Waste Collection Problem Research Articles

Multi-compartment waste collection vehicle routing problem with bin washer

This paper introduces a new variant of the Multi-Compartment Waste Collection Problem (MC-WCP), which we call the MC-WCP with Bin Washer (MC-WCP-BW). The problem involves a fleet of compressed refuse collection trucks equipped with a bin washer, which compresses each waste type in the corresponding compartment of the vehicle. The vehicles can also wash the bins. Separation sites and water refilling stations are considered in the problem. A subset of the bins must be washed when emptied, which is not mandatory for the others. The problem is modeled using a mixed-integer linear programming formulation incorporating multiple visits to separation sites and water refilling stations. An efficient Hybrid Variable Neighborhood Search (HVNS) algorithm is developed and evaluated on a set of instances from the literature and newly generated instances. Computational experiments show that our proposed algorithm can identify solutions of better quality in a shorter computational time, as compared with the current state-of-the-art algorithms. The potential benefits of the compaction operation under several scenarios are also examined via extensive analyses.

A bi-objective optimization model for waste collection problem under risk considering superior technology in waste refining and recycling: a case study

A bi-objective optimization model for waste collection problem under risk considering superior technology in waste refining and recycling: a case study

Smart Bin and IoT: A Sustainable Future for Waste Management System in Nigeria

As waste production is becoming more widely recognized as a significant issue, particularly in developing nations, rising food consumption and population growth have caused environmental degradation and health crises. Nigeria's waste collection and disposal problems are commonly associated with environmental pollution and health crises. Consequently, throwing garbage on roads during environmental sanitation, is a typical method of waste disposal, leading to large piles of refuse along the roadside, which hinders the nation's beauty. Therefore, for Nigeria's waste management system to be effective, sustainable smart bins with efficient Internet of Things (IoT) applications must be quickly adopted to create a green, clean atmosphere within cities. Smart bins with integrated IoT can provide a sustainable future for cities' waste management. This model seeks to develop a low-cost, intelligent waste bin system with IoT technology. Sensors and data sharing over a Wi-Fi network, allow for remote control of the waste bin, leading to improved optimization of the bin's level of waste. The economic benefits of this IoT-based system include remote access for efficient level control, lower labour costs, improved time and energy efficiency, and reduced congestion in waste bins.

A multi-day waste collection and transportation problem with selective collection and split delivery

This paper addresses a multi-day waste collection and transportation problem with selective collection and split delivery (MDWCTP-SCSD). Rather than emptying waste according to a fixed schedule, garbage trucks only visit community waste collection sites whose waste level reaches a predetermined threshold, reducing collection costs but increasing overflow risk. Collection sites likely to overflow the next day if unvisited today are also selected for preventive collection. Split delivery is permitted. The MDWCTP-SCSD is formulated as a mixed integer linear programming model. To solve this NP-hard problem, a heuristic combining variable neighborhood search and simulated annealing is proposed. In addition to classical operators, three new operators are developed to handle split delivery. Experiments demonstrate the proposed algorithm is competitive in solving two split delivery vehicle routing benchmark sets; outperforms CPLEX in solving small-scale instances within an hour; and quickly solves large-scale instances with high quality. Noteworthy findings indicate fleet costs and size can be reduced by approximately 50% if only visiting sites whose waste level reaches 80%.

Local Search-Based Metaheuristic Methods for the Solid Waste Collection Problem

The solid waste collection problem refers to truck route optimisation to collect waste from containers across various locations. Recent concerns exist over the impact of solid waste management on the environment. Hence, it is necessary to find feasible routes while minimising operational costs and fuel consumption. In this paper, in order to reduce fuel consumption, the number of trucks used is considered in the objective function along with the waste load and the travelling time. With the current computational capabilities, finding an optimal solution is challenging. Thus, this study aims to investigate the effect of well-known metaheuristic methods on this problem’s objective function and computational times. The routing solver in the Google OR-tools solver is utilised with three well-known metaheuristic methods for neighbourhood exploration: a guided local search (GLS), a tabu search (TS), and simulated annealing (SA), with two initialisation strategies, Clarke and Wright’s algorithm and the nearest neighbour algorithm. Results showed that optimal solutions are found in faster computational times than using only an IP solver, especially for large instances. Local search methods, notably GLS, have significantly improved the route construction process. The nearest neighbour algorithm has often outperformed the Clarke and Wright's methods. The findings here can be applied to improve operations in Saudi Arabia’s waste management sector.

