Optimal Distribution Plan Research Articles

Application of Trapezoidal Fuzzy Numbers in the Inventory Problem of Decision Science

The objectives of this research study focus on managing production networks efficiently through a systematic and integrated approach. The central idea is to combine inventory control and transportation scheduling to optimize the entire supply chain. The study introduces the concept of the overtime fuzzy inventory-transportation problem (FITP), specifically aiming to determine an optimal distribution plan that considers overtime to minimize total distribution costs. The use of trapezoidal fuzzy numbers (TrFN) is highlighted as a key methodology for addressing uncertainty and incomplete information in real-world problems. TrFNs are considered a generalization of crisp and fuzzy numbers, providing a flexible framework for modelling imprecision. The process of de-fuzzification is mentioned as a means to convert fuzzy numbers into crisp numbers for practical applications, thereby obtaining clear and precise solutions.

방재비축창고 위치 실증연구

Purpose – Gyeongsangbuk-do is a region prone to frequent natural disasters, and there are high expectations for the establishment of an Integrated National Management System of Disaster Resources. For this purpose, it is necessary to expand disaster prevention stockpiles in the province. However, currently, the logistics perspective has not adequately been considered, leading to network inefficiency. Therefore, this study proposes an optimal location-allocation plan for disaster stockpile warehouses in Gyeongsangbuk-do. Design/Methodology/Approach – This research applied a discrete-linear facility location problem based on maximal covering location algorithm. The optimal location is determined by prioritizing nearby areas and setting the service distance limit lower than the current maximum service distance. Findings – As a result, Gyeongju-si was identified as a more suitable facility location instead of Pohangsi, one of the current disaster stockpile center locations. Furthermore, the proposed optimal locationallocation plan is more efficient than the current network and reduces regional deviations due to the objective function of the applied algorithm. Research Implications – Based on the Integrated National Management System of Disaster Resources plan, this study holds potential for practical use by using the current operating warehouse locations in Gyeongsangbuk-do as parameters. Additionally, since logistics is often overlooked in disaster management, the optimal location and distribution plan proposed in this study can serve as an indicator of logistics efficiency.

IDENTIFYING THE POTENTIAL OF UNMANNED AERIAL VEHICLE ROUTING FOR BLOOD DISTRIBUTION IN EMERGENCY REQUESTS

This study is focusing on identifying the potential of Unmanned Aerial Vehicle (UAV) routing for blood distribution in emergency requests in Sri Lanka compared to existing transportation modes. Capacitated Unmanned Aerial Vehicle Routing Problem was used as the methodology to find the optimal distribution plan between blood banks directing emergency requests. The developed UAV routing model was tested for different instances to compare the results. Finally, the proposed distribution process via UAVs was compared with the current distribution process for the objective function set up in the model and other Key Performance Indicators (KPIs) including energy consumption savings and operational cost savings. The average percentage of distribution time re-duction, energy consumption cost reduction, and operational cost per day reduction utilizing UAVs were determined to be 58.57%, 96.35%, and 61.20%, respectively, for the instances tested using the model highlighting the potential of UAVs. Therefore, the deficiencies in Sri Lanka's present blood delivery system can be addressed using UAVs' potential for time, cost, and energy savings. The ability to save time through the deployment of UAVs to the fleet during emergency situations plays a crucial role in preventing the loss of human lives.

Target-Oriented Compromised Sales and Profit Approach for Production and Distribution Planning in a Supply Chain

This paper aims to determine optimal aggregate production and distribution plans in a supply chain system that simultaneously achieve two business targets of total profit and total sales, with uncertain parameters, e.g., production rate during regular time and overtime, inventory holding costs for a manufacturer and distribution centers, and transportation cost. A fuzzy multi-objective linear programming (FMOLP) model is developed to represent the planning problem. The proposed method that minimizes maximum deviation from satisfaction targets of fuzzy profits and sales is more effective, compared with the method that maximizes minimum satisfaction of fuzzy profits and sale, to determine various compromised solutions, which are Pareto-optimal, and to allow a planner to select the most desirable solution based on his/her opinion. This paper has made a significant contribution since it is the first one that proposes the FMOLP approach to determine compromised solutions with two target-based objectives of simultaneously achieving total fuzzy profit targets and total sales target.

