In a diverse and modern organization with high extent of competitiveness within the market, maintaining high performance is of necessity. Talent management practices, when implied and used properly can significantly contribute to an organizations’ degree of overall performance as it has been noted throughout the literature. Employees and individuals seeking professional careers are required to cope with fast-changing environments of their workplaces. The need to constantly improve oneself is a dire one. Current research paper analyzes mediation effect of organizational commitment on the relationship between proactive personality and talent management practices from employee perspective of university academic and administrative staff. Mediation regression analysis (PROCESS) has been used to analyze the gathered data from universities located in North Cyprus, and the accumulated results show a full mediation effect from organizational commitment on the aforementioned relationship. The study contributes to the literature through expansion of proposed model in context of talent management and proactive personality as well as analytical method alongside context of academia. Furthermore, this study provides tangible implications, which can be beneficial for university decision-makers.

This study aims to validate the dimensions of sharia experience quality and to determine the effect of sharia experience quality on customer loyalty by customer satisfaction as mediation variable. The sample technique of this study was a purposive sampling of 150 respondents who conducted financing at Islamic banks. SEM-PLS is used to test hypotheses. The results of the second order evaluation show that integration quality, physical environment quality, outcome quality, compliance sharia knowledge, and responsive religious experience had strong effects on sharia experience quality. Sharia experience quality had a significant effect on customer loyalty and customer satisfaction as a partial mediation of sharia experience quality on customer loyalty.

In this paper, we propose the binary version of the Social Group Optimization (BSGO) algorithm for solving the 0-1 knapsack problem. The standard Social Group Optimization (SGO) is used for continuous optimization problems. So a transformation function is used to convert the continuous values generated from SGO into binary ones. The experiments are carried out using both low-dimensional and high-dimensional knapsack problems. The results obtained by the BSGO algorithm are compared with other binary optimization algorithms. Experimental results reveal the superiority of the BSGO algorithm in achieving a high quality of solutions over different algorithms and prove that it is one of the best finding algorithms especially in high-dimensional cases.

The classical production-inventory model assumes that both demand and set-up costs are constant. However, in real manufacturing environment, managers usually embark on continuous improvement programmes that often lead to more effective use of tools and machineries and consequently reduction in set-up costs. In fact, constant emphasis on reduction of set-up costs is usually cited as one of the factors responsible for the efficiency of Japanese manufacturing methods. On the other hand, the demand for seasonal product is often characterized by a mixture of time-dependent patterns over the entire season. This paper investigates the effect of learning-based reduction in set-up costs on the optimal schedules and costs of a production-inventory system for deteriorating seasonal products. The demand pattern is a general three-phase ramp-type demand function that represents the various phases of demand commonly observed in many seasonal products in the market. A two-parameter Weibull-distribution function is used for the deterioration of items in order to make the model more generalized and realistic. The study further presents two different multi-period production strategies that can ensure a fast-response to customers’ demand and compare them with the usual single period strategy. The Numerical example and sensitivity analysis shows that learning-based reduction in set-up costs leads to higher production frequency and shorter production runs which are vital aspects of the just-in-time (JIT) philosophy.

The aim of this paper is to present a new genetic algorithm approach for large scale multiple resource-constrained project-scheduling problems (RCPSP). It also presents a heuristic approach to achieve proper solutions for large scale problems. This research area is very common in industry especially when a set of activities needs to be finished as soon as possible subject to two sets of constraints, precedence constraints and resource constraints. The emphasis in this research is on investigating the complexity of scheduling problems and developing a new GA approach to solve this problem in such a way that the advantages of GA are appropriately utilized by applying a novel method to reduce the complexity of the problem. Computational results are also reported for the most famous classical problems taken from the operational research literature.

Job Shop Scheduling Problem (JSSP) and Flow Shop Scheduling Problem (FSSP) are strong NP-complete combinatorial optimization problems among class of typical production scheduling problems. An improved Sheep Flock Heredity Algorithm (ISFHA) is proposed in this paper to find a schedule of operations that can minimize makespan. In ISFHA, the pairwise mutation operation is replaced by a single point mutation process with a probabilistic property which guarantees the feasibility of the solutions in the local search domain. A Robust-Replace (R-R) heuristic is introduced in place of chromosomal crossover to enhance the global search and to improve the convergence. The R-R heuristic is found to enhance the exploring potential of the algorithm and enrich the diversity of neighborhoods. Experimental results reveal the effectiveness of the proposed algorithm, whose optimization performance is markedly superior to that of genetic algorithms and is comparable to the best results reported in the literature.

Rotating discs work mostly at high angular velocity. High speed results in large centrifugal forces in discs and induces large stresses and deformations. Minimizing weight of such disks yields various benefits such as low dead weights and lower costs. In order to attain a certain and reliable analysis, disk with variable thickness and density is considered. Semi-analytical solutions for the elastic stress distribution in rotating annular disks with uniform and variable thicknesses and densities are obtained under plane stress assumption by authors in previous works. The optimum disk profile for minimum weight design is achieved by the Karush–Kuhn–Tucker (KKT) optimality conditions. Inequality constrain equation is used in optimization to make sure that maximum von Mises stress is always less than yielding strength of the material of the disk.

This paper presents our research works on integrating disassembly sequence planning with cost model for end-of-life (EOL) product. This paper has two objectives. The first objective is to optimize disassembly sequence of the EOL product. We integrate a traveling salesman problem approach with genetic algorithm in finding the optimal disassembly sequence for disassembling the EOL product. Based on this optimal sequence, the second objective is to identify the best EOL option. We employ EOL profits and net present value of parts and subassemblies of the EOL product to determine the best EOL option of components and parts of the EOL product. The predicted results showed that the developed cost model has reached a good correspondence with the established methods.

In this paper, a single facility centre location problem with a line barrier, which is uniformly distributed on a given horizontal route in the plane is proposed. The rectilinear distance metric is considered. The objective function minimizes the maximum expected barrier distance from the new facility to all demand points in the plane. An algorithm to solve the desired problem is proposed where a mixed integer nonlinear programming needs to be solved. The proposed model of this paper is solved using some already existed benchmark problem in the literature and the results are compared with other available methods.

Supplier evaluation and selection has been a vital issue of strategic importance for long time. Different multi-criteria decision making (MCDM) approaches have been proposed by the researchers in past, to solve the supplier evaluation and selection problem. In this paper, we present a review of various MCDM methodologies reported in the literature for solving the supplier evaluation and selection process. The review is solely based on sixty-eight research articles, including eight review articles in the academic literature from 2000 to 2011. We try to find out the most prevalent approach in the articles and thereby present the future scope of arriving at an optimal solution to the problem, based on the specifications, the strategies and the requirements of the buyers. The study presents that with the change in processes and the requirements, how the approach of the manufacturing industry has shifted from striving for operational effectiveness to the strategic partnership in the dyadic relationship.