Solving a real-life multi-period trailer-truck waste collection problem with time windows

By introducing local recovery networks, regional and local environmental authorities can play an important role in facilitating the circular economy. This paper studies a network design problem that encompasses the recovery of separately collected household waste streams, which are collected in containers at civic amenity sites. We formulate a generic tactical-operational container collection problem that will be solved using a mixed integer linear programming approach. This paper makes both a theoretical and practical contribution. We are the first to study a two container vehicle capacity restriction combined with collection site inventory capacities, time windows, shift break time, and shift duration constraints. The model is applied to a number of real-life test instances and scenarios. The results provide insight in how different combinations of scenarios exactly affect the fleet requirements. This not only includes the number of trucks or information under which circumstances an additional truck would (not) be needed, but also the 12-day collection schedule for the collection crews.

A logic-based Benders decomposition solution approach for two covering problems that consider the underlying transportation

We investigate two maximal covering location problems with capacity restrictions, minimum workload, and transportation. The problems are inspired by a waste collection problem in which large waste containers are scattered throughout the municipality, and the residents bring their waste to these containers. We take the residents’ preferences into account when allocating them to locations. When a container is full, a vehicle transports an empty container from the disposal facility (depot) to that location and replaces it. We propose a mixed-integer linear programming formulation for the problems in which vehicles can carry one or two containers, and apply a logic-based Benders decomposition approach for the latter. Here, the sub problem is a multi-period minimum weight perfect matching problem. We show that our logic-based Benders decomposition approach outperforms the direct formulation in terms of solution quality and speed. We further show that transportation of two containers at a time reduces the distance to be driven by 29.5% on average, without compromising the covering level. Furthermore, we analyze the effect of imposing a minimum workload as well as the effect of changing the focus between transportation and covering.

A decision support mechanism in the determination of organic waste collection and recycling center location: A sample application for Turkiye

The location problem of organic waste collection and recycling plants is a significant concern for policy makers in developing countries. Factors such as rapid population growth, unplanned urbanization, and limited resources pose challenges to the efficient management of organic waste. Inadequate waste disposal practices not only endanger the environment and human health but also hinder the development of sustainable and livable cities. Therefore, it is crucial to address the specific challenges associated with locating organic waste collection and recycling facilities to ensure effective waste management and promote environmental sustainability. This study aims to identify the most appropriate location for an organic waste collection and recycling facility for compost production. This study involves identifying the most influential aspects of organic waste collection and recycling center location selection through a literature review and expert opinion. The Analytical Hierarchy Process (AHP) methodology with fuzzy interval type-2 (T2F) sets is employed to weight the criteria. The COmbinative Distance-based Assessment (CODAS) approach is extended in a T2F environment to better reflect the uncertainty and evaluate the alternative locations. The study concludes by revealing the optimal location for the food waste collection center for Trabzon city in Turkiye. To verify the validity and reliability of the proposed integrated methodology, a comparative analysis was also performed using various decision-making methods.

Waste Collection Optimisation: A Path to a Green and Sustainable City of Makkah

Background: Saudi Arabia is a leading country endorsing a sustainable future, from policymaking and investment to infrastructure development. One of the rising concerns in Saudi Arabia's Vision 2030 is solid waste management, especially in Makkah. The Solid Waste Collection Problem (SWCP) refers to the route optimisation of waste collection trucks visiting containers across various locations. Manually generated routes might contain some mistakes, and constructing and revising designed solutions can take a long time. Thus, there is a need to find optimal and fast solutions to this problem. Solving this problem demands tackling numerous routing constraints while aiming to minimise the operational cost. Since solid waste has a significant impact on the environment, reducing fuel consumption must be an objective. Methods: Thus, a mixed-integer programming model is proposed in this paper while using the time-oriented nearest neighbour heuristic. The goal is to investigate their performance on nine existing instances of SWCP in the city of Makkah. The proposed model is implemented in the Gurobi solver. The time-oriented nearest neighbour heuristic constructs the initial solution and is then re-optimised using Google OR-tools. Results: Using the greedy method to construct a solution for this problem generated better solutions when compared to the results obtained without the greedy method. Computational times are also improved by 55.7% on the problem instances. Conclusions: The findings confirm the competitive performance of the proposed method in terms of computational times and solution quality.

