A hyper heuristic for the green vehicle routing problem with 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.

This paper deals with the Vehicle Routing Problem with Simultaneous Pick-up and Delivery and Time Limit (VRPSPDTL). Due to the combinatorial nature of the problem, heuristics methods are commonly used to generate good quality solutions within an acceptable computational time. Accordingly, an Iterated Local Search (ILS) procedure that uses a Variable Neighborhood Descent (VND) method to perform the local search is proposed. The algorithm was applied to test problems and, in most cases, was found to produce better results than those reported in the literature.KeywordsLocal SearchTravel Salesman ProblemVehicle Route ProblemReverse LogisticsCandidate ListThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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

The green vehicle routing problem (GVRP) is a relatively new topic, which aims to minimize greenhouse gasses (GHG) emissions produced by a fleet of vehicles. Both internal combustion vehicles (ICV) and alternative fuel vehicles (AFV) are considered, dividing GVRP into two separate subclasses: ICV-based GVRP and AFV-based GVRP. In the ICV-based subclass, the environmental aspect comes from the objective function which aims to minimize GHG emissions or fuel usage of ICVs. On the other hand, the environmental aspect of AFV-based GVRP is implicit and comes from using AFVs in transport. Since GVRP is NP-hard, finding the exact solution in a reasonable amount of time is often impossible for larger instances, which is why metaheuristic approaches are predominantly used. The purpose of this study is to detect gaps in the literature and present suggestions for future research in the field. For that purpose, we review recent papers in which GVRP was tackled by some metaheuristic methods and describe algorithm specifics, VRP attributes, and objectives used in them.

This paper addresses the Multi-Vehicle Routing Problem with Time Windows and Simultaneous Pickup and Delivery (MVRPTWSPD), aiming to optimize logistics distribution routes and minimize total costs. A vehicle routing optimization model is developed based on the operational requirements of the KS Logistics Center, focusing on minimizing vehicle dispatch, loading and unloading, operating, and time window penalty costs. The model incorporates constraints such as vehicle capacity, time windows, and travel distance, and is solved using a genetic algorithm to ensure optimal route planning. Through MATLAB simulations, 34 customer points are analyzed, demonstrating that the simultaneous pickup and delivery model reduces total costs by 30.13%, increases vehicle loading rates by 20.04%, and decreases travel distance compared to delivery-only or pickup-only models. The results demonstrate the significant advantages of the simultaneous pickup and delivery mode in reducing logistics costs and improving vehicle utilization, offering valuable insights for enhancing the operational efficiency of the KS Logistics Center.

This study develops a Green Vehicle Routing Problem (GVRP) model to address key logistics challenges, including time windows, simultaneous pickup and delivery, heterogeneous vehicle fleets, and multiple trip allocations. The model incorporates emissions-related costs, such as carbon taxes, to encourage sustainable supply chain operations. Emissions are calculated based on the total shipment weight and the travel distance of each vehicle. The objective is to minimize operational costs while balancing economic efficiency and environmental sustainability. A Genetic Algorithm (GA) is applied to optimize vehicle routing and allocation, enhancing efficiency and reducing costs. A Liquid Petroleum Gas (LPG) distribution case study in Yogyakarta, Indonesia, validates the model’s effectiveness. The results show significant cost savings compared to current route planning methods, alongside a slight increase in carbon. A sensitivity analysis was conducted by testing the model with varying numbers of stations, revealing its robustness and the impact of the station density on the solution quality. By integrating carbon taxes and detailed emission calculations into its objective function, the GVRP model offers a practical solution for real-world logistics challenges. This study provides valuable insights for achieving cost-effective operations while advancing green supply chain practices.

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.

The Vehicle Routing Problem (VRP) is a complex and high-level set of routing problems. Two of its important variants are the Dynamic Vehicle Routing Problem (DVRP) and the Green Vehicle Routing Problem (GVRP). The first one has become a challenging research topic in the last two decades, in which not all informations are known in advance, but are revealed as the system progresses. The second one is seen as a new application and solution in new logistics patterns, more specifically, for finding routes of vehicles to serve a set of customers while minimizing the total amount of CO 2 emissions, by increasing the loading rate and reducing the number of empty trips could reduce from 10% to 40% km traveled and therefore CO 2 emissions [1]. In this paper, we combine these two variants (DVRP and GVRP), where we try to minimize, in the dynamic environment, the greenhouse gas especially the carbon dioxide CO 2 , which is the immediate consequence of the depletion of the ozone layer and time duration. We will call this combination the Dynamic Green Vehicle Routing Problem (DGVRP). To study this new problem, we present the technique employed to estimate the amount of CO 2 emissions, the emission matrix and integrate them into the DGVRP model, then we propose the hybridization based on Ant Colony Optimization (ACO) algorithm with a Large Neighborhood Search (LNS) algorithm to solve our problem. The effectiveness of this approach is tested on a set of the dynamic green problems instances, which are adopted in this work from the static GVRP benchmark datasets.

