Parcel Pick-up Research Articles

Optimizing an express delivery mode based on high-speed railway and crowd-couriers

The rapid development of e-commerce has led to various challenges in the express delivery industry. To provide efficient and economical delivery services, this study introduces a joint transportation system for parcel delivery utilizing high-speed railway (HSR) and crowd-couriers. In this system, long-haul transportation between cities is conducted by HSR, which enables multiple transshipments in different operation lines. The pre-haul parcel pickup and end-haul delivery services within the city are performed by crowd-couriers, thus achieving door-to-door transportation. A mathematical model is developed to jointly optimize the freight allocation problem in the rail segment and the crowd-courier routing problem in the road segment. The freight allocation problem is formulated based on the concept of transportation plans. The transportation plans with and without transshipments are found by a depth-first search-based algorithm in the rail segment. A customized heuristic algorithm based on the adaptive large neighborhood search with multiple operators is designed to find a near-optimal solution. A set of numerical experiments demonstrates the speed and accuracy with which the proposed method solves different-sized versions of problem. A sensitivity analysis based on a real HSR network is also conducted to provide more reliable managerial insights for operators.

The Influence of Motivation and Work Discipline on the Performance of Courier Employees at J&T Express Juanda Sidoarjo

J&T Express is a company operating in the field of goods expedition, encompassing both documents and packages. Despite being a new company, J&T Express utilizes IT in its service offerings, providing the convenience of parcel pick-up, eliminating the necessity for customers to visit J&T offices for sending goods. Nonetheless, a phenomenon hindering the performance of courier employees in this company is observed, characterized by declining motivation and work discipline each year. This research aims to investigate the impact of work motivation and work discipline on the performance of courier employees at J&T Express Juanda Sidoarjo. The data were gathered through observations, interviews, and the distribution of questionnaires to respondents in the courier section. The sampling method employed in this research is the saturated sampling technique. The respondents involved in this research were 60 employees working in the courier section of J&T Express. The outer model test evaluates validity and reliability initially. When testing hypotheses using the SEM PLS method, the t-test, p-values, and original samples are determined. Based on the results of the conducted research, it can be concluded that there is a significant influence between the variables of work motivation and work discipline on the performance of employees in the courier section of J&T Express Juanda Sidoarjo.

Balancing resources for dynamic vehicle routing with stochastic customer requests

We consider a service provider performing pre-planned service for initially known customers with a fleet of vehicles, e.g., parcel delivery. During execution, new dynamic service requests occur, e.g., for parcel pickup. The goal of the service provider is to serve as many dynamic requests as possible while ensuring service of all initial customers. The allocation of initial services impacts the potential of serving dynamic requests. An allocation aiming on a time-efficient initial routing leads to minimal overall workload regarding the initial solution but may congest some vehicles that are unable to serve additional requests along their routes. An even workload division is less efficient but grants all vehicles flexibility for additional services. In this paper, we investigate the balance between efficiency and flexibility. For the initial customers, we modify a routing algorithm to allow a shift between efficient initial routing and evenly balanced workloads. For effective dynamic decision making with respect to the dynamic requests, we present value function approximations with different feature sets capturing vehicle workload in different levels of detail. We show that sacrificing some initial routing efficiency in favor of a balanced vehicle workload is a key factor for a flexible integration of later customer requests that leads to an average improvement of 10.75%. Further, we show when explicitly depicting heterogeneity in the vehicle workload by features of the value function approximation provides benefits and that the best choice of features leads to an average improvement of 5.71% compared to the worst feature choice.

IDEAL LOCATION SELECTION FOR CONTACTLESS PARCEL PICK-UP POINTS

The post-Covid era has witnessed the adoption of various new habits in our daily lives, particularly in relation to the ubiquitous e-commerce platforms that have become essential for urban populations. The surge in e-commerce activities and the intensified volume in delivery of packages during the pandemic sparked innovative ideas. This study explores one such creative concept: parcel pick-up points. We conducted a pioneering research endeavor to determine the optimal locations for these pick-up points in Istanbul, Turkey. Our methodology employed a novel hybrid approach, combining the Spherical Fuzzy Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The evaluation of criteria importance was facilitated by a literature review and experienced high-level managers in the cargo industry, who determined the criteria weights using the Spherical Fuzzy AHP method. Subsequently, the TOPSIS method was employed to identify the most ideal locations, leading to the selection of Kadikoy, Umraniye, and Atasehir. This study provides valuable insights into the selection of the optimal locations for parcel pick-up points in Istanbul, Turkey, which can inform policymakers, e-commerce companies, and logistics stakeholders. The proposed hybrid approach demonstrates the integration of modern smart technologies with fuzzy decision-making techniques, offering a robust framework for decision support in the field of e-commerce logistics. Future research can further explore the implementation and effectiveness of these pick-up points to enhance the efficiency and convenience of last-mile deliveries in urban areas.

