Intelligent Search Algorithm Research Articles

A web based tool for dynamic job rotation scheduling using multiple criteria

This paper presents a method implemented in a web based tool able to generate job rotation schedules for human based assembly systems. Through a user friendly web interface, production engineers can represent an assembly line, the tasks to be performed for each product, and the operator's characteristics. An intelligent search algorithm is used for generating alternative solutions to the scheduling problem. Multiple criteria decision making is used for evaluating the job rotation schedule alternatives, according to criteria deriving from industrial assembly line requirements. The tool is tested on a truck assembly case study and can provide high quality solutions.

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.

Complexity Research on <i>B</i> Algorithm

The time complexity of B algorithm, one of the intelligent search algorithms, is discussed. By anatomizing some instances, it is pointed out that the cost of calculating the value of heuristic function should be included in the range of time complexity analysis for B algorithm. And then, an algorithm of calculating the value of heuristic function is presented. By analyzing the cost of calculating the value of heuristic function, it is pointed out that the number of recursions in B algorithm is O(n!) in the worst case. Therefore, the time complexity of B algorithm is exponential instead of O(n2).

Theory of constraints product mix optimisation based on immune algorithm

Product mix optimisation is one of the most fundamental problems in manufacturing enterprise. As an important component in theory of constraints (TOC), product mix optimisation is solved by the TOC heuristic (TOCh) and some intelligent search algorithms, even though these approaches often cannot effectively obtain a good solution in the previous attempts, especially for the large-scale product mix optimisation. Aiming at this problem, a contribution has been made to the following aspects in the present paper. Firstly, a model of TOC product mix optimisation, which identifies and exploits the capacity constrained resource (CCR) to maximise system throughput is put forward and simplified by cutting down some constraints of non-CCRs. Secondly, an intelligent optimisation approach based on immune algorithm (IA) and TOC for product mix optimisation is presented to search optimal solution(s), whether it is a small-scale or large-scale instance. Thirdly, the immune mechanisms, such as the immune response mechanism, immune self-adaptive regulation and vaccination, are studied in detail, which not only greatly improves the searching ability and adaptability, but also evidently increases the global convergence rate of immune evolution. Fourthly, the proposed approach is implemented and applied in both small-scale and large-scale product mix optimisation. Finally, a comparison between the proposed approach and existing approaches is made. Simulation results show that the proposed approach is superior to the existing approaches, such as the TOCh, revised TOCh, integer linear programming (ILP), tabu search (TS), and genetic algorithms (GA).

A two-stage scheduling method for hot rolling and its application

Hot rolling scheduling is a difficult problem in the steel processing industry. It involves many objectives and constraints in both technical and practical respects. A two-stage scheduling method is proposed in this paper. Batch planning of staple material is formulated as a VRPTW, which is solved with a modified PGA. Then, batches of the established units are optimized by adjusting rolling sequences using intelligent search algorithms to reach higher hot charge ratios. This method has been applied to a hot strip mill belonging to Baosteel in China. Our results demonstrate that the proposed technique can improve production efficiency and offer significant economic benefits.

Routing in Optical and Non-Optical Networks using Boolean Satisfiability

Today, most routing problems are solved using Dijkstra’s shortest path algorithm. Many efficient implementations of Dijkstra’s algorithm exist and can handle large networks in short runtimes. Despite these advances, it is difficult to incorporate user-specific conditions on the solution when using Dijkstra’s algorithm. Such conditions can include forcing the path to go through a specific node, forcing the path to avoid a specific node, using any combination of inclusion/exclusion of nodes in the path, etc. In this paper, we propose a new approach to solving the shortest path problem using advanced Boolean satisfiability (SAT) techniques. SAT has been heavily researched in the last few years. Significant advances have been proposed and has lead to the development of powerful SAT solvers that can handle very large problems. SAT solvers use intelligent search algorithms that can traverse the search space and efficiently prune parts that contain no solutions. These solvers have recently been used to solve many problems in Engineering and Computer Science. In this paper, we show how to formulate the shortest path problem in non-optical networks as a SAT problem. We also show how to use SAT in finding routing and wavelength assignments in optical networks. Our approach is verified on various network topologies. The results are promising and indicate that using the proposed approach can improve on previous techniques.

