129 research outputs found

    Two-Echelon Vehicle and UAV Routing for Post-Disaster Humanitarian Operations with Uncertain Demand

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    Humanitarian logistics service providers have two major responsibilities immediately after a disaster: locating trapped people and routing aid to them. These difficult operations are further hindered by failures in the transportation and telecommunications networks, which are often rendered unusable by the disaster at hand. In this work, we propose two-echelon vehicle routing frameworks for performing these operations using aerial uncrewed autonomous vehicles (UAVs or drones) to address the issues associated with these failures. In our proposed frameworks, we assume that ground vehicles cannot reach the trapped population directly, but they can only transport drones from a depot to some intermediate locations. The drones launched from these locations serve to both identify demands for medical and other aids (e.g., epi-pens, medical supplies, dry food, water) and make deliveries to satisfy them. Specifically, we present two decision frameworks, in which the resulting optimization problem is formulated as a two-echelon vehicle routing problem. The first framework addresses the problem in two stages: providing telecommunications capabilities in the first stage and satisfying the resulting demands in the second. To that end, two types of drones are considered. Hotspot drones have the capability of providing cell phone and internet reception, and hence are used to capture demands. Delivery drones are subsequently employed to satisfy the observed demand. The second framework, on the other hand, addresses the problem as a stochastic emergency aid delivery problem, which uses a two-stage robust optimization model to handle demand uncertainty. To solve the resulting models, we propose efficient and novel solution approaches

    A case study of two-echelon multi-depot vehicle routing problem

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    The Vehicle Routing Problem (VRP) is a classic combinatorial optimization problem and a topic still studied for practical applications. Current research focuses on single echelon distribution systems such as distribution centers serving customers. However, in typical distribution, goods flows among regional distribution centers, local warehouses and customers, defined as a two-echelon network. The two-echelon multiple depot VRP problem is documented and applied to two stages illustrated by a small scale computational example. In the first stage, the simulated annealing algorithm is employed to determine the routes between local warehouses and final customers. For the second stage, trial-and-error is applied to obtain the number and location of regional distribution centers and the routes between regional distribution centers and local warehouses. Matlab is utilized to simulate annealing iterations and cost functions are analyzed. The convergence tendency of simulated annealing is depicted in figures by Matlab coding. Contributions include demonstration between the SA algorithm and a specific combinatorial optimization problem, and an application of the algorithm

    Urban Logistics in Amsterdam: A Modal Shift from Roadways to Waterway

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    The efficiency of urban logistics is vital for economic prosperity and quality of life in cities. However, rapid urbanization poses significant challenges, such as congestion, emissions, and strained infrastructure. This paper addresses these challenges by proposing an optimal urban logistic network that integrates urban waterways and last-mile delivery in Amsterdam. The study highlights the untapped potential of inland waterways in addressing logistical challenges in the city center. The problem is formulated as a two-echelon location routing problem with time windows, and a hybrid solution approach is developed to solve it effectively. The proposed algorithm consistently outperforms existing approaches, demonstrating its effectiveness in solving existing benchmarks and newly developed instances. Through a comprehensive case study, the advantages of implementing a waterway-based distribution chain are assessed, revealing substantial cost savings (approximately 28%) and reductions in vehicle weight (about 43%) and travel distances (roughly 80%) within the city center. The incorporation of electric vehicles further contributes to environmental sustainability. Sensitivity analysis underscores the importance of managing transshipment location establishment costs as a key strategy for cost efficiencies and reducing reliance on delivery vehicles and road traffic congestion. This study provides valuable insights and practical guidance for managers seeking to enhance operational efficiency, reduce costs, and promote sustainable transportation practices. Further analysis is warranted to fully evaluate the feasibility and potential benefits, considering infrastructural limitations and canal characteristics

    Decision support system for vendor managed inventory supply chain:a case study

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    Vendor-managed inventory (VMI) is a widely used collaborative inventory management policy in which manufacturers manages the inventory of retailers and takes responsibility for making decisions related to the timing and extent of inventory replenishment. VMI partnerships help organisations to reduce demand variability, inventory holding and distribution costs. This study provides empirical evidence that significant economic benefits can be achieved with the use of a genetic algorithm (GA)-based decision support system (DSS) in a VMI supply chain. A two-stage serial supply chain in which retailers and their supplier are operating VMI in an uncertain demand environment is studied. Performance was measured in terms of cost, profit, stockouts and service levels. The results generated from GA-based model were compared to traditional alternatives. The study found that the GA-based approach outperformed traditional methods and its use can be economically justified in small- and medium-sized enterprises (SMEs)

