Search Results (9)

Emergency material scheduling (EMS) is a core component of post-disaster emergency response, with its efficiency directly impacting rescue effectiveness and the satisfaction of affected populations. However, due to severe road damage, limited availability of resources, and logistical challenges after disasters, current EMS practices often suffer from uneven resource distribution. To address these issues, this paper proposes a priority-based, multi-stage EMS approach with drone coordination. First, we construct a three-level EMS network “storage warehouses–transit centers–disaster areas” by integrating the advantages of large-scale transportation via trains and the flexible delivery capabilities of drones. Second, considering multiple constraints, such as the priority level of disaster areas, drone flight range, transport capacity, and inventory capacities at each node, we formulate a bilevel mixed-integer nonlinear programming model. Third, given the NP-hard nature of the problem, we design a hybrid algorithm—the Tabu Genetic Algorithm combined with Branch and Bound (TGA-BB), which integrates the global search capability of genetic algorithms, the precise solution mechanism of branch and bound, and the local search avoidance features of Tabu search. A stage-adjustment operator is also introduced to better adapt the algorithm to multi-stage scheduling requirements. Finally, we designed eight instances of varying scales to systematically evaluate the performance of the stage-adjustment operator and the Tabu search mechanism within TGA-BB. Comparative experiments were conducted against several traditional heuristic algorithms. The experimental results show that TGA-BB outperformed the other algorithms across all eight test cases, in terms of both average response time and average runtime. Specifically, in Instance 7, TGA-BB reduced the average response time by approximately 52.37% compared to TGA-Particle Swarm Optimization (TGA-PSO), and in Instance 2, it shortened the average runtime by about 97.95% compared to TGA-Simulated Annealing (TGA-SA).These results fully validate the superior solution accuracy and computational efficiency of TGA-BB in drone-coordinated, multi-stage EMS. Full article

Natural disasters (e.g., floods, earthquakes) significantly impact citizens, economies, and the environment worldwide. Due to their sudden onset, devastating effects, and high uncertainty, it is crucial for emergency departments to take swift action to minimize losses. Among these actions, planning the locations of relief supply distribution centers and dynamically allocating supplies is paramount, as governments must prioritize citizens’ safety and basic living needs following disasters. To address this challenge, this paper develops a three-layer emergency logistics network to manage the flow of emergency materials, from warehouses to transfer stations to disaster sites. A bi-objective, multi-period stochastic integer programming model is proposed to solve the emergency location, distribution, and allocation problem under uncertainty, focusing on three key decisions: transfer station selection, upstream emergency material distribution, and downstream emergency material allocation. We introduce a multi-armed bandit algorithm, named the Geometric Greedy algorithm, to optimize transfer station planning while accounting for subsequent dynamic relief supply distribution and allocation in a stochastic environment. The new algorithm is compared with two widely used multi-armed bandit algorithms: the $ϵ$-Greedy algorithm and the Upper Confidence Bound (UCB) algorithm. A case study in the Futian District of Shenzhen, China, demonstrates the practicality of our model and algorithms. The results show that the Geometric Greedy algorithm excels in both computational efficiency and convergence stability. This research offers valuable guidelines for emergency departments in optimizing the layout and flow of emergency logistics networks. Full article

This paper considers a pickup and delivery problem in automobile logistics. In the daily operations of a third-party logistics company (3PL), decisions must be made for two kinds of demands: delivering finished automobiles from an outbound warehouse to distribution centers (DCs) and transferring automobiles among the DCs according to specific customer orders. The problem is to assign a set of automobiles to a set of heterogeneous auto-carriers and deliver them to their destinations considering the outbound and transfer demands. Each automobile is assigned a value indicating its urgency level to be handled and a car type: small, medium, or large. Each of the auto-carriers has a specific number of slots with different types indicating the largest size of an automobile that can be loaded into the slot. An integer programming (IP) model is formulated for the problem to maximize the total loaded value and minimize the total transportation cost depending on the routing of the carriers. An improved adaptive large neighborhood search algorithm is developed to solve the problem efficiently, where a heuristic generates an initial solution, and a series of operators update the solution iteratively. Experimental results based on multi-scale instances show that the proposed algorithm can generate near-optimal solutions in an acceptable amount of time, and outperforms solving the IP model directly by CPLEX to a large extent. The algorithm can help 3PL companies make efficient and economical decisions in daily operations. Full article

Focusing on the dynamic improvement of the underlying service network configuration, this paper aims to address a specific challenge of redesigning a multi-echelon city logistics distribution network. By considering the intra-echelon connection of facilities within the same layer of echelon, we propose a new distribution network design model by reformulating the classical quadratic assignment problem (QAP). To minimize the overall transportation costs, the proposed model jointly optimizes two types of decisions to enable agile distribution with dynamic “shortcuts”: (i) the allocation of warehouses to supply the corresponding distribution centers (DCs), and (ii) the demand coverage decision from distribution centers to delivery stations. Furthermore, a customized branch-and-bound algorithm is developed, where the lower bound is obtained by adopting Gilmore and Lawler lower Bound (GLB) for QAP. We conduct extensive computational experiments, highlighting the significant contribution of GLB-oriented lower bound, to obtain practical solutions; this type of efficient mathematical lower bounds offers a powerful tool for balancing theoretical research ideas with practical and industrial applicability. Full article