A New Circuit Augmentation Method for Modeling of Interconnects and Passive Components

This paper presents a new methodology for automated broadband model generation from S-parameter data for interconnects and passive components. The new methodology is based on augmenting an existing equivalent circuit model with a macromodel (black-box) network described by rational functions while simultaneously perturbing the equivalent circuit component values. The macromodel network is determined using standard least-squares or vector-fitting approaches. The perturbation of the equivalent circuit parameter values is achieved during the macromodel generation by means of global optimization based on intelligent search algorithms. The new approach is demonstrated on several two-port test example structures including a broadband probe tip structure and a CMOS spiral inductor.

Admissible Heuristic Functions For Flow-Shop Problems

A set of heuristic functions is developed for use in conjunction with an intelligent heuristic search algorithm (IHSA*) for solving flowshop problems involving two or three machines with any number of jobs. The admissibility of these functions is established and a subset of three admissible heuristic functions is identified in terms of the close proximity of their values to the minimum makespan of the problem. Experimental results indicate that, choosing among these three admissible heuristic functions, the one with the largest value improves performance characteristics of IHSA*. In addition, for the particular case where any one of the machines dominates the others, it is shown that exactly one of this subset of three heuristic functions can be identified immediately as having a value that is significantly closer to the minimum makespan than the others, and experimental results again illustrate the resulting improvement in the performance of IHSA*.

Recent Developments in Decision-Analytic Modelling for Economic Evaluation

The past few years have seen rapid changes in the methods of decision-analytic modelling of healthcare programmes for the purposes of economic evaluation. This paper focuses on four developments in modelling that have emerged over the past few years or have become more widely used. First, no one optimal method for extrapolating outcomes from clinical trials has yet been established. Modellers may draw from a set of varied assumptions about survival extrapolation that encompass a range of possibilities from highly optimistic to extremely cautious. Secondly, the practicality and appeal of microsimulation as a method for analysing healthcare decision problems has increased dramatically with the speed of computing technology. Individual instantiations of a system are generated by using a random process to draw from probability distributions a large number of times (also known as Monte Carlo or probabilistic simulation). Microsimulation is moving in new directions, such as discrete-event simulations that simulate sequences of events by drawing directly from probability distributions of event times; this approach is now being broadly applied to model situations where populations of patients interact with healthcare delivery systems. Microsimulation modelling of transmission systems at the population level is also rapidly developing. Thirdly, model calibration is emerging as a new tool that may offer health scientists a means of generating important fundamental knowledge about disease processes. Model calibration allows evidence synthesis in which observations on observable quantities are used to draw inferences about unobservable quantities. The methodology of model calibration has advanced considerably, drawing on theories of numerical analysis and mathematical programming such as gradient methods, intelligent grid search algorithms, and many more. As a fourth issue, an area of extraordinary activity is in the use of transmission models to analyse interventions for infectious diseases, including population-wide effects of vaccination. Transmission models use differential equations to simulate, deterministically for the most part, transitions among infection-related health states. Only recently have modelling methodologies been combined so that cost-effectiveness analyses can consider explicitly not only the patient-level benefits of interventions but also the secondary benefits through transmission dynamics. Advances in technology allow more realistic and complex healthcare models to be simulated more rapidly. However, decision makers will not readily accept results from models unless they can understand them intuitively and explain them to others in relatively simple terms. The challenge for the next generation of modellers is not only to harness the power available from these newly accessible methods, but also to extract from the new generation of models the insights that will have the power to influence decision makers.

ADMISSIBLE HEURISTIC FUNCTIONS FOR FLOW-SHOP PROBLEMS

A set of heuristic functions is developed for use in conjunction withan intelligent heuristic search algorithm (IHSA*) for solving flow-shop problems involving two or three machines with any numberof jobs.

Integration of genetic algorithms and GIS for optimal location search

Optimal location search is frequently required in many urban applications for siting one or more facilities. However, the search may become very complex when it involves multiple sites, various constraints and multiple‐objectives. The exhaustive blind (brute‐force) search with high‐dimensional spatial data is infeasible in solving optimization problems because of a huge combinatorial solution space. Intelligent search algorithms can help to improve the performance of spatial search. This study will demonstrate that genetic algorithms can be used with Geographical Information systems (GIS) to effectively solve the spatial decision problems for optimally sitting n sites of a facility. Detailed population and transportation data from GIS are used to facilitate the calculation of fitness functions. Multiple planning objectives are also incorporated in the GA program. Experiments indicate that the proposed method has much better performance than simulated annealing and GIS neighborhood search methods. The GA method is very convenient in finding the solution with the highest utility value.