The economic and environmental impacts of shared collection service systems for retired electric vehicle batteries

One of the impending consequences of the rapid penetration of electric vehicles (EVs) is that a substantial amount of expired EV batteries will present an increasing waste collection and management problem, particularly in the urban context. Motivated by a lack of research on this issue, this paper comprehensively evaluates the relative benefits of shared versus non-shared collection systems, where the service outlets are not exclusive to specified automakers. Using a mixed-integer optimization model, the analysis features spatiotemporal and multiple stakeholder complexities. Based on the historical monthly EV sales data from 2016 to 2021, a representative case study of Beijing, China is conducted, including 16 district centers, 32 major automobile manufacturers, 153 collection service outlets and 4 disposal centers. The results show that a shared collection service system leads to higher profitability, higher collection rates, increased environmental benefits and improved facility utilization. Consequently, this research contributes to supply chain liberalization to foster the efficient waste management of EV batteries. With a further model extension, it can also provide decision support for the policy-making of more countries.

Dynamic multi-period vehicle routing with touting

This paper introduces a dynamic multi-period vehicle routing problem with touting as demand management technique, where customers that have not yet placed an order can be actively encouraged to order a service sooner. Touting the right customers, such as those located nearby customers who already placed orders, allows for more efficient routes over time. However, it also increases the frequency of visits at such touted customers as they are serviced before they would normally require, which leads to smaller demand volumes per visit. To tackle this trade-off, we propose several strategies to decide which customers to tout and when, using the characteristics of the customers as well as the current plan at the time of touting. Specifically, using the demand and the location information, we approach the ones which are close to the current tour, relatively far from the depot and not likely to easily be covered in the near future. This information is then used as a part of different touting strategies, which are further embedded in a rolling-time horizon vehicle routing algorithm to address the multi-period nature of the problem. These different strategies are empirically compared in a simulation based on a real-world waste collection problem. We demonstrate that touting indeed allows to significantly reduce the travel distance in a dynamic vehicle routing problem.

Capacitated Waste Collection Problem Solution Using an Open-Source Tool

Population in cities is growing worldwide, which puts the systems that offer basic services to citizens under pressure. Among these systems, the Municipal Solid Waste Management System (MSWMS) is also affected. Waste collection and transportation is the first task in an MSWMS, carried out traditionally in most cases. This approach leads to inefficient resource and time expense since routes are prescheduled or defined upon drivers’ choices. The waste collection is recognized as an NP-hard problem that can be modeled as a Capacitated Waste Collection Problem (CWCP). Despite the good quality of works currently available in the literature, the execution time of algorithms is often forgotten, and faster algorithms are required to increase the feasibility of the solutions found. In this paper, we show the performance of the open-source Google OR-Tools to solve the CWCP in Bragança, Portugal (inland city). The three metaheuristics available in this tool were able to reduce significantly the cost associated with waste collection in less than 2 s of execution time. The result obtained in this work proves the applicability of the OR-Tools to be explored for waste collection problems considering bigger systems. Furthermore, the fast response can be useful for developing new platforms for dynamic vehicle routing problems that represent scenarios closer to the real one. We anticipate the proven efficacy of OR-Tools to solve CWCP as the starting point of developments toward applying optimization algorithms to solve real and dynamic problems.

Concept of Mobile Application for Organization of Separate Waste Collection

The article presents the results of devising a concept for organization of separate waste collection at the household level using special software. The problem of waste collection and disposal at the local level in Russian regions is substantiated. All key characteristics of the problem area are defined and the basic concepts of functional capabilities of a respective mobile application are formulated using divergence, synthesis, analysis, transformation and convergence methods. The primary concept defines the following elements: accessibility of waste collection areas depending on their type; awareness of waste content and their preparation and disposal methods in household conditions; involvement of people of various age into the waste preparation and disposal process in a game format.

