A robust bi-objective multi-trip periodic capacitated arc routing problem for urban waste collection using a multi-objective invasive weed optimization

Bi-objective optimization for last-mile routing of garbage trucks: Aesthetics and Public exposure

In the area of Solid Waste Management, transportation of the collected waste is a critical aspect considering the substantial time spent by garbage trucks on public roads. Studies have reported that transporting garbage has challenges related to public exposure and aesthetics. This study presents a generalised bi-objective formulation for the optimal routing of garbage trucks from transfer stations to recycling sites/landfills considering the trade-off between public exposure and aesthetic loss and constraining the operating cost. The formulation uses the novel link capacity function to account for the delay at traffic signals and the mix of trucks and cars on link performance. The proposed formulation is solved using the weighted sum and ε-constraint methods and applied to a realistic case study of the City of Chicago, USA. The Pareto Front obtained for the bi-objective formulation offers diverse trade-off solutions catering to distinct performance metrics. The results highlight the disparity across the solutions; the solution (P0.95 on Pareto Front) for minimum operating cost (or travel time or distance travelled) is very different from the solution (P0.4 on Pareto Front) for aesthetic cost and public exposure. The parametric study indicated that a modest operating budget may suffice for achieving aesthetic benefits, but minimising public exposure requires a higher operating budget. Finally, the proposed framework is adaptable to address various challenges pertaining to waste transportation, thereby serving as a valuable tool for evaluating policies and practices geared towards sustainability objectives.

Circular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approach

Environmental pollution has been a major concern for researchers and policymakers. A number of studies have been conducted to enquire the causes of environmental pollution which suggested numerous policies and techniques as remedial measures. One such major source of environmental pollution, as reported by previous studies, has been the garbage resulting from disposed hospital wastes. The recent outbreak of the COVID-19 pandemic has resulted into mass generation of medical waste which seems to have further deteriorated the issue of environmental pollution. This necessitates active attention from both the researchers and policymakers for effective management of medical waste to prevent the harm to environment and human health. The issue of medical waste management is more important for countries lacking sophisticated medical infrastructure. Accordingly, the purpose of this study is to propose a novel application for identification and classification of 10 hospitals in Iraq which generated more medical waste during the COVID-19 pandemic than others in order to address the issue more effectively. We used the Multi-Criteria Decision Making (MCDM) method to this end. We integrated MCDM with other techniques including the Analytic Hierarchy Process (AHP), linear Diophantine fuzzy set decision by opinion score method (LDFN-FDOSM), and Artificial Neural Network (ANN) analysis to generate more robust results. We classified medical waste into five categories, i.e., general waste, sharp waste, pharmaceutical waste, infectious waste, and pathological waste. We consulted 313 experts to help in identifying the best and the worst medical waste management technique within the perspectives of circular economy using the neural network approach. The findings revealed that incineration technique, microwave technique, pyrolysis technique, autoclave chemical technique, vaporized hydrogen peroxide, dry heat, ozone, and ultraviolet light were the most effective methods to dispose of medical waste during the pandemic. Additionally, ozone was identified as the most suitable technique among all to serve the purpose of circular economy of medical waste. We conclude by discussing the practical implications to guide governments and policy makers to benefit from the circular economy of medical waste to turn pollutant hospitals into sustainable ones.

Developing a Mathematical Model for a Green Closed-Loop Supply Chain with a Multi-Objective Gray Wolf Optimization Algorithm

Intense competition in today’s market and quick change in customer preferences, along with the rapid development of technology and globalization, have forced companies to work as members of a supply chain instead of individual companies. The success of the supply chain depends on the integration and coordination of all its institutions to form an efficient network structure. An efficient network leads to cost savings throughout the supply chain and helps it respond to customer needs faster. Accordingly, and with respect to the importance of the supply chain, in this study a developed mathematical model for the design of a green closed-loop supply chain is presented. In this mathematical model, the economic and environmental objectives are simultaneously optimized. In order to tackle this mathematical model, two methods of epsilon constraint and multi-objective gray wolf optimization (MOGWO) algorithm have been applied. The results of comparisons between the two mentioned methods show that MOGWO reduce the average solving time from about 1300 seconds to 88 seconds. In the last step of this research, in order to show the application of the proposed mathematical model and the method of solving the research problem, it was implemented in the supply chain of Dalan Kouh diary product and the Pareto optimal solutions were analyzed.

