Collection Vehicle Routing 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.

Using Simulated Annealing to Solve the Multi-Depot Waste Collection Vehicle Routing Problem with Time Window and Self-Delivery Option

This research introduces the Multi-Depot Waste Collection Vehicle Routing Problem with Time Windows and Self-Delivery Option (MDWCVRPTW-SDO). The problem comes from the waste bank operation implemented in Yogyakarta City, Indonesia. A set of vehicles is dispatched from the waste banks to pick up waste from residents’ locations within the time windows specified by the residents. Residents may be compensated for delivering their waste to a waste bank by themselves. The objective of MDWCVRPTW-SDO is minimizing the sum of investment costs, routing costs, and total compensation paid to the residents. We model this problem as a mixed integer linear programming model and propose Simulated Annealing (SA) as an effective solution approach. Extensive computational experiments confirm that SA is effective to solve MDWCVRPTW-SDO. Moreover, the number of waste banks, compensation paid to residents, and the distribution of residents of each type are crucial for the success of the implementation.

A hybrid approach to solve a raw material collecting vehicle routing problem

PurposeThe purpose of this study is to formulate a new class of vehicle routing problem with an objective to minimise the total cost of raw material collection and derive a new approach to solve optimization problems. This study can help to select the optimum number of suppliers based on cost.Design/methodology/approachTo model the raw material vehicle routing problem, a mixed integer linear programming (MILP) problem is formulated. An interesting phenomenon added to the proposed problem is that there is no compulsion to visit all suppliers. To guarantee the demand of semiconductor industry, all visited suppliers should reach a given raw material capacity requirement. To solve the proposed model, the authors developed a novel hybrid approach that is a combination of block and edge recombination approaches. To avoid bias, the authors compare the results of the proposed methodology with other known approaches, such as genetic algorithms (GAs) and ant colony optimisation (ACO).FindingsThe findings indicate that the proposed model can be useful in industries, where multiple suppliers are used. The proposed hybrid approach provides a better sequence of suppliers compared to other heuristic techniques.Research limitations/implicationsThe data used in the proposed model is generated based on previous literature. The problem derives from the assumption that semiconductor industries use a variety of raw materials.Practical implicationsThis study provides a new model and approach that can help practitioners and policymakers select suppliers based on their logistics costs.Originality/valueThis study provides two important contributions in the context of the supply chain. First, it provides a new variant of the vehicle routing problem in consideration of raw material collection; and second, it provides a new approach to solving optimisation problems.

An Improved NSGA-II for Energy-oriented Hot Rolling Production Scheduling Problems

With the sharply increasing energy demand and the shortage of energy, the scheduling goal of hot rolling production (HRP) is to select and sequence slabs to reduce cost and energy consumption. A multi-objective HRP optimal scheduling model is formulated and transferred as a multi-objective prize collecting vehicle routing problem (MO-PCVRP) with special constraints that simultaneously minimizes the jump cost of width, thickness, and hardness between adjacent slabs, the total length of unselected slabs, and the energy cost. Furthermore, this paper introduces a greedy strategy to intensify the local search of the standard NSGA-II algorithm, and names it local-intensification NSGA-II (LI-NSGA-II). The comparison experiments show that the LI-NSGA-II algorithm obtains better comprehensive performance than other compared algorithms in solving multi-objective scheduling problems of HRP.

AN ANALYSIS OF THE UTILISATION OF LANDFILL FACILITIES IN A WASTE COLLECTION VEHICLE ROUTING PROBLEM

Recent studies have addressed several constraints to ensure the effective applicability of numerous models. However, an exclusive model meant for landfill utilisation for landfill facilities is still untapped. As such, this paper proposes a new constraint faced by landfill facilities to ensure a more comprehensive model and its applicability to real-life issues. This idea is influenced by three main reasons. First, the lifespan of selected landfills is bound to decrease due to the increasing density of waste transported to landfills daily. The second reason is abandoning the available landfill sites due to inappropriate locations. Lastly, the government would need to bear high costs in establishing more landfills to accommodate the annually increasing waste. Hence, the Nearest Greedy method was applied to a benchmark problem of Waste Collection Vehicle Routing Problem (WCVRP) to construct feasible initial routes by considering the following three scenarios of landfills: (1) without capacity priority, (2) balance capacity priority, and (3) imbalance capacity priority. This study highlights the importance of using all available landfills to unload waste for the WCVRP. Weighing in this additional constraint may increase the life expectancy of the available landfill sites. However, the computational findings revealed that utilising all available landfill facilities (scenarios 2 and 3) can significantly increase the total distance travelled and the number of vehicles used compared to Scenario 1.