A well-planned robot dispatching platform reduces costs and increases efficiency for companies while also reducing carbon emissions and achieving sustainable development. At the moment, the solution to the difficulty of warehouse logistics is use of multiple distribution centers with autonomous mobile robots (AMR). To solve this problem, this paper establishes a semi-closed model of multiple distribution centers, considering the number of cycles and the number of vehicles. An improved ant colony algorithm is proposed to improve the heuristic function based on the node distance relationship to improve the quality of path search. Dynamic variable pheromone concentration and volatility factors are set to accelerate the convergence speed of the algorithm while effectively reducing the problem of the premature algorithm. The traditional ant colony algorithm and the improved ant colony algorithm are used to solve the established model. In addition, the results show that the traditional ant colony algorithm has a certain rate of dominance in the single-day cost of the closed distribution model, but the overall comprehensive cost is lower than that of the improved ant colony algorithm. The single-day cost of the semi-open multi-distribution center logistics and distribution model is lower than that of the closed multi-distribution center logistics and distribution model, and the 7 day average cost is reduced by 12%. The improved ant colony algorithm can save about 119 kWh of electricity under the same target volume requirement, which achieves the company’s goals of cost reduction and increased efficiency, as well as green and sustainable development. Full article

With the development of smart logistics, current small distribution centers have begun to use intelligent equipment to indirectly read bar code information on courier sheets to carry out express sorting. However, limited by the cost, most of them choose relatively low-end sorting equipment in a warehouse environment that is complex. This single information identification method leads to a decline in the identification rate of sorting, affecting efficiency of the entire express sorting. Aimed at the above problems, an express recognition method based on deeper learning and multi-information fusion is proposed. The method is mainly aimed at bar code information and three segments of code information on the courier sheet, which is divided into two parts: target information detection and recognition. For the detection of target information, we used a method of deeper learning to detect the target, and to improve speed and precision we designed a target detection network based on the existing YOLOv4 network, Experiments show that the detection accuracy and speed of the redesigned target detection network were much improved. Next for recognition of two kinds of target information we first intercepted the image after positioning and used a ZBAR algorithm to decode the barcode image after interception. The we used Tesseract-OCR technology to identify the intercepted three segments code picture information, and finally output the information in the form of strings. This deeper learning-based multi-information identification method can help logistics centers to accurately obtain express sorting information from the database. The experimental results show that the time to detect a picture was 0.31 s, and the recognition accuracy was 98.5%, which has better robustness and accuracy than single barcode information positioning and recognition alone. Full article

The routing strategy for order picking is an important factor in the efficiency of warehouse picking, and improvements to the warehouse layout provide more routing options for picking. The number of storage locations to be visited during the picking operation also has an impact on the selection of routing strategies. In order to achieve an effective improvement in the efficiency of picking operations in warehouse distribution centers, this paper focuses on the leaf warehouse layout based on the previous single-command operation strategy and extends it to study the multi-command operation strategy, in which three heuristic routing strategies, the S-shape, the return, and the composite, are introduced to solve the walking distance problem of picking operations, with the study of the selection of the routing strategy for different numbers of storage locations to be visited. Based on the distance equation between any two storage locations to be visited in the leaf layout warehouse, travel distance models corresponding to the three routing strategies in the picking operation are constructed, and the cuckoo search algorithm is introduced to solve and calculate the travel distance of the composite strategies for the experiments. In addition, the computational experiments of the three path strategies are carried out according to the different numbers of storage locations to be visited in the picking operation. By analyzing the numerical results, we find that the composite strategy has the best overall results among the three routing strategies, with the average values of optimization rates exceeding 30% (the S-shape) and 40% (the return), respectively. At the same time, the return strategy outperforms the S-shape strategy when the number of locations to be visited is less than seven. As the number of locations to be visited increases, the S-shape strategy gradually outperforms the return strategy. From a managerial and practical perspective, compared to the single-command operation strategy that is the focus of the current research, the multi-command operation strategy we studied is more relevant to the actual situation of order merging picking in warehouses and can effectively improve the efficiency of picking operations, the competitiveness of enterprises, and customer satisfaction of e-commerce enterprises. Full article

Utilizing local brick-and-mortar stores for same-day order fulfillment is becoming prominent in omni-channel retailing. Efficient in-store order picking is critical to providing timely value-added omni-channel delivery services. Despite numerous studies on order picking in traditional logistics warehouses and distribution centers, there is scant research focusing on in-store order fulfillment with the multi-skilled workforce in omni-channel retail stores. We studied the integrated Order Picking and Heterogeneous Picker Scheduling Problem (OPPSP-Het) in omni-channel retail stores. We characterized the OPPSP-Het in a mixed-integer linear optimization model with the objective of the minimization of total tardiness of all customer orders. A hybrid heuristic combining the genetic algorithm and variable neighborhood descent was designed to obtain effective solutions. Extensive experiments were conducted to validate the performance of the proposed approach relative to existing algorithms in recent literature. We further numerically showed the effects of order size and heterogeneous workforce on order fulfillment performance. We lastly emphasized the importance of workforce flexibility as a cost-effective approach to improving in-store order fulfillment performance. Full article

The current era of industrial economics necessitates warehouse and logistic distribution centers (DCs) to contribute productively toward an organization’s success. Playing such a critical productive role implies that logistics activities must be practiced effectively and efficiently. However, the indistinguishability between effectiveness and efficiency leads to a somewhat shallow interpretation, and consequently, a diluted evaluation. Hence, this paper aims to develop a productivity evaluation model for nine DCs belonging to an international automotive vehicles and spare parts company. The developed model was set up based on two multi-criteria decision making (MCDM) approaches: the Preference Ranking Organization Method for Enrichment of Evaluations II (PROMETHEE II) and data envelopment analysis (DEA). PROMETHEE II was employed to evaluate the effectiveness, while the DEA was utilized in order to measure the efficiency of the investigated DCs. The resulting hybrid model collectively creates what can conceptually and practically be considered a productivity evaluation model. The results also provide six different strategies through which distribution center locations can be evaluated in order to implement potential future initiatives. Full article