Multi-compartment Vehicles Research Articles

Enhancing efficiency in delivering petroleum products: optimizing the deteriorating inventory-routing problem with heterogeneous multi-compartment vehicles

Enhancing efficiency in delivering petroleum products: optimizing the deteriorating inventory-routing problem with heterogeneous multi-compartment vehicles

An adaptive differential evolution algorithm to solve the multi-compartment vehicle routing problem: A case of cold chain transportation problem

This research paper introduces an adaptive differential evolution algorithm (ADE algorithm) designed to address the multi-compartment vehicle routing problem (MCVRP) for cold chain transportation of a case study of twentyeight customers in northeastern Thailand. The ADE algorithm aims to minimize the total cost, which includes both the expenses for traveling and using the vehicles. In general, this algorithm consists of four steps: (1) The first step is to generate the initial solution. (2) The second step is the mutation process. (3) The third step is the recombination process, and the final step is the selection process. To improve the original DE algorithm, the proposed algorithm increases the number of mutation equations from one to four. Comparing the outcomes of the proposed ADE algorithm with those of LINGO software and the original DE based on the numerical examples In the case of small-sized problems, both the proposed ADE algorithm and other methods produce identical results that align with the global optimal solution. Conversely, for larger-sized problems, it is demonstrated that the proposed ADE algorithm effectively solves the MCVRP in this case. The proposed ADE algorithm is more efficient than Lingo software and the original DE, respectively, in terms of total cost. The proposed ADE algorithm, adapted from the original, proves advantageous for solving MCVRPs with large datasets due to its simplicity and effectiveness. This research contributes to advancing cold chain logistics with a practical solution for optimizing routing in multi-compartment vehicles.

Modeling and solving the fuel distribution problem with unloading precedence and loading sequence considerations

AbstractThis paper presents a real-world liquid fuel distribution problem involving a heterogeneous fleet of multi-compartment vehicles servicing a set of orders of different fuels. Two new features are introduced that affect significantly the performance of the fuel distribution process in terms of safety and efficiency: (i) loading trucks so that the payload remains balanced throughout each phase of a delivery route, and (ii) sequencing the requests for loading trucks at the depot loading facilities. A Mixed Integer Programming formulation is presented and an Adaptive Large Neighbourhood Search algorithm with various novel features is developed and benchmarked. A new loading model is formed and solved for allocating the ordered items (fuel) to vehicle compartments, as part of constructing/repairing delivery routes. The computational performance of the proposed solution approach has been tested on a series of benchmark problems. Moreover, a series of experiments were performed in order to assess the effect of the balanced loading constraints on the traveled distance. The results indicate that the effect of this type of constraint on the total traveled distance is kept at a reasonable level, reaching a maximum increase of 4.37%. The computational tools presented in this work may accommodate the dispatchers’ work in producing efficient and safe delivery routes while managing efficiently any potential bottleneck in the truck loading facilities.

MULTI-COMPARTMENT VEHICLES FOR SPLIT PICK-UP AND SPLIT DELIVERY PROBLEM WITH TIME WINDOW

In this article, we'll look at multi-compartment vehicles, which collect goods from suppliers and deliver them to different customers who place large orders. Because some products are incompatible with each other, these goods cannot be transported by single-compartment vehicles. In addition, because customers can place large orders, some suppliers and/or customers may be visited several times by different vehicles. In this work, the aim is to satisfy a group of customers while respecting the constraints linked to the capacity of each compartment and each type of product, and to ensure that each supplier is visited before the customer. Our first step is to model our problem mathematically and then solve it using an approximate method. Given its complexity, we use the genetic algorithm to solve the problem of split pick-up and delivery with time windows by multi-compartment vehicles. Our model allows us to determine a minimum distance and a minimum cost using a reasonable number of vehicles.

