Pickup Locations Research Articles

Dynamic path finding for multi-load agent pickup and delivery problem

Recently, the Multi-Agent Pickup and Delivery (MAPD) problem has attracted widespread attention from both academia and industry. In the MAPD problem, each task has its pickup and delivery locations, and the agent needs to pick this task up from the pickup location and deliver it to its delivery location. Therefore, existing works consider the MAPD problem as the core problem in industrial scenarios, e.g., logistics warehouse. Note that the agents considered in the MAPD problem are single-load agents that complete tasks one by one. However, many commercial companies have deployed agents with multi-load instead of single-load agents to improve efficiency and reduce costs. The agents with multi-load can complete multiple tasks at once, so existing solutions cannot work well with the MAPD problem for multi-agents. To solve this issue, we investigate a novel problem in this paper, namely the Multi-Load Agent Pickup and Delivery (MLAPD) problem, where the agents with multi-load not only need to complete assigned real-time tasks but also need to avoid conflicts with each other and the goal is to minimize the total cost in the warehouse. To address this novel problem, we develop a task assignment to complete the assignments between multi-load agents and online tasks in real-time and a dynamic path finding problem that enables multi-load agents to move along conflict-free paths. Finally, extensive experiments in two different warehouses examine the effectiveness of our solutions.

An online auction-based mechanism for pricing and allocation of instant delivery services

Recently, instant delivery has been growing rapidly, with numerous platforms emerging to offer such services. Requestors dynamically arrive at the platform to place delivery service requests that detail their pickup locations, recipient locations, package weights, departure times, and willingness-to-pay (WTP). The platform then uses its dedicated riders, scattered in different places, to fulfill these requests. Given the dynamic and fluctuating characteristics of the demand, coupled with limited rider resources and heterogenous pickup costs, the platform faces the critical problem of dynamically pricing the requests and assigning the riders to maximize social welfare. To address this problem, we propose an online auction-based transaction mechanism. Specifically, we first propose a scoring function to evaluate the values of the requests over multi-period operations taking into account the requests’ attributes, riders’ delivery costs, and resource availability. Based on the scoring function, we design a time-varying Vickrey–Clarke–Groves (VCG)-like payment rule that can reflect the impacts of fluctuating supply-demand imbalances. Under this rule, a requestor will pay more during undersupply periods than during oversupply periods. To carve out the different impact degrees of the supply-demand imbalances, we further consider the linear, quadratic, and exponential time-varying resource parameters to construct the payment rule. In addition, we develop an online instant delivery resource allocation model to efficiently assign the riders to fulfill the accepted requests. We show that the proposed mechanism has desirable properties (individual rationality, budget balance, and incentive compatibility) and is computationally efficient. Furthermore, we give a lower bound for the mechanism efficiency. To validate the practicality of our mechanism and get some managerial insights into the operations of the instant delivery platform, we conduct numerical studies to compare the performance of our mechanism to the First-in, first-out (FIFO) allocation mechanism and to investigate the impacts of pricing functions, rolling horizon configurations, and rider numbers on the mechanism's performance.

EVALUATING AND OPTIMIZING THE HAULED CONTAINER SYSTEM (HCS) IN PT. KAMIBOX MAJU SEJAHTERA'S WASTE MANAGEMENT OPERATION

This study evaluates the Hauled Container System (HCS) used in PT. Kamibox Maju Sejahtera's waste management operations. The HCS is a transportation method to transport containers filled with waste and recyclables from collection points to processing or disposal facilities, at PT. Kamibox Maju Sejahtera vehicles travel from a centralized hub in Cisauk to specific pickup locations to load containers. Each vehicle typically handles one location per operational cycle and then transports the loaded container to its destination. Our analysis shows that the HCS system currently enables approximately 2.45 trips per day, which translates to two practical daily trips. This system's simplicity and directness provide ease of management and reduce contamination risk, reassuring the audience about its current effectiveness. The study further explores fuel efficiency and costs, revealing that while the Gran Max is the most fuel-efficient, the Colt Diesel is the most cost-effective. Recommendations include optimizing vehicle use and considering more fuel-efficient alternatives to improve sustainability and reduce operational costs. The findings serve as a guide for PT. Kamibox Maju Sejahtera and similar enterprises were aiming for sustainable waste management.

