Generating Travel Itinerary Using Ant Collony Optimization

Travelling is one of the activities needed by everyone to overcome weariness. The number of information about the tourism destination on the internet sometimes does not provide easiness for oncoming tourists. This paper proposes a system capable of making travel itinerary, for tourists who want to visit an area within a few days. For generating itinerary, the system considers several criterias (Multi-criteria-based), which include the popularity level of tourist attractions to visit, tourist visits that minimize budgets or tourist visits with as many destinations as possible. To handle multi criteria-based itinerary, we use the concept of multi attribute utility theory (MAUT). The running time of multi criteria-based itinerary is not significantly different from time-based itinerary. In addition, the number of tourist attractions in the itinerary is more than time-based itinerary, because the combination of solutions from each ant becomes more diverse

APPLYING PRECEDENCE RELATIONSHIPS AND CAD/CAM SIMULATION IN TIME-BASED OPTIMIZATION OF PROCESS PLANNING

Optimization of process planning belongs to the group of combinatorial optimization problems which at the macro and micro level consists of the selection of machining operations, definition of sequences of machining operations and their grouping into processes, selection of manufacturing resources, machining parameters and strategies. The objective function used for evaluating process plans is mostly defined by manufacturing cost, manufacturing time, surface quality and surface accuracy. The main goal of this research refers to the process planning and optimization of manufacturing time by applying precedence relationships among machining operations, as well as the simulation technique within the CAD/CAM system. The precedence relationships are defined on the basis of dimensional, geometric, technological and economical precedence constraints. Based on these rules, precedence matrices for determining operation sequence for the given shaft part are formed, and afterwards, machining operations are grouped into appropriate processes. For the given rational variants of process plans, a simulation of machining process is performed within the Catia software system. The obtained output is the best variant of process plan for the shaft part on the basis of manufacturing time as the adopted objective function

An Integrated Intelligent CAD/CAPP Platform: Part II - Operation Sequencing Based on Genetic Algorithm

We present a platform for integrated CAD/CAPP part design based on Elementary Machining Features (EMF) and intelligent approach for setup planning and operation sequencing based on a genetic algorithm through two papers. In this paper, as Part II of this platform, CAD/CAPP integration was realized via information from the enriched EMF, as well as production rules and a genetic algorithm. This is done for the purpose of the automated machining operation sequencing. Operation sequencing was conducted by using the improved genetic algorithm (GA).The improved GA uses integer representation for operations and implements modified genetic operators, enabling the achievement of high results in a reasonable computational time. In the paper we present a comprehensive case study applied to some existing and one new industrial example, confirming a high level of usability of the proposed GA and overall platform. Experimental results show that the improved GA algorithm gives slightly better results than similar algorithms in literature. For industrial example, we use body of the hydraulics cylinder which consists of 52 EMF. After implementation of the proposed methodology, the optimal machining operation sequence was identified, as well as the total machining cost of 142.49 BAM

Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing

Grid computing is a distributed system with heterogeneous infrastructures. Resource management system (RMS) is one of the most important components which has great influence on the grid computing performance. The main part of RMS is the scheduler algorithm which has the responsibility to map submitted tasks to available resources. The complexity of scheduling problem is considered as a nondeterministic polynomial complete (NP-complete) problem and therefore, an intelligent algorithm is required to achieve better scheduling solution. One of the prominent intelligent algorithms is ant colony system (ACS) which is implemented widely to solve various types of scheduling problems. However, ACS suffers from stagnation problem in medium and large size grid computing system. ACS is based on exploitation and exploration mechanisms where the exploitation is sufficient but the exploration has a deficiency. The exploration in ACS is based on a random approach without any strategy. This study proposed four hybrid algorithms between ACS, Genetic Algorithm (GA), and Tabu Search (TS) algorithms to enhance the ACS performance. The algorithms are ACS(GA), ACS+GA, ACS(TS), and ACS+TS. These proposed hybrid algorithms will enhance ACS in terms of exploration mechanism and solution refinement by implementing low and high levels hybridization of ACS, GA, and TS algorithms. The proposed algorithms were evaluated against twelve metaheuristic algorithms in static (expected time to compute model) and dynamic (distribution pattern) grid computing environments. A simulator called ExSim was developed to mimic the static and dynamic nature of the grid computing. Experimental results show that the proposed algorithms outperform ACS in terms of best makespan values. Performance of ACS(GA), ACS+GA, ACS(TS), and ACS+TS are better than ACS by 0.35%, 2.03%, 4.65% and 6.99% respectively for static environment. For dynamic environment, performance of ACS(GA), ACS+GA, ACS+TS, and ACS(TS) are better than ACS by 0.01%, 0.56%, 1.16%, and 1.26% respectively. The proposed algorithms can be used to schedule tasks in grid computing with better performance in terms of makespan