A hybrid firefly and particle swarm optimization algorithm with local search for the problem of municipal solid waste collection: a real-life example

A hybrid firefly and particle swarm optimization algorithm with local search for the problem of municipal solid waste collection: a real-life example

A memetic algorithm for solving rich waste collection problems

Inspired by a real-life case in the Brussels Capital Region, Belgium, this paper provides a Memetic Algorithm with Sequential Split procedure (MASS) for solving a large variety of waste collection problems with multiple depots, a restricted vehicle fleet at each depot, multiple (intermediate) processing facilities, capacity restrictions per processing facility and partial pick-ups. MASS first generates satisfactory initial solutions which are feasible w.r.t. the shift duration and the vehicle capacity using a novel procedure in which (1) a giant tour is split into vehicle routes, (2) intermediate processing facilities are introduced and (3) waste pick-ups can be split further if profitable. Second, MASS improves these solutions through local search. New test instances are created, which are used to evaluate the performance of MASS’s components. We show that MASS provides high-quality feasible solutions by comparing MASS with an exact approach on a small example. Furthermore, MASS is tested on existing instances for the multiple-depot vehicle routing problem (MDVRP), (multi-depot) vehicle routing problem with intermediate facilities ((MD)VRPIF) and the multi-depot vehicle routing problem with inter-depot routes (MDVRPI). MASS shows competitive results for the MDVRP, the VRPIF and the MDVRPI. For the MDVRPIF, MASS obtains better results than those currently found in the literature. To assess its practical value, MASS is used to solve a real-life waste collection problem in the Brussels Capital Region in which alternative scenarios for municipal bio-waste collection and treatment are compared.

Plug-in hybrid electric refuse vehicle routing problem for waste collection

Commercial waste collection is an essential service requiring efficient and reliable provision for customers. At the operational level, one of the most challenging problems is to design a set of refuse vehicle routes to collect waste from a set of bins. To be used multiple times, these vehicles must be emptied regularly throughout the day. This paper investigates a waste collection problem with a homogeneous fleet of plug-in hybrid electric refuse vehicles powered by two different power sources, i.e., electricity and compressed natural gas (CNG). In addition, realistic fuel consumption functions are used to estimate total energy requirements for each type of fuel, including refueling and recharging, and the detailed energy consumption along the path between two nodes of interest. We propose a Hybrid Threshold Acceptance (HTA) algorithm for this problem and denote it as the Hybrid Waste Collection Problem (HWCP). Extensive computational experiments confirm that the proposed HTA algorithm provides good results against current state-of-the-art algorithms designed for the electric vehicle routing problem. Out detailed computational results demonstrate the performance of our method considering either full or partial recharging, as well as the effect of different battery/tank capacities. Compared to the standard CNG or electric vehicles, we also show the benefits of using a fleet of hybrid electric refuse vehicles in terms of operational costs and total distance traveled.

Region-Focused Memetic Algorithms With Smart Initialization for Real-World Large-Scale Waste Collection Problems

Memetic algorithm (MA) is widely applied to optimize routing problems as it provides one way to combine local search with global search. However, the local search in MA needs to be carefully designed according to the problem’s characteristics. In this article, we consider a real-world large-scale waste collection problem with multiple depots, multiple disposal facilities, multiple trips, and working time constraints. Vehicles with a limited capacity and working time can start from different depots, collect waste at different sites, and make multiple trips to different disposal facilities to empty the waste and return to its origin. While the existing work considered problems with multiple trips and time constraints, none have tackled problems with multiple depots, multiple disposal facilities, multiple trips, as well as working time constraints. The change from “single-depot” to “multidepot” not only reflects better the situation in real life but also leads to a qualitative different and more complex problem. In this article, we first model this complex problem mathematically. Then, a novel region-focused MA is proposed to tackle this new challenge. Compared to classic MA, this region-focused one is enhanced by two major components: 1) a new heuristic-assisted solution initialization algorithm and 2) a region-focused local search with novel heuristics. Comprehensive computational studies show that our proposed approaches significantly outperform several state-of-the-arts on our real problem of thousands of tasks. The new local search procedure and solution initialization method significantly improve the search ability in combination with global search ability of MA.

