow shop scheduling. The objectives considered in this study are minimum makespan (Cmax), as well as maximum tardiness of jobs (Tmax). This problem is known as NP-hard. Hence, three bi-objective optimization methods based on simulated annealing, called CWSA (classical weighted simulated annealing), NWSA (normalized weighted simulated annealing), and FSA (fuzzy simulated annealing), are developed to solve the problem with the goal of finding approximations of the optimal Pareto front. Due to the fact that meta-heuristic algorithms are very vigilant of parameter values, we proposed a new reliable method, by mixing the Taguchi method and a Multi-Objective Decision Making (MODM) approach, for achieving our purpose. The algorithms are evaluated by solving both small and large scale problems. The performances are evaluated in terms of a relative deviation index. Finally, the result of the study is discussed and concluded, and potential areas of further study are highlighted.
Parking lot site selection using a fuzzy AHP-Topsis framework in Tuyserkan, Iran
Journal of Urban Planning and Development · Sep 1, 2018
Abstract: The increasing population growth and subsequent rise in vehicle use highlight the necessity of efficient urban planning and supporting effective transportation systems. Experiencing the same challenges as many cities in developing countries, the study area targeted in this research suffers from inadequate public parking sites. The historical background of the city and hosting tourists, population growth, traditional design of the city with narrow streets and passages, and lack of effective municipal planning in the past are the factors that signify the necessity of establishing new public parking sites. Siting new parking lots requires application of an efficient location selection approach to minimize the corresponding economic and environmental costs. That is, siting a parking lot at a location is economically reasonable and socially acceptable when multiple economic, social, and environmental criteria are considered. Hence, the present study uses an efficient hybrid multicriteria decision-making (MCDM) method by coupling the analytic hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) methods under a fuzzy environment (i.e., fuzzy AHP-TOPSIS) to effectively address site selection criteria and uncertainties associated with a decision-making process for locating a new public parking lot.