Late Acceptance Hill Climbing Research Articles

A hybrid artificial bee colony algorithm for balancing two-sided assembly line with assignment constraints

Two-sided assembly line balancing problem (TALBP) is a vital design problem for the industries. In real production process, some complex constraints should be considered in the two-sided assembly line. To solve the practical TALBP, this paper proposes a hybrid algorithm (HABC) that combines the artificial bee colony (ABC) algorithm and late acceptance hill-climbing (LAHC) algorithm. In the proposed algorithm, a well-designed decoding scheme is embedded to tackle multiple assignment constraints. Moreover, two neighborhood search strategies are implemented by employed bee and onlooker bee to explore and exploit the new solution. A set of computational experiments is performed on benchmark problems. The comparison results, best solutions, standard deviation and relative percentage index demonstrate that the HABC algorithm outperforms other algorithms published in the literature and finds 5 brand new solutions for 15 instances.

Finding the train composition causing greatest fatigue damage in railway bridges by Late Acceptance Hill Climbing

This paper presents a method to identify the most damaging train with respect to fatigue damage in railway bridges. The problem is formulated as a combinatorial optimization problem in which the solution space is the set of possible trains. The exact solution can only be obtained for trivial cases where the train set is small and/or for simple structural details where the most damaging train can be identified manually by analysis. Three different heuristics are considered to solve the problem approximately. The Late Acceptance Hill Climbing heuristic is proposed as a general approach to finding the most damaging train due to its strong performance for different train sets and structural details and simplicity in implementation and application. The proposed methodology can be used in the design of new and in the assessment of existing railway bridges to verify sufficient fatigue endurance.

Late Acceptance Hill Climbing Algorithm For Solving Patient Facing Problems In Hospitals

The current study is to evaluate the patient facing problems by applying Late Acceptance Hill Climbing Algorithm (LAHC) in hospital settings. The recent proposed procedure of LAHC is based on metaheuristic algorithm which is linked with one-point clarification method. Patient’s satisfaction regarding the performance of the hospital is a composite mechanism. The optimization procedure is connected with NP-hard problems which is practically related to the problems faced by patients. These problems are concerned with assigning the group of patients visiting the hospital for receiving healthcare services. The common issues faced by patients include communication gap, response time to attend patients, early symptomatic relief, getting proper advice for dosage and usage of medicines, clean hospital environment. Moreover, patient education and guidance before discharge from hospital is also the missing element. The suggested algorithm of LAHC to PFP is developed and it has two phases: the first phase includes providing the initial feasible solution using communication-oriented methodology. The second phase uses three neighborhood framework which are implanted inside the segment of PFP based on LAHC to additionally upgrade the underlying feasible solution of the introductory phase.

Late acceptance hill climbing algorithm for solving patient admission scheduling problem

This article tackles a patient admission scheduling using Late Acceptance Hill Climbing Algorithm (LAHC). The LAHC algorithm is among the newly proposed metaheuristic-based algorithm which belongs to the one-point solution technique. Patient admission scheduling is a complex combinatorial optimization problem which have been proven to belongs to class of NP-hard problem in almost all its variations. This problem is concerns with assigning a set of patients arriving for medical services in the hospital to a set of rooms, timeslots and beds subject to satisfying a set of predefined constraints. The proposed adaptation of LAHC to the patient admission scheduling named LAHC-based PAS comes in two stages: the first stage involves generating of initial feasible solution using the room oriented-based approach (ROP), while the second stage utilizes three neighbourhood structures which are embedded within the component of LAHC-based PAS to further enhances the initial solution generated at the initial stage. The proposed LAHC method is evaluated using the standard benchmark dataset. The experimental results shows that the proposed method is an effective technique for tackling the PAS problem. It is observed that the method outperform many of the existing techniques when compared with the state-of-the-arts.