Optimal distribution of perishable foods with storage temperature control and quality requirements: An integrated vehicle routing problem

The quality of food products deteriorates continuously over time at a speed depending on the storage temperature. Therefore, they must be stored at low temperatures to prolong their shelf life, which increases storage costs significantly. In this paper, we study a distribution problem in which a temperature-controlled warehouse is responsible for supplying a perishable food product, using a homogenous fleet of capacitated vehicles, to multiple customers with minimum quality requirements and delivery time windows. The objective is to minimize the sum of the energy cost at the warehouse and the transportation cost to deliver the customer demand. Decisions include the storage temperature at the warehouse and delivery routes. The distribution problem is formulated as a Mixed-Integer Linear Programming (MILP) model. After presenting an analytical study of the problem, a heuristic based on General Variable Neighborhood Search (GVNS) is designed to solve the large-size problem instances. Using the case of a dairy product, the model is solved under various scenarios. It is observed that the distribution and temperature decisions are dependent. Moreover, the optimal distribution plan and storage temperature are significantly impacted by changes in factors related to food quality, such as truck and environmental temperatures and energy price.

Topology optimization and lattice lightweight design of the Outsole of the return tank considering compound working conditions

On the premise of satisfying the safety of landing impact, the realization of lightweight design of the spacecraft return module is one of the key issues. Aiming at the lightweight design of the spacecraft return capsule under different conditions of hard landing, from the perspective of structural topology optimization calculation, the problem is studied and given based on the dynamic topology optimization of the Hybrid Cellular Automata (HCA) algorithm. The specific design plan is based on the LS-DYNA platform, and the dynamic impact topology optimization model of the return cabin under different working conditions is established. After analysis, the optimal material distribution and stiffener layout plans on the outsole structure was determined, and the conceptual design model of the outsole was obtained. Then use the body-centered cubic lattice structures to fill different areas of the outsole,and combine the size optimization technology to obtain the final lightweight outsole design plan. Compared with the traditional rib-skinned outsole structure, the design meets the safety requirements of hard landing while reducing the weight ratio to 24%. It demonstrates the dynamic topology optimization method based on the HCA algorithm in the lightweight design technology of the spacecraft return capsule. The feasibility and superiority in the additive manufacturing lattice lightweight designs method.

PHYSICS-INFORMED NEURAL NETWORKS FOR INFORMED VACCINE DISTRIBUTION INMETA-POPULATIONS

Accurate numerical and physical models play an important role in modeling the spread of infectious disease as well as informing policy decisions. Vaccination programs rely on the estimation of disease parameters from limited, error-prone reported data. Using physics-informed neural networks (PINNs) as universal function approximators of the susceptible-infected-recovered (SIR) compartmentalized differential equation model, we create a data-driven framework that uses reported data to estimate disease spread and approximate corresponding disease parameters. We apply this to data from a London boarding school, demonstrating the framework's ability to produce accurate disease and parameter estimations despite noisy data. However, real-world populations contain sub-populations, each exhibiting different levels of risk and activity. Thus, we expand our framework to model meta-populations of preferentially-mixed subgroups with various contact rates, introducing a new substitution to decrease the number of parameters. Optimal parameters are estimated through PINNs which are then used in a negative gradient approach to calculate an optimal vaccine distribution plan for informed policy decisions. We also manipulate a new hyperparameter in the loss function of the PINNs network to expedite training. Together, our work creates a data-driven tool for future infectious disease vaccination efforts in heterogeneously mixed populations.

Firepower distribution method of anti-ship missile based on coupled path planning

Anti-ship missile coordinated attack mission planning is a complex multi-objective optimization problem with multiple combinations of platforms, strong decision-making constraints, and tightly coupled links. To avoid the coupling disorder between path planning and firepower distribution and improve the efficiency of coordinated attack mission planning, a firepower distribution model under the conditions of path planning is established from the perspective of decoupling optimization and the algorithm is implemented. First, we establish reference coordinate system of firepower distribution to clarify the reference direction of firepower distribution and divide the area of firepower distribution; then, we construct an index table of membership of firepower distribution to obtain alternative firepower distribution plans; finally, the fitness function of firepower distribution is established based on damage income, missile loss, ratio of efficiency and cost of firepower distribution, and the mean square deviation of the number of missiles used, and the alternatives are sorted to obtain the optimal firepower distribution plan. According to two simulation experiments, the method in this paper can effectively solve the many-to-many firepower distribution problem of coupled path planning. Under the premise of ensuring that no path crossing occurs, the optimal global solution can be obtained, and the operability and timeliness are good.