A Truthful Combinatorial Reverse Auction Mechanism for Crowdshipping

Crowdshipping is an emerging logistics paradigm that leverages excess capacities of occasional couriers to transport goods. This paper investigates the problem that a crowdshipping platform recruits occasional couriers to serve the parcel pickup and delivery requests. A combinatorial reverse auction framework is introduced to model the interactions between the platform and couriers. We design an auction mechanism that implements in polynomial time, which satisfies the computational requirements of crowdshipping. It can also achieve near-optimal social cost with the approximation ratio rigidly proved. Through both rigorous theoretical analyses and extensive simulations, we demonstrate that the proposed auction mechanism is truthful, individual rational and has high computation efficiency.

Load prediction of parcel pick-up points: model-driven vs data-driven approaches

ABSTRACT Pick-Up Points (PUPs) represent an alternative delivery option for online purchases. Parcels are delivered at a reduced cost to PUPs and wait until being picked up by customers or returned to the original warehouse if their sojourn time is over. When the chosen PUP is overloaded, the parcel may be refused and delivered to the next available PUP on the carrier tour. This paper presents and compares forecasting approaches for the load of a PUP to help PUP management companies balance delivery flows and reduce PUP overload. The parcel life-cycle has been taken into account in the forecasting process via models of the flow of parcel orders, the parcel delivery delays, and the pick-up process. Model-driven and data-driven approaches are compared in terms of load-prediction accuracy. For the considered example, the best approach (which makes use of the relationship of the load with the delivery and pick-up processes) is able to predict the load up to 4 days ahead with mean absolute errors ranging from 3.16 parcels (1 day ahead) to 8.51 parcels (4 days ahead) for a PUP with an average load of 45 parcels.

Sustainable last mile parcel delivery and return service using drones

Improving last mile delivery, in terms of both efficiency and sustainability, has recently been enhanced using truck/drone tandems. However, nearly all these approaches focus only on deliveries, while returns are ignored. We propose a new model for integrating both delivery and returns in the combined operation of a truck and a drone, which we term the Flying Sidekick Traveling Salesman Problem integrating Deliveries and Returns with Multiple Payloads (FSTSP-DR-MP). This approach is more sustainable than truck-only deliveries as the drones operate through battery power with no emissions and can move more directly than vehicles along a road network. We formulate the problem as a mixed-integer linear program to minimize the total service time of the system, where the truck and the drone can perform both delivery and pickup of parcels during a single sortie. Because drones have a capacity of multiple parcels, each can visit more than one customer per dispatch, increasing drone utilization and responsiveness to customer expectations regarding return services, along with greater environmental improvements. Small-size cases are solved exactly using the MILP implemented in the CPLEX Python API. Since the MILP is not practical for realistically sized cases, we propose a meta-heuristic derived from Variable Neighborhood Search (VNS), which iteratively builds truck and drone routes. We show that this works well for up to 100 customers, a typical number in last-mile logistics. We assess the trade-offs between the ratio of returns to deliveries and drone capacity to provide managerial insights to the benefits of this approach for sustainable last mile logistics. Computational experiments show that integrating delivery and return truck-drone operations significantly reduces total service time and truck travel time compared to both traditional delivery schemes (single truck) and the well-known FSTSP drone schemes (one truck and one drone) up to 36.2% and 22.9%, respectively, by exploiting the nature of each customer (delivery or return) to increase the number of stops of each drone sortie. We also conduct a comparison with multiple drones have a single payload under similar scenarios. The results demonstrate that our model outperforms this alternative with an average improvement of 3.9% in total service time. Our approach addresses an important gap in the literature by accommodating returns, which are ubiquitous in last mile logistics, as well as providing for improved drone utilization, sustainability, and cost-effectiveness.

A Cost Calculation Model for Outsourcing in Parcel Pick-up and Delivery by Commercial Postal Services Operators

A Cost Calculation Model for Outsourcing in Parcel Pick-up and Delivery by Commercial Postal Services Operators

The role of consumer participation readiness in automated parcel station usage intentions

Automated parcel stations (APS) offer an effective and efficient way to respond to increasing volumes of online commerce and to add value to traditional ‘last mile’ parcel pick-up and delivery options. We developed a theoretical sound model with consumer participation readiness (CPR) as the foundation construct and showed that technology anxiety and service convenience fully mediate the effects of CPR on usage intentions. The model explained over 80% of the variance in the dependant variable and our findings offer novel insights that have important theoretical and managerial implications.

Same-Day delivery with pickup stations and autonomous vehicles

Same-day delivery has become a very important challenge for e-commerce providers. However, effective delivery concepts are still not established. In this paper, we analyze the potential of combining parcel pickup stations and autonomous vehicles for same-day delivery. This combination may allow for consolidation of goods, automated delivery processes, and reduced operation costs. We consider the problem on the operational planning level. A depot, a set of pickup stations, and an autonomous delivery fleet are given. Customers dynamically order goods to a preferred pickup station and expect fast service. The goods need to be delivered from the depot to the preferred pickup station or another pickup station in the close neighborhood. Autonomous vehicles are directly dispatched between the depot and the stations. To decide where and when to dispatch a vehicle and about the corresponding goods to load, we present a policy function approximation (PFA). Our intuitive PFA allows real-time decision making while balancing the tradeoff between fast delivery and consolidation. We conduct an extensive computational study for the pickup station network of the city of Braunschweig. We show how our delivery concept allows fast delivery for up to 100 delivered goods per vehicle and day. We further pair the PFA with a value function approximation to account for heterogeneity in demand. We finally derive important managerial insights for strategical and tactical planning and present an extensive outlook on promising future research directions.