Modeling and Optimization of Multi-Model Waste Vehicle Routing Problem Based on the Time Window

With the development of China's economy, the urban floating population is also increasing, resulting in a sharp increase in the amount of urban waste. How to recycle and dispose of municipal waste more efficiently has become the top concern of municipalities and other relevant departments. In this article, the above problem is transformed into the municipal waste collection vehicle routing problem (MWCVRP) to solve the problem with the minimum total waste transportation cost. Because the carrying capacity of different models is different, this article introduces a cost calculation criterion that combines the total mileage of different models of transport vehicles and the number of station services. A multi-model garbage truck path optimization model is established, and then a heuristic-based task dynamic assignment algorithm is designed to solve the problem. The Solomon dataset is used to verify the feasibility and effectiveness of the model and algorithm through experiments.

Waste Collection Vehicle Routing Problem with TimeWindows for Route Optimization of Garbage Transport Vehicles

The waste collection vehicle routing problem with time windows is an optimization problem on the route of the waste transport vehicle which aims to determine the route of the vehicle by considering the travel time and windows. Garbage transport vehicles transport garbage in several work areas. This affects the optimal time and distance. The working hours of the garbage collectors are divided into two parts. The first working hours are 07.00 - 11.00 West Indonesian Time (WIT) and 16.00-20.00 WIT. The cleaning staff has a break of 5 hours. This study aims to optimize the route of waste transportation vehicles in the problem of transporting waste so as to minimize travel time and distance. Waste collection vehicle routing problem with time windows on determining the route of a garbage transport vehicle which is simulated on the problem of transporting garbage in the city of Palembang. The waste collection vehicle routing problem with the time windows model is solved with an exact approach using LINGO software. The results of this study indicate that the proposed optimization model provides optimization of the route of the garbage transport vehicle, the total travel time, and break time of the cleaning staff.

Designing a sustainable logistics network for hazardous medical waste collection a case study in COVID-19 pandemic

The process of collecting and transporting hazardous medical waste poses a potential threat to the environment and public safety. Furthermore, the waste management system faces higher transportation costs due to the increasing human activities related to rapid population growth. The absence of an efficient and safe logistics network for the timely collection and transportation of hazardous wastes may have negative effects on the environment and public health. Therefore, more sustainable transportation of hazardous waste services is a necessity This paper attempts to design a sustainable network for hazardous medical waste collection services during the COVID-19 pandemic. An electric medical waste collection vehicle routing problem is introduced to construct optimal routes and rosters for a fleet of electric vehicles as well as cover their choice of charging technologies, times and locations. This problem allows us to minimize the health risk of hazardous medical waste while providing cost-effective, zero-emission waste management logistics. Therefore, this problem covers environmental and economic objectives to achieve sustainable development. An effective heuristic that covers adaptive large neighbourhood search and a local search is designed to deal with the complex problem. A series of extensive computational experiments is carried out using real-life benchmark instances to assess the performance of the algorithm. A sensitivity analysis is also conducted to investigate the effect of multiple charger types on the cost and risk objectives. The experiment results indicate that mixed-use of different charger types can reduce the total energy cost and transport risk compared to the case of using only a single charger.

The aquaculture service vessel routing problem with time dependent travel times and synchronization constraints

This paper studies the Aquaculture Service Vessel Routing Problem (ASVRP), which is an important planning problem arising in sea-based fish farming. In the ASVRP, there is a set of fish farms located in the sea, where each fish farm has one or more service tasks to be performed by a given heterogeneous fleet of service vessels with different capabilities. Some service tasks require simultaneous operation of more than one vessel and might also have time windows and precedence requirements. Furthermore, varying weather conditions make the sailing times and the service times of the tasks time dependent. The objective of the ASVRP is to maximize the value of the service tasks performed within a given planning horizon. We propose a time discrete optimization model for the ASVRP, formulated as a time dependent, prize collecting vehicle routing problem with synchronization constraints and time windows. Furthermore, we present an Adaptive Large Neighborhood Search (ALNS) heuristic for solving the problem. Results on a number of test instances based on real world data show that both the ALNS heuristic and a commercial solver are able to find high quality solutions for small problem instances, while the ALNS heuristic is superior when the problem size increases.