A Dynamic Resource Allocation Model Based on SMDP and DRL Algorithm for Truck Platoon in Vehicle Network

The rapid development of self-driving cars and breakthroughs in key technologies have made the truck platoon possible. In addition to reducing truck fuel consumption and air pollution by reducing air resistance, effective platoon strategies can also maximize highway throughput while improving driving safety. However, the truck platoon strategy’s current resource allocation model is still in the preliminary research stage. Therefore, inspired by the successful experience of deep reinforcement learning (DRL) in solving resource allocation problems, this article proposes a dynamic resource allocation model for the truck platoon based on the semi-Markov decision process (SMDP) and DRL, which is used to maximize system revenue when considering the resource cost and income balance of the transportation system. Precisely, the proposed method first models the process of controlling the dynamic in and out of the truck platoon as SMDP. The action value in a specific state obtained by the planning algorithm is used as a DRL sample for model training. Finally, the SMDP is optimized through the trained model to obtain a truck platoon resource that approximates the optimal strategy distribution plan. The experimental results show that compared with the traditional greedy algorithm, value iteration, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> -learning scheme concerning solving the dynamic resource allocation model of the truck platoon, the Deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> -Network (DQN) used in this article can reduce the probability of request processing delay while causing the system to obtain higher rewards.

Algorithm for Energy Resource Allocation and Sensor-Based Clustering in M2M Communication Systems

Recent years have seen a surge in curiosity in machine-to-machine (M2M) collaborations between academics and industry. Machine-to-machine communication devices (MTCDs) are able to communicate automatically and with minimum human intervention in an M2M communications infrastructure. While MTCDs are anticipated to deliver a range of services, resource allocation and clustering approaches in M2M transceivers face issues and limits due to the diverse quality of service (QoS) needs in various network conditions. A major issue in M2M communication systems is how to distribute and cluster resources. This article presents a clustering technique and collaborative resource allocation for MTCD resource management. The clustering and integrated resource allocation challenge is characteristic as a maximization of energy efficiency problem. As a consequence of the original optimization model’s inability to tackle nonlinear fractional utilizations, we separate the issue into two subproblems: power redistribution and cluster. We begin by obtaining the optimal power distribution plan through an iterative energy efficiency maximization algorithm and then offer a modified K -means technique for clustering. The effectiveness of the proposed approach is shown by the numerical solution.

An Exercise for Teaching Transportation Problem Using Spatial Data

We present an exercise for teaching the transportation problem using a mix of spatial and randomly generated data. It illustrates the potential of using qualitative and quantitative data and is suitable for undergraduate or introductory business school courses on operations research (OR), logistics, and supply chain management. It poses two challenges: (i) given the demand locations and volume, open a certain number of warehouses to ensure customer responsiveness and (ii) given those warehouses with capacity limits, determine an optimal distribution plan that minimizes the total distribution cost. This exercise is developed with the active participation of MBA students in an introductory OR course. The participants, attending the class online from different parts of India during the COVID-19 pandemic, helped generate realistic customer locations by sharing their location data. Visualizing this spatial data (after masking) in Google My Maps helps the students decide on suitable warehouse locations by considering the proximity to customers as well as diverse socioeconomic, political, and environmental factors. Then, using these warehouse and customer data, the optimal distribution plan is obtained by employing OpenSolver. Students appreciate the exposure—starting from data set generation to deriving an optimal solution—offered by this data-driven decision-making exercise.

Relief goods distribution problem: Considering donation strategies, fairness, and interventions

Relief goods are donated to affected areas through the governmental organization (GO), non-governmental organizations (NGOs), and donors. This paper discusses a relief goods distribution problem composed of a GO, NGO, and donor base, the solution to which is an optimal distribution plan. A relief goods donation strategy wherein donors give not only to the NGO but also to affected areas is proposed. A model is proposed for the GO, NGO, and donor relief goods donation system. In this model, the GO is controlled by “fairness” among affected areas. The NGO is controlled by the amount of received relief goods and costs of performance and publicity. Donors are controlled by the utility received from the NGO and affected areas. Numerical experiments highlight the advantageous nature of considering support provided to the NGO by the GO. The effectiveness of the donors' strategy is verified. A series of suggestions are proffered to the GO based on the results of this work.

