A Deep Reinforcement Learning-Based Hyper-Heuristic for Time-Dependent Green Logistics With Crowdsourcing

This paper addresses the challenge of rising operational costs in last-mile delivery caused by end-customer no-shows. The study proposes a collaborative operational framework for last-mile delivery that accommodates roaming customers, enabling them to be serviced by multiple depots as they transition between different locations. A mixed-integer programming (MIP) model is formulated to minimize the operational costs of last-mile delivery under the proposed framework. To improve the model’s practicality and computational efficiency, an adaptive large neighborhood search (ALNS) algorithm is developed, incorporating tailored neighborhood structures. Furthermore, a late acceptance strategy is embedded within the algorithm to mitigate the risk of premature convergence to local optima. The experimental results demonstrate that, in the absence of depot collaboration, the multi-depot model achieves a 16.9% reduction in operational costs compared to the single-depot model. Moreover, when depot collaboration is enabled, the average cost reduction percentage significantly increases to 40.37%. Notably, under the multi-depot collaborative framework, considering customers' roaming behavior—as opposed to fixed single-location assumptions—leads to a substantial 54.6% reduction in operational costs.

Efficient feasibility checks and an adaptive large neighborhood search algorithm for the time-dependent green vehicle routing problem with time windows

An intelligent stochastic optimization approach for stochastic order allocation problems with high-dimensional order uncertainties

<p style='text-indent:20px;'>The Vehicle Routing Problem with Multiple Time Windows (VRPMTW) is a generalization of problems in real life logistics distribution, which has a wide range of applications and research values. Several neighborhood search based methods have been used to solve this kind of problem, but it still has drawbacks of generating numbers of infeasible solutions and falling into local optimum easily. In order to solve the problem of arbitrary selection for neighborhoods, a series of neighborhoods are designed and an adaptive strategy is used to select the neighborhood, which constitute the Adaptive Large Neighborhood Search(ALNS) algorithm framework. For escaping from the local optimum effectively in the search process, a local search based on destroy and repair operators is applied to shake the solution by adjusting the number of customers. The proposed method allows infeasible solutions to participate in the iterative process to expand the search space. At the same time, an archive is set to save the high-quality feasible solutions during the search process, and the infeasible solutions are periodically replaced. Computational experimental results on VRPMTW benchmark instances show that the proposed algorithm is effective and has obtained better solutions.</p>

Based on the current situation and problems of transportation "last mile" transportation distribution, this paper establishes a path optimization model based on user distribution methods from the perspective of market preference for transportation distribution methods, designs an Adaptive Large Neighborhood Search (ALNS) algorithm, and builds a user portrait based on the solution algorithm and the construction method. Based on the solution algorithm and the user portrait construction method, the solution scenario is established, and the distribution route and transportation distribution method are planned based on five real location data. Through the analysis of the solution scenarios, it can be obtained that after the optimization of the model, the transportation distribution cost of enterprises can be reduced, and the satisfaction of the transportation distribution service quality can be improved. The higher the complaint cost, the lower the total transportation and distribution cost, and the higher the satisfaction rate; the higher the time window penalty cost, the higher the total distribution cost, and the lower the satisfaction rate. Through several model comparisons, it is found that the optimized model has obvious advantages in transportation cost and good performance in transportation service satisfaction. To further strengthen the promotion and application of the distribution path optimization model, countermeasures are proposed in three aspects: establishing a unified end transportation information service platform, increasing the investment in end transportation path optimization, and strengthening the formulation of supporting policies to realize the optimization of end distribution services.

With the growing demand for delivery services and the escalating labor costs, much effort has been made to achieve faster and cost-efficient delivery. A promising emerging strategy involves the integration of autonomous delivery vehicles or drones into the last-mile delivery. This study presents a fully automated last-mile delivery system that synergistically integrates autonomous vehicles and drones. We also introduce a novel variant of the vehicle routing problem with drones, referred to as the hybrid autonomous vehicle-drone routing problem (HAVDRP). In HAVDRP, we employ three delivery tools: autonomous vehicles, vehicle-carried drones, and independent drones. The aim is to fully leverage the advantages of autonomous vehicles and drones to provide customers with more efficient last-mile delivery services. An improved adaptive large neighborhood search algorithm is developed to address this problem. The algorithm incorporates a tabu list and an adaptive mechanism specifically designed for the HAVDRP, thereby augmenting the search efficiency. Computational experiments are conducted to evaluate the efficiency of the designed algorithm. Additionally, sensitivity analyses are conducted to explore the influences of some key parameters on the total time, which includes the cumulative working time of autonomous vehicles and drones. Based on the results of sensitivity analyses, we propose some management recommendations for the fully automated last-mile delivery system utilizing autonomous vehicles and drones.

