Elektronik dizgi işlemlerinin eniyilenmesi ve değişken maliyetli seyyar satıcı problemi

Duman, Ekrem (Dogus Author)Bu projede baskılı devre kartları (BDK) dizgi işlemlerinde ortaya çıkan yeni bir gezgin satıcı problemi (GSP) üzerinde çalışmalar yapılmış, sıraya dayalı GSP adı verilen bu problemin önce matematiksel modeli geliştirilmiş daha sonra onu çözecek özgün yöntemler geliştirilmiştir. Bunun dışında, bu problemin görüldüğü iki tip dizgi makinesinin diğer ilgili optimizasyon problemleri (karesel atama problemi - KAP) de çözülerek toplam dizgi süreleri enazlanmıştır. KAP ve problemin bütününün çözümlerinde metasezgisel yöntemlerden yararlanılmıştır.Doğuş Üniversitesi; TÜBİTA

Heuristics for a generalization of TSP in the context of PCB assembly

Traveling Salesman Problem (TSP) is one of the most well-known NP-Hard combinatorial optimization problems. Adding new constraints to the problem yields different generalizations to the problem and each new generalization forms the basis of a new research area. In this study, we propose new techniques for a generalization of the TSP. In this problem, the cost of traveling between two cities does not only depend on the distance between these cities, but also on the visiting sequence. We analyzed the problem under different conditions; the first and last points (nodes) are set fixed or they are free and for solving the problem we propose several heuristics. After analyzing constructive heuristics, improvement heuristics are applied. As improvement heuristics, we implemented pair-wise exchange procedure (PEP) and record-to-record travel with local exchange moves (RTRLEM). Comparison of these approaches together with their parameter fine tuning are given.Doğuş Üniversitesi, TÜBİTA

Heuristics for the Canadian traveler problem with neutralizations

Canadian Traveler Problem with Neutralizations (CTPN) is a recently introduced challenging graph-theoretic path-planning problem. In CTPN, traversability status of some edges in the underlying graph is dependent on an a priori probability distribution. A traveling agent has two capabilities called disambiguation and neutralization. In the disambiguation case, the true status of an ambiguous edge (traversable or untraversable) is revealed when the agent is at either end of such edges. If the neutralization capability is exploited, the edge immediately becomes traversable. These capabilities are limited and may add a cost of increased path length. The goal of the agent is to find the shortest expected path length by devising an optimal policy that dictates when and where to disambiguate or neutralize. CTPN has important and practical applications within the context of expert and intelligent systems. These include autonomous robot navigation, adaptive transportation systems, naval and land minefield countermeasures, and navigation inside disaster areas for emergency relief operations. There is a recently proposed state-of-the-art exact algorithm that solves CTPN to optimality, called CAON* (AO* with Caching and Neutralizations). CAON* is based on an extension of the well-known AO* (AND-OR search) algorithm. Even though CAON* has significant improvements compared to its predecessors, it still has exponential run time and space complexity and it has been shown to solve only small instances of CTPN in practice. In this study, we introduce new heuristics for CTPN based on novel strategies that can be used to solve much larger and realistic problem instances. We provide computational experiments on Delaunay graphs to assess and compare the performance of these heuristics and CAON*, in terms of both run time and solution quality. Our computational experiments indicate that the proposed heuristics run extremely fast (well under a second in all cases) and they result in up to 58% improvement over existing heuristics with respect to expected path lengths with an overall improvement of 32% across our computational experiments

OPTIMAL NAVAL PATH PLANNING IN ICE-COVERED WATERS

The Northern Sea Route (NSR) links the Atlantic and Pacific oceans through the Arctic and it is critical for global trade as it provides a route between Asia and Europe that is significantly shorter than the alternatives. NSR is soon expected to open for intercontinental shipping due to global warming and thus presents tremendous opportunities for reductions in shipping time, cost, and environmental impacts. On the other hand, facilitating this route requires innovative approaches due to the navigation risks associated with its ice-covered waters. This study presents a graph-theoretical approach for optimal naval navigation in ice-covered sea routes with flexible turn angles based on the idea of large-adjacency grid graphs. Our model allows for asymmetric left and right turn radii as well as turn speeds that vary as a function of the turn angle and it offers natural-looking navigation paths

Capacity Planning in Non-uniform Depth Anchorages

Commercial vessels utilize anchorages on a regular basis for various reasons such as waiting for loading/unloading, supply, and bad weather conditions. Recent increase in demand for anchorage areas has mandated a review of current anchorage planning strategies. In particular, current state-of-the-art anchorage planning algorithms assume that the anchorage areas are of uniform depth, which is quite unrealistic in general. In this study, we introduce an algorithmic modification to current anchorage planning methods that takes into account non-uniformity of anchorages. By exploiting the depth non-uniformity, our algorithm significantly improves the number of vessels that can be accommodated in an anchorage and it can easily be incorporated into existing anchorage capacity planning decision support systems

