Multiple Distribution Centers Research Articles

An adaptive large neighborhood search heuristic for multi-commodity two-echelon vehicle routing problem with satellite synchronization

<p style='text-indent:20px;'>In considering route optimization from multiple distribution centers called depots via some intermediate facilities called satellites to final customers with multiple commodities request, we introduce the Multi-Commodity Two-Echelon Vehicle Routing Problem with Satellite Synchronization (MC-2E-VRPSS). The MC-2E-VRPSS involves the transportation from multiple depots to satellites on the first echelon and the deliveries from satellites to final customers on the second echelon. The MC-2E-VRPSS integrates satellite synchronization constraints and time window constraints for satellites on the two-echelon network and aims to determine cost-minimizing routes for the two echelons. The satellite synchronization constraints which trucks from the multiple depots to some satellites need to be coordinated guarantee the efficiency of the second echelon network. In this study, we develop a mixed-integer programming model for the MC-2E-VRPSS. For validating the model formulation, we conduct the computational experiments on a set of small-scale instances using GUROBI and an adaptive large neighborhood search (ALNS) heuristic which we develop for the problem. Furthermore, the computation experiments for evaluating the applicability of the ALNS heuristic compared with large neighborhood search (LNS) on a set of large-scale instances are also conducted, which proved the effectiveness of the ALNS.</p><p style='text-indent:20px;'> </p><p style='text-indent:20px;'>Addendum: The Acknowledgement “This study was supported by the Humanities and Social Sciences Foundation Program of the Ministry of Education (Project No.19YJA630122), and the "National Key R&D Program of China"(Grant No.2020YFB1712900)” is added. We apologize for any inconvenience this may cause.</p>

Research on Cold Chain Routing Optimization of Multi-distribution Center Considering Traffic Performance Index

In response to the practical situation where traffic congestion factors reduce the distribution efficiency and increase the cost of cold chain logistics in the actual transportation process, a mathematical model for optimizing the cold chain path of multiple distribution centers is established, with the goal of minimizing the comprehensive cost and incorporating a traffic congestion index. By analyzing routine factors such as customer requested time windows, loading and unloading time, and road congestion, an improved particle swarm algorithm that introduces exchange offspring is proposed. The proposed method is tested through experimental simulations and the results are compared with traditional methods. The results show that considering the traffic congestion index saves 7.12% of the comprehensive cost compared to not considering the traffic congestion index, and completes the delivery service with a shorter path length.

Optimization of a Logistics Transportation Network Based on a Genetic Algorithm

In order to solve the problem of genetic algorithm, the author proposes a research on optimization of a logistics transportation network. First, combined with the distribution characteristics of logistics, starting from the relevant research theories of vehicle routing problems, the vehicle travel distance is considered in the basic model of vehicle routing optimization, a vehicle routing model with soft time window is established to minimize the total cost, respectively, and a vehicle routing model with multiple distribution centers. Second, on this basis, according to the actual problems and model needs of the research, using the basic principle of genetic algorithm, the solution algorithm design of the built model is carried out and we use MATLAB for programming, so that the solution of the built model is finally realized. We set T = 100, S = 50, Pc = 0.95, Pm = 0.1. β1 = 0.005, β2 = 1, and β3 = 0.005. Finally, the actual distribution data of the G supermarket chain in the region are used as the experimental data; by picking two shipping instances, the two constructed models and the designed genetic algorithm are verified, respectively.

Research on Multi-Center Mixed Fleet Distribution Path Considering Dynamic Energy Consumption Integrated Reverse Logistics

The fleet operation model in which electric vehicles coexist with traditional vehicles is becoming increasingly popular. Because electric vehicles have certain disadvantages and usage limitations, the multi-center management of the distribution of mixed fleets is very complex. There is no research on the multi-center mixed vehicle routing problem based on the integration of reverse logistics and dynamic energy consumption. In response to this challenge, this study proposes a solution to the multi-center mixed vehicle routing problem considering dynamic energy consumption and integrated reverse logistics. Specifically, three studies were carried out: (1) Considering the influencing factors of the operating cost system of the mixed fleet, a system dynamics model was constructed. (2) On the basis of considering delaying the aging of electric vehicle batteries, a new charging station insertion strategy was designed. (3) Based on a novel charging station insertion strategy, a fast non-dominated sorting multi-objective genetic algorithm with an elite strategy was designed to solve this problem. We designed 15 groups of examples to prove the effectiveness of the model and algorithm. The experimental results show that 46.67% of the cases have more than 60% customer satisfaction. The average expenditure cost of 15 groups of cases is CNY 2018.33, which can improve the average customer satisfaction by 22.94%. This method helps companies to formulate transportation plans according to the actual situation, including providing a cost model that considers multiple influencing factors and improving the average customer satisfaction while reducing the total cost expenditure. We believe that the results of this research can provide methods and ideas for logistics companies with multiple distribution centers to formulate large-scale distribution plans.

