The research on cargo volume forecasting and manpower demand involves utilizing appropriate models and algorithms to predict future information based on historical cargo volume and personnel allocation data, thereby e...
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ISBN:
(数字)9798350389579
ISBN:
(纸本)9798350389586
The research on cargo volume forecasting and manpower demand involves utilizing appropriate models and algorithms to predict future information based on historical cargo volume and personnel allocation data, thereby enhancing resource optimization and driving development. After preprocessing the data provided by a certain enterprise, this study utilizes LSTM (Long Short-Term Memory) patterns with reference to the daily cargo volume of each sorting center over the past four months and the hourly cargo volume over the past 30 days. A stationary time series model is constructed, and LSTM neural network parameters are set. Through rolling forecasts conducted via Python programs, an effective prediction of the hourly cargo volume for each of the 57 sorting centers over the next 30 days is achieved. Subsequently, a linear programming model is established to determine the objective function for maximizing personnel arrangement efficiency. Constraints such as single-shift attendance and employee headcount limits are selected. The Branch and Bound algorithm is employed to initialize upper and lower bounds and determine the feasible space, effectively yielding personnel deployment plans for different time periods at each sorting center over the next 30 days. The forecasting methods and data obtained in this paper play a significant role in resource allocation and service quality improvement in related industries.
Integer linear programming (ILP) is an NP-complete combinatorial optimization problem (COP), suggesting that it is computationally challenging to solve due to its exponentially increased operations with scaling. As sh...
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ISBN:
(数字)9798350394061
ISBN:
(纸本)9798350394078
Integer linear programming (ILP) is an NP-complete combinatorial optimization problem (COP), suggesting that it is computationally challenging to solve due to its exponentially increased operations with scaling. As shown in Fig. 1, The ILP is relevant in various real-world scenarios such as computational biology [1], investment decision, automated driving, and electronic design automation [2]. An ILP solver aims to find a set of integer variables
$(x)$
to maximize a linear objective function
$(c\cdot x)$
, subject to a set of linear constraints
$(A\cdot x\leq b)$
. With the increasingly wide usage of ILP, various new solving algorithms [3] have been proposed, but the performance are limited by substantial memory access. ILP coefficients are fixed during solving, but software solvers on cache-register architectures frequently access cache to reload coefficients because of small register file size, causing up to a
$10^{14}\mathrm{x}$
disparity between stored and accessed memory bits. FPGA [4] and AISC [5] accelerated solvers improve the speed by customized processing element (PE), but they still need frequent accesses to Block-RAM or scratch pad. Compute-in-memory (CIM) solutions are well-suited for ILP solving which has extremely high data reuse, but existing CIM DNN accelerators incur precision loss with hardware tradeoffs, which is unacceptable for ILP where the feasibility checking must be correct. Previous all-digital CIM COP solver for Boolean variables [6] uses a customized
$6\mathrm{T}$
-6T{###}
$3\mathrm{T}$
cell, limiting their adaptability to different technologies.
MSC Codes 52C17, 11H31The Tammes problem delves into the optimal arrangement of N points on the surface of the n-dimensional unit sphere (denoted as Sn−1), aiming to maximize the minimum distance between any two point...
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Constrained partially observable Markov decision processes (CPOMDPs) have been used to model various real-world phenomena. However, they are notoriously difficult to solve to optimality, and there exist only a few app...
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In this note, we generalize the classical optimal partial transport (OPT) problem by modifying the mass destruction/creation term to function-based terms, introducing what we term "generalized optimal partial tra...
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We develop the fictitious play algorithm in the context of the linear programming approach for mean field games of optimal stopping and mean field games with regular control and absorption. This algorithm allows to ap...
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The global drive towards carbon neutrality has led to a significant increase in the number of power plants based on renewable energy sources (RES). Concurrently, numerous households are adopting RES to generate their ...
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Unitary equivariance is a natural symmetry that occurs in many contexts in physics and mathematics. Optimization problems with such symmetry can often be formulated as semidefinite programs for a dp+q-dimensional matr...
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The rate vs. distance problem is a long-standing open problem in coding theory. Recent papers have suggested a new way to tackle this problem by appealing to a new hierarchy of linear programs. If one can find good du...
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The shift towards high-bandwidth networks driven by AI workloads in data centers and HPC clusters has unintentionally aggravated network latency, adversely affecting the performance of communication-intensive HPC appl...
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