Optimization of Multi-Vehicle Routing Problem with Time Windows and Simultaneous Pickup and Delivery

This paper provides a green vehicle routing problem with simultaneous pickup and delivery with time windows. The objective of this study is to minimize total costs including fuel cost and carbon emission cost while satisfying customer pickup and delivery demands simultaneously with time windows and capacity constraints. In this paper, fuel consumption is computed considering vehicle load and distance. Firstly, a mathematical model is developed to describe the VRPSPDTW problem. This study proposes a genetic algorithm to optimize cost. The computational experiments are conducted under three crossover (one point, two point and cyclic crossover) and two mutation (swap and inverse) operator. The computation between swap and inverse mutation under three crossover are compared and the results show that swap mutation perform better than inverse mutation under every crossover operator.

In this work, a bi-objective mixed-integer linear programming model is formulated for the first time to design a green supply chain while considering both forward and reverse flows, location-inventory-routing problem with simultaneous pickup and delivery, scheduling of vehicles, and time window. This model is presented to manage the production and distribution of perishable products and aims to minimize total costs and lost demands simultaneously. To solve the proposed bi-objective model under uncertainty, an approach based on fuzzy theory is developed. The performance of the proposed model and solution approach is evaluated using data from a bread production and distribution industry in Alborz province. The results showed that Hashtgerd center should be set up as the production and separation center and suppliers 1 and 2 (i.e., Seifabad and Mahdasht) should be selected to purchase raw materials when designing this network. The results obtained from the model's implementation in the real world and the sensitivity analysis of coefficients of objective functions indicate the desired efficiency and effectiveness of the proposed model and solution approach.

Vehicle routing problems with simultaneous pickup and delivery with time windows are studied in this paper. In a closed-loop logistics network, vehicles visiting customers for pickup and delivery may be only allowed for certain time intervals. A mixed integer programming model of the problem is presented. It has been known that solving vehicle routing problems with time windows is NP-hard. A heuristic solution procedure is developed to solve the problem. The proposed heuristic is tested on a number of numerical examples based on modified benchmark problems in the literature.

An exact algorithm for Two-Echelon Location-Routing problem with simultaneous pickup and delivery

A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery

In the simultaneous pickup and delivery problem, every customer has both delivery demand and pick-up demand, and both demands need to be served simultaneously．Under this condition, a location-routing problem with simultaneous pickup and delivery model was established to minimize the sum of location cost, routing cost and transportation cost. For solving this model, a Hybrid Immune Algorithm was developed. The initial solution was generated by greedy clustering algorithm; The antibody was evaluated and sorted by the original immune algorithm; And the immune operation of the original algorithm was improved by the neighborhood search operation. Finally, the feasibility of the model and the effectiveness of the algorithm were verified by using the Hybrid Immune Algorithm, the original Immune Algorithm, the simulated annealing algorithm and the ant colony algorithm.

Logistics operations are crucial in the e-commerce supply chain as they deal with high costs as well as they have a significant environmental impact. The first-mile and last-mile delivery operations in e-commerce logistics are regarded as the operations with the highest costs. As a result, e-commerce service providers are keen to improve their first mile and last-mile delivery processes. Therefore, this study has been conducted to optimize transportation cost and distance of combined first-mile pickup and last-mile delivery operations while meeting some practical requirements such as a variety of package types, package compatibility on different types of vehicles, and a heterogeneous fleet of vehicles. After a careful literature review, this paper introduces a mathematical model to optimize the simultaneous first-mile pickup and last-mile delivery. The proposed mathematical model was simulated in SupplyChainGuru® modelling and simulation software. The study concluded that when first-mile pickup and last-mile delivery are routed simultaneously, rather than distributing and collecting the products as two separate operations, the overall cost is minimized by about 34%, and the distance is reduced by about 42% while reducing the number of vehicles on the route and utilising the maximum possible capacity of vehicles. This simultaneous pickup and delivery will improve the routing of the e-commerce logistic supply chain and will serve as a platform for extending the simultaneous pickup and delivery process to other industries as well.

The optimization of auto parts supply chain logistics plays a decisive role in the development of the automotive industry. To reduce logistics costs and improve transportation efficiency, this paper addresses the joint optimization problem of multi-vehicle pickup and delivery transportation paths under time window constraints, coupled with the three-dimensional loading of goods. The model considers mixed time windows, three-dimensional loading constraints, cyclic pickup and delivery paths, varying vehicle loads and volumes, flow balance, and time window constraints. Evaluation rules for the three-dimensional loading test of goods are also set, resulting in constructing a comprehensive optimization model for the inbound logistics of auto parts and components. In this study, a Greedy-Tabu Dual-Heuristic algorithm is proposed, which integrates an Improved Greedy Algorithm with an Enhanced Tabu Search Algorithm based on the -sampling strategy. The overall problem-solving process for the Improved Greedy Algorithm and the Tabu Search Algorithm is outlined. The superiority, efficiency, and stability of the improved algorithm are verified by solving cases of various sizes and analyzing the algorithm’s results before and after improvement. A case study involving the third-party logistics company R Enterprise compares the pickup and delivery-separated Milk-Run mode with the simultaneous delivery and pickup Milk-Run mode. The proposed method shows a 26.67% reduction in total distance traveled and a 46.60% decrease in waiting time compared to the traditional Milk-Run approach. Additionally, when evaluated against the standard 3D loading inspection method, the proposed approach improves average vehicle load utilization by 17% and vehicle volume utilization by 15%. These findings verify the applicability and superiority of the proposed algorithms and models in practical scenarios.