Logistics Network Distribution Optimization Based on Vehicle Sharing

The development of the sharing economy has provided new ideas for a vehicle-sharing urban logistics network cooperative distribution strategy. In view of the lack of dispatching capacity or transportation capacity of logistics enterprises with multiple distribution centers, this paper proposes a vehicle-sharing urban logistics network cooperative distribution strategy. Based on the comprehensive consideration of a multi-distribution center, multi-model, rental vehicle, load, speed, fuel consumption, and other factors, the calculation method of vehicle energy consumption is introduced, the network collaborative distribution model with vehicle sharing is established, and an adaptive genetic algorithm combined with a scanning algorithm is designed. Finally, the validity and reliability of the mathematical model and algorithm are validated and analyzed by an example. The research results show that vehicle sharing can improve the efficiency of the distribution network and effectively reduce costs.

Distribution Path Optimization for Intelligent Logistics Vehicles of Urban Rail Transportation Using VRP Optimization Model

Path optimization of logistics distribution vehicles is researched to improve the efficiency of intelligent logistics systems. The logistics distribution system based on urban rail transportation is analyzed. A mathematical model is constructed for the Vehicle Routing Problem (VRP) of multiple distribution centers. A novel Concentration-Immune Algorithm Particle Swarm Optimization (C-IAPSO) is proposed based on the respective advantages of C-IA and PSO in vehicle path optimization combining the concept of antibody concentration. C-IAPSO first calculates the concentration selection probability of particles in the swarm and updates the immune memory bank as per the optimal particle retention strategy to ensure the diversity of the antibody swarm. To assess the performance of C-IAPSO, seven standard test functions are selected for comparison experiments; results prove that it can provide the fastest convergence rate. On unimodal functions Sphere and Quadric, the accuracy of C-IAPSO gets improved significantly. The specific conditions of the distribution center and the demand spots are analyzed; the vehicle travels 508.40 km in total under the optimal distribution path calculated by C-IAPSO, which is a notable decrease compared with the 600.40 km of Adaptive PSO (APSO). To sum up, applying C-IAPSO to vehicle path optimization of intelligent logistics systems can improve transportation efficiency and reduce transportation costs.

Optimization of Urban Unmanned Logistics Distribution Path with Multiple Distribution Centers

Optimization of Urban Unmanned Logistics Distribution Path with Multiple Distribution Centers

A sequential approach for a multi-commodity two-echelon distribution problem

We address a Multi-Commodity two-echelon Distribution Problem (MC2DP) where three sets of stakeholders are involved: suppliers, distribution centers, and customers. Multiple commodities have to be sent from suppliers to customers, using multiple distribution centers for consolidation purposes. Commodities are collected from the suppliers and delivered to the distribution centers with direct trips, while a fleet of homogeneous vehicles distributes commodities to customers. Commodities are compatible, that is any vehicle can transport any set of commodities as long as its capacity is not exceeded. The goal is to minimize the total transportation cost from suppliers to customers. We present two sequential schemes based on the solution, in a different order, of a collection and a delivery subproblem. In both cases, the solution of the first subproblem determines the quantity of each commodity at each distribution center. The second subproblem takes this information as input. We also propose different strategies to guide the solution of the first subproblem in order to take into account the impact of its solution on the second subproblem. The proposed sequential heuristics are evaluated and compared both on randomly generated instances and on a case study related to a short and local fresh food supply chain. The results show the impact of problem characteristics on solution strategies.