A novel bi-objective credibility-based fuzzy model for municipal waste collection with hard time windows

In this study, a bi-objective vehicle routing mathematical model was proposed, in which the main objectives were minimizing the total economic cost, as well as the whole time for municipal waste collection. The considered waste collection network included bins, multiple depots, multiple heterogeneous vehicles, multiple intermediate facilities, and a landfill. Besides, in each day, municipal waste was collected several times in various hard time windows by heterogeneous vehicles, which all used vehicles could trip more than once per time window. It should be noted that the amount of waste generated was uncertain, which a fuzzy credibility theory was used to cope with this uncertainty. The exact solutions of some small problems were generated by the augmented ε-constraint method. Due to the complexity of this problem in more substantial sizes, the metaheuristic of Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used. Since the performance of metaheuristic algorithms is quite sensitive to their parameters, we used the self-adaptive method to tune the parameters of NSGA-II and also compared the results with the Taguchi method. The initial solutions of the metaheuristic approach were generated by a novel heuristic algorithm. Also, the waste collection vehicle routing problem of one of Tehran’s regions was solved by the proposed model. After solving this model, the presented Pareto optimal solutions showed the total economic cost and the total time of the waste collection were improved by about 1.4% and 1.1%, respectively, which means a significant reduction, due to the high volume of costs and time.

Solving practical waste collection with time windows in an urban area

In logistics, several algorithms can be implemented in order to solve the problems of the vehicle routing with variants in order to find near-optimal solutions. Waste Collection can be considered as an essential logistic activity within any area or city. This kind of paper is aimed to implement Iterated greedy (IG) and Adaptive Large Neighborhood Search (ALNS) to solve waste collection vehicle routing problem with time windows on a real-case study. The idea is to generate an efficient way to collect waste problems in an area located in Riyadh, Saudi Arabia. Moreover, generating a route plays a significant role in terms of serving all customers’ demands who have own different time windows of receiving goods. Also, the performance of the proposed algorithms according to all instances is examined and minimizing the total costs and meeting all constraints that related to capacity, time windows, and others. To evaluate the execution of the presented algorithms, the computational results showed essential improvements, and also ALNS algorithm generates reasonable solutions in terms of total costs and a reasonable amount of time, when compared to other algorithms. Â

Optimizing triple bottom-line objectives for sustainable health-care waste collection and routing by a self-adaptive evolutionary algorithm: A case study from tehran province in Iran

The methods to collect and transport various types of medical waste are vital to the management of healthcare waste. The lack of a safe plan for the timely collection and transportation of such wastes may have an undesirable impact on the environment and public health. The present research addresses a real-life healthcare waste collection vehicle routing problem (HWCVRP) for small medical centers in Iran that produce minor amounts of waste and lack on-site facilities to treat wastes of various kinds. This problem involves social, environmental, and economic objective functions, aiming to achieve sustainable development. The social objective function is optimized based on a novel definition of risk in medical waste collection and designed to reduce public health risk by minimizing the time of waste collection from centers that produce a larger volume of more hazardous waste. Furthermore, the present paper provides a comprehensive estimate of fuel consumption in vehicles to be accurately minimized to diminish the environmental hazards involved in sustainable transportation. Also, the economic objective function is set to minimize the variable and fixed costs of transportation. A self-adaptive multi-objective evolutionary algorithm is generated with numerous effective operators to solve the model. Specific metrics are employed to compare the performance of the proposed meta-heuristic algorithm with those of multiple other evolutionary algorithms. The results reveal the effectiveness of the proposed method in reaching high-quality non-dominated solutions. Ultimately, a real-life case study is used to implement the proposed approach and to evaluate its performance.