How to Reduce the Number of Inspectors: Operation Mode and Modeling Simulation of Centralized Image Recognition in Railway Passenger Security Inspection

Security inspection is an essential step for passengers entering railway stations in China. Under the existing security inspection operation mode, each X-ray machine needs an image inspector to recognize the X-ray image, which poses the issue of resource optimization for inspectors. Based on the networking of security inspection X-ray machines, an innovative centralized image recognition (CIR) operation mode for railway security inspections is proposed, and it is worth noting that network and device failures are risk factors for this mode. The method for assigning image recognition personnel is designed to reduce labor force needed for security inspection. Using a combination of agent-based modeling method and computer simulation technology, the four elements of passengers, security inspection X-ray machines, X-ray image inspectors, and secondary baggage inspection personnel are modeled, and CIR indicators to study the effectiveness of the operation are designed. Based on the real departing passenger data of the Zhangjiakou section of the Beijing Zhangjiakou Line in China, simulation is conducted in two scenarios: large passenger flow and small passenger flow. The research is conducted from three aspects: operational efficiency, travel experience, and system complexity. Experiments have shown that, with a slight increase in passenger waiting time, the average number of X-ray image inspectors is reduced by more than 70% compared with the existing operation mode. The best pick-up location for passengers is related to passenger flow. The proposed model can analyze the effectiveness of CIR operation mode and provide decisive support for the application of CIR operation mode.

Role of Earliest Arrival Flow in an Evacuation Network

Traffic route guidance, destination optimization, and optimal route choice are some of the approaches to accelerate the evacuation process of transit-based evacuation problems. Their effectiveness depends upon the evacuee arrival patterns at the pickup locations and their assignment in an appropriate way to the resources in the network. The arrival patterns of the evacuees at different pickup locations might be a constant or random variable, deterministic or stochastic, time-dependent, or flow-dependent, or can be categorized either way. In this work, we are focused on the collections of evacuees at the pickup locations from the danger zone in the earliest arrival flow patterns, which can be further assigned to the transit vehicles in the integrated evacuation network by treating such pickup locations as the sources for the subsequent process to minimize the overall network clearance time from the danger zone to the safety.

How do consumers’ fashion lifestyles differentiate their logistics preferences for fashion products?

PurposeThis study addresses the knowledge gap on consumers’ logistics preferences for online fashion retailing. Despite the effects of operational attributes, including fulfillment cost, speed, time, and convenience, this study further characterizes consumer heterogeneity in fashion shopping with a fashion lifestyle measurement scale, and examines how such heterogeneity differentiates logistics preferences. Design/methodology/approachA questionnaire that consists of attitudinal measurements on fashion lifestyles and a stated choice experiment is disseminated to an online panel in Singapore. The collected responses are analyzed using the Integrated Choice and Latent Variable model. FindingsThis study concludes the significant effects of fulfillment cost, speed, specificity of home delivery slot, and distance to pickup locations on consumers' logistics choices. Meanwhile, consumers’ fashion lifestyles, including fashion consciousness, brand focus, and enjoyment orientation, are found to have considerable correlations with their heterogeneous logistics preferences. Originality/valueThis study fills the knowledge gap regarding online fashion consumers’ logistics preferences. Despite the generic operational attributes, special attention is dedicated to the unique feature of fashion shopping with differentiated shopping motivations. With such treatments, this study advocates the incorporation of segment-specific attributes in future consumer logistics research. Meanwhile, this study pioneers the inspection of the nexus between shopping motivation and logistics preference, which paves a new direction for future exploration. Practical implicationsThis study allows practitioners to predict channel demand with consumer profiling, and make informed logistics decisions, such as inventory management. This can effectively strengthen practitioners’ economic sustainability to remain competitive in contemporary consumer-centric shopping landscapes.