An Improved Ant Colony Optimization Approach for Optimization of Process Planning

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environments (CIMs). In this paper, process planning problem is described based on a weighted graph, and an ant colony optimization (ACO) approach is improved to deal with it effectively. The weighted graph consists of nodes, directed arcs, and undirected arcs, which denote operations, precedence constraints among operation, and the possible visited path among operations, respectively. Ant colony goes through the necessary nodes on the graph to achieve the optimal solution with the objective of minimizing total production costs (TPCs). A pheromone updating strategy proposed in this paper is incorporated in the standard ACO, which includes Global Update Rule and Local Update Rule. A simple method by controlling the repeated number of the same process plans is designed to avoid the local convergence. A case has been carried out to study the influence of various parameters of ACO on the system performance. Extensive comparative experiments have been carried out to validate the feasibility and efficiency of the proposed approach

Pengoptimuman jarak laluan mata alat menggunakan algoritma koloni semut untuk proses pengisaran poket

Pada masa kini, proses pemesinan kisar poket menggunakan mesin Kawalan Komputer Berangka (CNC) banyak digunakan dalam pemotongan logam. Terdapat dua langkah pemesinan di dalam proses pengisaran poket iaitu pemesinan kasar dan kemasan. Pemesinan kasar mengambil masa lebih 50 % dari keseluruhan masa pemotongan kerana sejumlah besar bahan kerja dipotong sehingga hampir menyerupai bentuk yang dikehendaki. Oleh itu, adalah penting untuk mempercepatkan masa pemesinan kasar. Pemesinan kontur selari dapat menghasilkan masa pemesinan kasar yang lebih rendah berbanding zigzag dan satu hala. Walau bagaimanapun, terdapat satu masalah di dalam pemesinan kontur iaitu berlaku bahagian lebihan tidak terpotong pada bahagian bucu dan tengah. Kawasan lebihan tidak terpotong ini berlaku kerana penetapan nilai selang antara kontur (ω) yang melebihi jejari mata alat (r). Salah satu cara untuk memotong kawasan lebihan ini adalah dengan menambahkan satu laluan mata alat tambahan (Llt) ke atas laluan asal, iaitu laluan kontur selari. Kaedah penghasilan laluan mata alat tambahan yang diperkenalkan kajian terdahulu berjaya untuk memotong keseluruhan kawasan lebihan ini. Namun, laluan yang dihasilkan oleh kajian sebelum ini tidak mempertimbangkan pergerakan mata alat yang menyumbang kepada peningkatan jarak laluan mata alat dan masa pemesinan kasar. Oleh itu, objektif kajian ini adalah untuk mengoptimumkan laluan mata alat bagi menentukan jarak laluan mata alat yang minimum di dalam proses pengisaran poket berdasarkan pemesinan kontur selari menggunakan kaedah cerdik buatan (AI). Algoritma kontur selari (Algo-KS) dibina bagi menghasilkan laluan mata alat secara kontur selari dan untuk menentukan kawasan lebihan tidak dipotong. Algoritma Koloni Semut berdasarkan aturan peralihan baru (ACO-PB) telah diperkenalkan untuk menentukan pergerakan mata alat bagi memotong kawasan lebihan berdasarkan aturan peralihan dan jarak minimum di antara dua kawasan lebihan. ACO-PB telah diuji keberkesanannya ke atas dua model iaitu model pertama dan model kedua bagi menentukan masa pemesinan kasar (Tmk). Seterusnya, Tmk yang diperoleh ini disahkan keputusannya menggunakan proses uji kaji pemesinan. Uji kaji dilakukan dengan mempraktikkan laluan mata alat yang dihasilkan berdasarkan ACO-PB ke dalam mesin kisar CNC tiga-paksi. Bahan kerja Aluminium 6061 dan mata alat jenis keluli laju tinggi (HSS) hujung rata yang bersalut Titanium Nitrida digunakan sepanjang proses pemesinan kasar. Hasil kajian mendapati terdapat perbezaan Tmk sebanyak 7.2 % di antara Tmk ACO-PB dan uji kaji. Keputusan ini telah mengesahkan bahawa ACO-PB yang dibangunkan berupaya untuk meminimumkan jarak laluan mata alat dan dapat dipraktikkan di dalam proses pemesinan sebenar. Llt dan Tmk yang dihasilkan ACO-PB juga telah dibandingkan dengan Llt dan Tmk yang diperoleh berdasarkan kajian terdahulu. Keputusan simulasi menunjukkan ACO-PB telah menghasilkan laluan mata alat yang lebih pendek sebanyak 23.7 % dan pengurangan Tmk sebanyak 4.95 % berbanding kajian terdahulu. Kajian ini juga telah membandingkan Tmk yang diperoleh menggunakan ACO-PB dan Mastercam dan mendapati ACO-PB berjaya mengurangkan Tmk sebanyak 46.5 %. Sebagai kesimpulan, kajian ini telah berjaya membangunkan algoritma ACO-PB yang berupaya untuk meminimumkan jarak laluan mata alat di dalam pemesinan kontur selari dan mengurangkan masa pemotongan bagi proses pemesinan kasar

