Search results

This paper sets out to evaluate the effect of search method employed (broadly defined here as formal or informal) on the probability that an individual will move out of unemployment into either employment or inactivity. We take advantage of the longitudinal element contained within the Labour Force Survey and link 794 individual records through five quarters. The sample contains all those who are unemployed in Spring 1996 and records their labour market status for the four quarters ending in Spring 1997. Given that this element of the LFS is only just beginning to be used, we attempt to evaluate its usefulness for the estimation of a discrete time‐competing risks model. We do find an effect of search method employed in the final specification and the sign on the coefficient rejects our belief that those using informal search methods may do so in order to signal their higher levels of productivity. Instead we are led to conclude that those using informal search methods may do so because of a lack of skills needed to use more formal channels.

In the quest for continuous quality improvement of both products and processes, experimental design (ED) based on Shainin’s variables search method is still playing a major role in the manufacturing community. This article presents a simple methodology in a step‐by‐step manner for Shainin’s variables search method, in terms of uncovering the key process variables or factors which influence a response or quality characteristic of interest. In order to illustrate the potential of this powerful methodology as a problem solving tool, a simple catapult experiment was carried out. The authors believe that this simple experiment can be used in the classroom to train engineers and scientists on variables search method in order to identify the most critical variables which influence the process or product performance.

In the quest for continuous quality improvement for both products and processes, design of experiments based on Shainin’s Variables Search is still playing a pivotal role among engineers and quality control practitioners. Briefly describes the Variables Search Designs formulated and promoted by Dorian Shainin for identifying the most critical variables which influence the process performance. In order to illustrate the potential of this powerful method, a simple paper helicopter experiment was carried out by the author. The results of the study have shown that Shainin’s Variables Search Design is a powerful tool for identifying the key process variables and therefore should be utilised by the engineering fraternity in manufacturing organisations as a problem‐solving tool.

The purpose of this paper is to solve the capacitated location routing problem (CLRP), which is an NP-hard problem that involves making strategic decisions as well as tactical and operational decisions, using a hybrid particle swarm optimization (PSO) algorithm.

PSO, which is a population-based metaheuristic, is combined with a variable neighborhood strategy variable neighborhood search to solve the CLRP.

The algorithm is tested on a set of instances available in the literature and gave good quality solutions, results are compared to those obtained by other metaheuristic, evolutionary and PSO algorithms.

Local search is a time consuming phase in hybrid PSO algorithms, a set of neighborhood structures suitable for the solution representation used in the PSO algorithm is proposed in the VNS phase, moves are applied directly to particles, a clear decoding method is adopted to evaluate a particle (solution) and there is no need to re-encode solutions in the form of particles after applying local search.

The purpose of this research is to study the Multi-Skill Resource-Constrained Multi-Project Scheduling Problem (MSRCMPSP), where (1) durations of activities depend on the familiarity levels of assigned workers, (2) more efficient workers demand higher per-day salaries, (3) projects have different due dates and (4) the budget of each period varies over time. The proposed model is bi-objective, and its objectives are minimization of completion times and costs of all projects, simultaneously.

This paper proposes a two-phase approach based on the Statistical Process Control (SPC) to solve this problem. This approach aims to develop a control chart so as to monitor the performance of an optimizer during the optimization process. In the first phase, a multi-objective statistical model has been used to obtain control limits of this chart. To solve this model, a Multi-Objective Greedy Randomized Adaptive Search Procedure (MOGRASP) has been hired. In the second phase, the MSRCMPSP is solved via a New Version of the Multi-Objective Variable Neighborhood Search Algorithm (NV-MOVNS). In each iteration, the developed control chart monitors the performance of the NV-MOVNS to obtain proper solutions. When the control chart warns about an out-of control state, a new procedure based on the Conway’s Game of Life, which is a cellular automaton, is used to bring the algorithm back to the in-control state.

The proposed two-phase approach has been used in solving several standard test problems available in the literature. The results are compared with the outputs of some other methods to assess the efficiency of this approach. Comparisons imply the high efficiency of the proposed approach in solving test problems with different sizes.

The proposed model and approach have been used to schedule multiple projects of a construction company in Iran. The outputs show that both the model and the NV-MOVNS can be used in real-world multi-project scheduling problems.

Due to the numerous numbers of studies reviewed in this research, the authors discovered that there are few researches on the multi-skill resource-constrained multi-project scheduling problem (MSRCMPSP) with the aforementioned characteristics. Moreover, none of the previous researches proposed an SPC-based solution approach for meta-heuristics in order to solve the MSRCMPSP.