Optimization and profit distribution in a two-echelon collaborative waste collection routing problem from economic and environmental perspective

In order to reduce waste collection costs and realize sustainable urban development, this paper investigates a two-echelon collaborative waste collection vehicle routing problem (2E-CWCVRP), considering the cooperation and profit distribution between participants in the collection network. An optimization model for 2E-CWCVRP with the aim to minimize total costs and carbon emissions is constructed. Then, a three-stage solution approach is developed to solve this model, including a k-means clustering for simplifying the problem, and a hybrid heuristic for searching the optimal vehicle routes based on Clarke & Wright algorithm and an adaptive large neighborhood search algorithm (CW-ALNS). Finally, an improved Shapley value model is constructed for determining the costs and carbon emissions reduction amount and the best alliance sequence of each participant. The experiment results indicate that: (1) the effectiveness of CW-ALNS algorithm is verified through the benchmark instances; (2) the costs and carbon emissions of the collection network could be reduced simultaneously after the implementation of cooperation; (3) constructing a large collection and transfer network is more efficient than dividing the network into several individual parts. Finally, different alliance sequences are analyzed from the economics and environment perspective and the best alliance sequences are determined.

Waste Collection Vehicle Routing Problem Model with Multiple Trips, Time Windows, Split Delivery, Heterogeneous Fleet and Intermediate Facility

Waste Collection Vehicle Routing Problem (WCVRP) is one of the developments of a Vehicle Routing Problem, which can solve the route determination of transporting waste. This study aims to develop a model from WCVRP by adding characteristics such as split delivery, multiple trips, time windows, heterogeneous fleet, and intermediate facilities alongside an objective function to minimize costs and travel distance. Our model determines the route for transporting waste especially in Cakung District, East Jakarta. The additional characteristics are obtained by analyzing the characteristics of waste transportation in the area. The models are tested using dummy data to analyze the required computational time and route suitability. The models contribute to determining the route of transporting waste afterward. The WCVRP model has been successfully developed, conducted the numerical testing, and implemented with the actual characteristics such as split delivery, multiple trips, time windows, heterogeneous fleets, and intermediate facilities. The output has reached the global optimal for both dummy and real data.

A novel mathematical model for municipal waste collection and energy generation: case study of Kermanshah city

PurposeWaste production and related environmental problems have caused urban services management many problems in collecting, transporting and disposal of waste. Since collecting and transporting waste are important parts of waste management budget, it is necessary to apply an appropriate method to reduce costs of collection. The aim of this study is to design a new model for urban waste collection vehicle routing problem with time windows and energy generating from waste.Design/methodology/approachA multiobjective model is presented with the objectives of minimizing cost of waste collection, reducing emissions from environmental pollutants and increasing income of recycles trade and energy recovery. Concerning the complexity of the model and its inability to solve large-scale problems, hyperactive genetic algorithms and multiple objective particle swarm optimization are applied.FindingsThe proposed model not only affects costs and income but also reduces the emissions of environmental pollutants. To solve the formulated model, multitarget approaches are applied on the processing site of Kermanshah city as a case study. The solutions of these algorithms and the exact method of partial constraints are compared and the outcomes are verified by numerical analysis by solving various examples in small, medium and large scales. The proposed model helps to collect urban wastes in the shortest possible time in addition to reducing the total cost, revenues from the sale of recycled materials and energy generation.Research limitations/implicationsWaste collection is related to VRP issue. Considering the environmental requirements in waste management, the concept of green supply chain is approached. This study provides a better understanding of urban waste management by examining various articles and combining economic and environmental dimensions. Waste management with a green approach and energy production from disposable waste involves many common stakeholders.Practical implicationsThis study suggests that proper waste management can generate revenue from municipal waste. Moreover, it reduces cost of collecting and emission of environmental pollutants. These findings could motivate companies to waste management with a sustainable approach.Originality/valueThe proposed model boosts the current understanding of the waste management and energy generation of waste. The paper adds additional values 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.