Optimising inventory placement in a two-echelon distribution system with fulfillment-time-dependent demand

We study a two-echelon, single-product fulfillment system where a regional fulfillment center (FC) replenishes multiple independent local distribution center (LDC) orders within an internal committed resupply leadtime, and LDCs serve end customers within a committed demand fulfillment time, or committed delivery time. Expected system-wide demand depends on the product's price, the committed delivery time, and the number of LDCs in the system. Our proposed model determines the values of product price, committed resupply time, and committed delivery time that maximise expected system-wide profit per period, while accounting for product holding costs and fixed facility costs. We characterise key properties of optimal solutions that permit an efficient solution for a fixed number of LDCs, and consider the impacts of several proposed demand growth models as the number of LDCs increases. The results of a computational study provide interesting managerial insights on how operational constraints and the scale of the distribution system influence strategic stock placement and distribution system structure.

An integrated queuing-stochastic optimization hybrid Genetic Algorithm for a location-inventory supply chain network

We consider a location-inventory optimization model for supply chain (SC) configuration. It includes a supplier, multiple distribution centers (DCs), and multiple retailers. Customer demand and replenishment lead time are considered to be stochastic. Two classes of customer orders, priority and ordinary, are assumed based on their demand. The goal is to find the optimal locations for DCs and their inventory policy simultaneously. For this purpose, a two-phase approach based on queuing theory and stochastic optimization was developed. In the first phase, the stock level of DCs is modeled as a Markov chain process and is analyzed, while in the second phase, a mathematical program is used to determine the optimal number and locations of DCs, the assignment of retailers to DCs, and the order quantity and safety stock level at DCs. As solving this problem is NP-hard, a hybrid Genetic Algorithm (GA) was developed to make the problem computationally tractable.

Research on Intelligent Transportation Logistics Technology Based on Energy Saving and Emission Reduction

Through the analysis of the relationship between logistics and economy, energy and environment, a mathematical model for the path optimization of multiple logistics distribution centres for energy conservation and emission reduction in logistics is established. According to the characteristics of the problem, a genetic algorithm is constructed to solve the problem, and the path optimization problem of multiple logistics distribution centres is integrated and solved by a mathematical model. This thesis establishes a mathematical model of the path optimization problem of multiple logistics distribution centres. This article proposes the concept of invalid genes, which is not limited to making every gene in an individual must be expressed, thus enhancing the flexibility of coding. Simulation experiments prove the effectiveness and operability of the method.

Research on Location Selection of Agricultural Products Logistics Distribution Center Based on Two-stage Combination Optimization Algorithm

The development of agricultural product logistics has a great impact on agricultural development. With the advancement of modern agriculture, agricultural product logistics is also facing important changes. On the basis of spatial analysis to determine the candidate address of the distribution center, this paper takes the lowest comprehensive cost as the objective function, establishes the distribution center location model, and solves it through the immune algorithm. On the basis of determining the distribution center, a distribution route optimization model with hard time windows for multiple distribution centers is established, and the tabu search method is used to solve the problem. According to the demand for time-sensitive fruit and vegetable agricultural products in a certain area and the actual circulation and distribution of fruit and vegetable agricultural products in newly developed enterprises, it is combined with the work done by the fruit and vegetable time-sensitive cold chain location planning model. Taking the grape distribution of Xinfadi Co., Ltd. in a certain area as an actual example of the model, using LINGO to solve and analyze the site selection model constructed in the paper, it verifies that the site selection plan for agricultural products that takes into account the characteristics of a complete cold chain and time-efficiency in this paper has a comprehensive low cost advantage.

Two-echelon supply chain network design with trade credit

This study considers a two-echelon supply chain that is comprised of an outside vendor, multiple distribution centers (DCs), and multiple retailers. The retailers have access to trade credit offered by upstream suppliers. We propose an integrated DC-retailer network design model that optimizes trade credit terms and safety stock levels, in addition to the decisions of DC locations, DC-retailer assignments, and inventory replenishment policies typically considered in the literature. The operating and handling cost is concave and non-decreasing to capture economies of scale whereas most existing studies simply assume that such cost is linear in demand. Trade credit financing cost is characterized in a way that preserves the important mathematical structure of the classic warehouse retailer network design model. Leveraging on the submodularity property of the cost components, we developed a polymatroid cutting-plane solution algorithm, which is effective for practically sized problem instances in numerical experiments. The results show that incorporating trade credit financing into supply chain network design may substantially reduce the total cost. Further, our study suggests a more consolidated supply chain network when either the safety stock or financing cost increases. Interestingly, as financing cost rises, a high-volume and low-frequency reorder pattern is favored, but the opposite is recommended when financing gain increases. The variation of each individual cost component is also analyzed for an in-depth understanding of the impact of operational and financial parameters on supply chain optimization.

