Current issue in corporate waqf in Malaysia

Corporate waqf is still new for the contemporary Islamic studies in Malaysia. There are limited resources and guidance to explain about corporate waqf. The purposes of this study are to explore the issues, concept and development of corporate waqf. Current structure for corporate waqf also being explored as part of case studies. This paper provides several perspectives and suggestions to this issue. The methodology for this study is secondary data approach by using the data analysis from the related journal and paper. The subject for this study is Selangor Muamalat. This paper come out with the concept and development of the contemporary waqf focused on corporate waqf and there are five current issues identified in the contemporary waqf. Next, seven proposed action plans are suggested to cover the issues. Lastly, the structure of Selangor Muamalat is analyses by focusing on the management structure, financial and operational framework and the Shariah consideration towards Selangor Muamalat structure

An Efficient MILP-Based Decomposition Strategy for Solving Large-Scale Scheduling Problems

This paper presents a MILP-based decomposition algorithm for solving large-scale scheduling problems with assembly operations in flexible flow shop environments. First, a rigorous mixed-integer linear (MILP) formulation based on the general precedence notion is developed for the problem under study. Then, the MILP model is embedded within a decomposition algorithm in order to accelerate the resolution of large-size industrial problems. The proposed solution approach is tested on several examples derived from a real-world case study arising in a shipbuilding company.Sociedad Argentina de Informática e Investigación Operativ

An Efficient MILP-Based Decomposition Strategy for Solving Large-Scale Scheduling Problems

This paper presents a MILP-based decomposition algorithm for solving large-scale scheduling problems with assembly operations in flexible flow shop environments. First, a rigorous mixed-integer linear (MILP) formulation based on the general precedence notion is developed for the problem under study. Then, the MILP model is embedded within a decomposition algorithm in order to accelerate the resolution of large-size industrial problems. The proposed solution approach is tested on several examples derived from a real-world case study arising in a shipbuilding company.Sociedad Argentina de Informática e Investigación Operativ

An Efficient MILP-Based Decomposition Strategy for Solving Large-Scale Scheduling Problems

This paper presents a MILP-based decomposition algorithm for solving large-scale scheduling problems with assembly operations in flexible flow shop environments. First, a rigorous mixed-integer linear (MILP) formulation based on the general precedence notion is developed for the problem under study. Then, the MILP model is embedded within a decomposition algorithm in order to accelerate the resolution of large-size industrial problems. The proposed solution approach is tested on several examples derived from a real-world case study arising in a shipbuilding company.Sociedad Argentina de Informática e Investigación Operativ

Optimum Allocation of Inspection Stations in Multistage Manufacturing Processes by Using Max-Min Ant System

