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Scheduling of production in a wire rope factory is complicated by several features: (a) the simultaneous requirement for two types of limited resource, machines and bobbins; (b) multi‐stage production with normally two or three stranding and one or two closing operations; (c) queuing at the closing machines; the typical job splits into sub‐batches when passing from the stranding to the closing operation; these sub‐batches usually queue at the closing operations which, being faster than stranding operations, generally receive work from several queues; (d) alternative choices in the selection of machines and bobbin sizes for any given stranding or closing operation; (e) the presence of random elements in the timing of machine breakdowns and repairs. In this case study factory in a developing country, the existing control of production flows was ad hoc rather than according to a specified method. The management needed to know whether a scientific scheduling approach could significantly improve the low utilisation of machines. As a first attempt a strategy was synthesised based on well‐known concepts from the theory of scheduling in static and dynamic environments, taking into consideration certain effects of the complicating factors mentioned above. Simulation revealed that a significant improvement was possible.

The importance of mobile services in our everyday life is growing while at the same time new interoperability issues arise due to hardware and software heterogeneity. Therefore, new architectural paradigms and models are needed to enhance software engineering methodologies with regard to platform independence and interoperability. This paper describes an UML pattern based approach for developing reconfigurable autonomous mobile services. Through the analysis of an mcommerce project, the relevance of our proposed architecture will be explained. Our focus lays on a generic reconfiguration mechanism based on profile matching from software modules. This profiling part will be further described and discussed. Finally, the applicability of our approach is investigated within a project about reconfigurable indoor navigation computers and a project about robot assisted sensor networks.

Constructs a knowledge‐based simulation model for job shop scheduling simulation techniques to build the initial status of the knowledge base. Uses the simulation experiments to study the effect of technological factors, such as machine loading conditions and criteria of scheduling performance requirements, on the selection of scheduling priority rules. Implements the model with real workshop data to test its validity to cater for real‐world situations such as machine group centres, more than one machine and the availability of operators. The proposed model yields satisfactory results.

The purpose of this paper is to develop an effective and efficient approach to exploit meta‐heuristic in particle swarm optimization (PSO) for the job shop scheduling problem (JSP), a class of NP‐hard optimization problems. The approach is to be built on a PSO with multiple independent swarms. PSO was inspired by bird flocking and animal social behaviors. The particles operate collectively like a swarm that flies through the hyperdimensional space to search for possible optimal solutions. The behavior of the particles is influenced by their tendency to learn from their personal past experience and from the success of their peers to adjust their flying speed and direction. Research in fusing the multiple‐swarm concept into PSO is well‐established in solving single objective optimization problems and multimodal problems.

This study examines the optimization of the JSP via a search space division scheme and use of the meta‐heuristic method of PSO by assigning each machine in a JSP an independent swarm of particles. The use of multiple swarms in PSO is motivated by the idea of “divide and conquer” to reduce the computational complexity incurred through solving a NP‐hard combinatorial optimization problem. The resulted design, JSP/PSO algorithm, fully exploits the computing power presented by the multiple‐swarm PSO.

Simulation experiments show that the proposed JSP/PSO algorithm can effectively solve the JSP problems from small to median size. If certain mechanism of information sharing between swarms can be incorporated, it is believed that the new design could offer even more computing power to tackle the large‐sized problems.

The proposed JSP/PSO algorithm is effective in solving JSPs. The proposed algorithm shows considerable promise when searching the space of non‐delay schedules. It demands relatively lower number of function evaluations compared to other state‐of‐the‐art. The drawback to the JSP/PSO is that the GT scheduling adopted is too computationally expensive. Future works will address this concern.

This research aims to address the cost-oriented stochastic assembly line balancing problem (ALBP) and propose a chance-constrained programming model.

The cost-oriented stochastic ALBP is solved for small- to medium-sized problems. Owing to the non-deterministic polynomial-time (NP)-hardness problem, a multiple rule-based genetic algorithm (GA) is proposed for large-scale problems.

The experimental results show that the proposed GA has superior performance and efficiency compared to the global optimum solutions obtained by the IBM ILOG CPLEX optimization software.

To the best of the authors’ knowledge, only one study has discussed the cost-oriented stochastic ALBP using the new concept of cost. Owing to the NP-hard nature of the problem, it was necessary to develop a heuristic or meta-heuristic algorithm for large data sets; this research paper contributes to filling this gap.

Construction firms keep minimal resources to maintain productive working capital. Hence, resources are constrained and have to be shared among multiple projects in an organization. Optimal allocation of resources is a key challenge in such situations. Several approaches and heuristics have been proposed for this task. The purpose of this paper is to compare two approaches for multi-mode resource-constrained project scheduling in a multi-project environment. These are the single-project approach (portfolio optimization) and the multi-project approach (each project is optimized individually, and then heuristic rules are used to satisfy the portfolio constraint).

A direct search algorithm called Probabilistic Global Search Lausanne is used for schedule optimization. Multiple solutions are generated that achieve different trade-offs among the three criteria, namely, time, cost and quality. Good compromise solutions among these are identified using a multi-criteria decision making method, Relaxed Restricted Pareto Version 4. The solutions obtained using the single-project and multi-project approaches are compared in order to evaluate their advantages and disadvantages. Data from two sources are used for the evaluation: modified multi-mode resource-constrained project scheduling problem data sets from the project scheduling problem library (PSPLIB) and three real case study projects in India.