A hybrid MCDM-FMOO approach for sustainable supplier selection and order allocation

This paper tackles the growing challenges in urban logistics by presenting an optimal distribution network that integrates urban waterways and last-mile delivery, tailored for cities boasting extensive waterway networks. We examine Amsterdam's city center as a case study, prompted by the strain on quay walls, congestion, and emissions, urging a reevaluation of its urban logistics design. We formulate the problem as a two-echelon location routing problem with time windows and introduce a hybrid solution approach for effective resolution. Our algorithm consistently outperforms existing methods, with a superior solution quality, demonstrating its effectiveness across established and newly developed benchmark instances. In our case study, we evaluate the benefits of transitioning from a roadway-centric to a waterway-based system, showcasing significant cost savings (approximately 28 %), reductions in vehicle weight (approximately 43 %), and minimized travel distances (approximately 80 %) within the city center. The integration of electric vehicles enhances environmental sustainability, resulting in a total daily emission reduction of 43.46 kg. Our study underscores the untapped potential of inland waterways in easing urban logistics challenges. Inspired by Amsterdam's experience, global cities can adopt innovative approaches for sustainable logistics, providing valuable insights for managers striving to enhance efficiency, cut costs, and promote sustainable transportation practices.

This paper studies the supplier selection and order allocation (SS&OA) problem, where risks include a series of disruption scenarios with uncertain probability of occurrence. It is a challenge for industry decision-makers to balance the average cost and the level of risk under the ambiguity set for probabilities. To address this challenge, a two-stage distributionally robust (DR) Mean-CVaR model is presented for the SS&OA problem. A procedure is developed for constructing the ambiguity set, and Polyhedral and Box ambiguity sets are constructed to characterise the uncertain probabilities. The worst-case Mean-CVaR criterion is employed for the second-stage cost within the ambiguity set to trade off the expected cost and CVaR value. Three measures are incorporated to increase the resilience of the supply chain. The proposed robust model is reformulated into two mixed-integer linear programming models. A real case of the Huawei cell phone manufacturer is used to illustrate the validity of the proposed approach in numerical settings. Experimental results show that the new optimising approach can provide a robust SS&OA solution to immunise against the influence caused by uncertain probabilities. By comparative analyses, some management insights are obtained for industry decision-makers.

In this paper, we propose (a) fuzzy multiple objective linear programming models for the Supplier Selection and Order Allocation (SSOA) problem under fuzzy demand and volume/quantity discount environments, and (b) an analysis of how to select the suitable aggregation operator based on the risk preferences of decision makers. The aggregation operators under consideration are additive, maximin, and augmented operators while the risk preferences are classified as risk-averse, risk-taking, and risk-neutral ones. The suitabilities of aggregation operators and risk preferences of decision makers are analyzed by a statistical technique, considering the average and the lowest satisfaction levels of the supplier selection criteria, based on numerical examples. Analysis results reveal that decision makers with different risk preferences will prefer only some aggregation operators and models. Moreover, a particular aggregation operator and model may generate a dominated solution for some situations. Thus, it should be applied with caution.

An integrated methodology for the selection of sustainable suppliers and order allocation problem with quantity discounts, lost sales and varying supplier availabilities

PurposeOn one side, the purpose of this paper is to numerically analyze the emergency order allocation mechanism and help managers to understand the relationship between the emergency coefficient, uncertainty and emergency cost in two‐echelon logistics service supply chain. On the other side, the purpose of this paper is to help managers understand how to deal with the problem of order allocation in the two‐echelon logistics service supply chain better in the case of emergency.Design/methodology/approachThe paper presents a multi‐objective planning model for emergency order allocation and then uses numerical methods with LINGO 8.0 software to identify the model's properties. The application of the order allocation model is then presented by means of a case study.FindingsWith the augment of uncertainty, the general cost of logistics service integrator (LSI) is increasing, while the total satisfaction of all functional logistics service providers (FLSPs) is decreasing, as well as the capacity reliability; at the same time the emergency cost coefficient is closely correlative with the satisfaction and general penalty intensity of FLSPs; finally, the larger the emergency cost coefficient is, the more satisfaction of FLSPs, but the capacity reliability goes up first and down later.Research limitations/implicationsManagement should note that it is not better when emergency cost coefficient is bigger. The general satisfaction degree of FLSP increases with the augment of emergency cost coefficient, but there is an upper limit of the value, i.e. it will not increase indefinitely with the augment of emergency cost coefficient. This paper also has some limitations. The optional emergency cost coefficient only adopted a group of data to analyze while the trend of the reliability of logistics capacity needs to be further discussed. In addition, the algorithm of emergency order allocation model in the case of multi‐objective remains to be solved.Practical implicationsUnder emergency conditions, LSIs can adopt this kind of model to manage their FLSPs to obtain the higher logistics performance. But LSIs should be careful selecting emergency cost coefficient. In accordance with different degrees of emergency logistics demand, LSIs can determine reasonable emergency cost coefficient, but not the bigger, the better, on the premise that LSIs acquire maximum capacity guarantee degree and overall satisfaction degree of FLSPs. FLSPs can make contract bargaining of reasonable emergency coefficient with LSIs to make both sides get the best returns and realize the benefit balance.Originality/valueMany studies have emphasized the capacity allocation of manufactures, order allocation of manufacturing supply chain and scheduling model of emergency resources without monographic study of supply chain order allocation of logistics service. Because the satisfaction degree of FLSPs the cost of integrators needs to be considered in the process of order allocation, and the inventory cost of capacity does not exist, it is different from the issue of capacity allocation planning of manufacture supply chain. Meanwhile, the match of different kinds of logistics service capacity must be considered for the reason of the integrated feature of logistics service. Additionally, cost is not the most important decision objective because of the characteristics of demand uncertainty and weak economy. Accordingly, this paper considers these issues.