Application of Recently Proposed Metaheuristics to the Sequence Dependent TSP

The Sequence Dependent Traveling Salesman Problem (SDTSP) is a combinatorial optimization problem defined as a generalization of the TSP. It emerged during optimization of two kinds of commonly used placement machines for production of printed circuit boards. The difference between SDTSP and TSP is that the cost incurred by transition from one point to another is dependent not only the distance between these points but also subsequent k points. In this study, we applied Simulated Annealing (SA), Artificial Bee Colony (ABC) and Migrating Birds Optimization (MBO) to solve real-world and random SDTSP instances. The metaheuristics were tested with 10 neighbor functions. In our computational study, we conducted extensive computational experiments. Firstly, we obtained best parameter value combination for each metaheuristic. Secondly, we conducted experiments so as to determine best performing neighbor function for each metaheuristic. Computational experiments show that twoopt function can be considered as the most suitable function for all the three metaheuristics

Canadian Traveler Problem with Neutralizations

The Canadian Traveler Problem (CTP) and the Obstacle Neutralization Problem (ONP) are two well-studied graph-theoretic path planning problems in the literature and both problems have been shown to be computationally intractable. In CTP, certain edges in a graph are blocked by a known probability and their status is revealed only when the traversing agent is at either end of these edges using the agent's limited disambiguation capability. The goal is to minimize the expected length of the traversal between a starting and a termination vertex by devising a policy that dictates in real-time which edge to disambiguate. In ONP, an agent needs to safely and swiftly navigate from a given source location to a destination through an arrangement of obstacles in the plane. The agent has a limited neutralization capability and uses it to safely pass through an obstacle at a cost of increased traversal length. The agent's goal is to find the sequence of obstacles to be neutralized en route which minimizes the overall traversal length subject to the agent's limited neutralization capability. Both of these problems have important and practical applications within the context of expert and intelligent systems. These include: autonomous robot navigation, adaptive transportation systems, naval and land minefield countermeasures, and navigation inside disaster areas for emergency relief operations. In this study, we consider a new path planning problem in the simultaneous presence of disambiguation and neutralization capabilities. This appears to be the first of its kind in the literature despite the close and inherent relationship between CTP and ONP. We call this problem the Canadian Traveler Problem with Neutralizations (CTPN). We present a Markov decision process formulation of CTPN and propose an optimal algorithm. This is based on an extension of the well-known AO* search algorithm. We provide computational experiments on Delaunay graphs to assess the relative performance of this algorithm in comparison to the well-known value iteration and AO* algorithms. We then investigate the relative utility and importance of the disambiguation and neutralization capabilities in order to assist decision-makers with financial constraints as well as navigation performance decisions. (C) 2019 Elsevier Ltd. All rights reserved

Metaheuristic based solution approaches for the obstacle neutralization problem

The problem of finding shortest path under certain constraints is NP-Complete except for some trivial variants. In this study, we develop metaheuristics for the obstacle neutralization problem (ONP) which is a path planning problem where the goal is to safely and swiftly navigate an agent from a given source location to a destination through an arrangement of potential mine or threat discs in the plane. To solve the ONP, ant system, genetic algorithm, simulated annealing and migrating birds optimization algorithms are developed and customized. We provide computational experiments both on real-world and synthetic data to empirically assess their performance. The results of the algorithms are compared with exact solutions on small instances. The comparison results present that our algorithms finds near-optimal solutions in reasonable execution times. Furthermore, the results show that the proposed versions of the aforementioned algorithms can be applicable to similar problems. (C) 2014 Elsevier Ltd. All rights reserved

AN OPTIMAL ALGORITHM FOR THE OBSTACLE NEUTRALIZATION PROBLEM

In this study, an optimal algorithm is presented for the obstacle neutralization problem (ONP). ONP is a recently introduced path planning problem wherein an agent needs to swiftly navigate from a source to a destination through an arrangement of obstacles in the plane. The agent has a limited neutralization capability in the sense that the agent can safely pass through an obstacle upon neutralization at a cost added to the traversal length. The goal of an agent is to find the sequence of obstacles to be neutralized en route minimizing the overall traversal length subject to the neutralization limit. Our optimal algorithm consists of two phases. In the first phase an upper bound of the problem is obtained using a suboptimal algorithm. In the second phase, starting from the bound obtained from phase I, a k-th shortest path algorithm is exploited to find the optimal solution. The performance of the algorithm is presented with computational experiments conducted both on real and synthetic naval minefield data. Results are promising in the sense that the proposed method can be applied in online applications