Waste collection routing problem: A mini-review of recent heuristic approaches and applications

The waste collection routing problem (WCRP) can be defined as a problem of designing a route to serve all of the customers (represented as nodes) with the least total traveling time or distance, served by the least number of vehicles under specific constraints, such as vehicle capacity. The relevance of WCRP is rising due to its increased waste generation and all the challenges involved in its efficient disposal. This research provides a mini-review of the latest approaches and its application in the collection and routing of waste. Several metaheuristic algorithms are reviewed, such as ant colony optimization, simulated annealing, genetic algorithm, large neighborhood search, greedy randomized adaptive search procedures, and others. Some other approaches to solve WCRP like GIS is also introduced. Finally, a performance comparison of a real-world benchmark is presented as well as future research opportunities in WCRP field.

Carbon mechanism on sustainable multi-objective solid transportation problem for waste management in Pythagorean hesitant fuzzy environment

Waste management involved in various fields of global ecosystem that provides several positive effects for green environment and sustainable development. We devise a multi-objective solid transportation model of waste management problem in agriculture field and forest department for urban or rural development. Starting to end point of the problem covered by considering the objective functions as transportation cost, job opportunity and carbon emission. Carbon emission is restricted by the combination of several policies of carbon mechanism (carbon tax, cap-and-trade and offset policy). Various critical sitchs appear in such realistic process and uncertainty attached with related data. Here we prefer Pythagorean hesitant fuzzy environment to overcome deep uncertainty rather than single uncertainty. After that, we initiate a ranking approach to convert uncertain data into crisp data. To justify the appropriateness of the formulated model and to select the best policy of carbon mechanism, we study two industrial applications with various cases of such mechanism. To derive the Pareto-optimal solution of the problems, two fuzzy techniques, namely, fuzzy programming and Pythagorean hesitant fuzzy programming, are utilized here. Comparative study, model validation, sensitivity analysis, managerial insights and conclusions with future research scopes are outlined at last.

A novel model for sustainable waste collection arc routing problem: Pareto-based algorithms

Municipal solid waste (MSW) management is known as one of the most crucial activities in municipalities that requires large amounts of fixed/variable and investment costs. The operational processes of collection, transportation and disposal include the major part of these costs. On the other hand, greenhouse gas (GHG) emission as environmental aspect and citizenship satisfaction as social aspect are also of particular importance, which are inevitable requirements for MSW management. This study tries to develop a novel mixed-integer linear programming (MILP) model to formulate the sustainable periodic capacitated arc routing problem (PCARP) for MSW management. The objectives are to simultaneously minimize the total cost, total environmental emission, maximize citizenship satisfaction and minimize the workload deviation. To treat the problem efficiently, a hybrid multi-objective optimization algorithm, namely, MOSA-MOIWOA is designed based on multi-objective simulated annealing algorithm (MOSA) and multi-objective invasive weed optimization algorithm (MOIWOA). To increase the algorithm performance, the Taguchi design technique is employed to set the parameters optimally. The validation of the proposed methodology is evaluated using several problem instances in the literature. Finally, the obtained results reveal the high efficiency of the suggested model and algorithm to solve the problem.

Tackling the rich vehicle routing problem with nature-inspired algorithms

In the last decades, the classical Vehicle Routing Problem (VRP), i.e., assigning a set of orders to vehicles and planning their routes has been intensively researched. As only the assignment of order to vehicles and their routes is already an NP-complete problem, the application of these algorithms in practice often fails to take into account the constraints and restrictions that apply in real-world applications, the so called rich VRP (rVRP) and are limited to single aspects. In this work, we incorporate the main relevant real-world constraints and requirements. We propose a two-stage strategy and a Timeline algorithm for time windows and pause times, and apply a Genetic Algorithm (GA) and Ant Colony Optimization (ACO) individually to the problem to find optimal solutions. Our evaluation of eight different problem instances against four state-of-the-art algorithms shows that our approach handles all given constraints in a reasonable time.