INTEGRATED PRODUCTION AND DISTRIBUTION PLANNING FOR PERISHABLE PRODUCTS

Bir ürünün geçtiği aşamalar, ilk tedarikçisinden başlayarak fabrikadaki üretim hattında işlenmesi, dağıtımcılar ve perakendeciler yolunu izleyerek müşteriye ulaşması olarak sıralanabilir. Bu zincirin planlanmasında yazında ve uygulamada genel kanı olarak üretim ile dağıtım ayrı planlama aşamaları olarak kabul edilmiş, buna bağlı olarak kurulan modeller de üretimi ve dağıtımı ayrı modeller altında ele almıştır. Oysa dağıtım ve üretim birbirlerini zincirleme olarak etkileyen kararlar olduğundan birleşik bir üretimdağıtım planlaması çok daha sağlıklı ve verimli sonuçlar verecektir. Özellikle de planlaması yapılan ürün zamanla kalitesi ve değeri azalan, yani bozulabilir bir ürün ise; ürünün ne zaman, ne kadar üretildiği ile ne zaman, nasıl dağıtılacağı birbirini zincirleme olarak etkileyecektir. Bu çalışmada, bir fabrikada, birden fazla bozulabilir ürünün üretimi ve birden fazla dağıtım merkezine birden fazla araç ile dağıtımını birleşik ele alan bir karma tamsayılı doğrusal programlama modeli önerilmiştir. Modelin amacı, toplam üretim, dağıtım ve stok maliyetlerini (sabit ve değişken üretim, fabrikadaki ve dağıtım merkezlerindeki stok ile araçların sabit dağıtım maliyetleri toplamını) en az yapmaktır

Multi-Depot Split-Delivery Vehicle Routing Problem

The rapid advancements in information technologies and globalization change the way of distributing goods to customers. Many enterprises have multiple factories, warehouses, and distribution centers and strive for competitive efficiency in the distribution operations to minimize transportation costs. This study proposed the mixed-integer programming (MIP) model for the multi-depot split-delivery vehicle routing problems (MDSDVRPs) with hetero vehicles, allowing multiple visits to a customer. A genetic algorithm (GA) with a novel two-dimensional chromosome representation has been proposed with dynamic mutation policies. The process parameters of the proposed GA are optimized using the Taguchi method. The proposed algorithms showed the benefits of split-delivery in MDSDVRPs and showed the competitive performance even for the classical single-depot vehicle routing problems with no split-delivery.

Integrated planning of downstream petroleum supply chain: a multi-objective stochastic approach

In studying petroleum supply chain networks, past studies have largely segregated three critical decision-making aspects: integrated planning, uncertainties, and multi-objective setting. This study focuses on consolidating these aspects and proposes a stochastic, multi-objective, mixed-integer linear programming model for strategic and tactical planning of downstream petroleum supply chain (DPSC) networks. Demand, considered the uncertain parameter, is modeled using a two-stage stochastic approach based on scenarios. The model—designed for multiple supply centers, distribution centers, products, and transportation modes—also considers transshipment between the centers. The objective functions consider simultaneous minimization of transportation cost and product loss cost that is incurred during transportation between the centers. The application of the proposed model is demonstrated with a case study of a real-world DPSC network undergoing construction of new pipelines and expansion of storage facilities. The augmented ε-constraint method is used to solve the model. Interesting trade-offs in the case study are analyzed, aiding the decision-makers in exploiting the model as a decision-support tool to better understand the complexity, flexibility, and risk of integrated decision-making under uncertainty.

Reliable Fourth Party Logistics Location-Routing Problem Under the Risk of Disruptions

A smooth operation of the fourth party logistics supply chain network requires the cooperation of multiple distribution centers and third party logistics. Any unexpected disruption caused by a certain distribution center may lead to serious consequences to the whole fourth party logistics supply chain. Therefore, we study the reliable fourth party logistics location-routing problem in the network design considering disruption risk of distribution centers. In addition, to minimize the total cost of the proposed problem, a scenario-based integer programming model is established to locate distribution centers, to select proper third party logistic providers, and to accomplish the vehicle routing from distribution centers to customers. Then, a two-level heuristic framework is proposed to handle this NP-hard problem through dividing the original problem into two sub-problems. One is the reliable fourth party logistics location assignment problem, and the other is the fourth party logistics vehicle routing problem. These two sub-problems can be independently solved and iteratively composed to get to the solution of the original problem. Furthermore, numerical experiments are designed, and the effectiveness of the heuristic framework is tested by comparing the quality of solutions and running time with those calculated by using CPLEX. Managerial insights are also provided based on the calculation results.