Multiobjective Program and Hybrid Imperialist Competitive Algorithm for the Mixed-Model Two-Sided Assembly Lines Subject to Multiple Constraints

A mixed-model two-sided assembly line is a manufacturing system designed for the production of large-sized products. In order to describe the actual condition, this paper presents a novel multiobjective programming model for balancing a mixed-model two-sided assembly line subject to multiple constraints, in which, additional constraints including zoning, synchronous, and positional constraints are considered besides the traditional constraints, e.g., the precedence constraint. Two objectives are simultaneously to be optimized, one is to minimize the combination of the weighted line efficiency and the weighted smoothness index, and the other is to minimize the weighted total relevant costs per unit of a product. A novel multiobjective hybrid imperialist competitive algorithm (MOHICA) is proposed to solve this problem. In the presented MOHICA, the sigma method is employed to quantify every individual, a novel merging method is introduced to reserve better individuals into the evolutionary population, and late acceptance hill-climbing (LAHC) algorithm is presented as a local search algorithm to achieve accurate balance between intensification and diversification. The experimental results on the selected benchmark instances and a practical case show that the proposed multiobjective algorithm outperforms nondominated sorting genetic algorithm (NSGA)-II, multiobjective improved teaching-learning-based optimization, and NSGA-III existing in the literature.

The late acceptance Hill-Climbing heuristic

This paper introduces a new and very simple search methodology called Late Acceptance Hill-Climbing (LAHC). It is a local search algorithm, which accepts non-improving moves when a candidate cost function is better than it was a number of iterations before. This number appears as a single algorithmic input parameter which determines the total processing time of the search procedure. The properties of this method are experimentally studied in this paper with a range of Travelling Salesman and Exam Timetabling benchmark problems. Also, we present a fair comparison of LAHC with well-known search techniques, which employ a cooling schedule: Simulated Annealing (SA), Threshold Accepting (TA) and the Great Deluge Algorithm (GDA). In addition, we discuss the success of the method in winning an international competition to automatically solve the Magic Square problem. Our experiments have shown that the LAHC approach is simple, easy to implement and yet is an effective search procedure. For most of the studied instances (especially for the large sized ones), its average performance is better than competitor methods. Moreover, the LAHC approach has an additional advantage (in contrast to the above cooling schedule based methods) in its scale independence. We present an example where the rescaling of a cost function in the Travelling Salesman Problem dramatically deteriorates the performance of three cooling schedule based techniques, but has absolutely no influence upon the performance of LAHC.

Late acceptance hill-climbing for high school timetabling

The application of the Late Acceptance Hill-Climbing (LAHC) to solve the High School Timetabling Problem is the subject of this manuscript. The original algorithm and two variants proposed here are tested jointly with other state-of-art methods to solve the instances proposed in the Third International Timetabling Competition. Following the same rules of the competition, the LAHC-based algorithms noticeably outperformed the winning methods. These results, and reports from the literature, suggest that the LAHC is a reliable method that can compete with the most employed local search algorithms.

Hybrid bee colony optimization for examination timetabling problems

Swarm intelligence is a branch of artificial intelligence that focuses on the actions of agents in self-organized systems. Researchers have proposed a bee colony optimization (BCO) algorithm as part of swarm intelligence. BCO is a meta-heuristic algorithm based on the foraging behavior of bees. This study presents a hybrid BCO algorithm for examination timetabling problems. Bees in the BCO algorithm perform two main actions: forward pass and backward pass. Each bee explores the search space in forward pass and then shares information with other bees in the hive in backward pass. This study found that a bee decides to be either a recruiter that searches for a food source or a follower that selects a recruiter bee to follow on the basis of roulette wheel selection. In forward pass, BCO is supported along with other local searches, including the Late Acceptance Hill Climbing and Simulated Annealing algorithms. We introduce three selection strategies (tournament, rank and disruptive selection strategies) for the follower bees to select a recruiter to maintain population diversity in backward pass. The disruptive selection strategy outperforms tournament and rank selections. We also introduce a self-adaptive mechanism to select a neighborhood structure to enhance the neighborhood search. The proposed algorithm is evaluated against the latest methodologies in the literature with respect to two standard examination timetabling problems, namely, uncapacitated and competition datasets. We demonstrate that the proposed algorithm produces one new best result on uncapacitated datasets and comparable results on competition datasets.