Pengoptimuman jarak laluan mata alat menggunakan algoritma koloni semut untuk proses pengisaran poket

Pada masa kini, proses pemesinan kisar poket menggunakan mesin Kawalan Komputer Berangka (CNC) banyak digunakan dalam pemotongan logam. Terdapat dua langkah pemesinan di dalam proses pengisaran poket iaitu pemesinan kasar dan kemasan. Pemesinan kasar mengambil masa lebih 50 % dari keseluruhan masa pemotongan kerana sejumlah besar bahan kerja dipotong sehingga hampir menyerupai bentuk yang dikehendaki. Oleh itu, adalah penting untuk mempercepatkan masa pemesinan kasar. Pemesinan kontur selari dapat menghasilkan masa pemesinan kasar yang lebih rendah berbanding zigzag dan satu hala. Walau bagaimanapun, terdapat satu masalah di dalam pemesinan kontur iaitu berlaku bahagian lebihan tidak terpotong pada bahagian bucu dan tengah. Kawasan lebihan tidak terpotong ini berlaku kerana penetapan nilai selang antara kontur (ω) yang melebihi jejari mata alat (r). Salah satu cara untuk memotong kawasan lebihan ini adalah dengan menambahkan satu laluan mata alat tambahan (Llt) ke atas laluan asal, iaitu laluan kontur selari. Kaedah penghasilan laluan mata alat tambahan yang diperkenalkan kajian terdahulu berjaya untuk memotong keseluruhan kawasan lebihan ini. Namun, laluan yang dihasilkan oleh kajian sebelum ini tidak mempertimbangkan pergerakan mata alat yang menyumbang kepada peningkatan jarak laluan mata alat dan masa pemesinan kasar. Oleh itu, objektif kajian ini adalah untuk mengoptimumkan laluan mata alat bagi menentukan jarak laluan mata alat yang minimum di dalam proses pengisaran poket berdasarkan pemesinan kontur selari menggunakan kaedah cerdik buatan (AI). Algoritma kontur selari (Algo-KS) dibina bagi menghasilkan laluan mata alat secara kontur selari dan untuk menentukan kawasan lebihan tidak dipotong. Algoritma Koloni Semut berdasarkan aturan peralihan baru (ACO-PB) telah diperkenalkan untuk menentukan pergerakan mata alat bagi memotong kawasan lebihan berdasarkan aturan peralihan dan jarak minimum di antara dua kawasan lebihan. ACO-PB telah diuji keberkesanannya ke atas dua model iaitu model pertama dan model kedua bagi menentukan masa pemesinan kasar (Tmk). Seterusnya, Tmk yang diperoleh ini disahkan keputusannya menggunakan proses uji kaji pemesinan. Uji kaji dilakukan dengan mempraktikkan laluan mata alat yang dihasilkan berdasarkan ACOPB ke dalam mesin kisar CNC tiga-paksi. Bahan kerja Aluminium 6061 dan mata alat jenis keluli laju tinggi (HSS) hujung rata yang bersalut Titanium Nitrida digunakan sepanjang proses pemesinan kasar. Hasil kajian mendapati terdapat perbezaan Tmk sebanyak 7.2 % di antara Tmk ACO-PB dan uji kaji. Keputusan ini telah mengesahkan bahawa ACO-PB yang dibangunkan berupaya untuk meminimumkan jarak laluan mata alat dan dapat dipraktikkan di dalam proses pemesinan sebenar. Llt dan Tmk yang dihasilkan ACO-PB juga telah dibandingkan dengan Llt dan Tmk yang diperoleh berdasarkan kajian terdahulu. Keputusan simulasi menunjukkan ACO-PB telah menghasilkan laluan mata alat yang lebih pendek sebanyak 23.7 % dan pengurangan Tmk sebanyak 4.95 % berbanding kajian terdahulu. Kajian ini juga telah membandingkan Tmk yang diperoleh menggunakan ACO-PB dan Mastercam dan mendapati ACO-PB berjaya mengurangkan Tmk sebanyak 46.5 %. Sebagai kesimpulan, kajian ini telah berjaya membangunkan algoritma ACO-PB yang berupaya untuk meminimumkan jarak laluan mata alat di dalam pemesinan kontur selari dan mengurangkan masa pemotongan bagi proses pemesinan kasar