Integrating mobile parcel lockers into last-mile delivery networks: an operational design for home delivery, stationary, and mobile parcel lockers

PurposeThe e-commerce boom presents new challenges for last-mile delivery (LMD), which may be mitigated by new delivery technologies. This paper evaluates the impact of mobile parcel lockers (MPL) on costs and CO2 equivalent (CO2e) emissions in existing LMD networks, which include home delivery and shipments to stationary parcel lockers.Design/methodology/approachTo describe customers’ preferences, we design a multinomial logit model based on recipients’ travel distance to pick-up locations and availability at home. Based on route cost estimation, we define the operating costs for MPLs. We devise a mathematical model with binary decision variables to optimize the location of MPLs.Findings Our study demonstrates that integrating MPLs leads to additional cost savings of 8.7% and extra CO2e emissions savings of up to 5.4%. Our analysis of several regional clusters suggests that MPLs yield benefits in highly populous cities but may result in additional emissions in more rural areas where recipients drive longer distances to pick-ups.Originality/valueThis paper designs a suitable operating model for MPLs and demonstrates environmental and economic savings. Moreover, it adds recipients’ availability at home to receive parcels improving the accuracy of stochastic demand. In addition, MPLs are evaluated in the context of several regional clusters ranging from large cities to rural areas. Thus, we provide managerial guidance to logistics service providers how and where to deploy MPLs.

Abstract WMP41: Neighborhood Social Vulnerability is Associated With Lower Frequency of Stroke Scale Assessment Among Patients With Suspected Stroke

Introduction: High-quality prehospital care during initial assessment by EMS providers can affect downstream care for acute stroke patients. Prehospital guidelines strongly recommend assessing suspected stroke patients with a validated stroke scale and obtaining a blood glucose level, but EMS adherence varies widely. Neighborhood socioeconomic characteristics are associated with stroke incidence and outcomes, but there is little research evaluating their relationship to quality metrics in prehospital stroke care. Hypothesis: Greater neighborhood-level social vulnerability is associated with lower frequency of stroke scale assessment and blood glucose check by EMS for patients with suspected stroke. Methods: We conducted a cross-sectional evaluation of EMS activations for suspected stroke patients in the 2019 ESO Data Collaborative. We included 911 responses where EMS documented a field impression of stroke and excluded activations from an acute health care facility. Neighborhood-level social vulnerability, using CDC’s Social Vulnerability Index (SVI), was assigned to each encounter based on pick-up location. Descriptive statistics were calculated, and mixed effects logistic regression models were fit to assess any relationship between SVI and documentation of stroke scale and blood glucose assessments, controlling for patient and event characteristics and clustering by EMS agency. Results: Among 84,411 patients with suspected stroke, 46% were male, with an average age of 71 years. Stroke scale assessment was documented in 78% of cases, and blood glucose in 88%. Compared to patients in the least vulnerable SVI quartile, those in the most vulnerable SVI quartile had lower odds of documented stroke scale assessment (OR 0.91, 95% CI 0.82, 1.00, p=0.04), but similar odds of documented blood glucose (OR 0.97, 95% CI 0.89, 1.06, p=0.48). High agency-level variation was present for stroke scale (ICC 70%, 95% CI 69, 73). Conclusions: Higher social vulnerability areas may be associated with less frequent stroke scale assessment; however, high agency-level variation indicates that the best targets to improve EMS systems’ compliance with evidence-based guidelines for acute stroke care may be low-performing agencies, regardless of setting.

