The rapid expansion of satellite Internet deployments, driven by the rise of Space-Ground Integration Network (SGIN) construction, has led to a significant increase in satellite numbers. To address the challenge of ef...
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The rapid expansion of satellite Internet deployments, driven by the rise of Space-Ground Integration Network (SGIN) construction, has led to a significant increase in satellite numbers. To address the challenge of efficient networking between large-scale satellites and limited ground station resources, this paper presents a hybrid learning-assisted multi-parallel algorithm (HLMP). The HLMP features a multi-parallel solving and deconflicting framework, a learning-assisted metaheuristic (LM) algorithm combining reinforcement learning (RL) and Tabu simulated annealing (TSA), and a linear programming (LP) exact-solving algorithm. The framework first divides the problem into parallel sub-problems based on the time domain, then applies LM and LP to solve each sub-problem in parallel. LM uses LP-generated scheduling results to improve its own accuracy. The deconflicting strategy integrates and refines the planning results from all sub-problems, ensuring an optimized outcome. HLMP advances beyond traditional task-driven satellite scheduling methods by offering a novel approach for optimizing large-scale satellite-ground networks under the new macro paradigm of "maximizing linkage to the greatest extent feasible." Experimental cases involving up to 1,000 satellites and 100 ground stations highlight HLMP's efficiency. Comparative experiments with other metaheuristic algorithms and the CPLEX solver further demonstrate HLMP's ability to generate high-quality solutions more quickly.
For mathematical programming (MP) to have greater impact as a decision tool, MP software systems must offer suitable support in terms of model communication and modelling techniques. In this paper, modelling technique...
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For mathematical programming (MP) to have greater impact as a decision tool, MP software systems must offer suitable support in terms of model communication and modelling techniques. In this paper, modelling techniques that allow logical restrictions to be modelled in integer programming terms are described, and their implications discussed. In addition, it is illustrated that many classes of non-linearities which are not variable separable may be, after suitable algebraic manipulation, put in a variable separable form. The methods of reformulating the fuzzy linear programming problem as a max-min problem is also introduced. It is shown that analysis of bounds plays a key role in the following four important contexts: model reduction, reformulation of logical restrictions as 0-1 mixed integer programmes, reformulation of non-linear programmes as variable separable programmes and reformulation of fuzzy linear programmes. It is observed that, as well as incorporating an interface between the modeller and the optimizer, there is a need to make available to the modeller software facilities which support the model reformulation techniques described here.
The Single Source Capacitated Facility Location Problem (SSCFLP) stands as a pivotal yet highly complex challenge in facility location science, underpinning real-world supply chains where single-sourcing constraints a...
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This study considers a problem of coordinating production, transportation and sales in a multi-echelon supply chain network. A simulation model is built to generate the random customer demands at different locations, ...
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This study considers a problem of coordinating production, transportation and sales in a multi-echelon supply chain network. A simulation model is built to generate the random customer demands at different locations, which are affected by a marketing strategy. Customer demands need to be satisfied by the supply chain through production, transportation and distribution. The optimization problem for coordination of production, transportation and distribution is first formulated as a linear programming with demands as input parameters in the constraint. Our objective is to maximize the expectation of the optimal profit of the supply chain given random demands by selecting an optimal marketing strategy. A simulation optimization technique is proposed to control the generation of random demands and solve the linear programming for efficiently learning the optimal marketing strategy. Numerical results show that our method can significantly improve the expected profit of the supply chain and reduce the computational burden of solving linear programming for achieving a given level of probability of correct selection of the optimal marketing strategy. Furthermore, we extend the optimization problem to a mixed integer programming and also demonstrate the computational efficiency of our proposed method.
This paper provides sufficient conditions for the optimal value function of a given linear semi-infinite programming (LSIP) problem to depend linearly on the size of the perturbations, when these perturbations involve...
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This paper provides sufficient conditions for the optimal value function of a given linear semi-infinite programming (LSIP) problem to depend linearly on the size of the perturbations, when these perturbations involve either the cost coefficients or the right-hand side function or both, and they are sufficiently small. Two kinds of partitions are considered. The first concerns the effective domain of the optimal value as a function of the cost coefficients and consists of maximal regions on which this value function is linear. The second class of partitions considered in this paper concerns the index set of the constraints through a suitable extension of the concept of optimal partition from ordinary to LSIP. These partitions provide convex sets, in particular, segments, on which the optimal value is a linear function of the size of the perturbations, for the three types of perturbations considered in this paper.
This paper proposes a dynamic model-based optimisation method for cigarette production planning to meet market demand and optimise inventory management. The model contains three objectives: Minimising the difference b...
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This paper proposes a dynamic model-based optimisation method for cigarette production planning to meet market demand and optimise inventory management. The model contains three objectives: Minimising the difference between annual demand and production output, balancing monthly production and demand, and minimising the average age of inventory. The model sets annual demand constraints, production capacity constraints, and considers seasonal adjustment and contingency inventory. The model is solved by a combination of linear programming and genetic algorithms. The results show that the optimised production plan can effectively reduce total costs, avoiding the risks of under-supply and over-stocking. The analysis shows that the planned monthly production quantity closely matches the actual allocation quantity, the inventory management is effective, and hence the ability to cope with peak demand is enhanced.
Estimating the cardinality of the output of a query is a fundamental problem in database query processing. In this article, we overview a recently published contribution that casts the cardinality estimation problem a...
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This paper explores the potential of quantum computing for solving the choice-based deterministic linear program (CDLP) for network revenue management. The CDLP is a model that incorporates customer choice between par...
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This paper explores the potential of quantum computing for solving the choice-based deterministic linear program (CDLP) for network revenue management. The CDLP is a model that incorporates customer choice between parallel itineraries serving the same origin-destination market and has been shown to offer significant revenue benefits compared to traditional methods. However, the CDLP is computationally intractable for realistic network sizes due to the exponential growth of the offer set space. We propose a novel quantum algorithm that uses quantum column generation and quantum search to achieve a quadratic speed-up over the classical counterparts. We demonstrate the feasibility and performance of our quantum algorithm on a toy network example and discuss the implications and challenges for future applications. Our work stands as the first of its kind to apply quantum computing to the domain of airline revenue management.
Vehicle numbers have been on the increase with the current growth in the economy and the rise of the middle income social status, thus allocation of parking space becomes a problem which needs to be addressed over par...
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Vehicle numbers have been on the increase with the current growth in the economy and the rise of the middle income social status, thus allocation of parking space becomes a problem which needs to be addressed over parked lots. This study addresses the optimization of parking lot design and obtaining optimum parking spots for vehicles. Types of parking designs are in two main categories: parallel parking and angular parking. Techniques in mathematical linear programming are utilized in an effort to increase and maximize the capacity of vehicles parked in the lot. The study investigates different angles of inclination to determine the angle that best optimizes the available space. Five angles, mathematical special angles, are evaluated to determine the combination that provides the optimal number of spots. Mixed integer linear programing (MILP) and integer linear programing are used for optimization formulation. LINDO and LINGO API software are used for analysis. The results show that the parking lot using linear programing can accommodate 41 parking spots at an angle of 75 degrees.
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