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This paper aims to develop a new differential evolution algorithm (DEA) for solving the simple assembly line balancing problem of type 2 (SALBP-2).

Novel approaches of mutation operator and crossover operator are presented. A self-adaptive double mutation scheme is implemented and an elitist strategy is used in the selection operator.

Test and comparison results show that the proposed IDEA obtains better results for SALBP-2.

The presented DEA is called the integer-coded differential evolution algorithm (IDEA), which can directly deal with integer variables of SALBP-2 on a discrete space without any posterior conversion. The proposed IDEA will be an alternative in evolutionary algorithms, especially for various integer/discrete-valued optimization problems.

Marine transportation has been faced with an increasing demand for containerized cargo during the past decade. Marine container terminals (MCTs), as the facilities for connecting seaborne and inland transportation, are expected to handle the increasing amount of containers, delivered by vessels. Berth scheduling plays an important role for the total throughput of MCTs as well as the overall effectiveness of the MCT operations. This study aims to propose a novel island-based metaheuristic algorithm to solve the berth scheduling problem and minimize the total cost of serving the arriving vessels at the MCT.

A universal island-based metaheuristic algorithm (UIMA) was proposed in this study, aiming to solve the spatially constrained berth scheduling problem. The UIMA population was divided into four sub-populations (i.e. islands). Unlike the canonical island-based algorithms that execute the same metaheuristic on each island, four different population-based metaheuristics are adopted within the developed algorithm to search the islands, including the following: evolutionary algorithm (EA), particle swarm optimization (PSO), estimation of distribution algorithm (EDA) and differential evolution (DE). The adopted population-based metaheuristic algorithms rely on different operators, which facilitate the search process for superior solutions on the UIMA islands.

The conducted numerical experiments demonstrated that the developed UIMA algorithm returned near-optimal solutions for the small-size problem instances. As for the large-size problem instances, UIMA was found to be superior to the EA, PSO, EDA and DE algorithms, which were executed in isolation, in terms of the obtained objective function values at termination. Furthermore, the developed UIMA algorithm outperformed various single-solution-based metaheuristic algorithms (including variable neighborhood search, tabu search and simulated annealing) in terms of the solution quality. The maximum UIMA computational time did not exceed 306 s.

Some of the previous berth scheduling studies modeled uncertain vessel arrival times and/or handling times, while this study assumed the vessel arrival and handling times to be deterministic.

The developed UIMA algorithm can be used by the MCT operators as an efficient decision support tool and assist with a cost-effective design of berth schedules within an acceptable computational time.

A novel island-based metaheuristic algorithm is designed to solve the spatially constrained berth scheduling problem. The proposed island-based algorithm adopts several types of metaheuristic algorithms to cover different areas of the search space. The considered metaheuristic algorithms rely on different operators. Such feature is expected to facilitate the search process for superior solutions.

The purpose of this paper is to solve commitment problem of generating units in thermal power plants and to find the optimal dispatches of the committed units.

The unit commitment (UC) problem has been solved in two stages. In the first stage, the optimal units are identified using contribution factor. Initially, the generating units to be committed for each interval in the time horizon are obtained without considering the unit operational constraints such as minimum up time, minimum down time and initial state. Then the unit operational constraints are enforced and the optimal UC schedule is obtained. In the second stage, sequential approach with a matrix framework has been proposed to obtain the optimal dispatches of the committed units.

The simple methodologies have been developed for unit selection and to find the optimal dispatches of the committed units. The results of proposed methodology illustrate an improvement in the savings of total cost. The proposed approach is computationally efficient for solving large‐scale systems and successive UC problems.

UC has a major role in electric thermal power plant operation. The problem with one day and one week scheduling horizon has a large potential of use, especially for small‐ and medium‐scale power systems. It reflects reality in a closer way and provides a more complete and realistic knowledge about the system in operation. The techniques developed for UC problem will provide a support to electric power companies for their economic operation and the concepts presented are useful in both graduate teaching and research to understand the UC problem.

The contribution of the paper is the simple methodologies which have been developed for unit selection and economic dispatch.

The purpose of this paper is to develop an intelligent system for fashion style selection for non-standard female body shapes.

With the goal of creating natural aesthetic relationship between the body shape and the shape of clothing, garments designed for the upper and lower body are combined to fit different female body shapes, which are classified as V, A, H and O-shapes. The proposed intelligent system combines genetic algorithm (GA) with a neural network classifier, which is trained using the particle swarm optimization (PSO). The former, called genetic search, is used to find the optimal design parameters corresponding to a best fit for the desired target, while the task of the latter, called neural classification, is to evaluate fitness (goodness) of each evolved new fashion style.

The experimental results are fashion styling recommendations for the four female body shapes, drawn from 260 possible combinations, based on variations from 15 attributes. These results are considered to be a strong indication of the potential benefits of the application of intelligent systems to fashion styling.

