scholarly journals A Simulated Annealing Approach for the Train Design Optimization Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Federico Alonso-Pecina ◽  
David Romero
Keyword(s):  
Simulated Annealing ◽  
Design Optimization ◽  
Optimization Problem ◽  
Ad Hoc ◽  
Heuristic Method ◽  
Combinatorial Problem ◽  
Second Step ◽  
Railway Network ◽  
Optimal Decisions ◽  
Initial Feasible Solution

The Train Design Optimization Problem regards making optimal decisions on the number and movement of locomotives and crews through a railway network, so as to satisfy requested pick-up and delivery of car blocks at stations. In a mathematical programming formulation, the objective function to minimize is composed of the costs associated with the movement of locomotives and cars, the loading/unloading operations, the number of locomotives, and the crews’ return to their departure stations. The constraints include upper bounds for number of car blocks per locomotive, number of car block swaps, and number of locomotives passing through railroad segments. We propose here a heuristic method to solve this highly combinatorial problem in two steps. The first one finds an initial, feasible solution by means of an ad hoc algorithm. The second step uses the simulated annealing concept to improve the initial solution, followed by a procedure aiming to further reduce the number of needed locomotives. We show that our results are competitive with those found in the literature.

scholarly journals Optimizing Virtual Private Network Design Using a New Heuristic Optimization Method

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Hongbing Lian ◽  
András Faragó
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Design Optimization ◽  
Optimization Problem ◽  
Optimization Methods ◽  
Optimization Method ◽  
Virtual Private Network ◽  
Successful Model ◽  
Search Speed ◽  
Private Network

In virtual private network (VPN) design, the goal is to implement a logical overlay network on top of a given physical network. We model the traffic loss caused by blocking not only on isolated links, but also at the network level. A successful model that captures the considered network level phenomenon is the well-known reduced load approximation. We consider here the optimization problem of maximizing the carried traffic in the VPN. This is a hard optimization problem. To deal with it, we introduce a heuristic local search technique called landscape smoothing search (LSS). This study first describes the LSS heuristic. Then we introduce an improved version called fast landscape smoothing search (FLSS) method to overcome the slow search speed when the objective function calculation is very time consuming. We apply FLSS to VPN design optimization and compare with well-known optimization methods such as simulated annealing (SA) and genetic algorithm (GA). The FLSS achieves better results for this VPN design optimization problem than simulated annealing and genetic algorithm.

Optimal Allocation of Design Margins in Additive Remanufacturing

2021 ◽  
Author(s):  
Khalil Al Handawi ◽  
Massimo Panarotto ◽  
Petter Andersson ◽  
Ola Isaksson ◽  
Michael Kokkolaras
Keyword(s):  
Additive Manufacturing ◽  
Design Optimization ◽  
Optimization Problem ◽  
Optimal Allocation ◽  
Optimization Method ◽  
Structure Design ◽  
Design Decisions ◽  
Optimal Decisions ◽  
Different Types ◽  
Temperature Loads

Abstract Often, coping with changing requirements results in substantial overdesign, because of the ways in which design margins are allocated at the beginning of the design process. In this paper, we present a design optimization method for minimizing overdesign using additive manufacturing. We use recently defined constituents of design margins (buffer and excess) as metrics in a design optimization problem to minimize overdesign. The method can be used to obtain optimal design decisions for changing requirements. We demonstrate our method by means of a turbine rear structure design problem where changes in the temperature loads are met by depositing different types of stiffeners on the outer casing. The optimal decisions obtained by optimization minimize overdesign but ensure that requirements are met throughout the product’s lifecycle.