On the periodic hierarchical Chinese postman problem

This paper presents a mathematical formulation and a heuristic approach for a new variant of the Hierarchical Chinese Postman Problem (HCPP). Indeed, we introduce the concept of periodicity, and we define and solve, for the first time, the Periodic-HCPP, denoted as P-HCPP. Given that the resulting integer programming model makes use of a big number of binary variables and given the extended time horizon considered, 30 days in our case, the problem is characterized by a high level of complexity. However, our developed heuristic is able to solve instances having up to 40 nodes, 520 arcs and 5 hierarchies, whereas a general-purpose solver like Gurobi was not able to provide solutions for instances having more than 10 nodes. While the collected results are very encouraging, we provide at the end of this paper a set of possible future extensions of this work.

Cost-benefit analysis of demolition waste management via agent-based modelling: A case study in Shenzhen

The economic instrument is an effective approach to encourage demolition contractors to conduct low-impact waste management. It is essential for project managers and decision-makers to better understand the cost-benefit of demolition waste (DW) management, to promote development of an effective waste management plan. This study explores the interactive dynamics and adaptive nature between stakeholders, where the cost-benefit of DW management is analysed through the agent-based modelling approach. Shenzhen, a leading city in China in the management of DW, was selected as the study area. It was revealed that if the traditional demolition method is adopted as the primary choice, the net benefit of demolition of buildings in the study case will reach -131.4 billion yuan, i.e. the cost will surpass the revenue. If the selective demolition method is widely used by demolition contractors, simulation results indicate that the net benefit will reach 33.3 billion yuan, an increase of 125.34%, compared to the situation in which the traditional demolition method is widely implemented. Based on the simulation, an optimal management framework for DW management stakeholders was constructed. The research results can provide a decision-making basis for the government and relevant departments to formulate DW management measures.

A sustainable medical waste collection and transportation model for pandemics

We are currently experiencing a critical period for the prevention and control of the COVID-19 pandemic. COVID-19 related waste is a threat to global public environmental health. Medical waste management during this pandemic is one of the major issues facing public service organizations such as municipalities, which is of great importance in terms of logistics, environment and social aspects. The discussion of logistics operations is related to the collection, transportation and disposal of waste, which imposes high expenses. Many methods have been applied to develop and improve waste management policies in the literature. Apart from these studies, very few researchers have improved vehicle operations in waste management considering environmental aspects and the possibility of outsourcing. In this paper, by examining the gaps in the field, we try to explain and formulate the sustainable medical waste management problem for pandemics. Finally, by designing several practical examples with different scales, we solve the problem using CPLEX solver, compare different conditions and discuss the practical implications using the sensitivity analysis of demand parameter.

Municipal waste management and electrical energy generation from solid waste: a mathematical programming approach

Purpose

Waste production and related environmental problems have caused urban services management many problems in collecting, transporting and disposal of waste. The purpose of this study is to design a new model for municipal waste collection vehicle routing problems with time windows and energy generating from waste. To this purpose, a bi-objective model is presented with the objectives of increasing the income of waste recycles and energy generation from waste and reducing emissions from environmental pollutants.

Design/methodology/approach

A bi-objective model is presented with the objectives of increasing income of recycles trade and energy generation and reducing emissions from environmental pollutants. Concerning the complexity of the model and its inability to solve large-scale problems, non-dominated sorting genetic algorithms and multi-objective particle swarm optimization algorithms are applied.

Findings

In this research, an integrated approach to urban waste collection modeling that coordinates the various activities of waste management in the city of Kermanshah and energy generation from waste are provided. Besides, this study calculates the criteria that show the environmental effects of municipal waste. The proposed model helps to collect municipal wastes in the shortest possible time in addition to reducing the total cost, revenues from the sale of recycled materials and energy production.

Originality/value

The proposed model boosts the current understanding of the waste management and energy generation of waste. The paper adds additional value by unveiling some key future research directions. This guidance may demonstrate possible existing and unexplored gaps so that researchers can direct future research to develop new processes.

A hybrid approach for selecting sustainable suppliers and determining order allocation based on interval type-2 fuzzy sets

Purpose

This study aims to create a model for defining the best supplier for a company and allocating order that considers sustainability criteria beyond the traditional selection criteria.

Design/methodology/approach

In this paper, sustainable supplier Selection and order allocation (SSS and OA) problem is managed based on a multiobjective linear programming (MOLP) model that incorporates sustainability dimensions. First, an interval type-2 fuzzy analytic hierarchy process (FAHP) method is applied for the main criteria and subcriteria to determine the weight of the selected criteria. Then, these values are used to convert the proposed MOLP model into a single-objective model.