The sustainable reverse inventory-routing problem with time windows and simultaneous pickup and delivery

This study investigates the Automated Guided Vehicle (AGV)scheduling problem under pickup and delivery constraints and introduces a new formulation, termed AGV Scheduling for Simultaneous Pickup and Delivery with Time Windows (AGV-SPDTW). A scheduling model is developed with the objective of minimizing total costs, including transportation costs, fixed usage costs, and penalty costs for time window violations. To address this model, a Bi-objective Deep Q-Network (B-DQN) algorithm enhanced with a multi-neighborhood search strategy is proposed. In this framework, a multi-neighborhood search action space is constructed, and the Deep Q-Network determines actions to guide route exploration. Moreover, a reward–penalty mechanism based on Pareto dominance is designed to autonomously optimize the decision network. Finally, simulation experiments are conducted to validate the effectiveness of the proposed algorithm. The results confirm that B-DQN is an efficient approach for solving the AGV-SPDTW problem and can substantially improve the operational efficiency of automated warehouse systems.

The aim of this work is to develop an intelligent optimization software based on enhanced VNS meta-heuristic to tackle Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD). An optimization system developed based on enhanced Variable Neighborhood Search with Perturbation Mechanism and Adaptive Selection Mechanism as the simple but effective optimization approach presented in this work. The solution method composed by combining Perturbation based Variable Neighborhood Search (PVNS) with Adaptive Selection Mechanism (ASM) to control perturbation scheme. Instead of stochastic approach, selection of perturbation scheme used in the algorithm employed an empirical selection based on each perturbation scheme success along the search. The ASM help algorithm to get more diversification degree and jumping from local optimum condition using most successful perturbation scheme empirically in the search process. A comparative analysis with a well-known exact approach is presented to test the solution method in a generated VRPSPD benchmark instance in limited computation time. Then a test to VRPSPD scenario provided by a liquefied petroleum gas distribution company is performed. The test result confirms that solution method present superior performance against exact approach solution in giving best solution for larger sized instance and successfully obtain substantial improvements when compared to the basic VNS and original route planning technique used by a distributor company.

Electric vehicles (EVs) are a promising option to reduce air pollution and shipping costs, especially in urban areas. To provide scientific guidance for the growing number of logistics companies using EVs, we investigated an electric-vehicle-routing problem with simultaneous pickup and delivery that also considers non-linear charging and load-dependent discharging (EVRPSPD-NL-LD). The objective was to minimize the total number of EVs and the total working time, including travel time, charging time, waiting time, and service time. We formulated the problem as a mixed integer linear program (MILP), and small-size problems could be solved to optimality in an acceptable amount of time using the commercial solver IBM ILOG CPLEX Optimization Studio (CPLEX). In view of the fact that the problem is NP-hard, an adaptive large neighborhood search (ALNS) metaheuristic method was proposed to solve large-size problems. Meanwhile, new operators and a time priority approach were developed to provide options for different scenarios. The results of our computational investigation and sensitivity analysis showed that the proposed methods are effective and efficient for modified benchmark instances.

This paper addresses a new variant of the location routing problem (LRP), namely the heterogeneous fleet LRP with simultaneous pickup and delivery and overloads (HFLRPSPDO) which has not been previously tackled in literatures. In this problem, the heterogeneous fleet is comprised of vehicles with different capacities, and the vehicle overloads up to a specified upper bound is allowed. This paper proposes a polynomial-size mixed integer linear programming formulation for the problem in which a penalty function, allowing capacity violations of vehicles, is integrated into objective function. Furthermore, two heuristic algorithms, respectively based on tabu search and simulated annealing, are proposed to solve HFLRPSPDO. Computational results on simulated instances show the effectiveness of the proposed problem formulation and the efficiency of the proposed heuristic algorithms.

We study a class of vehicle‐routing problems with simultaneous pickup and delivery (VRPSPD). In VRPSPDs, each customer may require a certain quantity of goods delivered from the depot and a quantity of goods to be picked up and returned to the depot. Besides the standard VRPSPD, we address (1) the VRPSPD with time limit (VRPSPDTL), which imposes a time limit on the routes of the transportation vehicles, (2) the VRPSPD with time windows (VRPSPDTW), which takes customer time windows into account, (3) the VRP with divisible deliveries and pickups (VRPDDP), which allows for fulfilling the delivery and pickup requests of each customer in two separate visits, (4) the previously unstudied VRP with restricted mixing of divisible deliveries and pickups (VRPRMDDP), which accounts for the difficulty of rearranging the vehicle load by additionally requiring that a certain percentage of the vehicle capacity must remain unoccupied when both types of demand are simultaneously loaded, and (5) the previously unstudied VRPDDP with time windows (VRPDDPTW). We develop a hybrid heuristic solution method which combines an adaptive large neighborhood search algorithm with a path relinking approach, called ALNS‐PR, and we demonstrate the competitiveness of our algorithm on benchmark instances proposed in the literature. Especially on VRPSPDTL, VRPSPDTW, and VRPDDP instances, our ALNS‐PR proves to be superior to the majority of comparison algorithms and is able to obtain numerous new best solutions.

This paper addresses the two-echelon location routing problem with simultaneous pickup and delivery (2E-LRPSPD). The 2E-LRPSPD deals with optimally locating primary and secondary facilities, and integrating goods distribution from depots and collection from customers and secondary depots. To the best of our knowledge there is no previous study on this problem. We propose two mixed integer programming formulations for the 2E-LRPSPD. While the first formulation is a two-index node-based formulation, the second one is a two-index flow-based formulation. Moreover, a family of valid inequalities are adapted from the literature to strengthen the formulations. In order to evaluate the performances of the formulations and valid inequalities, we conduct an experimental study on the instances derived from the literature. The computational results show that the flow-based formulation produces better lower bounds than the node-based formulation on small and medium-size problems.