Due to variations in carrying capacity and visiting periods of vessels calling at terminals, certain transshipped containers may not be able to be transported immediately after arrival. Instead, they must be temporari...
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Due to variations in carrying capacity and visiting periods of vessels calling at terminals, certain transshipped containers may not be able to be transported immediately after arrival. Instead, they must be temporarily stored in the yard and scheduled for later transportation. Increased container storage time in the yard as a consequence of transit delays not only incurs additional costs for shipping companies, but also introduces new issues for terminal yard management. Therefore, this study investigates the daily storage yard management challenges that arise in marine container terminals that integrate space allocation and yard crane deployment decisions while considering the delayed transshipment of containers. A mixed-integer linear programming model is proposed to describe the problem, whose objectives are to minimize the transportation costs for container loading and discharge operations, penalty costs associated with the delayed transshipment of containers, transportation costs associated with containers not transshipped within the planning horizon, and yard crane inter-block movement costs. The benders decomposition algorithm is applied to address this problem, and the traditional decomposition approach is improved by adding characteristics such as the development of strong optimality cuts and the generation of a powerful initial solution to the master problem. Extensive experiments are performed to validate the effectiveness of the model and efficiency of the proposed improved benders decomposition algorithm.
Recent research on distributionally robust (DR) machine scheduling has used a variety of approaches to describe the region of ambiguity of uncertain processing times by imposing constraints on the moments of the proba...
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Recent research on distributionally robust (DR) machine scheduling has used a variety of approaches to describe the region of ambiguity of uncertain processing times by imposing constraints on the moments of the probability distributions. One approach that has been employed outside machine scheduling research is the use of statistical metrics to define a distance function between two probability distributions. Adopting such an approach, we study Wasserstein distance-based DR parallel-machine scheduling, where the ambiguity set is defined as a Wasserstein ball around an empirical distribution of uncertain processing times corresponding to finitely many samples. The objective is to minimize a DR objective that concerns the worst-case expected total completion time-related cost over all the distributions arising from the Wasserstein ambiguity set, subject to DR chance constraints on the machine service capacity. We show that the problem can be equivalently re-formulated as a mixed-integer linear program (MILP), which has a more simplified formulation when the bounded support set reduces to a left bounded one. To solve the resulting model, we develop a tailored branch-and-benders-cut algorithm incorporating some enhancement strategies, including in-out benders cut generation, aggregated sample group cut generation, and two-stage benders cut generation, which significantly outperforms the CPLEX solver. Experiment results on comparing our model with the deterministic and stochastic counterparts and the model with first-order moment ambiguity set illustrate the benefits of considering distributional ambiguity and Wasserstein ambiguity set.
作者：
Ashok S. KumarT. SudhaM-Tech Student
Electronics and Communication EngineeringNSS College of Engineering Palakkad 678008 Kerala India Professor
Electronics and Communication Engineering NSS College of Engineering Palakkad 678008 Kerala India
In this paper, our objective is to reduce the interference in the direction of Primary Users (PUs) and to decrease the power cost of Secondary Users (SUs) in the Cognitive underlay system. Artificial Bee Colony (ABC) ...
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In this paper, our objective is to reduce the interference in the direction of Primary Users (PUs) and to decrease the power cost of Secondary Users (SUs) in the Cognitive underlay system. Artificial Bee Colony (ABC) algorithm, one of the most recently introduced meta-heuristic optimization algorithms is used here as the clustering algorithm. Here relay stations are introduced in cooperative beamforming technique, which are equipped with directional antennas that can efficiently improve the spectral efficiency and can reduce the interference to primary users in a cognitive underlay systems. In the proposed research, flexible cooperation is considered as a general case while full cooperation and inter-cell cooperation are considered as special cases. We jointly optimize the beamforming and the clustering to reduce the overall power consumption of the secondary users and to satisfy the primary users interference limits. Inorder to find optimal solution, an iterative algorithm called generalized bendersdecomposition method is applied. We have investigated the benefits of ABC algorithm and showed the advantages of the relays in cognitive wireless networks.
This paper presents a novel reliable hierarchical location-allocation model where facilities are subject to the risk of disruptions. Based on the relationship between various levels of system, a multi-level multi-flow...
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This paper presents a novel reliable hierarchical location-allocation model where facilities are subject to the risk of disruptions. Based on the relationship between various levels of system, a multi-level multi-flow hierarchy is considered. The heterogeneous probabilistic disruptions are investigated in which the constructed facilities have different site-dependent and independent failure rates. In the occurrence of facility disruptions, to achieve system reliability, the mitigation operation is considered in such a way as to reassign the demand nodes to other operational facilities that can provide services. The problem is modeled from both cost and risk perspectives such that the fixed installation cost as well as the expected costs in normal disruption-free and disruptive conditions are minimized. A benders decomposition algorithm is developed which seeks to find exact solution of the proposed model. Two efficient accelerating techniques including valid inequalities and knapsack inequalities are also proposed to expedite the convergence of solution procedure. The numerical results illustrate the applicability of the proposed model as well as the efficiency of the designed solution procedure.
Unmanned Aerial Vehicles (UAVs), or drones, are gaining attention in emergency response for their rapid mobility in dynamic scenarios. Constrained by limited endurance and payload, UAVs typically operate in a "de...
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Unmanned Aerial Vehicles (UAVs), or drones, are gaining attention in emergency response for their rapid mobility in dynamic scenarios. Constrained by limited endurance and payload, UAVs typically operate in a "depot-customer-depot" paradigm. Thus, optimally locating multiple depots is critical to achieving operational efficiency and flexibility. Traditional location models, which rely on circular coverage, fail to capture the actual reachable area for UAV round-trips between multiple depots within a given endurance range. This drawback restricts deployment flexibility or results in excessive redundancy, even making it impractical. To address this limitation, we introduce an ellipse-based locating method for flexible UAV deployment, inspired by UAV reachability and process flexibility in manufacturing. This approach attempts to optimize the redundancy of multi-depot coverage for demand points to achieve a better balance between deployment flexibility and resource requirements. To tackle the model's computational challenge, we present an improved benders decomposition algorithm that speeds up the solution process by analytically addressing subproblems and implementing dominance rules to manage the master problem's size. Simulations show that the proposed model greatly improves the ability to handle uncertainties by incorporating slight redundancy in emergency resources, and the fulfillment rate of demand fluctuations is increased by 5%-20%, which shows the superiority of enhancing the mobility and flexibility of UAV deployment.
This paper focuses on the development of a model for optimal transmission switching procurements within the operating reserve where energy and reserve are jointly dispatched. In this paper a stochastic mix-integer lin...
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ISBN:
(纸本)9781457721588
This paper focuses on the development of a model for optimal transmission switching procurements within the operating reserve where energy and reserve are jointly dispatched. In this paper a stochastic mix-integer linear programming model is utilized. Transmission switching can provide economic benefits when compared with other control methods such as generation unit rescheduling or load shedding for management contingency. The main purpose of this paper is to determine a sufficient amount of spinning reserve for a power system that considers the probabilistic behavior of system's components and transmission switching for reducing spinning reserve cost. Transmission switching considering in contingency for determined optimal spinning reserve and utilized for management contingencies.
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