Over the two last decades, distribution companies have been aware of the importance of paying attention to the all aspects of a distribution system simultaneously to be successful in the global market. These aspects are the economic, the environmental, the social and the safety aspects. In the Vehicle Routing Problem (VRP) literature, the economic issue has often been used, while the environmental, the safety and the social concerns have been less proportion of studies. The Green vehicle routing problem (GVRP) is one of the recent variants of the VRP, dealing with environmental aspects of distribution systems. In this paper, two developed mixed integer programming models are presented for the GVRP with social and safety concerns. Moreover, a Genetic Algorithm (GA) is developed to deal efficiently with the problem in large size. Different numerical analyses have performed to validate the presented algorithm in comparison to exact solutions and investigate the influence of several key factors like the effect of increasing the cost of safety aspect on route balancing, and customer waiting time. The results confirm that the proposed algorithm performs well and has more social and safety benefits (such as more balanced tours and fewer customers waiting time than the classic GVRP.

Green vehicle routing problem with queues

This study proposes a three-index flow-based mixed integer formulation to solve a two-echelon location routing problem with simultaneous pickup and delivery. In this formulation, pickup and delivery demands can be addressed using the same vehicle in each echelon of the network to reduce costs and increase logistics efficiency. We solve such NP-hard problem by developing a multistart hybrid heuristic with path relinking (MHH-PR) which is composed of local search and a variable neighbourhood descent algorithm. In the algorithm, three constructive heuristics are applied to generate diversified initial solutions, and path relinking is introduced for intensification and postoptimisation. Results indicate that MHH-PR can reduce the gap between the near optimal and global optimal solutions by 1%-2%. The proposed algorithm significantly improves computational efficiency by reducing the computational time of more than 10 min for existing cases involving 20 nodes to less than 10 s.

GVRP considered oil-gas recovery in refined oil distribution: From an environmental perspective

Landing aircraft safely is an important operation that air traffic controllers have to deal with on a daily basis. For each arriving aircraft a runway and a landing time must be allocated. If these allocations can be done in an efficient way, it could give the airport a competitive advantage. The Aircraft Landing Problem (ALP) aims to minimize the deviation from a preferred target time of each aircraft. It is an NP-hard problem, meaning that we may have to resort to heuristic methods as exact methods may not be suitable, especially as the problem size increases. This paper proposes an iterated local search (ILS) algorithm for the ALP. ILS is a single solution based search methodology that successively invokes a local search procedure to find a local optimum solution. A perturbation operator is used to modify the current solution in order to escape from the local optimum and to provide a new solution for the local search procedure. As different problems and/or instances have different characteristics, the success of the ILS is highly dependent on the local search, the perturbation operator(s) and the perturbation strength. To address these issues, we utilize four perturbation operators and a time varying perturbation strength which changes as the algorithm progresses. A variable neighborhood descent algorithm is used as our local search. The proposed ILS generates high quality solutions for the ALP benchmark instances taken from the scientific literature, demonstrating its efficiency in terms of both solution quality and computational time. Moreover, the proposed ILS produces new best results for some instances.

In this paper, we investigate some modified local search (LS) heuristics for the solution of symmetric traveling salesman problems (TSPs). These modifications are mainly due to the use of extended neighborhood structures. In addition, we are concerned with several new sets of the moves (transitions of solutions) based on the extended configurations of edge exchanges. We are also examining the performance of these extensions being used in an iterated local search (ILS) paradigm. The results from the experiments with the benchmark TSP instances from the TSP library (TSPLIB) demonstrate that the introduced improvements enable to seek solutions of higher quality without substantially increasing computational complexity.DOI: http://dx.doi.org/10.5755/j01.itc.42.3.1301