Multi-Compartment Vehicle Routing Problem Considering Traffic Congestion under the Mixed Carbon Policy

The use of multi-compartment vehicles (MCVs) in urban logistics distribution is increasing. However, urban traffic congestion causes high carbon emissions in the logistics distribution, resulting in unsustainable development in urban transportation. In addition, the application of the mixed carbon policy has gradually become the first choice for energy conservation and emission reduction in some countries and regions. The transportation industry is a major carbon-emitting industry, which needs to be constrained by carbon emission reduction policies. In this context, the research on the multi-compartment vehicle routing problem (MCVRP) considering traffic congestion under the mixed carbon policy is carried out. Firstly, a mathematical model of MCVRP considering traffic congestion under the mixed carbon policy is constructed. Secondly, a two-stage variable neighborhood threshold acceptance algorithm (VNS-TA) is proposed to solve the above mathematical model. Thirdly, 14 adapted standard examples of the MCVRP are used to verify the effectiveness and optimization ability of the two-stage VNS-TA algorithm. A simulation example of the MCVRP considering traffic congestion under the mixed carbon policy is used to conduct sensitivity analyses for different scenarios. Finally, the following conclusions are drawn: (1) the two-stage VNS-TA algorithm is effective and has strong optimization ability in solving the basic MCVRP, and (2) the two-stage VNS-TA algorithm can solve and optimize the MCVRP considering traffic congestion under the mixed carbon policy, which has the effects of cost saving and energy conservation and emission reduction.

Designing sustainable logistics networks for classified municipal solid wastes collection and transferring with multi-compartment vehicles

The success stories of waste classification and processing systems in reducing emissions and conserving natural resources have inspired many countries to adopt similar systems. The current research on waste collection logistics network design is limited in capturing the features of waste classification systems. The environmental objectives, the use of multi-compartment vehicles to separate waste and reduce driving distance, and the joint decision-making of transfer station locations and vehicle routes are not integrated into the same optimization framework. This study aims to investigate the strategic planning of transfer stations and vehicle fleet dispatch in a city-level classified waste collection system. The problem is formulated as an integer programming problem that minimizes total travel costs while considering greenhouse gas (GHG) emissions. To solve the problem effectively, we propose a fast hybrid heuristic algorithm based on column generation (CG) and Adaptive Large Neighborhood Search (ALNS). The proposed algorithm framework is tested on various instances and applied to a real-sized logistics network in Chengdu, China. Computational results indicate that the proposed algorithm achieves high efficiency while maintaining a satisfactory gap. The results confirm that introducing the waste classification system can help reduce total travel costs and greenhouse gas emissions.

Solving the multi-compartment vehicle routing problem by an augmented Lagrangian relaxation method

In real-world routing applications such as waste collection or fuel distribution, multiple products that cannot be mixed should be jointly collected or delivered. Multi-compartment vehicles are applied with great flexibility in routing and assignment decisions for these real-world applications. This study concentrates on the multi-compartment vehicle routing problem with fixed compartment size and fixed assignment products to compartments. For this problem, we develop two new formulations in the space–time network and state-space–time network. For the state-space–time network formulation, we dualize the task allocation constraints to the objective function and decompose the Lagrangian relaxed problem (LR) into a series of identical routing subproblems. To produce high-quality feasible solutions, the augmented Lagrangian relaxed problem (ALR) with superior convergence and feasibility is further applied to this problem. The ALR is decomposed into many solvable routing subproblems by alternating direction method of multipliers. A time-dependent dynamic programming algorithm is developed to settle the obtained subproblems of the LR and ALR. The subgradient optimization method solves the Lagrangian dual problems. The solution quality can be evaluated by the relative gap between the global lower and upper bounds at each iteration. In numerical experiments, the proposed method produces solutions with good integrality gaps. Based on Lagrangian relaxation and decomposition techniques, this research provides a novel solving scheme for the multi-compartment vehicle routing problem.

A multi-compartment electric vehicle routing problem with time windows and temperature and humidity settings for perishable product delivery

Perishable product delivery has been a notable challenge due to the nature of perishability and high customer expectations. Recently, emerging technologies such as electric and multi-compartment vehicles have been applied to facilitate perishable product deliveries with less deterioration and carbon emissions. Accordingly, new management problems are placed including setting environmental conditions of compartments, routing and scheduling the recharging of vehicles. To deal with these difficulties, we introduce the multi-compartment electric vehicle routing problem with time windows and temperature and humidity settings (MCEVRP-th). A mathematical formulation is proposed, and a hybrid adaptive large neighborhood search and tabu search heuristic (ALNS/TS), which allows generating infeasible solutions, is developed. Extensive experiments assess the performance of the proposed algorithm based on the modified benchmark and real-world instances, and several management insights are derived.