Findings

The economic criterion (0.438) was the most important criterion for SSS in the agricultural machinery sector, followed by the social criterion (0.333) and the environmental criterion (0.229).

Practical implications

The results show that the proposed framework can be utilized by the agricultural machinery industry for SSS and OA.

Originality/value

The proposed framework provides to develop an integrated model by interval type-2 fuzzy sets for SSS and OA, taking into account the relationships between qualitative and quantitative evaluation criteria with different priorities. The validity of the developed model is confirmed by a case study of the agricultural machinery industry in Turkey.

A fuzzy optimization approach to the capacitated node-routing problem for municipal solid waste collection with multiple tours: A case study

In many countries, municipal solid waste management is considered a very important challenge, and the most relevant costs in this field are dedicated to the collection process. Therefore, this study aimed to propose a mathematical model with multiple depots and multiple intermediate facilities to minimize fixed and variable costs of waste collection. Intermediate facilities are used in the developed countries in their waste collection network, because these facilities reduce the long-term costs of waste management and increase the quality of the waste collection process. Also, in reality, the amount of waste generated per day is not deterministic, so, to cope with the issue of uncertainty in the amount of waste, a fuzzy optimization approach was considered. Furthermore, a system where vehicles that could collect the wastes in multiple tours, with a maximum number of tours for each vehicle, was also considered. Due to the high complexity of this model, a genetic algorithm was elaborated. Further, the efficiency of the proposed algorithm was confirmed by comparison with the exact solution in small dimensions. It should be noted that the initial solution of this algorithm was obtained by a proposed heuristic algorithm. Finally, a case study on the vehicle routing of municipal solid waste was conducted in a district of Tehran, Iran. Moreover, the solutions of the model were validated by comparing the results of the proposed model and the current real-life situation. The contractors could improve vehicle routes and reduce costs by implementing the results of the proposed model, without any additional cost.

A robust green location-allocation-inventory problem to design an urban waste management system under uncertainty

The excessive expansion of cities and the consequent increase in urban population, especially in recent years, have led to a significant increase in consumptions and generations of different types of municipal solid waste (MSW). In this research, a robust green location-allocation-inventory problem (LAIP) is investigated to design an efficient MSW management system. Since the exact amount of MSW composition in different regions is not known and is uncertain, robust optimization technique is applied to formulate the problem as a mixed-integer linear programming (MILP) model. The objective is to minimize the total cost including fixed locational costs of collection and processing/disposal facilities, operational costs of facilities, transportation costs, penalty costs of non-collected waste and costs arising from pollution emissions. The validation of the proposed model is performed by different problems based on real-life data in deterministic and uncertain conditions using CPLEX solver of GAMS software. Then, the effects of greenness are evaluated by performing a sensitivity analysis on the parameter of pollution cost. Finally, the concept of robustness worthiness threshold (RWT) under budget constraint is introduced and discussed.

A Chance-Constrained Vehicle Routing Problem for Wet Waste Collection and Transportation Considering Carbon Emissions

In order to solve the optimization problem of wet waste collection and transportation in Chinese cities, this paper constructs a chance-constrained low-carbon vehicle routing problem (CCLCVRP) model in waste management system and applies certain algorithms to solve the model. Considering the environmental protection point of view, the CCLCVRP model combines carbon emission costs with traditional waste management costs under the scenario of application of smart bins. Taking into the uncertainty of the waste generation rate, chance-constrained programming is applied to transform the uncertain model to a certain one. The initial optimal solution of this model is obtained by a proposed hybrid algorithm, that is, particle swarm optimization (PSO); and then the further optimized solution is obtained by simulated annealing (SA) algorithm, due to its global optimization capability. The effectiveness of PSOSA algorithm is verified by the classic database in a capacitated vehicle routing problem (CVRP). What's more, a case of waste collection and transportation is applied in the model for acquiring reliable conclusions, and the application of the model is tested by setting different waste fill levels (WFLs) and credibility levels. The results show that total costs rise with the increase of credibility level reflecting dispatcher's risk preference; the WFL value range between 0.65 and 0.75 can obtain the optimal solution under different credibility levels. Finally, according to these results, some constructive proposals are propounded for the government and the logistics organization dealing with waste collection and transportation.