Constructive Heuristic Approach Research Articles

Simultaneous scheduling of maintenance crew and maintenance tasks in bus operating companies: a case study

PurposeThe purpose of this paper is to describe an integer linear programming model to schedule the maintenance crew and the maintenance tasks in a bus operating company.Design/methodology/approachThe proposed methodology relies on an integer linear programming model that finds feasible maintenance schedules. It minimizes the costs associated with maintenance crew and the costs associated with unavailability. The model is applied in a real-world case study of a Portuguese bus operating company. A constructive heuristic approach is put forward, based on solving the maintenance scheduling problem for each bus separately.FindingsThe heuristic finds better solutions than the exact methods (based on branch-and-bound techniques) in a much lower computational time.Practical implicationsThe results suggest the relevance of such heuristic approaches for maintenance scheduling in practice.Originality/valueThis proposed model is an effective decision-making support method that provides feasible maintenance schedules for the maintenance technicians and for the maintenance tasks in a fleet of buses. It also complies with several operational, technical and labour constraints.

Minimization of total completion time on a batch processing machine with arbitrary release dates: an effectual teaching–learning based optimization approach

In this research study, a single machine batch-processing problem with release dates to minimize the total completion times of jobs is considered. The machine is able to process at most a certain number of jobs at the same time and the total size of the jobs allocated to a batch cannot exceed the machine capacity. Since the research problem has been shown to be NP-hard, an effective Teaching–Learning Based Optimization (TLBO) is proposed. A constructive heuristic approach is developed to generate initial feasible solutions for the TLBO. In order to enhance the efficiency of the proposed TLBO, a Tabu Search (TS) with three different neighborhood generation mechanisms is incorporated into the teaching phase and learner phase separately. To validate the outcomes of the proposed TLBO, we carry out an experimental study and compare its outcomes with the best-known results obtained by several meta-heuristic methods on a set of benchmark instances derived from the literature. The computational results show that the proposed TLBO with the incorporation of TS in its learning phase is able to come up with very good quality solutions.

Regular Expression Based Medical Text Classification Using Constructive Heuristic Approach

Medical text classification assigns medical related text into different categories such as topics or disease types. Machine learning based techniques have been widely used to perform such tasks despite the obvious drawback in such “black box” approach, leaving no easy way to fine-tune the resultant model for better performance. We propose a novel constructive heuristic approach to generate a set of regular expressions that can be used as effective text classifiers. The main innovation of our approach is that we develop a novel regular expression based text classifier with both satisfactory classification performance and excellent interpretability. We evaluate our framework on real-world medical data provided by our collaborator, one of the largest online healthcare providers in the market, and observe the high performance and consistency of this approach. Experimental results show that the machine-generated regular expressions can be effectively used in conjunction with machine learning techniques to perform medical text classification tasks. The proposed methodology improves the performance of baseline methods (Naive Bayes and Support Vector Machines) by 9% in precision and 4.5% in recall. We also evaluate the performance of modified regular expressions by human experts and demonstrate the potential of practical applications using the proposed method.

Multi-period and multi-resource operating room scheduling under uncertainty: A case study

Abstract Efficient planning and scheduling is essential for timely treatment of patients and improving the quality of operating room services and activities. In the present study, attempts are made to investigate a multi-period and multi-resource operating room integrated planning and scheduling problem under uncertainty. To this end, a mixed integer linear programming model has been developed for minimizing the tardiness in surgeries, overtime and idle time. Constraints related to human resources, equipment, as well as beds in pre-operative holding unit, recovery unit, ward and intensive care unit are taken into consideration. The durations of surgeries and recoveries are assumed uncertain, and a robust optimization approach has been used to manage the uncertainty. Due to the complexity of the model and the inability to solve large-scale problems, a metaheuristic method based on the genetic algorithm and a constructive heuristic approach have been proposed. After setting the parameters of the solution approaches using the Taguchi method, numerical experiments are performed based on various instances, and the results obtained from solving the mathematical model are compared to the results of the proposed metaheuristic and heuristic approaches. The results indicate that the proposed methods have a very good performance and the heuristic approach outperforms the genetic approach because the objective function of the proposed constructive heuristic is on average, about 19% better than the objective function of the genetic approach. A case study is also conducted in a hospital. The results obtained from the comparison of the proposed approaches with the hospital scheduling show that overtime and idle time are significantly improved in the proposed approaches.

A multiple-rule based constructive randomized search algorithm for solving assembly line worker assignment and balancing problem

This paper proposes a constructive heuristic approach for the assembly line worker assignment and balancing problem (ALWABP). ALWABP arises when the operation time for every task differs according to the worker who executes the task. Since the operation times of tasks vary due to the workers, the problem requires a simultaneous solution to the double assignment problem. Tasks must be assigned to workers and workers to stations, concurrently. This problem is especially proposed in sheltered work centers for the disabled. However, it is not only important for the assembly lines with the disabled, but also for manually operated assembly lines with high labor turnover. In this paper, a multiple-rule based constructive randomized search (MRBCRS) algorithm is proposed in order to solve the ALWABP. Thirty nine task priority rules and four worker priority rules are defined. Performance of the proposed MRBCRS is compared with the relevant literature on benchmark data. Experimental results show that the proposed MRBCRS is very effective for benchmark problems. The results show that the algorithm improves upon the best-performing methods from the literature in terms of solution quality and time.

