Simultaneous Pickup Research Articles

CMSA based on set covering models for packing and routing problems

AbstractMany packing, routing, and knapsack problems can be expressed in terms of integer linear programming models based on set covering. These models have been exploited in a range of successful heuristics and exact techniques for tackling such problems. In this paper, we show that integer linear programming models based on set covering can be very useful for their use within an algorithm called “Construct, Merge, Solve & Adapt”(CMSA), which is a recent hybrid metaheuristic for solving combinatorial optimization problems. This is because most existing applications of CMSA are characterized by the use of an integer programming solver for solving reduced problem instances at each iteration. We present applications of CMSA to the variable-sized bin packing problem and to the electric vehicle routing problem with time windows and simultaneous pickups and deliveries. In both applications, CMSA based on a set covering model strongly outperforms CMSA when using an assignment-type model. Moreover, state-of-the-art results are obtained for both considered optimization problems.

An exact algorithm for simultaneous pickup and delivery problem with split demand and time windows

This study introduces a new variant of the vehicle routing problem (VRP) called the simultaneous pickup and delivery problem with split demand and time windows (SPDP-SDTW). The motivation behind this study stems from real-life urban and rural delivery scenarios, encompassing features such as split demand, simultaneous pickup and delivery, many-to-many pickup and delivery, and time windows. The study thoroughly investigates the properties of the optimal solution for the SPDP-SDTW. Based on these properties, an arc flow model is developed for the SPDP-SDTW. Dantzig Wolfe (DW) decomposition techniques are employed to obtain the master problem and the pricing subproblem. In order to effectively address the SPDP-SDTW, an improved branch and price (I-BP) algorithm is proposed, incorporating a tailored column generation (CG) algorithm, branching strategies, and dual stabilization strategies. The proposed CG algorithm provides a framework that combines the improved adaptive degree heuristic (I-AGH) algorithm and the solver Gurobi. This integration substantially mitigates the computational burden involved in solving the subproblem. Extensive computational experiments conducted on datasets of varying sizes, including small, medium, and large instances, consistently demonstrate that the I-BP algorithm performs the best in both solution quality and computational efficiency when compared to existing exact and heuristic algorithms.

An efficient heuristic for very large-scale vehicle routing problems with simultaneous pickup and delivery

The paper discusses two variants of the Capacitated Vehicle Routing Problem (CVRP), in which each customer to be visited can require both pickup and delivery, or only either pickup and delivery but not both. These problems are referred to, respectively, as the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) and as the Vehicle Routing Problem with Mixed Pickup and Delivery (VRPMPD). Both VRPSPD and VRPMPD are particularly relevant in practical logistics distribution scenarios, especially when dealing with a substantial number of pickup and delivery locations. The aim of this work is to provide an algorithm which extends, through a variety of specializations, the FILO framework, originally proposed and specifically designed for the CVRP. This variant, called FSPD, has been developed to accomplish two objectives: first, being competitive with the state-of-the-art algorithms for the VRPSPD and VRPMPD; second, efficiently solving new benchmark instances for these problems with a very large number of customers, while maintaining linear scalability of the computing time with respect to the problem size. The extensive computational study and detailed analysis of the algorithm components conducted in this paper demonstrate the successful achievement of both objectives.

Research on vehicle path planning of automated guided vehicle with simultaneous pickup and delivery with mixed time windows

AbstractThe authors investigate new Automated Guided Vehicle (AGV) Routing Problem with Simultaneous Pickup and Delivery with Mixed Time Windows (VRPSPDMTW) in smart workshops, a variation of the classic Vehicle Routing Problem (VRP). A mixed time window vehicle routing model was developed for simultaneous deliveries. This model reduces the cost of AGVs used and distribution cost, along with time window penalties. To address this complex challenge, a Hybrid Adaptive Genetic Algorithm using Variable Neighbourhood Search (AGA‐VNS) is proposed. This algorithm enhances the genetic algorithm's local search capabilities while preserving solution diversity, thereby improving both efficiency and quality of solutions. Comprehensive computational experiments are conducted, which include both VRPSPDTW test benchmark and real‐world smart factory instance studies. The outcomes reveal that the AGA‐VNS algorithm outperforms both professional solver software and advanced heuristic methods significantly. Moreover, the newly developed mixed time window model is more aligned with the requirements of real‐world production processes compared to the traditional time window model. Thus, this research not only presents novel insights into the domain of vehicle routing problems but also demonstrates its significant applicability and potential in the background of intelligent workshops.