    The Two-Echelon Vehicle Routing Problem with Pickups, Deliveries, and Deadlines

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    This paper introduces the Two-Echelon Vehicle Routing Problem with Pickups, Deliveries, and Deadlines (2E-VRP-PDD), a new and emerging routing variant addressing the operations of logistics companies connecting consumers and suppliers in megacities. Logistics companies typically organize their logistics in such megacities via multiple geographically dispersed two-echelon distribution systems. The 2E-VRP-PDD is the practical problem that needs to be solved within each of such a single two-echelon distribution setting, thereby merging first and last-mile logistics operations. Specifically, it integrates forward flow, reverse flow, and vehicle time-synchronization aspects such as parcel time windows, satellite synchronization, and customer-dependent deadlines on the arrival of parcels at the hub. We solve the 2E-VRP-PDD with a tailored matheuristic that combines a newly developed Adaptive Large Neighborhood Search (ALNS) with a set-partitioning model. We show that our ALNS provides high-quality solutions on established benchmark instances from the literature. On a new benchmark set for the 2E-VRP-PDD, we show that loosening or tightening time restrictions, such as parcel delivery deadlines at the city hub, can lead to an 8.5% cost increase; showcasing the overhead associated with same-day delivery compared to next-day delivery operations. Finally, we showcase the performance of our matheuristic based on real-life instances which we obtained from our industry collaborator in Jakarta, Indonesia. On these instances, which we share publicly and consists of 1500 - 2150 customers, we show that using our ALNS can significantly improve current operations, leading to a 17% reduction in costs

    A stochastic variable size bin packing problem with time constraints

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    In this paper, we extend the classical Variable Size Bin Packing Problem (VS-BPP) by adding time features to both bins and items. Speciffically, the bins act as machines that process the assigned batch of items with a fixed processing time. Hence, the items are available for processing at given times and are penalized for tardiness. Within this extension we also consider a stochastic variant, where the arrival times of the items have a discrete probability distribution. To solve these models, we build a Markov Chain Monte Carlo (MCMC) heuristic. We provide numerical tests to show the different decision making processes when time constraints and stochasticity are added to VSBPP instances. The results show that these new models entail safer and higher cost solutions. We also compare the performance of the MCMC heuristic and an industrial solver to show the effciency and the effcacy of our method

    Adaptive large neighborhood search algorithm – performance evaluation under parallel schemes & applications

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    Adaptive Large Neighborhood Search (ALNS) is a fairly recent yet popular single-solution heuristic for solving discrete optimization problems. Even though the heuristic has been a popular choice for researchers in recent times, the parallelization of this algorithm is not widely studied in the literature compared to the other classical metaheuristics. To extend the existing literature, this study proposes several different parallel schemes to parallelize the basic/sequential ALNS algorithm. More specifically, seven different parallel schemes are employed to target different characteristics of the ALNS algorithm and the capability of the local computers. The schemes of this study are implemented in a master-slave architecture to manage and assign loads in processors of the local computers. The overall goal is to simultaneously explore different areas of the search space in an attempt to escape the local minima, taking effective steps toward the optimal solution and, to the end, accelerating the convergence of the ALNS algorithm. The performance of the schemes is tested by solving a capacitated vehicle routing problem (CVRP) with available wellknown test instances. Our computational results indicate that all the parallel schemes are capable of providing a competitive optimality gap in solving CVRP within our investigated test instances. However, the parallel scheme (scheme 1), which runs the ALNS algorithm independently within different slave processors (e.g., without sharing any information with other slave processors) until the synchronization occurs only when one of the processors meets its predefined termination criteria and reports the solution to the master processor, provides the best running time with solving the instances approximately 10.5 times faster than the basic/sequential ALNS algorithm. These findings are applied in a real-life fulfillment process using mixed-mode delivery with trucks and drones. Complex but optimized routes are generated in a short time that is applicable to perform last-mile delivery to customers