A 2-phase constructive algorithm for cumulative vehicle routing problems with limited duration

The Clarke & Wright (C&W) algorithm is one of the most widely used classical heuristics in capacitated Vehicle Routing Problems (VRPs) in which a linear function of distance is considered as the objective function. The C&W algorithm is very simple and easy to implement, and produces fairly good solutions very fast. In this study, the C&W algorithm is adopted for the cumulative VRP with limited duration (CumVRP-LD) where load is also considered in the objective function as well as distance. The most common applications of cumulative VRPs are the determination of routing policies that minimize total fuel consumption. A 2-phase constructive heuristic approach including the K-means clustering algorithm is proposed to improve the computational performance of the modified C&W algorithm for CumVRP-LD. The main contribution of this study is the definition of a new extended formulation that captures truck-load and travel distance by considering the unique characteristics of the problem and to develop a fast and easy implemented constructive algorithm for CumVRP-LD. Such approaches are necessary for the development of systems that respond fast, possibly online, to changes in the real problem situations.

A Constructive Heuristic for Two-Dimensional Bin Packing

Two-dimensional bin packing is encountered in various applications where small rectangular items are packed into a minimum number of large rectangular objects (bins). Aiming at an optimal area utilization, the paper presents an effective constructive heuristic approach to two-dimensional bin packing. The heuristic approach integrates ranking, placement and search strategies along with an effective handling method of the remaining areas during the packing process. In order to obtain an optimal arrangement in a given area, all items are examined for possible positions and orientations using the search strategies. In addition, an effective handling method of the remaining areas is applied through appropriate partitioning and merging to minimize waste areas. Tests with a number of standard test and real world instances have shown that the performance of the proposed approach is superior to that of other approaches published.

A simulated rebounding algorithm applied to the multi-stage security-constrained transmission expansion planning in power systems

This research discusses the multi-stage security-constrained transmission network expansion planning. In modern power systems, the problem is formulated as a large-scale, mixed-integer, non-linear programming problem, which for a real power systems is very difficult to solve. Although remarkable advances have been made in optimization techniques, finding an optimal solution to a problem of this nature can still be extremely challenging. In this paper, a new constructive heuristic approach, based on a local controlled random search (simulated rebounding algorithm) is proposed to choose the decision variables. The model can produce better solutions than other references techniques such as particle swarm optimization, evolutionary particle swarm optimization, genetic algorithms, and simulated annealing algorithm, among other evolutionary methods. The methodology is applied to assess the capabilities of the proposed approach in the Ecuadorian and Chilean Power Systems as an example of application. Simulation results show that the proposed approach is accurate and very efficient, and it has the potential to be applied to real power system planning problems. The algorithm has been presented and applied to the multi-stage security-constrained transmission expansion planning.

Capacitated location of collection sites in an urban waste management system

Urban waste management is becoming an increasingly complex task, absorbing a huge amount of resources, and having a major environmental impact. The design of a waste management system consists in various activities, and one of these is related to the location of waste collection sites. In this paper, we propose an integer programming model that helps decision makers in choosing the sites where to locate the unsorted waste collection bins in a residential town, as well as the capacities of the bins to be located at each collection site. This model helps in assessing tactical decisions through constraints that force each collection area to be capacitated enough to fit the expected waste to be directed to that area, while taking into account Quality of Service constraints from the citizens’ point of view. Moreover, we propose an effective constructive heuristic approach whose aim is to provide a good solution quality in an extremely reduced computational time. Computational results on data related to the city of Nardò, in the south of Italy, show that both exact and heuristic approaches provide consistently better solutions than that currently implemented, resulting in a lower number of activated collection sites, and a lower number of bins to be used.

A Multiyear Security Constrained Hybrid Generation-Transmission Expansion Planning Algorithm Including Fuel Supply Costs

This paper addresses the problem of a multiyear security constrained hybrid generation-transmission expansion planning. It is assumed that the overall generation requirements of a network are known along the planning horizon, but their allocations are unknown. Moreover the fuel cost throughout the network is not uniform. By allocating the overall generation capacity among the grid nodes, and determining the new transmission element additions along the planning horizon, the overall cost of the system is minimized. The problem is formulated as a mixed integer nonlinear programming problem, which for a large-scale system is very difficult to solve. In this paper a new constructive heuristic approach is proposed, so that the problem can be readily solved. To assess the capabilities of the proposed approach, two networks are studied: the Garver test grid as a small grid and the Iranian power grid as a large-scale grid.

A multi-objective iterated greedy search for flowshop scheduling with makespan and flowtime criteria

In this paper, we tackle the problem of total flowtime and makespan minimisation in a permutation flowshop. For this, we introduce a multi-criteria iterated greedy search algorithm. This algorithm iterates over a multicriteria constructive heuristic approach to yield a set of Pareto-efficient solutions (a posteriori approach). The proposed algorithm is compared against the best-so-far heuristic for the problem under consideration. The comparison shows the proposal to be very efficient for a wide number of multicriteria performance measures. Aside, an extensive computational experience is carried out in order to analyse the different parameters of the algorithm. The analysis shows the algorithm to be robust for most of the considered performance measures.