Arc routing with trip-balancing and attractiveness measures — A waste collection case study

This work focuses on a household (or door to door) waste collection problem in the Portuguese municipality of Seixal, which is modelled as a generalisation of a mixed capacitated arc routing problem (MCARP). The MCARP is known to be NP-hard. The proposed methodology uses: i) a GIS (geographic information system), for input/output and to reduce problem dimensions; ii) a matheuristic that iteratively solves a new hybrid model; and iii) two versions of a two-phase matheuristic. The latter pursues the generation of connected and compacted trips. During the first phase, called initial assignment, some links requiring service are assigned to vehicle services. In the second phase, the hybrid model finishes the assignment and generates a feasible set of trips. The quality of the generated solutions is assessed through the total time, as well as by some attractiveness measures. These measures evaluate the adequacy of the solutions for the real case-study, a crucial aspect for trips that need to be accepted by practitioners. With this purpose, a new attractiveness measure that introduces space dependent penalisation of overlaps, named weighted hull overlap, is also proposed. Computational results with 18 Seixal instances, with 265–1223 nodes and 492–2254 links, point to the good performance of the proposed methodology.

Optimization of Waste Collection and Transportation in Ratmalana Area in Sri Lanka

At present solid waste management has become a national concern where all the local governments, provincial governments, and the central governments seek for sustainable solutions. Much of the Municipal Solid Waste (MSW) management cost in Sri Lanka is allocated for waste collection and transportation rather than for waste disposal and treatment. The objective of this study is to minimize the total cost of solid waste collection and transportation. This enables a direct reduction in the fuel cost, labour cost, emission due to transportation which results in a significant reduction in the total cost of MSW management. The waste collection process was studied in Ratmalana area, which is a suburb in Colombo District in the Western Province of Sri Lanka, where many households and industries are located, and the factories discharge raw untreated waste into open drains leading to natural waterways or low-lying lands. The current practice of waste collection and transportation refers to collecting all the waste from households and factories and other institutions to the disposal station at Karadiyana, Pilyandala using four tractors along the settled routes. The study mainly relies on secondary data in relation to collected and transported quantities of waste by date, by four tractors in the month of September 2020. Linear programming (LP) techniques were applied to route optimization to reduce the daily total traveling distance of waste collection and transportation. Compared to the existing solid waste collection and transportation process from sources to the collection center along six (6) roads in Ratmalana area, the proposed heuristic solution for solid waste collection and transportation problem will significantly be reduced the daily average transporting distance from 86.29 km to 55.06 km with a reduction of 31.23 km (decrease in total distance is 36.2%). As a result, it is required only three (3) tractors (with 3,000 kg capacity) instead of 4 carriers deployed currently and the labour requirement will be reducing from 12 labours to 9 labours. In addition to those advantages of the proposed LP model, the number of vehicles reduced from 4 to 3 on a daily basis. The application of LP techniques will provide many advantages in terms of minimizing number of vehicles deployed, fuel cost, vehicle maintenance cost and labour cost and reduction in emission with the optimization of the resource utilization.
 Keywords: Waste, Collection, Transportation, Linear programming

Optimisation of sustainable urban recycling waste collection and routing with heterogeneous electric vehicles

Solid waste management faces increasing challenges due to rapid urbanisation and population growth. In addition, the different types of waste sources are scattered in different geographical regions within the city increases the transportation and collection costs, as well as increases emissions. Therefore, a cost-effective and environmentally friendly solution can be found to optimise waste collection procedures and transportation operations. This study introduces the electric waste collection problem (EWCP) in which a heterogeneous fleet of electric vehicles has to be assigned to carry out a number of visits to the places where the waste bins are located. This problem is a generalisation of the well-known vehicle routing problem. We consider multiple types of wastes, time windows, multi-compartment, split deliveries, and waste bin-vehicle compatibility. This paper aims to optimise waste collection and transportation operations in a sustainable way. We mathematically formulate the problem as a mixed-integer programming (MIP) model and develop an adaptive variable neighbourhood search (AVNS) to solve the EWCP efficiently. We have generated a new instance set for the problem based on the real-life case study and conducted extensive computational experiments with our extended heuristic. The results indicate that the AVNS is highly effective compared to the MIP model with small-size instances. Our obtained results suggest that using an electric vehicle fleet in waste collection operations will help reduce the total travel costs and harmful gas emissions. • A new electric waste collection problem with multiple types of wastes is addressed. • Constraints such as split deliveries and bin-vehicle compatibility are considered. • An adaptive variable neighbourhood search heuristic is offered to solve the problem efficiently. • Extensive computational experiments are conducted for several insights. • The impact of the fleet mix and traffic congestion are also explored in the study.