Abstract WMP40: A Geospatial Information Services Assessment of Prehospital Bypass on Overtriage for Large Vessel Occlusion and Hemorrhagic Stroke

Introduction: To increase the number of patients with large vessel occlusion (LVO) acute ischemic strokes (AIS) transported directly to Comprehensive Stroke Centers (CSCs), on December 1, 2021, the threshold score for our county EMS agency’s quantitative stroke severity scale (FAST-ED) for bypassing a non-CSC decreased from 7 to 6. The goal of this work was to evaluate the impact of and adherence to this change, and its effect on overtriage. Methods: Two large regional health systems and the county EMS agency collaborated on this study. Using latitude and longitude coordinates from our EMS agency’s database, we geocoded the pick-up location of all patients who had a FAST-ED score of ≥6 from September 2019 to December 2022. We used ArcGIS to calculate drive times to the closest facility among 2 CSCs and 7 non-CSCs, and linked EMS records to hospital records to determine LVO AIS involving the intracranial ICA, M1, M2 or basilar artery. For patients without a CSC as the closest destination facility, we defined overtriage as routing to a CSC for a non-LVO or non-hemorrhagic stroke patient. Results: Over our 40-month study period, among patients with a FAST-ED score performed (n=13,913), 824 had a score ≥6. Actual versus ArcGIS drive times in minutes [mean(SD)] compared favorably [14.8(8.5) vs 14.4(6.4), with an ICC of 0.68 (95% CI 0.64-0.72). For 799/824 patients (97%), one of the county’s two CSCs was not the closest destination facility, and among this group 99% (n=787/799) of EMS records were successfully linked to the hospital record. Among these patients, prior to December 1, 2021, 67% (n=219/326) patients with FAST-ED score ≥7 were bypassed to a CSC, with an overtriage rate of 49% (n=107/219), while after that date 88% (n=230/262) patients with a FAST-ED score ≥6 were bypassed to a CSC, with an overtriage rate of 55% (n=126/230). Conclusion: It is feasible to use drive times calculated from ArcGIS to evaluate the performance of EMS stroke triage and destination plans. Lowering our county’s FAST-ED score threshold for bypass increased adherence to the bypass protocol with a modest increase in overtriage. Future work will incorporate undertriage rates with the ultimate goal of optimizing our regional stroke and triage destination plan.

A systematic analysis of design choices in short-term taxi demand prediction models

Accurate prediction of short-term taxi demand allows taxi services to strategically position idle vehicles in areas with insufficient supply, thus reducing idle times for taxis and waiting times for passengers. Towards this end, state-of-the-art approaches use Machine Learning (ML) models based on historic demand data, such as Long Short-Term Memory (LSTM), Fully Connected Feed Forward Neural Network (FCNN), Convolutional Neural Network (CNN), Graph Convolutional Network (GCN), or a combination of these. The majority of approaches use a grid representation of the environment to discretize the pickup locations of trips and time bins to order the temporal dimension of the large multi-target regression problem. While these models show good accuracy, many \ extit{design choices}, such as the size of the grid cells or the number of previous time steps inserted into a model, are made without justification or without proper tuning. This paper systematically investigates these design decisions. Our hypothesis is that considering them jointly rather than in isolation can improve accuracy. We investigate the influence of (1) the selected grid cell size, (2) the number of input maps, (3) the usage of time shift to enlarge the training data, and (4) the potential of transfer learning on the accuracy of short-term taxi demand prediction. We selected different network types and evaluated them on two large real-world datasets. The evaluation shows that our model outperforms state-of-the-art short-term taxi demand prediction models.