Optimizing Gate Assignments at Airport

2014 ◽  
Vol 556-562 ◽  
pp. 4178-4184
Author(s):  
Pan Zheng ◽  
Jing Li ◽  
Ying Hui Liang
Keyword(s):  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Objective Function ◽  
Assignment Problem ◽  
Optimization Problem ◽  
High Efficiency ◽  
Heuristic Method ◽  
Combinatorial Optimization Problem ◽  
The Core ◽  
Gate Assignment

Airport gate assignment is to appoint a gate for the arrival or leave flight and to ensure that the flight is on schedule. Assigning the airport gate with high efficiency is a key task among the airport ground busywork. As the core of airport operation, aircraft gate assignment is known as a kind of complicated combinatorial optimization problem. In this paper, we consider the over-constrained Airport Gate Assignment Problem where the number of flights exceeds the number of gates available, and where the objective is to minimize the overall variance of slack time (OVST). According to the intrinsic characteristics of the objective function itself, we design a meta-heuristic method and simulated annealing to solve the problem. Finally, the illustrative examples show the validity of the proposed approach.

scholarly journals A Hybrid Simulated Annealing/Linear Programming Approach for the Cover Printing Problem

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Federico Alonso-Pecina ◽  
David Romero
Keyword(s):  
Linear Programming ◽  
Simulated Annealing ◽  
Optimization Problem ◽  
Ad Hoc ◽  
Combinatorial Optimization Problem ◽  
Programming Approach ◽  
Printing Cost ◽  
Hybrid Simulated Annealing ◽  
Programming Techniques ◽  
Hard Cover

TheNP-hard cover printing problem addressed here consists in determining the number and composition of equal size impression grids, as well as the number of times each grid is printed, in order to fulfill the demand of different book covers at minimum total printing cost. The considered costs come from printing sheets and for composing grids. Thus, to deal with this combinatorial optimization problem we investigated two heuristics: one combines simulated annealing and linear programming techniques and the other is a hybrid of Tabu Search and an ad hoc procedure. Through intensive testing on available instances, these algorithms proved to be superior to previous approaches.

Penjadwalan Produksi untuk Meminimasi Makespan dengan Implementasi Algoritma Simulated Annealing

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Sawaluddin ◽  
Rosnani Ginting
Keyword(s):  
Simulated Annealing ◽  
Production Scheduling ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Job Scheduling ◽  
Initial Temperature ◽  
Heuristic Method ◽  
Reduction Factor ◽  
Annealing Algorithm ◽  
First Job

PT. ABC adalah perusahaan manufaktur yang memproduksi gelas plastik berdasarkan pesanan pelanggan (job order). Perusahaan menerapkan penjadwalan produksi dalam urutan pekerjaan pada pesanan, di mana setiap pekerjaan pertama datang harus diselesaikan terlebih dahulu dari pekerjaan lain (yang memiliki batas waktu kerja yang sama). Ini berdampak pada keterlambatan pengiriman produk ke konsumen. Untuk menghindari keterlambatan pengiriman produk, perlu menjadwalkan produksi di perusahaan untuk meminimalkan waktu penyelesaian produk (makespan). Penelitian ini menggunakan Algoritma Simulated Annealing. Algoritma Simulated Annealing adalah jenis metode heuristik karena memiliki potensi besar untuk menyelesaikan masalah optimisasi, di mana parameter yang digunakan adalah suhu awal (Ti) 2000C, suhu faktor reduksi adalah 0,95, jumlah iterasi adalah 15 kali. Algoritma Simulated Annealing sama dengan 20149,89 menit. Dapat dilihat bahwa dengan menggunakan metode yang diusulkan, ada pengurangan makespan dari 4418,86 menit = 75,65 jam = 3,06 hari. Sehingga penjadwalan pekerjaan dapat dipenuhi tepat waktu dan tidak ada penundaan tanggal jatuh tempo yang ditetapkan 14 hari. Jadi dapat disimpulkan algoritma Annealing Simulasi lebih efektif daripada metode First Come First Served.   PT. ABC is a manufacturing company that produces plastic cups based on customer orders (job order). Companies apply production scheduling in the order of jobs on the order, where every first job comes must be completed first from another job (which has the same working time limit). This has an impact on the delay in delivering products to consumers. To avoid delays in product shipments, it is necessary to schedule production at the company in order to minimize the time of product completion (makespan). This research uses Simulated Annealing Algorithm. The Simulated Annealing algorithm is a type of heuristic method because it has great potential to solve the optimization problem, where the parameters used are the initial temperature (Ti) of 2000C, the reduction factor temperature (s) is 0.95, the number of iterations is 15 times. The Simulated Annealing Algorithm is equal to 20149,89 minutes. It can be seen that by using the proposed method, there is a reduction of makespan of 4418.86 minutes = 75.65 hours = 3.06 days. So that job scheduling can be fulfilled on time and no delay of due date set by 14 days. So it can be concluded Simulated Annealing algorithm is more effective than First Come First Served method.