    A unified race algorithm for offline parameter tuning

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    This paper proposes uRace, a unified race algorithm for efficient offline parameter tuning of deterministic algorithms. We build on the similarity between a stochastic simulation environment and offline tuning of deterministic algorithms, where the stochastic element in the latter is the unknown problem instance given to the algorithm. Inspired by techniques from the simulation optimization literature, uRace enforces fair comparisons among parameter configurations by evaluating their performance on the same training instances. It relies on rapid statistical elimination of inferior parameter configurations and an increasingly localized search of the parameter space to quickly identify good parameter settings. We empirically evaluate uRace by applying it to a parameterized algorithmic framework for loading problems at ORTEC, a global provider of software solutions for complex decision-making problems, and obtain competitive results on a set of practical problem instances from one of the world's largest multinationals in consumer packaged goods

    Two-stage network design in humanitarian logistics.

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    Natural disasters such as floods and earthquakes can cause multiple deaths, injuries, and severe damage to properties. In order to minimize the impact of such disasters, emergency response plans should be developed well in advance of such events. Moreover, because different organizations such as non-governmental organizations (NGOs), governments, and militaries are involved in emergency response, the development of a coordination scheme is necessary to efficiently organize all the activities and minimize the impact of disasters. The logistics network design component of emergency management includes determining where to store emergency relief materials, the corresponding quantities and distribution to the affected areas in a cost effective and timely manner. In a two-echelon humanitarian relief chain, relief materials are pre-positioned first in regional rescue centers (RRCs), supply sources, or they are donated to centers. These materials are then shipped to local rescue centers (LRCs) that distribute these materials locally. Finally, different relief materials will be delivered to demand points (also called affected areas or AAs). Before the occurrence of a disaster, exact data pertaining to the origin of demand, amount of demand at these points, availability of routes, availability of LRCs, percentage of usable pre-positioned material, and others are not available. Hence, in order to make a location-allocation model for pre-positioning relief material, we can estimate data based on prior events and consequently develop a stochastic model. The outputs of this model are the location and the amount of pre-positioned material at each RRC as well as the distribution of relief materials through LRCs to demand points. Once the disaster occurs, actual values of the parameters we seek (e.g., demand) will be available. Also, other supply sources such as donation centers and vendors can be taken into account. Hence, using updated data, a new location-allocation plan should be developed and used. It should be mentioned that in the aftermath of the disaster, new parameters such as reliability of routes, ransack probability of routes and priority of singular demand points will be accessible. Therefore, the related model will have multiple objectives. In this dissertation, we first develop a comprehensive pre-positioning model that minimizes the total cost while considering a time limit for deliveries. The model incorporates shortage, transportation, and holding costs. It also considers limited capacities for each RRC and LRC. Moreover, it has the availability of direct shipments (i.e., shipments can be done from RRCs directly to AAs) and also has service quality. Because this model is in the class of two-stage stochastic facility location problems, it is NP-hard and should be solved heuristically. In order to solve this model, we propose using Lagrangian Heuristic that is based on Lagrangian Relaxation. Results from the first model are amounts and locations of pre-positioned relief materials as well as their allocation plan for each possible scenario. This information is then used as a part of the input for the second model, where the facility location problem will be formulated using real data. In fact, with pre-positioned items in hand, other supplies sources can be considered as necessary. The resulting multi-objective problem is formulated based on a widely used method called lexicography goal programming. The real-time facility location model of this dissertation is multi-product. It also considers the location problem for LRCs using real-time data. Moreover, it considers the minimization of the total cost as one of the objectives in the model and it has the availability of direct shipments. This model is also NP-hard and is solved using the Lagrangian Heuristic. One of the contributions of this dissertation is the development of Lagrangian Heuristic method for solving the pre-positioning and the real- time models. Based on the results of Lagrangian Heuristic for the pre-positioning model, almost all the deviations from optimal values are below 5%, which shows that the Heuristics works acceptably for the problem. Also, the execution times are no more than 780 seconds for the largest test instances. Moreover, for the real-time model, though not directly comparable, the solutions are fairly close to optimal and the execution time for the largest test instance is below 660 seconds. Hence, the efficiency of the heuristic for real-time model is satisfactory
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