Two phase algorithm for bi-objective relief distribution location problem.

BackgroundBlood banks have to be thoughtful about supply chain decisions to effectively satisfy the blood product demand of hospitals. These decisions include the number and locations of distribution centers (DC), as this has a strong impact on both transportation cost and the ability to deliver emergency orders in time.Study Design and MethodsWe propose a mixed‐integer linear programming approach to find optimal DC locations for supplying individual hospitals. The model maximizes the number of hospitals reachable from a DC within a given time‐limit, and minimizes transportation cost. The minimal amount of data required is a set of hospital locations. The model can be further attuned to the user's needs by adding various model extensions. The model's use is demonstrated by two case studies, considering the blood banks of the Netherlands and Finland.ResultsFor both case studies re‐locating the DCs would result in a reduction of transportation cost of about 10% without affecting the reliability of delivery. In addition, to save facility exploitation costs, the number of DCs may be reduced in both countries while maintaining the reliability of delivery. The model was also shown to be robust against changes in hospital ordering behavior.DiscussionWe demonstrated the general usability and added value of the model by successfully optimizing the blood supply chains of the Netherlands and Finland, which differ substantially. Nonetheless, in both countries potential savings in both transportation and facility exploitation cost could be shown. The model code is open source and freely accessible online.

Arctic puffin optimization: A bio-inspired metaheuristic algorithm for solving engineering design optimization

Quick responses to sudden-onset disasters and the effective allocation of rescue and relief resources are vital for saving lives and reducing the suffering of the victims. This article deals with the problem of positioning medical and relief distribution facilities after a sudden-onset disaster event. The background of this study is the situation in Padang Pariaman District after the West Sumatra earthquake. Three models are built for the resource location and deployment decisions. The first model reflects current practice where relief distribution and victim evacuation are performed separately and relief is distributed by distribution centers within administrative boundaries. The second model allows relief to be distributed across boundaries by any distribution center. The third model further breaks down functional barriers to allow the evacuation and relief distribution operations share vehicles. These models are solved directly for small problems and by using a direct approach as well as heuristics for large problems. Test results on small problems show that resource sharing measures, both across boundaries and across different functions, improve on current practice. For large problems, the results give similar conclusions to those for small problems when each model is solved using its own best approach.

Bayesian Optimization (BO) is a widely-used method for optimizing expensive-to-evaluate black-box functions. Traditional BO assumes that the learner has full control over all query variables without additional constraints. However, in many real-world scenarios, controlling certain query variables may incur costs. Therefore, the learner needs to balance the selection of informative subsets for targeted learning against leaving some variables to be randomly sampled to minimize costs. This problem is known as Bayesian Optimization with cost-varying variable subsets (BOCVS). While the goal of BOCVS is to identify the optimal solution with minimal cost, previous works have only guaranteed finding the optimal solution without considering the total costs incurred. Moreover, these works assume precise knowledge of the cost for each subset, which is often unrealistic. In this paper, we propose a novel algorithm for the extension of the BOCVS problem with random and unknown costs that separates the process into exploration and exploitation phases. The exploration phase will filter out low-quality variable subsets, while the exploitation phase will leverage high-quality ones. Furthermore, we theoretically demonstrate that our algorithm achieves a sub-linear rate in both quality regret and cost regret, addressing the objective of the BOCVS problem more effectively than previous analyses. Finally, we show that our proposed algorithm outperforms comparable baselines across a wide range of benchmarks.

Purpose The purpose of this paper is to maximize the total demand covered by the established additive manufacturing and distribution centers and maximize the total literal weight assigned to the drones. Design/methodology/approach Disaster management or humanitarian supply chains (HSCs) differ from commercial supply chains in the fact that the aim of HSCs is to minimize the response time to a disaster as compared to the profit maximization goal of commercial supply chains. In this paper, the authors develop a relief chain structure that accommodates emerging technologies in humanitarian logistics into the two phases of disaster management – the preparedness stage and the response stage. Findings Solving the model by the genetic and the cuckoo optimization algorithm (COA) and comparing the results with the ones obtained by The General Algebraic Modeling System (GAMS) clear that genetic algorithm overcomes other options as it has led to objective functions that are 1.6% and 24.1% better comparing to GAMS and COA, respectively. Originality/value Finally, the presented model has been solved with three methods including one exact method and two metaheuristic methods. Results of implementation show that Non-dominated sorting genetic algorithm II (NSGA-II) has better performance in finding the optimal solutions.