Computational results prove the superiority of the single-project approach over heuristic priority rules (multi-project approach). The single-project approach identifies better solutions compared to the multi-project approach. However, the multi-project approach involves fewer optimization variables and is faster in execution.

It is feasible to adopt the single-project approach in practice; realistic resource constraints can be incorporated in a multi-objective optimization formulation; and good compromise solutions that achieve acceptable trade-offs among the conflicting objectives can be identified.

An integer programming model was developed in this research to optimize the multiple objectives in a multi-project environment considering explicit resource constraints and maximum daily costs constraints. This model was used to compare the performance of the two multi-project environment approaches. Unlike existing work in this area, the model used to predict the quality of activity execution modes is based on data collected from real construction projects.

Productivity, based on estimated and actual hours, of most maintenance workers is only 30 to 50 per cent. Given the significance of maintenance to manufacturing competitiveness, it is surprising how little research is being carried out. Scheduling is a crucial component of maintenance management and is a focus of research. Identifies the areas of concern in maintenance scheduling and surveys representative work from the academic and practitioner literature. Specific points of practice and theory which need further investigation are pinpointed.

The purpose of this paper is to introduce an agent‐based model for partners' selection in inter‐organizational supply‐chains. Screening activities undertaken by the main contracting organization for the case of large project deliveries and parameters to be considered are identified, a multi‐level quantitative framework to support the decision‐making process in effectively selecting an efficient and a compatible set of partners is proposed. Different agent behaviours are introduced and modelled; their impact on the results of the bidding processes is analysed through a simulation model specifically developed for this purpose.

Among the biggest challenges, companies confront with is the finding of appropriate action lines, methods or IT tools to overcome problems introduced by the saturation of the market, fierce competition and downfall of entry‐barriers. The paper uses a paradigm that is seen as one of the most promising in responding to the needs of modern companies: the agent‐based paradigm.

Designing an efficient supply network greatly influences the level of uncertainty that a company is encountering. An agent‐based model may efficiently help the inter‐organizational networks partners' selection process. To operate effectively in open environments such as supply chains, agents must be adaptive. System/agents performance can be improved by doing what‐if simulation runs.

A further possible work direction, considering that nowadays a society must find a compromise between being internationally competitive and having a healthy environment, would be the introduction in the model of a special type of agent, namely the project delivery waste management agent.

In addition to contributing to the scientific literature, this research also aims at supporting practical business situations in the industry by creating a normative decision‐making model based on an agent‐based approach. The simulation model developed supports the tuning of parameters used in the evaluation process and makes possible analyzing the effect of different agent behaviours.

Research that identifies means to constrain the pre‐contracting selection problem for specific delivery chains as bridge constructions is needed. The paper details the particularities of such chains, and makes a presentment of roles of actors with impact on the assessment process. One specific of the approach is that in addition to participants as suppliers and main contracting organization also issues (goals) relevant in the supplier's appraisal process are represented by independent agents.

This research proposes weighted grey relational analysis (WGRA) method to evaluate the performance of 325 multilevel dispatching rules in the wafer fabrication process.

The research methodology involves multilevel dispatching rule generation, simulations, WGRA and result analysis. A complete permutation of multilevel dispatching rules, including the partial orders, is generated from five basic rules. Performance measures include cycle time, move, tool idling and queue time. The simulation model and data are obtained from a wafer fab in Malaysia. Two seasons varying in customer orders and objective weights are defined. Finally, to benchmark performance and investigate the effect of varying values of coefficient, the models are compared against TOPSIS and VIKOR.

Results show that the seasons prefer different multilevel dispatching rules. In Normal season, the ideal first basic dispatching rule is critical ratio (CR) and CR followed by shortest processing time (SPT) is the best precedence pairing. In Peak season, the superiority of the rule no longer heavily relies on the first basic rule but rather depends on the combination of tiebreaker rules and on-time delivery (OTD) followed by CR is considered the best precedence pairing. Compared to VIKOR and TOPSIS, WGRA generates more stable rankings in this study. The performance of multicriteria decision-making (MCDM) methods is influenced by the data variability, as a higher variability produces a much consistent ranking.

As research implications, the application illustrates the effectiveness and practicality of the WGRA model in analyzing multilevel dispatching rules, considering the complexity of the semiconductor wafer fabrication system. The methodology is useful for researchers wishing to integrate MCDM model into multilevel dispatching rules. The limitation of the research is that the results were obtained from a simulation model. Also, the rules, criteria and weights assigned in WGRA were decided by the management. Lastly, the distinguishing coefficient is fixed at 0.5 and the effect to the ranking requires further study.

The research is the first deployment WGRA in ranking multilevel dispatching rules. Multilevel dispatching rules are rarely studied in scheduling research although studies show that the tiebreakers affect the performances of the dispatching rules. The scheduling reflects the characteristics of wafer fabrication and general job shop, such as threshold and look-ahead policies.