GIS-Based Optimization for Gas Distribution Route Design: A Case Study of PT Gagas Energi Indonesia

PT Gagas Energi Indonesia is a company engaged in the trading and natural gas business, one of which is Gaslink C-Cyl. Route determination for Gaslink C-Cyl distribution is carried out subjectively based on the proximity of locations between customers, thus creating inefficient routes, affecting the number of vehicles needed, and causing high transportation costs. Based on this problem, this study aims to minimize transportation costs by determining the optimal route and number of vehicles needed using the Multi-Trip Vehicle Routing Problem Time Windows Simultaneous Pickup and Delivery (MTVRPTW-SPD). This problem is solved using Geographic Information System (GIS) based optimization. To evaluate GIS-based optimization performance, comparison between existing condition and optimization are done for 50 demand points. Then, two scenario was developed, 100 and 200 demand points to explain the decision implication related to demand uncertainty. Based on the optimization process for 50 demand points gives better solution than existing condition in term of number of fleets needed, total distance travelled, and total transportation cost which result 3 units, 46,5%, and 43,5% respectively.

A cooperative vehicle routing problem with delivery options for simultaneous pickup and delivery services in rural areas

The booming e-commerce is challenging express companies by requiring higher logistics service efficiency in rural China. This study investigates a cooperative vehicle routing problem for simultaneous pickup and delivery requests, in which the delivery orders can be fulfilled either by home delivery or customer pickup. The delivery option for each order is complex due to the optional cooperative relationships among express companies, the inter-dependence between self-pickup site selection and the overall online vehicle routing scheme, and the distance-dependent compensation to self-pickup customers. The problem is formulated as a mixed integer linear programming model and solved by a tailored adaptive large neighborhood search algorithm. A cost-saving allocation mechanism based on the core guaranteed Shapley value is applied to carrier collaboration. The numerical results show that the newly introduced cooperative distribution system can significantly reduce operational costs. Such improvements are robust even if the total demand changes or under different rural logistics network topologies.

Two-echelon time-dependent vehicle routing problem with simultaneous pickup and delivery and satellite synchronization

The multi-echelon distribution strategy has been widely applied in city logistics systems. This study develops a mixed-integer linear programming model for a new variant of vehicle routing problem, namely, a two-echelon time-dependent vehicle routing problem with simultaneous pickup and delivery and satellite synchronization, in which customers are divided into multiple customer regions according to different geographical characteristics. The pickup and delivery activities with hard time windows are performed simultaneously by the same vehicles from depots to satellites in the first echelon and from satellites to customers in different customer regions in the second echelon. A vehicle speed function is developed for each echelon based on traffic conditions. The function depends on commuter traffic and urbanization level. Satellites with storage buffer capacities for split and consolidation are allowed. Thus, demand unloading, storage, and reloading are synchronized. Costs of transportation, loading/unloading, inventory, and environment during one complete distribution process are considered. A memetic algorithm (MA) is proposed including a split algorithm for chromosome decoding and a three-phase construction heuristic for initial population generation. Self-adaptive operators of crossover, mutation, and local search are designed, and a neighborhood route improvement strategy is devised that includes distance-related destroy, route-based destroy, distance-based greedy repair, and time-distance cluster repair operators. Results of different scales of numerical experiments and sensitivity analysis show the effectiveness of the model and MA by comparing it with an exact algorithm, CPLEX, and adaptive large neighborhood search.

Research on a Joint Distribution Vehicle Routing Problem Considering Simultaneous Pick-Up and Delivery under the Background of Carbon Trading

In order to explore the positive impact of the joint distribution model on the reduction in logistics costs in small-scale logistics enterprises, considering the demand on enterprises for simultaneous pick-up and delivery, as well as the cost of carbon emissions, this study considers the vehicle routing problem of simultaneous pick-up and delivery under a joint distribution model. First of all, an independent distribution model and a joint distribution model including fixed transportation, variable transportation, time penalty, and carbon emissions costs are established; second, by adding the self-adaption cross-mutation probability and the destruction and repair mechanism in the large-scale neighborhood search algorithm, the genetic algorithm is improved to adapt to the solution of the model in this paper, and the effectiveness of the improved algorithm is verified and analyzed. It is found that the improved genetic algorithm is more advantageous than the original algorithm for solving the problems of both models designed in this paper. Finally, the improved genetic algorithm is used to solve the two models, and the results are compared and analyzed. It is found that the joint distribution model can reduce the total cost by 6.61% and the carbon emissions cost by 5.73%. Additionally, the impact of the carbon trading mechanism on the simultaneous pick-up and delivery vehicle routing problem under the joint distribution model is further explored. The results of this study prove that enterprises can effectively reduce costs, improve profits, reduce carbon emissions, and promote the sustainable development of logistics enterprises under the condition of joint distribution.

