Green logistics location-routing problem with eco-packages

Highlights

• Optimize a location-routing problem with eco-packages in a state-space-time network.

• Propose a two-phase optimization model for green eco-packages’ pickup and delivery.

• Design a Lagrangian relaxation-based heuristic algorithm to solve the model.

• Explore eco-package route sequences in resource sharing pickup and delivery process.

• Study synchronization and cost-effectiveness in transport and service phases.

Abstract

Optimization of the green logistics location-routing problem with eco-packages involves solving a two-echelon location-routing problem and the pickup and delivery problem with time windows. The first echelon consists of large eco-package transport, which is modeled by a time-discretized transport-concentrated network flow programming in the resource sharing state–space–time (SST) network. The second echelon focuses on small eco-package pickups and deliveries, established by the cost-minimized synchronization-oriented location routing model that minimizes the total generalized cost, which includes internal transportation cost, value of eco-packages, short-term benefits and environmental externalities. In addition, the Gaussian mixture clustering algorithm is utilized to assign customers to their respective service providers in the pickup and delivery process, and a Clarke–Wright saving method-based non-dominated sorting genetic algorithm II is designed to optimize pickup and delivery routes, and improve their cost-effectiveness and degree of synchronization. Different strategy testing results are used in the service phase as input data to calculate the cost of the transport phase, which is solved through a Lagrangian relaxation approach. The 3D SST network representation innovatively captures the eco-package route sequence and state transition constraints over the shortest path in the pickup and delivery at any given moment of the transport phase. A large-scale logistics network in Chengdu, China, is used to demonstrate the proposed model and algorithm, and undertake sensitivity analysis considering the life cycle of green eco-packages.

Introduction

The convenience of online shopping platforms and the continuous growth of the world’s commodity consumption rate are essential driving forces for modern logistics operations. The diversification and personalization of customer demands constitute emerging challenges to maintaining green logistics activities, including sustainable package delivery and pickup operations. In addition to meeting daily, diverse customer demands, logistics companies must consider the societal and environmental effect of logistics operations, and support sustainable logistics activities. Statistics show that more than 19 billion corrugated boxes were used for packaging in China in 2017, which means over 50 million tons of raw paper was consumed, resulting in about 70 million trees cut down for paper-making. Such huge resource consumption makes a green logistics strategy in packaging a focus of governments, enterprises, and customers (Liu et al., 2020a). In the life cycle of a conventional standard package, paper materials are used by the merchant for packaging products, transported by the logistics company to the customer, and ultimately discarded in most situations, as shown in Fig. 1. By contrast, in the case of green eco-packages, packaging materials are recycled and used multiple times after the customer takes out the product. On the one hand, green eco-packages are evidently better than conventional standard packages from the key perspective of green-oriented logistics and environmental protection. On the other hand, logistics companies tend to focus more on the cost and benefit of using green eco-packages. Thus, it is critically important to use Table 1 to compare the costs and benefits of the two types of packages. One can refer to Mahmoudi and Parviziomran (2020) to learn more about environmental and economic costs of these packages.

Table 1 compares the cost between a conventional standard package and a green eco-package for decision-making. From the manufacturing cost perspective, a green eco-package costs more than a conventional standard package (Nordin and Selke, 2010). The conventional standard package has no short-term benefit, that is, it has a high environmental externality due to the lack of recycling, whereas the green eco-package presents a relatively high short-term benefit and a low externality due to recycling. As a result, many companies have started to explore green logistics strategies that use green eco-packages to achieve cost savings from reduced waste of resources. In 2017, Suning invested 50,000 green eco-packages that can be recycled more than 1000 times to reduce the use of conventional standard packages by 6.5 million, and the percentage of cost savings exceeded 60%. In addition, the use of eco-packages enables resource sharing in terms of transporting and customer services across different facilities.

The location of the pickup facilities has a significant role on the recycling rate of green eco-package (Tiew et al., 2019). In a multi-echelon logistics network design for conventional package delivery, how to select the pickup facilities from the existing delivery facilities to recycle the green eco-package is a major challenge. Massive delivery and pickup requests are generated daily by online shopping, creating the need for logistics operators to synchronize logistics activities in multi-echelon networks, especially under resource constraints. Furthermore, achieving sustainability in recycling green eco-packages requires a strong focus on the selection of the facilities’ locations along with the design of the routing network.

In this study, we address the challenging problem of simultaneous location-routing decision, reflecting that the usually dispersed distribution of customer nodes over the entire city causes difficulties in reducing the underutilization of resources. We approach this problem as a green logistics location-routing problem with eco-packages (GLLRPE) considering the state, space, and time aspects of operations as well as operational synchronization and resource sharing between facilities. Finally, we propose an optimization framework for large-scale multi-echelon logistics delivery and pickup networks to solve the GLLRPE for logistics operations’ sustainability.

In general, a multi-echelon logistics delivery and pickup network consists of one or more pickup centers (PCs) and delivery centers (DCs), multiple delivery satellites (DSs) and pickup satellites (PSs) at intermediate echelons, and many customers (Govindan et al., 2014, Wang et al., 2017a). In a metropolis, where the sheer number of customers leads to large variations in pickup and delivery requirements, PSs and DSs should be wisely installed to satisfy customers’ actual expectations and keep companies profitable (Wang et al., 2018a). The state of each recycled green eco-package should also be tracked to examine its life cycle such that the logistics operators can decide on its reuse. Green eco-packages can be divided into three categories, namely, initial-, medium-, and critical-grade recoveries in actual operations (Cruz et al., 2012, Da et al., 2014). For example, no pickup services are usually provided for eco-packages exceeding their thresholds of critical-grade recovery owing to the exorbitant recovery cost. Moreover, each customer or logistics facility with a specific geographical location has service time constraints (time) and is subject to a delivery or pickup decision (state), based on the space capacity of the vehicle (space) and recycling times. Hence, the entire network can be referred to as state–space–time (SST) network (Mahmoudi and Zhou, 2016, Xu et al., 2018, Mahmoudi et al., 2019a, Mahmoudi et al., 2019b). In these complicated green logistics networks, large green eco-packages need to be transported from the first-echelon logistics facilities to the second-echelon satellites by semitrailer trucks, and reassigned to multiple small eco-packages for delivery to customers with a high degree of synchronization. Moreover, in SST networks, the theory proves that resource sharing can provide a higher degree of synchronization between multi-echelon logistics facilities than if each facility operates on its own.