Since the report of the 19th National Congress of the Communist Party of China proposed the implementation of the rural revitalization strategy, the rural economy has developed rapidly, the effect of industrial agglomeration has been further deepened, user consumption levels and procurement needs have continued to increase, and logistics activities have increased sharply. The logistics distribution is a key link in the logistics service supply chain, and the study of logistics distribution path planning is of great significance to the development of rural revitalization. This paper aims to study the application of swarm intelligence algorithm in logistics distribution route planning under the background of rural revitalization. This article first expounds the problems and reasons that hinder rural logistics distribution, puts forward the form and optimization goal of logistics distribution path planning, and explains its definition, classification and solution methods respectively. This paper proposes the Intelligent Ant Colony Algorithm (IACA) to solve the logistics distribution path planning and solve the problem of multiple distribution centers. Finally, the algorithm is compared with the Cat Group Algorithm (CGA) for simulation experiments to verify the effectiveness of the algorithm. The experimental results show that when the number of distribution centers is 14, the optimal number of iterations for CGA and IACA is 41 and 30, respectively. When the number of distribution centers is 48, the optimal number of iterations for CGA and IACA is 202, respectively. 100 times, it shows that the algorithm proposed in this paper can be well used in the path planning of logistics distribution, improve the efficiency of distribution, and reduce the cost of logistics distribution.KeywordsRural revitalizationIntelligent algorithmLogistics distributionRoute planning

This paper addresses the design of single commodity stochastic distribution networks. The distribution network under consideration consists of a single supplier serving a set of retailers through a set of distribution centers (DCs). The number and location of DCs are decision variables and they are chosen from the set of retailer locations. To manage inventory at DCs, the economic order quantity (EOQ) policy is used by each DC, and a safety stock level is kept to ensure a given retailer service level. Each retailer faces a random demand of a single commodity and the supply lead time from the supplier to each DC is random. The goal is to minimize the total location, shipment, and inventory costs, while ensuring a given retailer service level. The introduction of inventory costs and safety stock costs leads to a nonlinear NP-hard optimization problem. A Lagrangian relaxation approach is proposed. Computational results are presented and analyzed showing the effectiveness of the proposed approach.

Assortment Allocation to Distribution Centers to Minimize Split Customer Orders

In long-range air defense multi-missile cooperative interception missions, due to the low accuracy of indication information and the lack of ground radar support, significant errors exist in estimating the true position of the target. This makes it difficult for the interception regions of multiple missiles to cooperatively cover the target error region. To address this issue, a multi-missile cooperative coverage guidance strategy based on an improved snake optimization (ISO) algorithm is proposed. First, a missile cooperative coverage model is established, with the interception regions treated as nodes in the coverage optimization problem, and the highest regional interception probability used as the optimization criterion. Next, the snake optimization (SO) algorithm is improved by enhancing its initialization, exploration, and exploitation phases, resulting in the ISO algorithm. Based on this, a multi-missile cooperative coverage guidance strategy is proposed, where the interception regions of each missile are optimally allocated within the target error region, expanding the overall interception region and improving the cooperative interception probability across the coverage area. Finally, the effectiveness of the proposed strategy is validated through numerical simulations.

The performance of a distribution center is typically judged on throughput-based criteria. Order picking consumes 30% to 40% of operation time in a typical distribution center. To effectively execute an order picking operation in a distribution center depends upon coordinating the formulation of the storage strategy, order processing, and planning the order picking route. However, it is usually too complicated to employ traditional optimization methods, such as linear programming, to solve this kind of problem. As a result, we applied the Particle Swarm Optimization (PSO) Algorithm to schedule order picking routes. PSO is one of the latest swarm intelligence algorithms; consequently, when compared to previous sophisticated algorithms such as genetic algorithms and simulated annealing, the study of its properties and applications is still in its infancy. This research considers the convergence rate, the convergent reliability (i.e., solution precision), and the performance test function (i.e., fitness function) in scheduling order picking routes. We apply genetic algorithms to determine the initial solution in order to locate the optimal solution faster by PSO. This paper also compares the effects of different parameters on particle swarm optimization. In order to verify the result, we also made a comparison with the Ant system in finding the optimal solution in order route planning. Overall, the research result will enhance the system of order picking in distribution centers and improve the efficiency of order picking operations.

There are several disasters every year that affect millions of people and trigger the need for relief items such as food, blankets, or tents. Humanitarian logistics networks organize the flow of relief items from various suppliers to the people in need. In the scientific literature, various OR models have been proposed for setting-up and running humanitarian logistics networks. Their practical application, however, is challenging because humanitarian organizations lack the necessary resources and often have not been included in the model development process. In this thesis, the concept of an online OR toolkit for humanitarian logistics is proposed. Such a toolkit should enable practitioners in the field of humanitarian logistics to tap the full potential of the published OR models. Two major steps were necessary to develop the structure of the toolkit. In the first step, characteristics of disaster management and humanitarian logistics were identified. In the second step, available OR models for humanitarian logistics were analyzed. The resulting structure divides models into those for tasks in the disaster preparedness phase and those for the disaster response phase. Subsequently, models are assigned to the specific task they are meant to support, e.g. specifying the locations and relief item stocks of temporary warehouses. Then, the available models for a specific task are grouped by the OR methodology they are based on. Finally, each model is described by its mathematical formulation, by a translation of the mathematical formulation into a suitable program code, and by the problem that is captured in the mathematical formulation. A problem structuring scheme is used to analyze consistently the problems that are captured in OR models for humanitarian logistics. The proposed structure of the toolkit is exemplarily filled with nine published mathematical programs. Three of them support the specification of stationary warehouses; three support the specification of local distribution centers; and the others support the specification of transportation activities. Thereby, the cornerstones of any humanitarian logistics network can be laid with the present state of the toolkit’s content: stationary warehouses as sources of relief items, local distribution centers as the sinks of relief items, and transportation activities to realize the flow of relief items in between. It is shown that the proposed problem structuring scheme can characterize different types of mathematical programs in a consistent way. Furthermore, it is indicated how the proposed structure of the toolkit can support practitioners in finding, adapting, implementing, building databases for, and combining available OR models for humanitarian logistics as well as supporting academics in identifying open research questions.