Heuristic Algorithms for Heterogeneous and Multi-Trip Electric Vehicle Routing Problem with Pickup and Delivery

In light of the widespread use of electric vehicles for urban distribution, this paper delves into the electric vehicle routing problem (EVRP): specifically addressing multiple trips per vehicle, diverse vehicle types, and simultaneous pickup and delivery. The primary objective is to minimize the overall cost, which encompasses travel expenses, waiting times, recharging costs, and fixed vehicle costs. The focal problem is formulated as a heterogeneous and multi-trip electric vehicle routing problem with pickup and delivery (H-MT-EVRP-PD). Additionally, we introduce two heuristic algorithms to efficiently approximate solutions within a reasonable computational time. The variable neighborhood search (VNS) algorithm and the adaptive large neighborhood search (ALNS) algorithm are presented and compared based on our computational experiences with both. Through solving a series of large-scale real-world instances for the H-MT-EVRP-PD and smaller instances using an exact method, we demonstrate the efficacy of the proposed approaches.

The Vehicle Routing Problem with Simultaneous Pick-Up and Delivery under Fuzziness Considering Fuel Consumption

The increasing interest in environmental protection has propelled reverse logistics as a challenging field in supply chain optimization. This paper addresses the vehicle routing problem with simultaneous pick-up and delivery (VRPSDP) while considering fuzzy payloads, with the primary objective of minimizing fuzzy fuel consumption. The VRPSDP with fuzzy payloads poses a computationally intractable challenge, as it involves a fleet of vehicles departing from a central depot to both deliver and collect goods from a dispersed group of customers. To effectively tackle this problem, a genetic algorithm is applied that incorporates the concept of fuzziness. This problem diverges from the traditional VRPSDP by explicitly considering fuel consumption reduction towards environmental sustainability. To validate and assess the feasibility of the proposed approach, a series of test instances are utilized. The numerical results exhibit the efficiency of the proposed method and place emphasis on the influence of uncertainty in the quantities of goods collected and delivered by customers on the resulting solution.

Integrated location and routing for cold chain logistics networks with heterogeneous customer demand

The critical interdependence between facility location and vehicle routing is a fundamental component of cold chain logistics management (CCLM). Furthermore, integrating information within CCLM has the potential to enhance operational efficiency, reduce costs, improve risk management, and elevate product quality, ultimately ensuring that temperature-sensitive goods are delivered in best condition. This paper introduces a novel non-linear multi-objective model designed to concurrently optimize warehouse facility location and vehicle routing, addressing the challenges inherent in cold chain logistics processes. The model seeks to minimize the aggregate costs related to transportation, facility location, and delivery tardiness. The study accounts for several pragmatic assumptions to address real-world scenarios: multiple delivery requests per customer, handling mixed commodities, and distributing mixed commodities using a single vehicle. This paper firstly studies simultaneous pickup and delivery with multiple requests and heterogeneous customer demands, each of which should be preserved in a different range of temperatures and needs different vehicle types. The epsilon-constraint method is employed to validate the proposed model, and a set of advanced, hybrid multi-objective evolutionary algorithms (MOEA) are presented to tackle the problem in a real-world context. A comprehensive set of performance metrics is utilized supported by rigorous statistical testing.

A Hybrid Fuzzy C-Means Heuristic Approach for Two-Echelon Vehicle Routing with Simultaneous Pickup and Delivery of Multi-Commodity

A Hybrid Fuzzy C-Means Heuristic Approach for Two-Echelon Vehicle Routing with Simultaneous Pickup and Delivery of Multi-Commodity

A Memetic Algorithm for Vehicle Routing with Simultaneous Pickup and Delivery and Time Windows

A Memetic Algorithm for Vehicle Routing with Simultaneous Pickup and Delivery and Time Windows

Solution of green simultaneous pickup and delivery vehicle routing problem with time window using metaheuristic methods