A hybrid grey wolf optimizer for process planning optimization with precedence constraints

Process planning optimization is a well-known NP-hard combinatorial problem extensively studied in the scientific community. Its main components include operation sequencing, selection of manufacturing resources and determination of appropriate setup plans. These problems require metaheuristic-based approaches in order to be effectively and efficiently solved. Therefore, to optimize the complex process planning problem, a novel hybrid grey wolf optimizer (HGWO) is proposed. The traditional grey wolf optimizer (GWO) is improved by employing genetic strategies such as selection, crossover and mutation which enhance global search abilities and convergence of the traditional GWO. Precedence relationships among machining operations are taken into account and precedence constraints are modeled using operation precedence graphs and adjacency matrices. Constraint handling heuristic procedure is adopted to move infeasible solutions to a feasible domain. Minimization of the total weighted machining cost of a process plan is adopted as the objective and three experimental studies that consider three different prismatic parts are conducted. Comparative analysis of the obtained cost values, as well as the convergence analysis, are performed and the HGWO approach demonstrated effectiveness and flexibility in finding optimal and near-optimal process plans. On the other side, comparative analysis of computational times and execution times of certain MATLAB functions showed that the HGWO have good time efficiency but limited since it requires more time compared to considered hybrid and traditional algorithms. Potential directions to improving efficiency and performances of the proposed approach are given in conclusions.Web of Science1423art. no. 736

Dividing Complex Parts into Multiple Pieces for Advanced Joining and Additive Manufacturing

Poor material utilization is inherent to conventional manufacturing processes, leading to high material waste and machining times. Additive manufacturing processes attempt to solve this issue by allowing production of near-net shapes, but the processes may be too expensive or infeasible. By leveraging both processes in a single part, the waste and cost of manufacturing can be reduced. For example, a complex part could have the main body produced by bar stock and machining, while small protruding features are joined onto the main body by additive manufacturing. This thesis presents a method to divide complex parts into smaller pieces to be built up through advanced joining and additive manufacturing. A beam search algorithm is applied to consider the vast number of manufacturing and joining options and converge on the lowest cost solutions. The algorithm runs by intelligently identifying cutting planes and iteratively applying them to solid geometry to create possible manufacturing alternatives. Each manufacturing alternative is then evaluated based on cost, and the lowest cost alternatives are presented to the designer to aid in determining better manufacturing plans. Application of this method will reduce material waste and machining to offset the added joining costs. This thesis presents the development, implementation, and testing of this approach