Interpretation and evaluation of the γ index and the γ index angle for the verification of IMRT hybrid plans

In IMRT, the method for a quantitative comparison of two-dimensional dose distributions is still under development. The γ evaluation method proposed by Low et al is the most accepted approach and has been adapted by many groups. Based on the concept of Low et al we developed a software tool with an intelligent search algorithm to minimize the calculation time. For the interpretation of deviations a γ angle distribution and other tools (dose difference map, profiles, γ area histograms, etc) are integrated in the software package. Ten hybrid plans are included in the verification study containing 6 IMRT head and neck cases, 2 IMRT prostate cases and one IMRT paravertebral case as well as a standard uniform intensity conformal 4 field box treatment for comparison. IMRT plans are realized with a segmental MLC delivery technique. The fields of a hybrid plan are applied at once and dose distributions are measured with films in three planes of a verification phantom. All γ vector calculations are based on a 3% dose criterion and a 3 mm DTA acceptance criterion. The mean value γmean (mean value in the γ distribution) of the various IMRT plans is 0.45 ± 0.10 (1 SD). On average, the percentage of points exceeding the acceptance criteria of γ ⩽ 1 (γ>1) is 5.8 ± 5.4% (1 SD). The mean value of γ1% (1% of points have an equal or higher γ value) is 1.47 ± 0.59 (1 SD) for IMRT plans. In 5 out of 27 planes, γ>1 is substantially larger than the average. This is also indicated in γ area histograms. Planes with large areas outside the tolerance criteria were further evaluated using γ angle distributions. This additional information indicates that the large areas with high γ values are dominated by the dose difference. It is shown that the deviations are influenced by tongue and groove effects. From the statistical evaluation of γ values (e.g. γ area histogram), acceptance criteria for IMRT hybrid plans can be defined. For the interpretation of the γ maps, distributions of the γ angle and traditional evaluation methods, such as dose profiles, are still very useful.

SIMULATION OF DESIGN AND ANALYSIS OF A CONTAINER REVERSE-LOGISTICS SYSTEM

ABSTRACT The contemporary relevance of environmental protection has gained greater importance around the world, to the point where many countries, such as Germany, Holland, Japan, European Union, and Taiwan, have taken heed and initiated a range of policies. Concepts such as green manufacturing and sustainable management are used to describe interventions that utilize appropriately natural resources in an endeavor to decrease any harm that may result when enterprises handle the wastes they produce. The literature emphasizes the issues relating to recycling products rather than recycling containers; that in-turn could attract considerable cost-benefits. This paper discusses the development of a simulation model that incorporates reverse logistics. Application and testing of the model is facilitated using the case of transportation containers used by Toyota automobile company. Empirical findings indicate that the operational cost-benefits using reusable containers are better than the expendable containers. The concept of reusable containers as depicted in the model is further explored to identify the optimum economic quantities. Model architecture used a method of integrated bisection algorithm and an artificial intelligence search algorithm.

Design and Evaluation of an Algorithm Animation of State Space Search Methods

We describe the design and evaluation of an algorithm animation of artificial intelligence search algorithms. As well as the usual single step mode for assistance in learning the individual steps of an algorithm, the system supports an innovative burst mode for visualizing qualitative characteristics. A formal empirical study was conducted with the aim of determining if this algorithm animation of search methods would make a suitable alternative laboratory activity to the more traditional activity of programming implementations of search methods. Results provide good evidence that the animation laboratory activity is a viable alter native for reinforcing students understanding about state space search methods.

Adaptive production scheduling of virtual production systems using object-oriented Petri nets with changeable structure

Virtual Production Systems (VPSs) are logically constructed by organizing production resources belonging to one or more physical manufacturing systems. VPSs can enhance the agility of manufacturing systems. However, an effective scheduling approach is required to cope with disturbance and changes to these systems. An adaptive production scheduling method is proposed. Object-oriented Petri nets with changeable structure (OPNs-CS) formulate the scheduling problem of VPSs. To resolve resource constraints in a VPS, the OPNs-CS is modified by introducing limited token available time and by revising the enabling and firing rules. The artificial intelligent heuristic search (A*) algorithm is modified and applied to generate the optimal or near optimal schedule. When a VPS encounters any disturbance, an estimate of the effects of the disturbance can be estimated by simulation on the OPNs-CS model. If the scheduling target (completion time) is not affected, rescheduling is not required. Whenever there is a change to the VPS, the TOPNs-CS model is updated to refresh VPS schedule. A case study is presented to demonstrate the procedures for applying the proposed scheduling approach. The given case study shows that the proposed approach is capable of scheduling a VPS dynamically in response to disturbances and changes are involved.