Araç rotalama problemi, merkezi bir depodan farklı koordinatlarda yer alan müşterilere belirli kapasiteye sahip araçlarla yapılacak dağıtım için en kısa dağıtım rotasının belirlendiği bütünleşik bir optimizasyon problemidir. Artan çevresel duyarlılık ve problemin gerçek hayata daha uygun hale getirilmesi için zaman, eş zamanlı toplama ve dağıtım, rota uzunluğu, çoklu depo, teslimat bölme, yakıt tüketimi ve karbon emisyonu gibi kısıtlar probleme eklenerek yeni varyantlar ortaya konmuştur. Bu çalışmada, çevresel duyarlılığın ön plana çıktığı yeşil araç rotalama problemi, zaman pencereli ve eş zamanlı topla dağıt araç rotalama problemleri bütünleşik olarak ele alınmaktadır. Bu noktada, toplama ve dağıtım talepleri, siparişlerin teslim zamanları ve dağıtım esnasında sürdürülebilirliğin sağlanabilmesi için çevresel faktörler de önemli bir etken olarak göz önüne alınmıştır. Çalışma kapsamında Yeşil Zaman Pencereli ve Eş Zamanlı Topla Dağıt Araç Rotalama Problemi (YZPETDARP) için yeni karma tamsayılı doğrusal olmayan matematiksel model oluşturulmuş, belirli şartlar altında model doğrusallaştırılarak farklı yöntemler ile çözüm aranmıştır. YZPETPARP’nin çözümü için metasezgisel arama algoritmaları olan Genetik Algoritma (GA) ve Ağırlıklı Süperpozisyon Çekim Algoritması (ASÇA) önerilmiş, literatürdeki ilgili veriler entegre edilerek test verileri oluşturulmuştur. Deneysel çalışmalar sonucunda çözüm uygunluk değeri ve çözüm süresi bakımından GA ile daha iyi sonuçlara ulaşılmış, or-opt sezgiseli ile entegre edilen ASÇA ise GA ile elde edilen sonuçlara yakın ve tatmin edici sonuçlar vermiştir.

An artificial bee colony algorithm for medical goods distribution and pharmacological waste collection by hybrid vehicles considering environmental criteria

PurposeThis paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively.Design/methodology/approachHere, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used.FindingsThen, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance.Originality/valueIn this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function.

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

This study considers an extension of the two-Echelon Location-Routing Problem (2E-LRP) namely two-Echelon Location-Routing Problem with Simultaneous Pickup and Delivery (2E-LRPSPD). A Mixed Integer Programming (MIP) formulation is developed and strengthened by the use of valid inequalities to solve the 2E-LRPSPD, and then a Branch and Cut-based exact algorithm (B&C) is presented. To improve the upper bounds, Iterated Local Search and Variable Neighborhood Search-based metaheuristic algorithms, namely, ILS-VNS and VNS, are developed and embedded into the B&C. Computational results, conducted on instances produced from the literature, demonstrate that the B&C yields tight lower and upper bounds, and also is capable of solving some instances optimally with up to 50 customers and 10 satellites in a reasonable time. Furthermore, sensitivity analysis reveals that the heuristic approaches and an increase in the runtime limit assist the algorithm to reach tight the upper bounds, whereas the number of potential depots does not have any effect on the performance of the algorithm. Besides, further computational analysis on the 2E-LRPSPD with a single depot, which is studied in the literature, shows that the B&C improves some best-known solutions while proving the optimality of some instances. Finally, the B&C is applied to a two-echelon supermarket distribution system in Türkiye. The computational results show that simultaneous pickup and delivery operations, as well as depot location decisions are critical elements to consider in the design of cost-effective distribution networks.

A unified exact approach for a broad class of vehicle routing problems with simultaneous pickup and delivery

The vehicle routing problem (VRP) with simultaneous pickup and delivery (VRPSPD) is a classical combinatorial optimization problem in which one aims at determining least-cost routes, starting and ending at the depot, while simultaneously meeting the pickup and delivery demands of geographically dispersed customers in a single visit, and without violating the capacity of the vehicle. In this work, we propose a unified branch-cut-and-price (BCP) algorithm to solve ten variants of the problem, considering not only the standard version but also those including additional attributes such as a heterogeneous fleet of vehicles, time windows, route duration, multiple depots, and location decisions. The resulting problem that generalizes all of these variants can be formulated as a heterogeneous location routing problem with simultaneous pickup and delivery and time windows (HLRPSPDTW), for which we propose a compact formulation to serve as the basis for an extended formulation associated with the BCP approach. The proposed exact method includes many of the features developed in recent years, such as a bucket graph-based labeling algorithm, rank-1 cuts with limited memory, and dynamic ng-sets. Extensive computational experiments were performed on more than 550 benchmark instances and our algorithm was capable of obtaining a number of new optimal solutions, as well as improved lower bounds, for all variants addressed in this paper.