Shared Bicycle Distribution Connected to Subway Line Considering Citizens’ Morning Peak Social Characteristics for Urban Low-Carbon Development

The transport sector has produced numerous carbon emissions in China, and it is important to promote low carbon commuting. As an emerging mode of urban low-carbon transportation in China, shared bicycles have been used by more and more citizens on a daily basis, with advantages of green and low-carbon emissions to environment, flexibility for short trips, and convenience for covering the distance between the normal low-carbon transportation and destinations. However, the imbalanced distribution of shared bicycles along subway lines, especially during the morning peak hours, has directly restricted their performance in urban traffic. In this paper, an integer linear program model (ILPM) is proposed to obtain an optimal low-carbon distribution plan of shared bicycles connecting with the subway line (SBCSL) during the morning peak hours. First, an objective function is built to improve the carbon emission reduction of SBCSL. Second, constraint functions are extracted considering the quantity of bicycles to be distributed to the subway line as well as the distribution limits of each subway station. At last, a case study is conducted on the distribution of shared bicycles in Beijing Subway Line 13 of China during the morning peak hours. The results show that the ILPM is of significance to provide optimal distribution scheme of shared bicycles in subway line with different station types including office-oriented, residential-oriented, and hybrid-oriented stations.

TRANSPORTATION OPTIMIZATION MODEL USING THE DISTANCE MATRIX: A CASE OF CEMENT DISTRIBUTION FROM SELECTED COMPANIES TO DISTRIBUTION CENTERS IN EBONYI STATE

Ascertaining an optimal cement distribution plan for cement companies in Nigeria has remained a challenge. The absence or fluctuation of data for estimating the cost of transporting cement from each source to each distribution center is a big stumbling block whenever modeling attempts are being made via transportation algorithms. This work has succeeded in removing these challenges by providing a Transportation Optimization Model for cement distribution using transportation Distance Matrix instead of transportation Cost Matrix. This research seeks to improve supply in the Nigerian cement industry. Three selected factories (Gboko, Port-Harcourt and Calabar) and four major distribution centers (Abakaliki, Onueke, Ohaozara and Afikpo) in Ebonyi state were considered for this work. The result of the findings using the Vogel Approximation Method, minimized the total transportation distance and by implication the total transportation costs.

An efficient optimization framework for tracking multiple quality attributes in supply chains of perishable products

Improved supply chain optimization strategies can play a major role in addressing global food security and safety in years to come. In particular, tighter safety regulations, changing consumer quality requirements and more stringent market competition call upon integrated supply chain decision-making frameworks that explicitly consider product quality control. This effort requires metrics of quality that accurately reflect product physico-chemical properties, as well as consumer purchasing preferences. However, a critical challenge linked to embedding the complex dynamics of the evolution of product quality in time within supply chain models is the large-scale nature of the ensuing optimization problems, which are computationally intractable even for moderate-size, single-item systems. In the present work, we introduce a computationally efficient optimal production and distribution planning framework for perishable products having multiple quality attributes that evolve in time as a function of environmental conditions during shipment and storage. We also propose a model reduction strategy and a decomposition framework that enhance the scalability of our approach. We perform extensive numerical simulations using different network instances to validate our theoretical findings, as well as to demonstrate the advantages of the proposed supply chain management scheme.

A kernel search heuristic for the multivehicle inventory routing problem

AbstractIn this paper an inventory routing problem is studied in which the goal is to determine an optimal distribution plan to replenish a set of customers by routing a limited fleet of capacitated vehicles over a discrete planning horizon. Each customer consumes a per period quantity of product and has a maximum inventory capacity. The goal is to minimize the total distribution cost that comprises the routing and the inventory holding costs. A matheuristic is presented, which uses the information gathered by a tabu search to build a sequence of mixed‐integer linear programming problems of small size. Extensive computational experiments are conducted on a large set of benchmark instances. The results show that the matheuristic outperforms other state‐of‐the‐art algorithms in terms of average solution quality.