A city as a system that constitutes one of the most important areas of human activities. The significant role to fulfill their expectations pay the goods transport and deliveries. These issues are the subject of urban logistics. In broad terms, urban logistics may be construed as a number of processes focused on freight flows, which are completed in cities, including deliveries, supply, goods transfer, services, etc. Due to the different urban logistics stakeholders’ expectations, these systems generate many challenges for managers, especially in the context of city users’ needs and their quality of life. Today, there is a lack of broadened approach and methodology to support them from the processes’ efficiency perspective. To fulfill this gap, the purpose of this paper is to apply the Six Sigma method as a support in last mile delivery management. Six Sigma method plays important role in production systems processes management. However, it could be useful in much wider perspective, including transport and logistics processes. The Authors emphasize that the Six Sigma method could be efficient approach in the last mile delivery processes’ analysis in the context of their efficiency. It helps positioning the customer satisfaction level and quantify the delivery processes defects, related to the undelivered goods. Following that it could improve significantly the last mile delivery processes efficiency. The concept is illustrated by a sample evaluation of one of the urban logistics processes: completion of deliveries. To this end, urban logistics processes were defined. In particular, the delivery completion process was mapped, and the process client was defined along with their expectations. The defects that occurred in the process were identified and analyzed. A DPMO (Defect per Million Opportunities) indicator was established for three randomly selected months. In addition, a root cause analysis of errors was performed. The sigma level for the studied process ranged from 2.61 to 2.89. The factor that had the greatest impact on the number of defects defined as failed deliveries was customer’s absence. The obtained results indicate that the delivery process should be examined in more detail.

The locomotive routing problem (LRP) considers the transportation of non-motorised containers called torpedo ladle cars (TPCs) within a steelworks. Locomotives are responsible for picking up and delivering TPCs to fulfil requests for various facilities, including steelmaking, ironmaking, heating, and repair facilities. This study introduces the many-to-many locomotive routing problem (M2MLRP), specifically tailored for the steel industry. M2MLRP was formulated using pattern- and routing-based models, and a logic-based Benders-decomposition-based matheuristic algorithm was developed. The pattern-based model successfully solved 20 small randomly generated instances, while the proposed matheuristic algorithm demonstrated the rapid generation of near-optimal solutions. Notably, unlike the pattern- and routing-based models, the proposed algorithm extended its capability to generate good feasible solutions for medium- and large-sized instances. Across all instances, the matheuristic algorithm outperformed both the routing-based model and an adaptive large neighbourhood search algorithm, showcasing its superior solution quality. This study provides a significant contribution to the field by being the first to address M2MLRP in the context of the steel industry.

Attended Home Delivery, where customer attendance at home is required, is an essential last-mile delivery challenge, e.g., for valuable, perishable, or oversized items. Logistics service providers are often faced no-show customers. In this paper, we consider the delivery problem in which customers can be revisited on the same day by a courier in the case of a failed first delivery attempt. Specifically, customer presence uncertainty is considered in a two-stage stochastic program, where penalties are introduced as recourse actions for failed deliveries. We build on the notion of a customer availability profile defined as a profile containing historical time-varying probability information of successful deliveries. We tackle this stochastic program by developing an efficient parallelized Adaptive Large Neighborhood Search algorithm. Our results show that by achieving a right balance between increasing the hit rate and reducing travel cost, logistics service providers can realize costs savings as high as 32% if they plan for second visits on the same day.