The aim of the study is to show that merging two areas of mathematics – topology and discrete optimization – could result in a viable option to solve classical or specialized integer problems.

In the paper, discrete topology concepts are applied to propose a metaheuristic algorithm that is capable to solve binary programming problems. Particularly, some of the homotopy for paths principles are used to explore the solution space associated with four well-known NP-hard problems herein considered as follows: knapsack, set covering, bi-level single plant location with order and one-max.

Computational experimentation confirms that the proposed algorithm performs in an effective manner, and it is able to efficiently solve the sets of instances used for the benchmark. Moreover, the performance of the proposed algorithm is compared with a standard genetic algorithm (GA), a scatter search (SS) method and a memetic algorithm (MA). Acceptable results are obtained for all four implemented metaheuristics, but the path homotopy algorithm stands out.

A novel metaheuristic is proposed for the first time. It uses topology concepts to design an algorithmic framework to solve binary programming problems in an effective and efficient manner.

The purpose of this paper is to efficiently solve disassembly line balancing problem (DLBP) and the sequence-dependent disassembly line balancing problem (SDDLBP) which are both known to be NP-complete.

This manuscript utilizes a well-proven metaheuristics solution methodology, namely, variable neighborhood search (VNS), to address the problem.

DLBPs are analyzed using the numerical instances from the literature to show the efficiency of the proposed approach. The proposed algorithm showed superior performance compared to other techniques provided in the literature in terms of robustness to reach better solutions.

Since disassembly is the most critical step in end-of-life product treatment, every step toward improving disassembly line balancing brings us closer to cost savings and compelling practicality.

This paper is the first adaptation of VNS algorithm for solving DLBP and SDDLBP.

The purpose of this paper is to identify user-generated content (UGC) features that determine web purchase decision making.

The authors embed a spike-and-slab Bayesian variable selection mechanism into a logistic regression model to identify the UGC features that are critical to web purchase intent. This enables us to make a highly reliable analysis of survey data.

The results indicate that the web purchase decision is driven by the relevance, up-to-dateness and credibility of the UGC information content.

The results show that the characteristics of UGC are seen as positive and the medium enables consumers to sort information and concentrate on aspects of the message that are similar to traditional word-of-mouth (WOM). One important implication is the relative importance of credibility which has been previously hypothesized to be lower in the electronic word-of-mouth (e-WOM) context. The results show that consumers consider credibility important as the improved technology provides more possibilities to find out about that factor. A limitation is that the data are not fully representative of the general population but our Bayesian method gives us high analytical quality.

The study shows that UGC impacts consumer online purchase intentions. Marketers should understand the wide range of media that provide UGC and they should concentrate on the relevance, up-to-dateness and credibility of product information that they provide.

The analytical quality of the spike- and- slab Bayesian method suggests a new way of understanding the impact of aspects of UGC on consumers.

The purpose of this research is to improve efficiency of the traditional scheduling methods and explore a more effective approach to solving the scheduling problem in supply networks with genetic algorithms (GAs).

This paper develops two methods with GAs for detailed production scheduling in supply networks. The first method adopts a GA to job shop scheduling in any node of the supply network. The second method is developed for collective scheduling in an industrial cluster using a modified GA (MGA). The objective is to minimize the total makespan. The proposed method was verified on some experiments.

The suggested GAs can improve detailed production scheduling in supply networks. The results of the experiments show that the proposed MGA is a very efficient and effective algorithm. The MGA creates the manufacturing schedule for each factory and transport operation schedule very quickly.

For future research, an expert system will be adopted as an intelligent interface between the MRPII or ERP and the MGA.

From the mathematical point of view, a supply network is a digraph, which has loops and therefore the proposed GAs take into account loops in supply networks. The MGA enables dividing jobs between factories. This algorithm is based on operation codes, where each chromosome is a set of four‐positions genes. This encoding method includes both manufacture operations and long transport operations.

Blockmodeling is viewed often as a data reduction method. However, this is a simplistic view of the class of methods designed to uncover social structures, identify subgroups, and reveal emergent roles. Worse, this view misses the richness of the method as a tool for uncovering novel human resource management (HRM) insights. Here, we provide a brief overview of some essentials of blockmodeling and discuss research questions that can be addressed using this approach in applied HRM settings. Finally, we offer an empirical example to illustrate blockmodeling and the types of information that can be gleaned from its implementation.