Customer Satisfaction and Timely Delivery: The Order Picking Scheduling Problem

In recent years, electronic commerce (EC) using a portable terminal has become familiar, and the number of people using online shops is increasing. Moreover, the number of companies entering the online shopping market is increasing, thus intensifying the competition between companies. As a result, a company that manages business to consumer (B2C) EC sites must consider providing more and varied services to attract customers. To capture more customers, these companies may also introduce protocols to optimize current service provision by, say, ensuring timely delivery. Therefore, cooperation is essential between companies that manage B2C EC sites and those that deliver the goods. This study aims to develop an order picking scheduling model that considers parcel pickup time and investigates the in?uence of various parameters.

Design and modeling of a crowdsource-enabled system for urban parcel relay and delivery

This paper proposes a crowdsource-enabled system for urban parcel relay and delivery. We consider cyclists and pedestrians as crowdsources who are close to customers and interested in relaying parcels with a truck carrier and undertaking jobs for the last-leg parcel delivery and the first-leg parcel pickup. The crowdsources express their interests in doing so by submitting bids to the truck carrier. The truck carrier then selects bids and coordinates crowdsources’ last-leg delivery (first-leg pickup) with its truck operations. The truck carrier's problem is formulated as a mixed integer non-linear program which simultaneously i) selects crowdsources to complete the last-leg delivery (first-leg pickup) between customers and selected points for crowdsource-truck relay; and ii) determines the relay points and truck routes and schedule. To solve the truck carrier problem, we first decompose the problem into a winner determination problem and a simultaneous pickup and delivery problem with soft time windows, and propose a Tabu Search based algorithm to iteratively solve the two subproblems. Numerical results show that this solution approach is able to yield close-to-optimum solutions with much less time than using off-the-shelf solvers. By adopting this new system, truck vehicle miles traveled (VMT) and total cost can be reduced compared to pure-truck delivery. The advantage of the system over pure-truck delivery is sensitive to factors such as penalty for servicing outside customers’ desired time windows, truck unit operating cost, time value of crowdsources, and the crowdsource mode.

A Multi-Agent System based on the Multi-Objective Simulated Annealing Algorithm for the Static Dial a Ride Problem

AbstractThe Dial a Ride Problem (DRP) is to take passengers from a place of departures to places of arrivals. Different versions of the Dial a Ride Problem are found in every day practice; transportation of people in low-density areas, transportation of the handicapped and elderly persons and parcel pick-up and delivery service in urban areas. The ultimate aim is to offer an alternative to displacement optimized individually and collectively. Indeed the DRP is a multi-criteria problem, the proposed solution of which aims to reduce both route duration in response to a certain quality of service provided. In this work, we offer our contribution to the study and solving the DRP in the application using a multi agent system based on the Multi-Objective Simulated Annealing Algorithm. Tests show competitive results on (Cordeau and Laporte, 2003) benchmark datasets while improving processing times to obtain a pareto solution for the problem in concern.

A fuzzy logic approach to dynamic Dial-A-Ride problem

Different versions of the dynamic Dial-A-Ride problem are found in every day practice; transportation of people in low-density areas, transportation of the handicapped and elderly persons and parcel pick-up and delivery service in urban areas. Carrier, which is providing the transportation service, receives calls for transportation. The problem is to assign every new passenger request to one of the vehicles and to design a new route and schedule for this vehicle. This assignment must be done in real time. Two approximate reasoning algorithms are developed in this paper. Using the first approximate reasoning algorithm we make the decision about which vehicle will accept the new request. The second developed approximate reasoning algorithm was used to design the new route and schedule for the vehicle chosen to serve the new request. The model developed has been tested on a greater number of different numerical examples. The results obtained are very promising.

Design of multiple-vehicle delivery tours satisfying time constraints

We studied the following organization for the letter and parcel pick-up and delivery problem in an urban environment: the working day is divided in time periods during which each vehicle starts from a sorting center, travels to a precise area of the region, picks up and delivers letters and parcels, and then returns to the depot to have the collected material sorted out for delivery during the following period. The intent of this approach is to have each zone serviced by one vehicle only to facilitate the work of the dispatcher and the routing of each vehicle. We have developed a method of partitioning an urban region into zones so as to minimize the total number of vehicles used or the total distance traveled by all the vehicles. The first part of the method determines the zones and the average number of points to visit. Then, a first refinement of the method takes into account the daily variability of the demand in each zone, and a second refinement examines the advantage of allowing some overlapping of zones for the purpose of having an overloaded vehicle relieved by one from an adjacent zone.