This paper considers a single-sourcing network design problem for a three-level supply chain. For the first time, a novel mathematical model is presented considering risk-pooling, the inventory existence at distribution centers (DCs) under demand uncertainty, the existence of several alternatives to transport the product between facilities, and routing of vehicles from distribution centers to customer in a stochastic supply chain system, simultaneously. This problem is formulated as a bi-objective stochastic mixed-integer nonlinear programming model. The aim of this model is to determine the number of located distribution centers, their locations, and capacity levels, and allocating customers to distribution centers and distribution centers to suppliers. It also determines the inventory control decisions on the amount of ordered products and the amount of safety stocks at each opened DC, selecting a type of vehicle for transportation. Moreover, it determines routing decisions, such as determination of vehicles' routes starting from an opened distribution center to serve its allocated customers and returning to that distribution center. All are done in a way that the total system cost and the total transportation time are minimized. The Lingo software is used to solve the presented model. The computational results are illustrated in this paper.

This paper addresses frequent and foreseeable floods in the short‐term preparedness of an imminent event using a multicriteria optimization model integrated with a geographical information system to simulate flood levels, determine the best strategies, and update information. The proposed model takes into account the four main relief operations: location of emergency facilities (i.e., distribution centers, shelters, and meeting points), prepositioning of humanitarian aid, evacuation, and distribution of humanitarian aid. Three criteria are considered in the formulation to minimize: the maximum evacuation flow‐time, the maximum distribution flow‐time, and total cost of relief operations. The approximation to the efficient frontier is built using multiobjective programming through the use of commercial software. The usefulness and robustness of the model are verified using data from one of the worst Mexican floods considering various flood levels created from three key elements in humanitarian logistics. The strategies provided by the proposed methodology are compared with those implemented by the Mexican authorities during the studied disaster.

With the rapidly growing number of wireless users and systems more frequency bands are needed, which led to the spectrum scarcity problems. Cognitive radio (CR) is an emerging technology that enables the dynamic spectrum access by optimizing the usage of the available spectrum through giving the unlicensed Secondary Users (SU) the opportunity to exploit the spectrum without causing any interference with the Primary Users’ (PU) usage. Security threats is one of the substantial problems issues that must be addressed in CR networks. Primary User Emulation (PUE) attacks are one of the main threats in deploying CRN, where the Malicious Users (MU) mimic the PU signal to confuse the other SUs and corrupt the spectrum sensing process. In this paper, a hybrid Genetic Whale Optimization Algorithm (GWOA) is proposed to optimize the spectrum utilization and to combat the PUE attacks. GWOA integrates the crossover and mutation operations with WOA algorithm to reach the balance between exploitation and exploration phases, to find the optimal solution based on a fitness function. The simulations results show promising performance of GWOA, when compared with recent detection algorithms.

The increasing complexity of modern plant systems and the limitations of precise mathematical modeling have led to a shift towards data-driven control methods. These methods present an effective alternative that treats plants as black-box systems without requiring explicit models. The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. Nonetheless, control performance can be limited by the fixed probability coefficient used in the original SEDA to disrupt design parameters, especially when balancing exploration and exploitation phases. This study proposes an enhanced version of the SEDA: the modified SEDA (MSEDA), to address this issue. The MSEDA introduces a dynamic probability coefficient that decreases with each iteration. The adjustment improves the balance between exploration and exploitation phases, which enhances control accuracy. The MSEDA was used to tune the brain emotional learning-based intelligent controller (BELBIC) together with a proportional-integral-derivative (PID) controller. The result was the BELBIC-PID controller inspired by the limbic system of the human brain, which has high precision. The effectiveness of the proposed MSEDA-BELBIC-PID was validated using simulations on multi-input multi-output (MIMO) systems, with a focus on tracking performance and computational efficiency. The statistical analysis of 30 independent trials demonstrated that the proposed MSEDA-BELBIC-PID was significantly improved over the original SEDA-BELBIC-PID. Wilcoxon's rank test yielded a fitness function p-value < 0.05, which confirmed the robustness and effect of the proposed enhancement. The comparative results demonstrated that the MSEDA-BELBIC-PID consistently performed better than the original approach and had improved fitness function values, reduced total integral square error, and lower total integral square input. These findings underscored the MSEDA suitability as a data-driven tool for controller design parameter optimization. Furthermore, the low computational burden of MSEDA rendered it a strong alternative to heuristic multi-agent algorithms, which frequently encounter high computational costs with large controller design parameters.