scholarly journals A simulated annealing-based algorithm for selecting balanced samples

2021 ◽  
Author(s):  
Roberto Benedetti ◽  
Maria Michela Dickson ◽  
Giuseppe Espa ◽  
Francesco Pantalone ◽  
Federica Piersimoni
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Sample Selection ◽  
Auxiliary Information ◽  
Real Data ◽  
Simulation Experiments ◽  
Balanced Sampling ◽  
Inclusion Probabilities ◽  
Random Method ◽  
Annealing Algorithms

AbstractBalanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed algorithm.

scholarly journals SAT modulo discrete event simulation applied to railway design capacity analysis

2021 ◽  
Author(s):  
Bjørnar Luteberget ◽  
Koen Claessen ◽  
Christian Johansen ◽  
Martin Steffen
Keyword(s):  
Discrete Event Simulation ◽  
Construction Projects ◽  
Software Verification ◽  
Ad Hoc ◽  
Discrete Event ◽  
Capacity Analysis ◽  
Railway Network ◽  
Event Simulation ◽  
Design Engineers ◽  
Railway Capacity

AbstractThis paper proposes a new method of combining SAT with discrete event simulation. This new integration proved useful for designing a solver for capacity analysis in early phase railway construction design. Railway capacity is complex to define and analyze, and existing tools and methods used in practice require comprehensive models of the railway network and its timetables. Design engineers working within the limited scope of construction projects report that only ad-hoc, experience-based methods of capacity analysis are available to them. Designs often have subtle capacity pitfalls which are discovered too late, only when network-wide timetables are made—there is a mismatch between the scope of construction projects and the scope of capacity analysis, as currently practiced. We suggest a language for capacity specifications suited for construction projects, expressing properties such as running time, train frequency, overtaking and crossing. Such specifications can be used as contracts in the interface between construction projects and network-wide capacity analysis. We show how these properties can be verified fully automatically by building a special-purpose solver which splits the problem into two: an abstracted SAT-based dispatch planning, and a continuous-domain dynamics with timing constraints evaluated using discrete event simulation. The two components communicate in a CEGAR loop (counterexample-guided abstraction refinement). This architecture is beneficial because it clearly distinguishes the combinatorial choices on the one hand from continuous calculations on the other, so that the simulation can be extended by relevant details as needed. We describe how loops in the infrastructure can be handled to eliminate repeating dispatch plans, and use case studies based on data from existing infrastructure and ongoing construction projects to show that our method is fast enough at relevant scales to provide agile verification in a design setting. Similar SAT modulo discrete event simulation combinations could also be useful elsewhere where one or both of these methods are already applicable such as in bioinformatics or hardware/software verification.

Adaptive Complex Method for Efficient Design Optimization

2007 ◽  
Author(s):  
Marcus Pettersson ◽  
Johan O¨lvander
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Global Optimum ◽  
Direct Search ◽  
Complex Method ◽  
Efficient Design ◽  
Direct Search Methods ◽  
Simulation Based ◽  
Set Of Points ◽  
Quasi Newton

Box’s Complex method for direct search has shown promise when applied to simulation based optimization. In direct search methods, like Box’s Complex method, the search starts with a set of points, where each point is a solution to the optimization problem. In the Complex method the number of points must be at least one plus the number of variables. However, in order to avoid premature termination and increase the likelihood of finding the global optimum more points are often used at the expense of the required number of evaluations. The idea in this paper is to gradually remove points during the optimization in order to achieve an adaptive Complex method for more efficient design optimization. The proposed method shows encouraging results when compared to the Complex method with fix number of points and a quasi-Newton method.

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