Ride-Hailing Assignment Problem under Waiting Time Uncertainty using Interval-Valued Fuzzy Quadratic

Ride-hailing is a creative idea created by transportation supported by science and technology. Ride-hailing services can help daily community activities. The issue with ride-hailing is that traffic conditions are unpredictable, implying that waiting times are uncertain. The time passengers spend waiting from when they book a ride service until the driver arrives at the pick-up location is called waiting time. This study suggests a quadratic programming technique for minimizing waiting time while accounting for the unpredictability of pick-up travel time. The interval-valued fuzzy quadratic programming method handles the uncertainty and imprecision of the anticipated journey time. When allocating drivers to pick up passengers, interval-valued fuzzy numbers can provide a more realistic representation of waiting time uncertainty. As a result, the interval-valued fuzzy quadratic programming model can handle the uncertainty in waiting time for ride-hailing assignment problems. The model's performance is evaluated using waiting time and the number of people served. The model's performance is demonstrated numerically using the simulation-based case study. This study shows how to utilize a mathematical method to solve real-world problems with uncertainty and improve user welfare.

Online flash delivery from multiple depots

ABSTRACT We study routing for on-demand last-mile logistics with two crucial novel features: i) Multiple depots, optimizing where to pick-up every order, ii) Allowing vehicles to perform depot returns prior to being empty, thus adapting their routes to include new orders online. Both features result in shorter distances and more agile planning.We propose a scalable dynamic method to deliver orders as fast as possible. Following a rolling horizon approach, each time step the following is executed. First, define potential pick-up locations and identify which groups of orders can be transported together, with which vehicle and following which route. Then, decide which of these potential groups of orders will be executed and by which vehicle by solving an integer linear program. We simulate one day of service in Amsterdam that considers 10,000 requests, compare results to several strategies and test different scenarios. Results underpin the advantages of the proposed method.

Microtransit adoption in the wake of the COVID-19 pandemic: Evidence from a choice experiment with transit and car commuters

On-demand mobility platforms play an increasingly important role in urban mobility systems. Impacts are still debated, as these platforms supply personalized and optimized services while also potentially exacerbating sustainability challenges. To alleviate these concerns, microtransit projects have emerged, promising to combine the advantages of pooled on-demand rides with more sustainable fixed-route public transit services. Specifically, microtransit provides, dynamic rider-driver matching to serve demand with fewer vehicles and design optimal routes if riders accept to wait to board vehicles at curbside boarding locations. The shift to microtransit calls for new research on user behavior, motivations, and acceptability to understand demand and its role in existing mobility systems. The COVID-19 pandemic context adds an additional layer of complexity. This study investigates the potential demand for microtransit options against the background of the pandemic. We use a pivoted efficient choice experiment to study the decision-making of Israeli public transit and car commuters when offered to use novel microtransit options (sedan vs. passenger van). By estimating commuter group-specific Integrated Choice and Latent Variable models with error component terms for the microtransit alternatives, we investigate the tradeoffs related to traditional fare and travel time attributes, along with microtransit features: walking time to the pickup location, vehicle sharing, waiting time, minimum advanced reservation time, and shelter at designated boarding locations. We analyzed two latent constructs: the attitudes toward sharing and the experiences and risk perceptions related to the COVID-19 pandemic. The results reveal three key takeaways. (1) New modal attributes significantly affect the utility of the microtransit alternatives, with a notable aversion to walking and waiting among drivers; (2) car and transit commuters have structural differences in attribute elasticities; (3) significant differences are noted for the magnitude of the latent variable effects. Sharing experience and COVID Comfort play a key role for drivers evaluating the choice of microtransit.

An agent-based simulation and logistics optimization model for managing uncertain demand in forest supply chains

This paper aims to model and minimize transportation costs in collecting tree logs from several regions and delivering them to the nearest collection point. This paper presents agent-based modeling (ABM) that comprehensively encompasses the key elements of the pickup and delivery supply chain model and presents the units as autonomous agents communicating. The modeling combines components such as geographic information systems (GIS) routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. ABM models the entire pickup and delivery operation, and modeling outcomes are presented by time series charts such as the number of trucks in use, facilities inventory, and travel distance. In addition, various simulation scenarios are used to investigate potential facility locations and truck numbers and determine the optimal facility location and fleet size.