A dynamic approach for the multi-compartment vehicle routing problem in waste management

Urban areas worldwide face a significant environmental challenge which is increasing municipal solid waste rate. Addressing its negative consequences necessitates advancements in waste management systems. Although the previous research focused on the static routing approach in the collection phase, this paper adds a dynamic municipal solid waste collection scheme to optimize vehicle routing, accounting for fluctuations in waste generation and changes in transportation systems. This study employs, for the first time, the application of a discrete choice model (DCM) to streamline the process of re-optimization in dynamic vehicle routing problems (DVRP). At each decision epoch, DCM is applied to determine the likelihood of choosing the next geographical zone to visit bins based on current waste generation levels and traveling costs. Moreover, the multi-compartment vehicles are considered to preserve waste segregation during transportation, thereby increasing operational efficiency and regulatory compliance. Another contribution of this paper is to determine visiting priority for each bin by adjusting the time window based on the threshold waste level. Hence, this paper proposes a framework for sustainable, efficient, and effective waste management practices by integrating the benefits of dynamic and multi-compartment routing. Furthermore, a hybrid Genetic and Particle Swarm Optimization algorithm has been designed to find the best solution for the studied problem as well as some of the latest and most proficient metaheuristic algorithms. Finally, the Best Worst Method is applied to find the best-proposed algorithm to solve the presented problem, indicating that the hybrid algorithm has the highest performance in providing high-quality route plans.

On optimizing healthcare waste routing systems using waste separation policies: A case study

This article investigates the transportation task of Health-care waste (HCW). The particularity of HCW, compared to ordinary waste, is that it may contain infectious and/or toxic substances. The disposal of this waste involves transporting it from healthcare facilities (HCF) to treatment centers. This step can be threatening to humans and the environment if not managed with the utmost care. To ensure the safe management of HCW, the separation policy between dangerous and normal waste should be honored. Hence, a vehicle routing problem with multi-compartment vehicles is adopted with the underlying objective of minimizing the total travel distance. Given the NP-hardness of the problem, an adaptive evolutionary algorithm is proposed based on the results of pre–post statistical tests of the population. The proposed algorithm is compared to the best so far solutions on medium and large instances using benchmarks from the literature. The experimental study confirms the efficiency of the proposed approach against the competing algorithms. Finally, a case study is provided to illustrate the managerial insights of the studied problem.

A holistic, integrated supply-production–distribution problem in the dairy industry under uncertain supply and demand

Although the simultaneous planning of several relevant operations in the form of an integrated model has been gaining prestige in recent years on miscellaneous topics, the dairy industry, due to multifaceted and complex planning aspects of each level, has been less investigated. Hence, this study introduces an unprecedented, integrated supply-production–distribution problem under uncertain supply and demand in the dairy industry. In this regard, a novel mathematical model is developed to minimize the total cost of the supply chain, wherein a broad range of decisions, including assignment, routing, and scheduling, are reflected. More importantly, each stage encompasses a wide range of realistic concerns, to name a few, a comprehensive supply network, the blending process, various time limitations, and coordination among means of transportation, such as lorries and different multi-compartment vehicles, are considered in the supply phase. Additionally, a multi-period, multi-product, multi-stage production system with various production capacities and perishability requirements is considered in the production phase. The considerations of multi-compartment vehicles alongside the assignment of product-oriented time windows and products' early and late delivery are also reflected in the distribution phase. Furthermore, a set-induced robust optimization technique with the interval-ellipsoidal-polyhedral uncertainty set is applied to handle supply and demand uncertainties. Finally, a real case study is investigated to demonstrate the applicability and validation of the proposed model and the uncertainty approach. The obtained results revealed that considering external cooling units and the blending process can diminish the system’s total costs by approximately 16% and 11%, respectively.

Scheduling and routing of multiple heterogeneous vehicles in a milk collection problem with blending in compartments and time windows

A raw milk collection problem is consistently the focus of the dairy and milk supply chain, wherein a broad range of concerns need to be reflected, including coordinating heterogeneous vehicles and harmonising various activities under miscellaneous time restrictions. So, this study offers a general milk collection network with different milk qualities to formulate a scheduling and routing problem with multiple multi-compartment heterogeneous vehicles, where associated activities are performed under various time constraints, including time windows, maximum available time, and planning horizon. Moreover, the blending process in multi-compartment vehicles is also considered to increase total profit as the primary purpose of the network. Additionally, a novel hybrid meta-heuristic algorithm that encompasses variable neighbourhood search (VNS) and adaptive large neighbourhood search (ALNS) is proposed to solve the proposed model, wherein a new heuristic algorithm for the blending process is developed to accelerate the offered meta-heuristic algorithm. Finally, a case study and extensive numerical experiments are offered to illustrate the applicability and validity of the proposed model and solution approach. The obtained results reveal that the considerations of the blending process and multi-compartment vehicles significantly impact the system's profit.

