Crew scheduling in civilian ships is a combinatorialoptimization problem with various constraints. Traditional methods struggle with large-scale scheduling, while classic algorithms often fail to balance performance ...
详细信息
ISBN:
(数字)9798350363609
ISBN:
(纸本)9798350363616
Crew scheduling in civilian ships is a combinatorialoptimization problem with various constraints. Traditional methods struggle with large-scale scheduling, while classic algorithms often fail to balance performance and practicality. this paper presents an Improved Grey Wolf optimization (IGWO) algorithm. We establish a mixed-integerprogramming model that considers hard and soft constraints to maximize crew-position matching. the IGWO algorithm enhances the standard Grey Wolf optimization (GWO) framework by integrating strategies like heuristic initialization, adaptive encirclement search, elite leader selection, and constraint violation repair. Simulation on random and real-world datasets show that IGWO outperforms manual methods, rule-based heuristics, and the standard GWO algorithm in objective function value, constraint satisfaction, and employee satisfaction. this research advances heuristic optimization in combinatorial problems.
In light of significant complexity of the byproduct gas system in steel industry (which limits an ability to establish its physics-based model), this study proposes a data-based predictive optimization (DPO) method to...
详细信息
In light of significant complexity of the byproduct gas system in steel industry (which limits an ability to establish its physics-based model), this study proposes a data-based predictive optimization (DPO) method to carry out real-time adjusting for the gas system. Two stages of the method, namely the prediction modeling and real-time optimization, are involved. At the prediction stage, the states of the optimized objectives, the consumption of the outsourcing natural gas and oil, the power generation and the tank levels, are forecasted based on a proposed mixed Gaussian kernel-based prediction intervals (PIs) construction model. the Jacobian matrix of this model is represented by a kernel matrix through derivation, which greatly facilitates the subsequent calculation. At the second stage, a rolling optimization based on a mathematical programming technique involving continuous and integer decision-making variables is developed via the prediction intervals.
the present paper deals with using different mathematical tools and approaches for the data fitting. A comparison of two types of mathematical models, which solutions are used to fit experimental data is done, where t...
详细信息
the present paper deals with using different mathematical tools and approaches for the data fitting. A comparison of two types of mathematical models, which solutions are used to fit experimental data is done, where the error-of-fit and the computation time are taken as the fitting benchmarks. Both types of the defined models consist of differential equations, one uses fractional, the other integer orders of differentiation. the first advantage of the fractional-order models is the fact that fractional-order differential equation (FDE) has one degree of freedom more (the order of differentiation lies in the interval (0,1)), whereas the integer-order differential equation has the order of differentiation constant, equal to 1. the other advantage besides the “freedom in order” is that FDEs provide a powerful instrument for description of memory and hereditary properties of systems in comparison to the integer-order models, where such effects are neglected or difficult to incorporate. the first aspect of this work consist in choosing a suitable optimization method for finding the parameters of the defined models based on minimizing chosen fitting criterion. the Medium-scale optimization, using the tools of sequential quadratic programming, Quasi-Newton and line-search algorithms (performed by the MATLAB function fmincon) is compared withthe evolutionary - genetic algorithm (performed by the MATLAB function ga). the second aspect lies in using two different approaches in the formulation of the optimization criterion. For the time domain identification the classical least squares method (LSM) and so the sum of vertical offsets will be used. the state-space identification will use the total least squares method (TLSM or the so-called orthogonal distance fitting criterion - ODF), which uses the sum of orthogonal (perpendicular) distances between the experimental points and the fitting curve. Several examples are presented in the form of figures. the efficiency of computat
this work is devoted to development of threshold algorithms for one static probabilistic competitive facility location and design problem in the following formulation. New Company plans to enter the market and to loca...
详细信息
ISBN:
(纸本)9783319734415;9783319734408
this work is devoted to development of threshold algorithms for one static probabilistic competitive facility location and design problem in the following formulation. New Company plans to enter the market and to locate new facilities with different design scenarios. Clients of each point choose to use the facilities of Company or its competitors depending on their attractiveness and distance. the aim of the new Company is to capture the greatest number of customers thus serving the largest share of the demand. this share for the Company is elastic and depends on clients' decisions. We offer three types of threshold algorithms: Simulated annealing, threshold improvement and Iterative improvement. Experimental tuning of parameters of algorithms was carried out. A comparative analysis of the algorithms, depending on the nature and value of the threshold on special test examples up to 300 locations is carried out. the results of numerical experiments are discussed.
Supplier selection is a challenging decision that has strategic importance for organizations. Cost is no longer the sole factor in the selection of suppliers, and the complexity of this issue arises from the interplay...
详细信息
ISBN:
(纸本)9781479925056
Supplier selection is a challenging decision that has strategic importance for organizations. Cost is no longer the sole factor in the selection of suppliers, and the complexity of this issue arises from the interplay of several situation-specific criteria (such as total cost, CO_(2e) emissions, development time, lead time) as well as the combinatorial nature of this problem. this paper proposes an approach based on combinatorialoptimization (integer linear programming) combined with multi-criteria value analysis to establish priorities and trade-offs among the defined criteria for combinatorial bidding. the approach was employed in a real-world decision, the selection of a supplier for a cosmetics packaging set for a new product line. the obtained solution is compared against standard multi-criteria optimization (without a combinatorial auction formulation) and also against single criterion optimization. the paper also reports on the challenges and advantages of applying the framework in the case study.
