Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries

Abstract

In this work, a multidepot multiperiod vehicle routing problem with pickups and deliveries (MDPVRPPD) is solved by optimizing logistics networks with collaboration and resource sharing among logistics service providers. The optimal solution can satisfy customer demands with periodic time characteristics and incorporate pickup and delivery services with maximum resource utilization. A collaborative mechanism is developed to rearrange both the open and closed vehicle routes among multiple pickup and delivery centers with improved transportation efficiency and reduced operational costs. The effects of resource sharing strategies combining customer information sharing, facility service sharing, and vehicle sharing are investigated across multiple service periods to maximize resource utilization and refine the resource configuration. A multiobjective optimization model is developed to formulate the MDPVRPPD so that the minimum total operational costs, waiting time, and the number of vehicles are obtained. A hybrid heuristic algorithm incorporating a 3D clustering and an improved multiobjective particle swarm optimization (IMOPSO) algorithm is introduced to solve the MDPVRPPD and find Pareto optimal solutions. The proposed hybrid heuristic algorithm is based on a selective exchange mechanism that enhances local and global searching capabilities. Results demonstrate that the proposed IMOPSO outperforms other existing algorithms. We also study profit allocation issues to quantify the stability and sustainability of long-term collaboration among logistics participants, using the minimum costs remaining savings method. The proposed model and solution methods are validated by conducting an empirical study of a real system in Chongqing City, China. This study contributes to the development of efficient urban logistics distribution systems, and facilitates the expansion of intelligent and sustainable supply chains.

The Impacts of the Cross-Chain Collaboration Center Model on Transportation Performance: A Case Study of a Bulk Transportation Network in Thailand

“…This route sequence can be written in route format as {depot>1>2>2>3>1>2>depot}, and it can be written in operation sequence format as {depot>job1>job2>job1> depot}. We define the fuel cost (fvw) and the travel time (Svw) from operation sequence v to w. After obtaining route rk, we defined the loading terminal (av) and unloading terminal (bv) of operation sequence v to calculate tvw and cvw, as shown in Equations ( 19) and (20).…”

“…Maneengam and Udomsakdigool [19] proposed a covering model with a screening technique to reduce the initial problem size for collaborative bidirectional multi-period vehicle routing problems under profit-sharing agreements in bulk transportation. Wang et al [20] developed a multi-objective optimization model to formulate the multi-depot multiperiod vehicle routing problem with pickups and deliveries. Moreover, they proposed a hybrid heuristic algorithm to solve the problem and used the minimum cost remaining savings method to allocate the cost for each participant in the collaboration.…”

“…More considerations of other performance indicators have been added, including environmental, social impact, quality, and storage costs. A study that examined and compared collaboration with non-collaboration settings showed that CTM provides superior performance and cost benefits when compared to non-collaborative solutions [3]- [4], [11], [16]- [17], [20]- [22], [25]- [31], [33], [35], [37], [36]- [39]. Although we know that the 4C model (4CM) can lower the total cost, it can possibly increase the cost for some parties, and change is almost impossible if there remains a barrier due to human factors.…”

Collaborative multicenter logistics delivery network optimization with resource sharing

“…The external repository not only stores the nondominated vectors obtained along the search process but also plays a role in guiding the population to the latest Pareto frontier. In addition, the Pareto frontier can be extended to the Pareto front surface to select the optimal solution for solving problems involving more than two objective functions [ 57 ]. The specific process of AGPSO is explained as follows, and the pseudo-code procedure is shown in Table 4 .…”

Multidepot Recycling Vehicle Routing Problem with Resource Sharing and Time Window Assignment

“…Molina et al [38] established a mathematical model that aims at maximizing the number of served customers and minimizing the total cost in the case of RS. Wang et al [39] studied the RS strategy in multiple service periods and multidepot to enhance resource efficiency and refine the resource configuration.…”