WITHDRAWN: An improved genetic algorithm for multi-compartment vehicle selection with time window

The main problem to be solved in this article is how to choose a mixed fleet of single and multi-compartment vehicles when delivering multiple orders, and when considering the time-dependence, time window, and deterioration loss, to minimize the total distribution cost of the distribution center, namely the time dependence of multi-compartment vehicle selection problem TDMCVRPTWs. By establishing the heuristic algorithm pre self-organized clustering improved genetic algorithm (SOMGA), and based on the framework of SOMGA algorithm, We build a pre self-organizing clustering improved genetic algorithm of vehicle type selection (SOMGAs), SOMGAs to solve the hybrid fleet that can make the distribution cost the lowest. In order to verify the rationality of SOMGA algorithm, this paper will check the calculation through Solomon’s 25, 50 and 100 customer sets with time windows, and compare the results with HPSO improved particle swarm optimization algorithm. In addition, the optimal values of ACS, HAC, HVANS and HABC are compared. Through analysis, SOMGA algorithm has strong algorithm advantages. In order to verify the feasibility of the pre-self-organization cluster genetic algorithm of vehicle type selection (SOMGAs) based on the framework of SOMGA algorithm, through the Solomon 50 customer, 100 customer set, the hybrid fleet can significantly reduce the distribution cost. And in the process of vehicle class selection, the vehicle path of each vehicle is optimized. Finally, it is concluded that SOMGAs algorithm can solve a reasonable mixed fleet in 80% of cases, reducing the total distribution cost by 0% to 24%. • A new algorithm for solving the multi-compartment vehicle routing problem is proposed. • It provides a new method for studying hybrid fleet. • Various traffic environmental factors and food environmental factors are considered.

Designing Sustainable Logistics Networks for Classified Municipal Solid Wastes Collection and Transferring with Multi-Compartment Vehicles

Designing Sustainable Logistics Networks for Classified Municipal Solid Wastes Collection and Transferring with Multi-Compartment Vehicles

Genetic Algorithm for the Pick-up and Delivery Problem with Time Window by Multi-Compartment Vehicles

Abstract In this article we focus on multi-compartment vehicles that have to pick-up goods from suppliers and deliver them to various customers. Usually these goods cannot be transported by single-compartment vehicles due to the fact that some products are harmful to others: incompatibility between products. It is therefore a question of satisfying a set of customers while respecting the constraints linked to the capacity of each vehicle compartment, each type of product and ensuring that each supplier is visited before his customer. Our work consists firstly of mathematically modelling the problem described and secondly of solving it using methods due to its complexity. In this case we use the genetic algorithm to solve the pick-up and delivery problem of goods with a time window provided by multi-compartment vehicles. Our model allows to find a minimum distance and a minimum cost in the tour carried out by a reasonable number of vehicles.

Variable Neighborhood Descent for Multi-Compartment and Multi-Objective Vehicle Routing Problem in Refined Product Distribution

Refined product distribution is an application of the Multi-compartment Vehicle Routing Problem (MCVRP), which simultaneously considers the vehicle routing, the assignment of heterogonous vehicles and loading policies of multi-compartment. First, we develop an optimization model with the objective of delivering on-time and minimizing transportation cost. Then we propose a Multi-objective Variable Neighborhood Descent Algorithm (MOVND), where the [Formula: see text]-constraint method transforms the original problem into a series of sub-problems of single objective with constraints. Finally, the efficiency of the proposed algorithm is verified by conducting a large number of small and large instances. Mainly including (i) compared with the classical NSGA-2 algorithm for multi-objective VRP, MOVND provides better performance in terms of convergence, spread and distribution; (ii) the multi-compartment vehicles are able to carry a variety of products simultaneously, which can improve the effective utilization of vehicle space and meet the needs of different customers in a single transportation. In addition, heterogonous vehicles that take full advantage of the characteristics of different vehicles are superior to homogeneous ones in terms of operating cost in the practice of the refined product distribution.

