A study of the reserve allocation problem by a mixed integer approach

Author(s):  
J. Kubokawa
2021 ◽  
Vol 30 (04) ◽  
pp. 2150017
Author(s):  
Nataša Kovač ◽  
Tatjana Davidović ◽  
Zorica Stanimirović

This study considers the Dynamic Minimum Cost Hybrid Berth Allocation Problem (DMCHBAP) with fixed handling times of vessels. The objective function to be minimized consists of three components: costs of positioning, waiting, and tardiness of completion for all vessels. A mathematical formulation of DMCHBAP, based on Mixed Integer Linear Programming (MILP), is proposed and used within the framework of commercial CPLEX 12.3 solver. As the speed of finding high-quality solutions is of crucial importance for an efficient and reliable decision support system in container terminal, two population-based metaheuristic approaches to DMCHBAP are proposed: combined Genetic Algorithm (cGA) and improvement-based Bee Colony Optimization (BCOi). Both cGA and BCOi are evaluated and compared against each other and against state-of-the-art solution methods for DMCHBAP on five sets of problem instances. The conducted computational experiments and statistical analysis indicate that population-based metaheuristic methods represent promising approaches for DMCHBAP and similar problems in maritime transportation.


2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Burak Yuksek ◽  
N. Kemal Ure

We consider the integrated problem of allocation and control of surface-to-air-missiles for interception of ballistic targets. Previous work shows that using multiple missile and utilizing collaborative estimation and control laws for target interception can significantly decrease the mean miss distance. However, most of these methods are highly sensitive to initial launch conditions, such as the initial pitch and heading angles. In this work we develop a methodology for optimizing selection of multiple missiles to launch among a collection of missiles with prespecified launch coordinates, along with their launch conditions. For the interception we use 3-DoF models for missiles and the ballistic target. The trajectory of the missiles is controlled using three-dimensional extensions of existing algorithms for planar collaborative control and estimation laws. Because the dynamics of the missiles and nature of the allocation problem is highly nonlinear and involves both discrete and continuous variables, the optimization problem is cast as a mixed integer nonlinear programming problem (MINP). The main contribution of this work is the development of a novel probabilistic search algorithm for efficiently solving the missile allocation problem. We verify the algorithm by performing extensive Monte-Carlo simulations on different interception scenarios and show that the developed approach yields significantly less average miss distance and more efficient use of resources compared to alternative methods.


2006 ◽  
Vol 505-507 ◽  
pp. 511-516
Author(s):  
Ta Cheng Chen ◽  
Tung-Chou Hsu

This paper considers nonlinearly mixed integer tolerance allocation problems in which both tolerance and process selection are to be decided simultaneously so as to minimize the manufacturing cost. The tolerance allocation problem has been studied in the literature for decades, usually using mathematical programming or heuristic/metaheuristic optimization approaches. The difficulties encountered for both methodologies are the number of constraints and the difficulty of satisfying the constraints. A penalty-guided artificial immune algorithm is presented for solving such mixed integer tolerance allocation problems. Numerical examples indicate that the proposed artificial immune algorithms perform well for the tolerance allocation problem considered in this paper. In particular, as reported, solutions obtained by artificial immune algorithm are as well as or better than the previously best-known solutions.


2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Guomei Gan ◽  
Yanhu Huang ◽  
Qiang Wang

Device to device (D2D) communication has recently attracted a lot of attentions since it can significantly improve the system throughput and reduce the energy consumption. Indeed, the devices can communicate with each other in a D2D system, and the base station (BS) can share the spectrum with D2D users, which can efficiently improve the spectrum and energy efficiency. Nevertheless, spectrum sharing also raises the difficulty of resource allocation owing to the serious cochannel interference. To reduce the interference, the transmit power of the D2D pairs and BS to cellular users should be further optimized. In this paper, we consider the resource allocation problem of D2D networks involving the power allocation and subcarrier assignment. The resource allocation problem is formulated as a mixed integer programming problem which is difficult to solve. To reduce the computational complexity, the original problem is decomposed as two subproblems in terms of the subcarrier assignment and power allocation. For the subcarrier assignment problem, the particle swarm optimization (PSO) is adopted to solve it since the subcarrier assignment is an integer optimization problem, and it is difficult to be tackled using the traditional optimization approach. When the subcarrier assignment is fixed, there are only the power allocation variables in the original resource allocation problem. The difference of convex functions (DC) programming is adopted to solve the power allocation problem. Simulation results demonstrate the effectiveness of the proposed resource allocation scheme of D2D networks.


2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Hongtao Hu ◽  
Haotian Xiong ◽  
Yuanfeng You ◽  
Wei Yan

A mixed integer programming model is proposed to solve supplier selection and order allocation problem for a manufacturer. In this model, quality, delivery performance, and purchasing cost are chosen as three criteria to select suppliers and set as objectives. Inventory level, goods flow balance, service level, supply ability, and marketing demand are considered as constraints. In the proposed model, the three objectives have different weights which are given by experts. However, the experts score the weight by many subjective factors. So, the fuzzy analytic hierarchy process (FAHP) based approach is used to calculate the weighted values. In the end, a case study illustrates the advantage of weighted values solved by FAHP. And the result shows that a weighted model is more advantageous for supplier selection and order allocation.


Author(s):  
Nguyen Hoang Son ◽  
Nguyen Van Hop

In this work, a mixed-integer linear programming model is formulated to allocate the appropriate orders to the right suppliers for recyclable raw materials. We modify the previous model for the supplier selection and order allocation problem for stochastic demand to cope with the supply risks of recyclable raw materials such as insufficient supply quantity, defective rate, and late delivery. The optimal solution of the mathematical model is the benchmark for small-sized problems. Then, a hybrid meta-heuristic of Particles Swarm Optimization and Grey Wolf Optimization (PSO-GWO) is proposed to search for the best solution for large-sized problems. A real-life case study of a steel manufacturer with two factories in Vietnam is presented to validate the proposed approach. Some experiments have been tested to confirm the performance of the hybrid PSO-GWO approach.


2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Ming Zeng ◽  
Wenming Cheng ◽  
Peng Guo

The gantry crane scheduling and storage space allocation problem in the main containers yard of railway container terminal is studied. A mixed integer programming model which comprehensively considers the handling procedures, noncrossing constraints, the safety margin and traveling time of gantry cranes, and the storage modes in the main area is formulated. A metaheuristic named backtracking search algorithm (BSA) is then improved to solve this intractable problem. A series of computational experiments are carried out to evaluate the performance of the proposed algorithm under some randomly generated cases based on the practical operation conditions. The results show that the proposed algorithm can gain the near-optimal solutions within a reasonable computation time.


2021 ◽  
pp. 0734242X2098661
Author(s):  
Carina Letelier ◽  
Carola Blazquez ◽  
Germán Paredes-Belmar

In the commune of Renca in Santiago, Chile, the household waste is currently collected on the kerbside and then thrown in the rear of a compactor truck. This system becomes inefficient when not all citizens are always serviced, yielding high overall collection costs and negative impacts on the environment and the society. Recently, recyclable waste collection sites have been situated throughout the commune, which need to be examined with respect to population coverage and average travel distances to these sites. This study employs mixed-integer linear programming models and geographic information systems to solve the bin location–allocation problem for household and recyclable waste separately. The results are shown for different values of waste generation, bin capacities, and travel distances, in addition to considering the users’ and municipality’s preferences in the decision-making process. The proposed recyclable waste bin locations present a more efficient solution than the existing collection sites in the commune of Renca since more users are serviced within a shorter travel distance to dispose of their recyclable waste.


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