Research on logistics management layout optimization and real-time application based on nonlinear programming

Abstract To solve the problem of long logistics delivery time in supply chain, a Mixed Integer Non-linear Program (MINLP) model is built by using Mixed Integer nonlinear programming theory. Firstly, the General algebraic modeling system (GAMS) is used to build the model to fully integrate each parameter of logistics transportation, the total distribution time of the supply chain network, the coverage radius of the logistics base, the number of users, the total capacity of the logistics base, the mode of railway and road transportation, the nonlinear programming model is built and solved by DICOPT solver in GAMS. The cost of logistics can be decreased, transportation time can be reduced, and the logistics system's operating efficiency can be increased in the long term with the help of this algorithm. The proper operation of the logistics system is critical in encouraging the supply chain circulation of various industries and has a direct impact on the society's economic development. The optimal logistics distribution plan with 5 logistics bases covered users of 18 and railway capacity of 2. With the same railway capacity and the same total budget, the larger the number of covered users, the greater the total distribution time increases, but the larger the total budget, the growth of the total distribution time slows down significantly. Experiments show that MINLP model can solve the problem of logistics-based layout optimization in nonlinear logistics management.

Linear programming with fuzzy parameters for inventory routing problem in effective management of personal protective equipment: a case study of corona virus disease 2019

ABSTRACT Coronavirus disease 2019 (COVID-19) brings about severe disruptions for human living, economics, and environmental balance. This pandemic has boosted the demand for personal protective equipment (PPE) and thus, medical wastes. Thus, a research hypothesis is stated whether it would be possible to provide more efficient distribution and inventory planning to optimize the PPE availability in a fuzzy environment. To confirm the hypothesis, in addition to addressing a scientific novelty considering integration of linear programming with fuzzy parameters, named as the Jimenez’s method and clustering heuristic methods, the practicality of these approaches is provided on a real case study and generated instances from Turkey. The computational results reveal that clustering the 32% of the hospitals ensures optimum total cost and solution time while feasibility degree changes between 0, 4, and 1 values of the Jimenez are not more effective on the results. Improved cost results reveal the economic importance of the healthcare supply chain management and optimal PPE distribution and inventory planning can break down the disease propagation and reduce the detrimental impacts on human life. The proposed approach, for the first time, ensures practical and theoretical insights of distribution and storage decisions across disciplines, namely, engineering and health-care organization authorities.

Drone transports medical supplies to Puerto Rico based on shortest path

Puerto Rico suffered the worst hurricane which caused devastating damage in 2017. In order to deliver medical supplies in time and patrol the lines, NGOs try to improve the response capabilities of its by designing a portable disaster response system called DroneGo.DroneGo needs to find the optimal delivery path and the optimal equipment utilization rate for delivery according to the disaster relief conditions and schedule. In this paper, the integration of integer programming algorithm and Dijkstra algorithm is used to construct the shortest distribution path model for medical supplies transported by rescue drones in disaster areas. Using integer programming method, 7 kinds of drones and 3 kinds of medical packages are distributed, and then the DIjkstra algorithm is used to obtain the optimal distribution plan: San Juan Port is equipped with medical kits and three C-type drones, Arecibo Port allocates B-type drone patrol lines and medical kits for rescue, and Guayama Port only allocates drones for patrol lines. Using this distribution scheme greatly improves the efficiency of rescue, and distributes rescue materials reasonably and effectively, achieving the goals of optimal delivery path and optimal equipment utilization.

Relatively optimal production-distribution plans for stock management in supply chains over a finite time horizon

A model of the centralized dynamic production - distribution control is considered. In a supply chain a vendor supplies a product to a buyer, sending the products to the buyer’s warehouse in equal-size batches. Costs are attached to manufacturing the batch, the delivery of shipment and stockholding at both agents, and defined on production-distribution cycles (PDCs). There is only one production setup in a cycle with the restriction that in each warehouse the beginning and the ending inventory levels are the same. It is shown that the average cost of “sensible” PDCs is a continuous piece-wise convex function of their production batch sizes. For infinite time horizon the optimal production distribution plan is a constant sequence of PDCs with lowest average cost - economic production-distribution cycles (EPDCs).