Going My Way? Understanding Curb Management and Incentive Policies to Increase Pooling Service Use and Public Transit Linkages in the San Francisco Bay Area

Despite lower user costs, only 20% to 40% of transportation network company (TNC) users select a pooled, or shared, ride option. Why are existing TNC users not selecting the pooled option or using TNCs to connect to public transit, and what role do built environment features and incentives play in their decision? This study explores the factors that influence TNC user decisions through a multi-method approach comprising photovoice small group discussions and a workshop. Between March 2021 and May 2021, 15 San Francisco Bay Area TNC users shared photographs they took of TNC pick-up locations through two-to-three-person guided small group discussions. The photos revealed that users prefer waiting in retail or in well-lit, good-visibility locations. Participants’ primary concern was personal safety, particularly female users who may take additional precautions when walking to pick-up locations and waiting for and taking rides. In July 2021, 12 photovoice participants and 5 stakeholders provided feedback on key findings from the photography discussions. The pooling improvement strategies identified include the following: designated TNC stops with lighting and marked pick-up areas; enhanced in-app safety features; TNC partnerships with employers and retailers to incentivize riders; and mode transfer discounts for connecting TNCs to public transit. The findings suggest that safety related to the built environment plays an outsized role in a TNC user’s decision to pool or connect to public transit, and the out-of-vehicle portion of the TNC trip should be equally considered when developing policies to increase pooling.

Spatial CRM and location strategy: E-commerce solutions in the furniture industry. Case of IKEA pick-up points in Poland

E-commerce in the furniture industry is specific as delivery of heavy and oversized parcels with traditional channels is not feasible. Alternative solutions, aside from stationary shops, include pick-up points which combine e-commerce purchasing with stationary collection. Convenient pick-up locations that support environmental sustainability and offer cost savings is a domain of Spatial Customer Relationship Management (CRM), which is not enough discussed in the literature. In this paper we develop Spatial CRM for the location strategy of pick-up points in the furniture industry, using IKEA Poland as a case study. We provide the methodology for analysing the spatial range of population potential for collection points and stationary shops based on radial catchment areas of 20 km and precise population data from a grid 1 km × 1 km. We compare this market coverage with customers' attraction to IKEA shops expressed through Google Trends searches to evaluate location strategies for furniture retailers. Our regional approach is responsive to local needs, improves CRM, and remains cost-effective for businesses. Given the similarity of European furniture markets in terms of consumer perspective and e-commerce development, our analysis methodology can be adapted for use in other countries and markets.

Compensation guarantees in crowdsourced delivery: Impact on platform and driver welfare

Crowdsourced delivery and other sharing economy platforms attract freelance workers by offering them flexibility in scheduling their own work hours. Those platforms, however, have been criticized for the lack of protection they offer workers. Since workers are treated as independent contractors, they do not receive minimum wage and other protection measures under labor law. In this paper, we examine the integration of driver compensation guarantees in a platform’s dynamic matching decisions. We study the problem of designing dynamic matching policies that guarantee a particular level of compensation for active workers over a time period, while maintaining work hour flexibility. We model three types of policies, that are either wage-based or utilization-based. We propose an MDP model to capture the dynamic and stochastic nature of the problem, then design a value function approximation algorithm to efficiently solve the large-scale MDP model. Extensive computational testing is conducted to assess the performance of the proposed solution methodology and the compensation guarantees, using synthetic and real-world datasets. Our findings suggest that the utilization policy results in the highest earning for drivers, though at the expense of longer empty miles from drivers’ origins to the pickup locations of matched orders. On the other hand, the effective wage policy leads to shorter average distance to pickup, but slightly lower earning to drivers. Both policies result in only a slight decrease in platform profit as compared to the base case, and exhibit lower dispersion in the distribution of driver earning while active. In contrast, the nominal wage policy shows a comparable trend to the base-case policy in terms of average driver earnings, suggesting minimal benefits for drivers.