An improved estimation of distribution algorithm for multi-compartment electric vehicle routing problem

The multi-compartment electric vehicle routing problem (EVRP) with soft time window and multiple charging types (MCEVRP-STW&MCT) is studied, in which electric multi-compartment vehicles that are environmentally friendly but need to be recharged in course of transport process, are employed. A mathematical model for this optimization problem is established with the objective of minimizing the function composed of vehicle cost, distribution cost, time window penalty cost and charging service cost. To solve the problem, an estimation of the distribution algorithm based on Levy flight (EDA-LF) is proposed to perform a local search at each iteration to prevent the algorithm from falling into local optimum. Experimental results demonstrate that the EDA-LF algorithm can find better solutions and has stronger robustness than the basic EDA algorithm. In addition, when comparing with existing algorithms, the result shows that the EDA-LF can often get better solutions in a relatively short time when solving medium and large-scale instances. Further experiments show that using electric multi-compartment vehicles to deliver incompatible products can produce better results than using traditional fuel vehicles.

A hybrid FJA-ALNS algorithm for solving the multi-compartment vehicle routing problem with a heterogeneous fleet of vehicles for the fuel delivery problem

This paper proposes a new hybrid algorithm to solve the multi-compartment vehicle routing problem (MCVRP) with a heterogeneous fleet of vehicles for the fuel delivery problem of a previous study of twenty petrol stations in northeastern Thailand. The proposed heuristic is called the Fisher and Jaikumar Algorithm with Adaptive Large Neighborhood Search (FJA-ALNS algorithm). The objective of this case is to minimize the total distance, while using a minimum number of multi-compartment vehicles. In the first phase, we used the FJA to solve the MCVRP for the fuel delivery problem. The results from solving the FJA were utilized to be the initial solutions in the second phase. In the second phase, a hybrid algorithm, namely the FJA-ALNS algorithm, has been developed to improve the initial solutions of the individual FJA. The results from the FJA-ALNS algorithm are compared with the exact method (LINGO software), individual FJA and individual ALNS. For small-sized problems (N=5), the results of the proposed FJA-ALNS and all methods provided no different results from the global optimal solution, but the proposed FJA-ALNS algorithm required less computational time. For larger-sized problems, LINGO software could not find the optimal solution within the limited period of computational time, while the FJA-ALNS algorithm provided better results with much less computational time. In solving the four numerical examples using the FJA-ALNS algorithm, the result shows that the proposed FJA-ALNS algorithm is effective for solving the MCVRP in this case. Undoubtedly, future work can apply the proposed FJA-ALNS algorithm to other practical cases and other variants of the VRP in real-world situations.

Optimizing routing and delivery patterns with multi-compartment vehicles

Retailers usually apply repetitive weekly delivery patterns when scheduling the workforce for shelf replenishment, defining cyclic transportation routes and managing warehouse capacities. In doing so, all logistics subsystems are jointly scheduled. Grocery products require different temperature zones. As long as transport was in separated vehicles due to temperature requirements, it was not possible to coordinate deliveries across different temperature zones. The recent introduction of multi-compartment trucks has changed this and allows joint deliveries. This simultaneous delivery of multiple product segments impacts repetitive weekly delivery patterns as, for example, low volume segments can be delivered more frequently if they are transported together with high volume segments.We address the problem of defining delivery patterns for delivery with multi-compartment vehicles. After deriving decision-relevant costs, we propose a novel model that defines the Periodic Multi-Compartment Vehicle Routing Problem. The model is solved by an integrated framework that determines delivery patterns within an Adaptive Large Neighborhood Search in combination with a Large Neighborhood Search for solving the routing problem. We analyze the impact of selecting delivery patterns across product segments and show the efficiency of our integrated planning approach using numerical studies. Joint planning generates cost savings of up to 15%. Furthermore, we show that the algorithm provided can also improve single-segment problems by 3% compared to a state-of-the art benchmark. Beyond that we demonstrate the applicability and advantage of our approach in a case study with a large German grocery retailer.

A two-stage procedure for efficiently solving the integrated problem of production, inventory, and distribution of industrial products

This paper deals with the problem of optimally planning the production, inventory and distribution of products transported via multi-compartment vehicles. It assumes that facilities in the distribution network have preservation-storing devices to inventory products on-site. Production activities may be performed on any time period of the planning horizon. Due to problem complexity, a two-stage solution strategy that first generates a set of multi-period distribution routes through a column generation approach is proposed. The routes are used for feeding the MILP formulation of the problem. Several valid inequalities are proposed for expediting the MILP resolution. The aim is to maximize the profit obtained by the company that fabricates and distributes the products. This profit is computed as the total income from sales minus the sum of all costs incurred along the planning horizon. The effectiveness of the two-stage solution strategy is tested on an extensive set of realistic instances.