We present two approaches to the construction of scaling functions and wavelets that generate nearly cardinal and nearly symmetric wavelets on the line. the first approach casts wavelet construction as an optimization...
详细信息
ISBN:
(数字)9781728137414
ISBN:
(纸本)9781728137421
We present two approaches to the construction of scaling functions and wavelets that generate nearly cardinal and nearly symmetric wavelets on the line. the first approach casts wavelet construction as an optimization problem by imposing constraints on the integer samples of the scaling function and its associated wavelet and with an objective function that minimizes deviation from cardinality or symmetry. the second method is an extension of the feasibility approach by Franklin, Hogan, and Tam to allow for symmetry by considering variables generated from uniform samples of the quadrature mirror filter, and is solved via the Douglas-Rachford algorithm.
this paper is concerned with probabilistic evaluation of multiple-frame data association hypotheses in multiple-target tracking problems, in particular, when targets are not necessarily independent a priori. Multiple-...
详细信息
this paper is concerned with probabilistic evaluation of multiple-frame data association hypotheses in multiple-target tracking problems, in particular, when targets are not necessarily independent a priori. Multiple-target tracking problems with dependent targets naturally arise whenever targets interact with each other, as they move in congested traffic, or as they actively coordinate their movements in other situations. this paper develops a Bayesian data association hypothesis evaluation formula for dependent targets. Because the resulting formula does not have a multiplicative or log-linear form, the best hypothesis cannot be selected by integer linear programming or multi-dimensional assignment algorithms commonly used to solve data association problems in multiple target tracking. Instead, we propose to use Reuven Rubinstein's cross-entropy method as a possible solution. A K-best hypothesis selection extension will be discussed as an application of the generalized Murty's algorithm. this paper focuses on the theoretical aspects as the first step of a solution concept development.
An effective spare part supply system planning is essential to achieve a high capital asset availability. We investigate the design problem of a repair shop in a single echelon repairable multi-item spare parts supply...
详细信息
Introduction: Efficient port yard storage is a critical challenge at Dar es Salaam Port, where limited space, diverse cargo types, and strict regulatory requirements complicate operations. Traditional optimization met...
ISBN:
(数字)9781837242931
Introduction: Efficient port yard storage is a critical challenge at Dar es Salaam Port, where limited space, diverse cargo types, and strict regulatory requirements complicate operations. Traditional optimization methods often fail to address these complexities adequately. this study presents a hybrid approach that combines Mixed-integer Linear programming (MILP) with Genetic Algorithms (GA) to optimize storage operations, aiming to improve efficiency and adaptability. Methods: the methodology involves a two-phase optimization strategy. In the first phase, a robust MILP model is developed using the PuLP library in Python to determine optimal cargo allocations based on volume, compatibility, and equipment constraints. the second phase refines these allocations using a Genetic Algorithm, facilitated by the DEAP library, to explore and optimize solutions further. this hybrid approach is designed to handle the complexities of real-world port operations by leveraging the strengths of both MILP and GA. Results: the MILP model successfully allocated cargos to storage locations, optimizing space utilization and reducing handling costs. the largest storage area, Location L5, was utilized up to 95% of its capacity, reflecting an efficient allocation strategy. the Genetic Algorithm further enhanced the initial solution, increasing overall utility by 3% and providing multiple near-optimal configurations. Sensitivity analysis revealed the system's resilience to changes in cargo volumes and handling times. Discussion: the integration of MILP and GA proved effective in optimizing port yard storage, providing both high operational efficiency and strategic flexibility. the study's findings indicate that continuous optimization and infrastructure expansion, alongside technological integration, can significantly improve port operations. the results also underscore the importance of dynamic modeling for future enhancements. Conclusion: this hybrid optimization approach combining MILP
A well cores reused-based wrapper design is an important approach to minimize SOC test application time and test costs. the combinatorialoptimization problem of core wrapper design has been proven to be a NP-hard pro...
详细信息
A well cores reused-based wrapper design is an important approach to minimize SOC test application time and test costs. the combinatorialoptimization problem of core wrapper design has been proven to be a NP-hard problem. In this paper, a wrapper scan chain balance algorithm with entropy increase hybrid discrete differential evolution (EIHDE) is proposed to solve the core wrapper problem, which is inspired by thermodynamic system principle of entropy increase and outstanding global searching ability of Differential Evolution (DE). the proposed approach develops a cooperative mutation strategy based on entropy increase for the problem to preserving its interesting search mechanism for discrete domains. In the proposed model, two cooperative encode modes of individuals are introduced for standard differential mutation and the cooperative entropy increase mutation: integer encode mode and binary encode mode. EIHDE controls the search space by differential mutation, and search for superior individual in local space by entropy increase mutation. the combination of two kinds of mutation operations promotes the optimization ability considerably and achieves a better tradeoff between exploitation and exploration. the experimental results of the ITC'02 SOC test benchmarks show that EIHDE can achieve more balanced results compared with other algorithms.
暂无评论