Stochastic optimization for transportation planning in disaster relief under disruption and uncertainty

Purpose This study aims to minimize the expected arrival time of relief vehicles to the affected areas, considering the destruction of potential routes and disruptions due to disasters. In relief operations, required relief items in each affected area and disrupted routes are considered as uncertain parameters. Additionally, for a more realistic consideration of the situations, it is assumed that the demand of each affected area could be met by multiple vehicles and distribution centers (DCs) and vehicles have limited capacity. Design/methodology/approach The current study developed a two-stage stochastic programming model for the distribution of relief items from DCs to the affected areas. Locating the DCs was the first-stage decisions in the introduced model. The second-stage decisions consisted of routing and scheduling of the vehicles to reach the affected areas. Findings In this paper, 7th district of Tehran was selected as a case study to assess the applicability of the model, and related results and different sensitivity analyses were presented as well. By carrying out a simultaneous sensitivity analysis on the capacity of vehicles and the maximum number of DCs that can be opened, optimal values for these parameters were determined, that would help making optimal decisions upon the occurrence of a disaster to decrease total relief time and to maximize the exploitation of available facilities. Originality/value The contributions of this paper are as below: presenting an integrated model for the distribution of relief items among affected areas in the response phase of a disaster, using a two-stage stochastic programming approach to cope with route disruptions and uncertain demands for relief items, determining location of the DCs and routing and scheduling of vehicles to relief operations and considering a heterogeneous fleet of capacitated relief vehicles and DCs with limited capacity and fulfilling the demand of each affected area by more than one vehicle to represent more realistic situations.

Vehicles robust scheduling of hazardous materials based on hybrid particle swarm optimisation and genetic algorithm

This paper takes the vehicles scheduling of hazardous materials as the research object. First, considered the four objectives of risk minimization, cost minimization, risk equilibrium value minimization, and duration minimization, the vehicles robust scheduling of hazardous materials in multiple distribution centers with risk balance is constructed. Then, uncertain models are transformed into peer-to-peer models through the idea of robust discrete optimization. By introducing the crossover and mutation operators of genetic algorithm into particle swarm algorithm, a hybrid particle swarm algorithm is constructed, and three-stage coding rules are adopted. Finally, case study is exemplified to prove the feasibility of the model and algorithm. The results show that the obtained scheduling scheme for the vehicles of hazardous materials minimizes the risk value and makes the risk distribution more balanced for the government regulatory department, and the vehicle can avoid certain road sections with relatively large risk values. For transportation enterprises, the vehicle scheduling scheme for transporting hazardous materials minimizes the cost and shortens the task duration at the same time. In the case of risk uncertainty, different robust control parameters are introduced to make the vehicles robust scheduling of hazardous materials have different degrees of stability.

Inventory replenishment planning of a distribution system with storage capacity constraints and multi-channel order fulfilment

In this paper, an inventory replenishment planning problem in a two-echelon distribution system of Alibaba with a central distribution centre (CDC) and multiple front distribution centers (FDCs) is studied. In the system, the CDC replenishes its inventories of multiple products from external suppliers, while each FDC replenishes its inventories of the products from the CDC. These products are jointly replenished at each stock. This problem arises when Alibaba prepares its annual “double 11” promotion. Facing stochastic demands of the products in the promotion period, the objective of the problem is to optimally determine the inventory replenishment quantities of the products for each stock before the promotion so that the expected total profit of the system is maximized, subject to the storage capacity constraint of each stock. During the promotion, customer orders may be fulfilled through multiple channels, that is, a customer order may be fulfilled by its local FDC, other FDCs, or the CDC. This stochastic optimization problem is formulated as a convex nonlinear programming model and solved optimally by a piecewise linear approximation approach. The effectiveness of the model and the approach is demonstrated by numerical experiments on instances generated based on data of Alibaba. The results show that our model can lead to a significantly higher expected total profit compared with a deterministic planning model.