Using iterated greedy and randomized iterated greedy algorithms to solve urban area waste collection in Riyadh city

This paper addresses the real-life waste collection vehicle routing problem by applying Iterated Greedy (IG) and Randomized Iterated Greedy (RIG) in order to improve the processes. This kind of problem becomes more complex in developing countries in several aspects such as costs and fuel. Nowadays, the waste collection is considered as one of the interesting areas. There are three types of waste: commer-cial, residential and roll-on-roll-off. In this paper, we mainly consider the residential waste collection problem. The problem can be summa-rized as follows: a vehicle has to satisfy the demand at each customer location while satisfying the capacity of the vehicle for reducing the total cost. We report a case study that is related to waste collection in Riyadh, Kingdom of Saudi Arabia. To solve the case study problem, IG and RIG were employed. Experiments have been done on the case study data and show a better performance when compared IG algo-rithm results with RIG algorithm results. Â

A Solution for the Full-Load Collection Vehicle Routing Problem With Multiple Trips and Demands: An Application in Beijing

Municipal solid waste (MSW) collection has become a major challenge for clean city management and social sustainable development in developing economies. A new variant of the collection vehicle routing problem (CVRP) is addressed with the characteristics of full loads and multiple trips of the collection vehicles, and multiple demands of the garbage facilities, which is called the collection vehicle routing problem of the garbage facilities (CVRPGF) in this study. Dummy customers are introduced to equivalently transform the CVRPGF problem to the vehicle routing problem with simultaneous pickup-delivery and time windows (VRPSPDTW). A parallel simulated annealing algorithm (Par-SAA) is developed to solve the VRPSPDTW problem. When applied to an international benchmark dataset, the computational results prove the superiority of the proposed algorithm, in which the number of collection vehicles (NV) in four instances is reduced by one. Finally, when the model and algorithm are applied to a real CVRPGF problem in the Xuanwu District of Beijing, the NV needed is reduced by 30%, and the total travel time is decreased by 12%. Thus, the effectiveness of the Par-SAA is demonstrated, and the proposed solution has practical value in China.

Reducing pollutant emissions in a waste collection vehicle routing problem using a variable neighborhood tabu search algorithm: a case study

This paper focuses on designing waste collection routes with a single landfill using eco-efficiency as a performance indicator. In this problem, there are a limited number of heterogeneous vehicles based at a single depot. Empty vehicles leave the depot, collect waste from a set of locations and drop off the collected waste at a specific landfill. Then, vehicles leave the landfill and may collect more waste from other locations or return empty to the depot. Traditional performance indicators in vehicle routing problems are mainly focused on economic objectives, not explicitly considering environmental issues. In this paper, a mathematical model is presented with an eco-efficient objective function that takes into account external costs (climate change and air pollution). The COPERT model is used for estimating fuel consumption, carbon dioxide and pollutant emissions. The problem is first heuristically solved using a semi-parallel construction algorithm. Then, solutions are improved by a variable neighborhood tabu search algorithm developed for this problem. The algorithm is validated for a real problem in the municipality of Alcala de Guadaira, within the metropolitan area of Seville (Spain). Results obtained on a set of case studies improve the solution that is currently implemented in the municipality, in terms of total distance traveled, carbon dioxide emissions and pollutant emissions.

A Metaheuristic Approach to the Waste Collection Vehicle Routing Problem with Stochastic Demands and Travel Times

A Metaheuristic Approach to the Waste Collection Vehicle Routing Problem with Stochastic Demands and Travel Times

Efficiency of Heuristic Algorithms in Solving Waste Collection Vehicle Routing Problem: A Case Study

This paper investigated the efficiency of six heuristic algorithms from prior studies in the attempt to solve issues related to waste collection, namely: (i) Nearest Greedy (NG), (ii) Further from Depot (FFD), (iii) Different Initial Customer (DIC), (iv) Savings Approach, (v) Sweep Algorithm, and (vi) Different Initial Customer based on Sweep Algorithm. In fact, these heuristics have been employed to solve several routing problems in past studies, but the performance of each heuristic has never been compared. Hence, this paper looked into the efficiency of these heuristics by testing them on a real case study of waste collection problem in a district located at the north of Peninsular Malaysia. Several solutions obtained from these heuristics were compared with solutions implemented by the waste collection company, especially in terms of the total distance travelled. As a result, the computational results exhibited that DIC generated the best solutions, when compared to other heuristics, with a 12% reduction of the total travel distance.