Using animation of state space algorithms to overcome student learning difficulties

We describe an algorithm animation system for artificial intelligence search algorithms. We have identified a number learning difficulties experienced by students studying search algorithms and designed the animation system to help students to overcome these difficulties. As well as the usual single step mode for assistance in learning the individual steps of an algorithm, the system supports an innovative burst mode for visualising qualitative behaviour and facilitating comparisons between different algorithms and heuristic functions. The system has successfully been used in the classroom for 4 years and survey results indicate use of the system improves understanding. An empirical study comparing a group of 15 students using the animation system and 15 students who wrote programs for the algorithms revealed a generally similar level of understanding, however the animation group was better at dealing with questions about qualitative behaviour.

Component-based software development for integrated construction management software applications

This paper presents a framework and a prototype for designing Integrated Construction Management (ICM) software applications using reusable components. The framework supports the collaborative development of ICM software applications by a group of ICM application developers from a library of software components. The framework focuses on the use of an explicit software development process to capture and disseminate specialized knowledge that augments the description of the ICM software application components in a library. The importance of preserving and using this knowledge has become apparent with the recent trend of combining the software development process with the software application code. There are three main components in the framework: design patterns, design rationale model, and intelligent search algorithms. Design patterns have been chosen to represent, record, and reuse the recurring design structures and associated design experience in object-oriented software development. The Design Recommendation and Intent Model (DRIM) was extended in the current research effort to capture the specific implementation of reusable software components. DRIM provides a method by which design rationale from multiple ICM application designers can be partially generated, stored, and later retrieved by a computer system. To address the issues of retrieval, the paper presents a unique representation of a software component, and a search mechanism based on Reggia's setcover algorithm to retrieve a set of components that can be combined to get the required functionality is presented. This paper also details an initial, proof-of-concept prototype based on the framework. By supporting nonobtrusive capture as well as effective access of vital design rationale information regarding the ICM application development process, the framework described in this paper is expected to provide a strong information base for designing ICM software.

Optimal Placement of Passive Dampers on Seismic and Wind-Excited Buildings using Combinatorial Optimization

The performance of energy dissipative systems for the response reduction of complex civil engineering structures depends on their locations in the structure. In this paper, the optimal locations of energy dissipative systems have been investigated using combinatorial optimization based integer heuristic programming techniques. Three intelligent search algorithms, namely, sequential, Worst-Out-Best-In (WOBI) and Exhaustive Single Point Substitution (ESPS) search methods, have been used to determine optimal locations for different types of objective functions. The applications of the proposed approach for the determination of optimal locations have been demonstrated for three seismic-excited buildings and one wind-excited building. It is shown that the optimal locations obtained by minimizing the peak interstory drift of buildings depend on the specific design earthquake. The WOBI and ESPS methods have been found to be quite effective in improving the locations obtained by the sequential search method.

Artificial Intelligence Search Algorithms for Dynamic Traffic Routing

The development of sound routing strategies for dynamic traffic routing is a complex task that requires the use of an automated traffic-routing decision support system (DSS). In this study, heruistic search algorithms were used to build prototype DSSs for on-line traffic routing. Specifically, two DSSs were developed for a real-world highway network: the first was developed by using simulated annealing (SA) and the second was constructed by using genetic algorithms (GAs). The use of such algorithms allows more accurate modeling of traffic dynamics than is possible by traditional analytical techniques. To evaluate and compare the performances of the developed DSSs, a test set consisting of 10 randomly generated problems was used. Results demonstrated that the DSSs are capable of yielding high-quality solutions in a reasonable amount of time. In addition, for on-line traffic routing, SA tends to perform better than GAs.

Heuristic evaluation functions in artificial intelligence search algorithms

We consider a special case of heuristics, namely numeric heuristic evaluation functions, and their use in artificial intelligence search algorithms. The problems they are applied to fall into three general classes: single-agent path-finding problems, two-player games, and constraint-satisfaction problems. In a single-agent path-finding problem, such as the Fifteen Puzzle or the travelling salesman problem, a single agent searches for a shortest path from an initial state to a goal state. Two-player games, such as chess and checkers, involve an adversarial relationship between two players, each trying to win the game. In a constraint-satisfaction, problem, such as the 8-Queens problem, the task is to find a state that satisfies a set of constraints. All of these problems are computationally intensive, and heuristic evaluation functions are used to reduce the amount of computation required to solve them. In each case we explain the nature of the evaluation functions used, how they are used in search algorithms, and how they can be automatically learned or acquired.