An Improved TLBO Algorithm for Balancing Stochastic Two-Sided Assembly Line

The stochastic two-sided assembly line is a special line in which workers perform assembly tasks in both sides of the line and the task time is not deterministic. In the improved teaching-learning-based optimization (ITLBO) algorithm, teacher and learner phases are modified for the discrete problem, a novel merging method is proposed to construct the new evolutionary population and Late Acceptance Hill-Climbing (LAHC) is integrated into the self-learning phase to obtain high quality solutions. The experimental results validate the effectiveness of the proposed algorithm.

A multi-objective TLBO algorithm for balancing two-sided assembly line with multiple constraints

Two-sided assembly line is often designed to produce large-sized high-volume products such as cars, trucks and engineering machinery. However, in real-life production process, besides the elementary constraints in the one-sided assembly line, additional constraints, such as zoning constraints, positional constraints and synchronous constraints, may occur in the two-sided assembly line. In this paper, mathematical formulation of balancing multi-objective two-sided assembly line with multiple constraints is established, and some practical objectives, including maximization of the line efficiency, minimization of the smoothness index and minimization of the total relevant costs per product unit (Tcost), have been considered. A novel multi-objective optimization algorithm based on improved teaching---learning-based optimization (ITLBO) algorithm is proposed to obtain the Pareto-optimal set. In the ITLBO algorithm, teacher and learner phases are modified for the discrete problem, and late acceptance hill-climbing is integrated into a novel self-learning phase. A novel merging method is proposed to construct a new population according to the ordering relation between the original and evolutionary population. The proposed algorithm is tested on the benchmark instances and a practical case. Experimental results, compared with the ones computed by other algorithm and in current literature, validate the effectiveness of the proposed algorithm.

Two-sided assembly line balancing with operator number and task constraints: a hybrid imperialist competitive algorithm

Two-sided assembly line balancing problems usually occur in plants producing large-sized high-volume products such as buses, trucks, or cars. Comparing to traditional simple assembly lines, the two-sided assembly line can shorten the length of line, enhance the utilization of tools and fixtures, and improve the production rate of operators, etc. Whereas in a two-sided assembly line, due to a couple of constraints such as the operation direction constraint and the sequence-dependent finish time constraint, the balancing procedure is much more complex than for a simple assembly line. In this paper, besides some fundamental constraints of the two-sided assembly lines, we put forward additionally the restriction of operator number at each workstation, and take into account concurrently more constraints such as the positional constraints, zoning constraints, and the synchronous task constraints. A novel hybrid imperialist competitive algorithm (ICA) combined with the late acceptance hill-climbing (LAHC) algorithm is employed to solve the multiconstraint problem. For the evaluation of the proposed algorithm, the performance of the hybrid ICA is examined over benchmarks from the literature and compared with the LAHC algorithm and the lower bounds of the instances. Furthermore, a practical two-sided assembly line balancing problem from an engineering machinery company is solved with the proposed approach. The results confirm that the hybrid ICA can effectively shorten the line length and reduce the amount of operators for the existing two-sided assembly line.

A late acceptance hill-climbing algorithm for balancing two-sided assembly lines with multiple constraints

Two-sided assembly lines are widely applied to produce the large-sized high-volume products, such as buses and trucks. Balancing the lines is a vital design problem for the industries, and the problem is NP-hard. Besides the fundamental constraints of the conventional line balancing problem, some specific constraints may occur in the two-sided assembly line problem, including the zoning constraints, the positional constraints, and the synchronism constraints, which make the problem more complex. In this paper, an integer programming (IP) model is constructed and solved for the two-sided assembly line balancing problem which contains the above three constraints. A novel metaheuristic named late acceptance hill-climbing (LAHC) is also proposed to solve the problem effectively. The proposed algorithm is tested on several sets of instances. The computational results of the LAHC algorithm are compared with those of IP and the lower bounds of the instances. The experiment validates the effectiveness of the LAHC algorithm.