CarGo: A Smart Car Rental App with Real-Time Tracking and Notifications - Powered by React Native

"CarGo" is a state-of-the-art car rental system developed in React Native that provides a seamless and easy-to-use experience for people looking for car rental services. This innovative app includes user registration, hassle-free booking management, real-time tracking capabilities, an integrated notification system, and much more. Through an intuitive interface, users can easily log in, browse available vehicles, view detailed vehicle information and book with preferred date, time and pickup location. A standout feature of this app is its real-time tracking integration, which allows users to monitor the location of their rental vehicle, ensuring safety and convenience throughout the rental period. "CarGo" is seamlessly compatible with Android and iOS platforms, ensuring a consistent and powerful experience for all users. We promise to redefine the car rental industry with a modern and efficient approach..

New Orleans' school meals distribution in response to the COVID-19 pandemic: operational details and geographic coverage.

To facilitate continuation of school feeding during COVID-19 school lockdowns, U.S. Congress authorized waivers to allow for school meals to be picked up by parents/guardians in non-school settings. We summarized school meals distribution and characterized reach in socially vulnerable neighborhoods in New Orleans, a city prone to environmental disasters, with a city-wide charter school system, and historically high levels of child poverty and food insecurity. School meals operations data were obtained from New Orleans, Louisiana (NOLA) Public Schools for 3/16/2020-5/31/2020. For each pick-up location, we estimated: average meals available (weekly), average meals served (weekly), number of weeks of operation, and rate of meal pick-up ([meals served/meals available]*100). These characteristics were mapped in QGIS v3.28.3, along with neighborhoods' Social Vulnerability Index (SVI). Pearson correlation and ANOVA were run to assess differences between operations characteristics and neighborhood SVI. From 38 meal sites, 884,929 meals were available for pick-up; 74% of pick-up sites were in moderately/highly socially vulnerable areas. Correlations between average meals available and served, weeks of operation, rate of meal pick-up, and SVI were weak and not statistically significant. SVI was associated with average rate of meal pick-up but not other operations characteristics. Despite the disaggregated nature of the charter school system, NOLA Public Schools successfully pivoted to providing children with pick-up meals due to COVID-19 lockdowns, with 74% of sites located in socially vulnerable neighborhoods. Future studies should describe the types of meals provided to students during COVID-19, in terms of diet quality and nutrient adequacy.

Solid Waste Handling Scenarios, Management and Economic Study in Erbil City

Solid waste management has become an important issue for developing countries. Solid waste management is also a weighty problem in Erbil City, Kurdistan Region, Iraq. To date, weak points have been observed in Erbil City, particularly in the handling and transportation of municipal solid waste. Consequently, this issue affects the environment, human health, management, finances, and aesthetics of the city. This research focused on handling scenarios, management, and an economic study of solid waste in New Zanco Village, Erbil City. Data on the quantity and generation rate (GR) of solid waste and current routes for solid waste collection were collected directly. Additionally, truck volume, handling options, and economic subjects were studied. Untimorous software was applied to find the optimum routes for solid waste collection in the studied area. Results revealed that the average GR for 48 samples was 1.114 kg/capita/day. The minimum and maximum values were 0.24 kg/capita/day and 3.28 kg/capita/day, respectively. The optimized capacity for the trucks was 7 m3. The number of pick-up locations per truck was 92. From the current waste collection route, waste is collected in eight cycles each week and the total weekly travel length equals 321.12 km. Based on the results obtained from OptimoRoute software, waste is collected in seven cycles and the total weekly travel length equals 294.03 km. The waste collection will reduce the 27.09 km of travel length in each week, which equals a reduction of 8.43% of the total distance. The operation cost will be reduced by 8.43% per week. Additionally, the total yearly travel length will be reduced by 1,300.32 km and the total yearly cost will be reduced by about $2,730.67.