Volleyball premier league algorithm with ACO and ALNS for simultaneous pickup–delivery location routing problem

The reverse logistics problem, which involves customer returns and incurs high additional logistics costs, has become a topic of increasing interest. This problem is modeled as the location routing problem with simultaneous pickup and delivery under time constraints (LRPSPDTW), which is a more complex version of the location routing problem (LRP). Previous studies have shown that existing algorithms are unable to provide satisfactory solution accuracy for this problem. In order to address this, a novel algorithm called the hybrid volleyball premier league (HVPL) algorithm is proposed. Drawing inspiration from the competition mechanism of the original volleyball premier league (VPL) algorithm, the HVPL algorithm incorporates adaptive large neighborhood search (ALNS) and improved ant colony optimization (ACO) as sub-algorithms, allowing them to work together synergistically and strike a balance between exploration and exploitation. The learning and elimination phases of the VPL algorithm have been redesigned to enhance convergence speed. Furthermore, the Bellman’s algorithm has been enhanced to determine depot locations and construct vehicle paths for the solution. Prior to solving the LRPSPDTW problem, benchmark testing was conducted using 35 latest benchmark functions. Experimental results and Friedman mean rank test demonstrated that HVPL exhibits superior exploration and exploitation capabilities. HVPL was subsequently tested on 66 instances based on the modified Solomon’s benchmark and the NEO research group benchmarks, and the results showcased its outperformance over other methods in the majority of cases.

Providing an integrated multi-depot vehicle routing problem model with simultaneous pickup and delivery and package layout under uncertainty with fuzzy-robust box optimization method

This paper modeled and solved an integrated multi-depot vehicle routing problem (MDVRP) with simultaneous pickup and delivery (SPD) with package layout under unpredictable pickup, delivery, and transfer costs. The model described in this paper is divided into two stages. In the first stage, the SCA algorithm is used to optimize the package dimensions (a collection of commodities consumers need). The NSGA II and MOALO algorithms are used in the second stage to optimize the three objective functions of 1 simultaneously) minimizing total costs, 2) minimizing co2 emissions, and 3) minimizing the maximum working hours of drivers based on the optimal dimensions (length, width, and height) obtained from solving the first stage model. Determining the quantity and ideal location of possible warehouses, the best route for trucks to take to deliver and collect customer items, and the distribution of customers to warehouses are the key goals of the second stage. Since the model is unclear, the problem's uncertainty parameters are controlled using a novel fuzzy-robust box optimization (FRBO) technique. This technique, which combines the advantages of fuzzy programming with robust box-based optimization, produces excellent results when used to optimize objective functions. The numerical calculations in the numerical example show that the total network costs and CO2 emissions increased in the second stage in the presented model with an increasing uncertainty rate. At the same time, the maximum working hours of drivers decreased due to the shortened communication route and the number of vehicles increasing. Finally, the MOALO algorithm was used to resolve a case study at Safir Broadcasting Company because of its excellent efficiency in resolving the created model, the findings of which revealed the presence of 13 potential effective solutions. The quantity of greenhouse gas emissions rose by 1.11%, the overall expenditures climbed by 1.72%, and the number of hours that drivers worked fell by 11.98% when the uncertainty rate was raised from 0.5 to 0.7, according to research on the FRBO.

Multi-objective optimization of a sustainable two echelon vehicle routing problem with simultaneous pickup and delivery in construction projects

In many construction projects, the delivery of raw materials from the warehouse to the project place and the return of waste materials from the project place to the warehouse are planned simultaneously. This issue has many complexing assumptions. Accordingly, in this research, the multi-objective two-echelon vehicle routing with simultaneous pickup and delivery is investigated. For this purpose, a multi-objective mathematical model has been developed. Objective functions include economic, environmental and social criteria to achieve sustainability. Moreover, as the problem is known as a hard-NP problem, two meta-heuristic algorithms, NSGA-II and MOPSO, are proposed to solve this problem. In order to evaluate the performance of the proposed meta-heuristic algorithms, 10 test problems of different sizes were generated. The solutions obtained with the proposed algorithms for each of these example problems were evaluated using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method in eight indicators. The achieved numerical results show that the proposed model and the applied algorithms have sufficient efficiency. Moreover, it is revealed that the NSGAII and MOPSO algorithms have a desirability weight of 0.4321 and0.5679, respectively. Therefore, it can be concluded that the MOPSO algorithm has higher efficiency for solving the model.