The proposed intelligent system combines the effective searching capabilities of two approaches. The first approach uses the GA for identifying best fits to the target shape of the body in the solution space. The second is the PSO for finding optimal (with respect to training mean-squared error) weight and threshold parameters of the neural classifier, which is able to evaluate the fitness of successively evolved fashion styles.

DataLib is a comprehensive, interactive information resource management system. Utilizing state‐of‐the‐art database management techniques, it supports the full set of automated library functions: cataloging, retrieval, circulation, acquisitions, and serials management. It runs on the DEC VAX and Data General MV‐series computers. A UNIX V version is also available. The first installation of the current system was 1984; 24 installations are now in place.

Changing ecosystems with digitalisation, the value of information affects all sub-ecosystems, and due to societal changes, management approaches have started to be implemented. This new administration digitalisation, which is the basis of their strategy, made it mandatory for them to adapt to technology. This article details the theoretical basis of the business ecosystem to investigate the integration of human-computer interaction (HCI) technology and platform business ecosystem into the artificial intelligence (AI) environment and to create practicality for the intellectual development of platform business strategy ecosystem management. Then, in the context of textile e-commerce, the intelligent clothing ecosystem is recommended, and the structure of the smart clothing ecosystem is created. A flexible production planning model with multiple constraints and maximum cost savings has been developed within system constraints and objective function scope. The standard genetic algorithm after optimisation was used to solve the aim of model function (based on double-layer integer coding). In the context of the parameters of the algorithm and textile layout, the algorithm was calculated with the MATLAB simulation tool, and then a textile experiment was carried out to solve the problem of system management. Finally, the audio, interactive customer survey results were analysed with structural equation modelling, and a human-computer interactive customer trust model was proposed. The results indicate that the calculation results of the objective function with 6 order sizes and 40 order sizes tend to be good and that the 6th order, respectively, tends to be good. The Gantt chart, calculated by the optimisation algorithm, can show the equipment selection strategy, start time and end time of each order in different workshops. The combination of each operation planning strategy is the production planning chart that corresponds to the order. It is thought that this study, AI-oriented HCI technology for future studies, can solve the strategic management problem of the platform business ecosystem, realise the efficient ecological functioning of the system and benefit businesses in this context.

This paper describes the application of heuristic techniques for designing gravity wastewater collection systems. Designing sewer networks can be a time‐consuming task that is largely based on trial and error where suitable pipe diameters and slopes combinations for all pipelines between manholes must be identified. Since there is a large range of possible slopes, diameters and roughness coefficients of pipes, only a small number of combinations of these parameters are usually analyzed in traditional design processes. Identifying a minimum cost design is an important issue when constructing sewer networks. In this paper, genetic algorithms and tabu search techniques are implemented to solve this difficult optimization problem. An adaptive rule and a dynamic search strategy were developed to assist the search procedures find better solutions.

The purpose of this paper is to introduce to scientific community a new optimization technique and its application to the radiation synthesis case.

The immunity tactic is a new powerful optimization tool inspired by immune system. It was used with success to achieve the conformal antenna radiation synthesis in an acceptable processing time.

Radiation synthesis of conformal antenna arrays based on immunity tactic generates very good results compared with other optimization methods. The comparison is very satisfactory as regards accuracy and processing time.

The improvement of convergence and accuracy will be done certainly by use of other variants of the technique or combination with others.

The paper exposes with details a new optimization technique based on immune system and its behavior. The results, for the special case of conformal antenna arrays radiation synthesis, are very satisfactory and very encouraging. The impact of the new technique will be, without doubt, positive on optimization field.

In October 1976 the University of Kent at Canterbury Library implemented an on‐line circulation control system. Based on an Interdata 74 mini computer and using Plessey light pens, a Pertec VDU and a teletype, the system (KLACS) operates for 84 hours a week. The system was designed to include all the features available under the manual system together with the automatic production of lists, recall notices and statistical data. The file specification and the input/output facilities are described in detail.

This paper aims to develop a model for selecting project team members. In this model, while knowledge sharing among individuals is maximized, the project costs and the workload balance among employees are also optimized.

The problem of project team formation is formulated as a fuzzy multi-objective 0-1 integer programming model. Afterward, to deal with uncertainty in the decision-making on the candidates’ abilities and the project requirements, the fuzzy multi-objective chance-constrained programming approach is adopted. Finally, by combining the non-dominated sorting genetic algorithm II and the fuzzy simulation algorithms, a method is proposed to solve the problem.

The computational results of the proposed model in a case study of project team formation in a large Iranian company from the shipbuilding industry evidently demonstrated its effectiveness in providing Pareto-optimal solutions for the team composition.

Seemingly for the first time, this paper develops a model to optimize knowledge sharing and improve the project efficiency through the selection of appropriate project team members.