In multistage manufacturing processes it is common to locate inspection stations after some or all of the processing workstations. The purpose of the inspection is to reduce the total manufacturing cost, resulted from unidentified defective items being processed unnecessarily through subsequent manufacturing operations. This total cost is the sum of the costs of production, inspection and failures (during production and after shipment). Introducing inspection stations into a serial multistage manufacturing process, although constituting an additional cost, is expected to be a profitable course of action. Specifically, at some positions the associated inspection costs will be recovered from the benefits realised through the detection of defective items, before wasting additional cost by continuing to process them. In this research, a novel general cost modelling for allocating a limited number of inspection stations in serial multistage manufacturing processes is formulated. In allocation of inspection station (AOIS) problem, as the number of workstations increases, the number of inspection station allocation possibilities increases exponentially. To identify the appropriate approach for the AOIS problem, different optimisation methods are investigated. The MAX-MIN Ant System (MMAS) algorithm is proposed as a novel approach to explore AOIS in serial multistage manufacturing processes. MMAS is an ant colony optimisation algorithm that was designed originally to begin an explorative search phase and, subsequently, to make a slow transition to the intensive exploitation of the best solutions found during the search, by allowing only one ant to update the pheromone trails. Two novel heuristics information for the MMAS algorithm are created. The heuristic information for the MMAS algorithm is exploited as a novel means to guide ants to build reasonably good solutions from the very beginning of the search. To improve the performance of the MMAS algorithm, six local search methods which are well-known and suitable for the AOIS problem are used. Selecting relevant parameter values for the MMAS algorithm can have a great impact on the algorithm’s performance. As a result, a method for tuning the most influential parameter values for the MMAS algorithm is developed. The contribution of this research is, for the first time, a methodology using MMAS to solve the AOIS problem in serial multistage manufacturing processes has been developed. The methodology takes into account the constraints on inspection resources, in terms of a limited number of inspection stations. As a result, the total manufacturing cost of a product can be reduced, while maintaining the quality of the product. Four numerical experiments are conducted to assess the MMAS algorithm for the AOIS problem. The performance of the MMAS algorithm is compared with a number of other methods this includes the complete enumeration method (CEM), rule of thumb, a pure random search algorithm, particle swarm optimisation, simulated annealing and genetic algorithm. The experimental results show that the effectiveness of the MMAS algorithm lies in its considerably shorter execution time and robustness. Further, in certain conditions results obtained by the MMAS algorithm are identical to the CEM. In addition, the results show that applying local search to the MMAS algorithm has significantly improved the performance of the algorithm. Also the results demonstrate that it is essential to use heuristic information with the MMAS algorithm for the AOIS problem, in order to obtain a high quality solution. It was found that the main parameters of MMAS include the pheromone trail intensity, heuristic information and evaporation of pheromone are less sensitive within the specified range as the number of workstations is significantly increased

AI for in-line vehicle sequence controlling: development and evaluation of an adaptive machine learning artifact to predict sequence deviations in a mixed-model production line

Customers in the manufacturing sector, especially in the automotive industry, have a high demand for individualized products at price levels comparable to traditional mass production. The contrary objectives of providing a variety of products and operating at minimum costs have introduced a high degree of production planning and control mechanisms based on a stable order sequence for mixed-model assembly lines. A major threat to this development is sequence scrambling, triggered by both operational and product-related root causes. Despite the introduction of just-in-time and fixed production times, the problem of sequence scrambling remains partially unresolved in the automotive industry. Negative downstream effects range from disruptions in the just-in-sequence supply chain to a stop of the production process. A precise prediction of sequence deviations at an early stage allows the introduction of counteractions to stabilize the sequence before disorder emerges. While procedural causes are widely addressed in research, the work at hand requires a different perspective involving a product-related view. Built on unique data from a real-world global automotive manufacturer, a supervised classification model is trained and evaluated. This includes all the necessary steps to design, implement, and assess an AI artifact, as well as data gathering, preprocessing, algorithm selection, and evaluation. To ensure long-term prediction stability, we include a continuous learning module to counter data drifts. We show that up to 50% of the major deviations can be predicted in advance. However, we do not consider any process-related information, such as machine conditions and shift plans, but solely focus on the exploitation of product features like body type, powertrain, color, and special equipment

Unified Concept of Bottleneck

The term `bottleneck` has been extensively used in operations management literature. Management paradigms like the Theory of Constraints focus on the identification and exploitation of bottlenecks. Yet, we show that the term has not been rigorously defined. We provide a classification of bottleneck definitions available in literature and discuss several myths associated with the concept of bottleneck. The apparent diversity of definitions raises the question whether it is possible to have a single bottleneck definition which has as much applicability in high variety job shops as in mass production environments. The key to the formulation of an unified concept of bottleneck lies in relating the concept of bottleneck to the concept of shadow price of resources. We propose an universally applicable bottleneck definition based on the concept of average shadow price. We discuss the procedure for determination of bottleneck values for diverse production environments. The Law of Diminishing Returns is shown to be a sufficient but not necessary condition for the equivalence of the average and the marginal shadow price. The equivalence of these two prices is proved for several environments. Bottleneck identification is the first step in resource acquisition decisions faced by managers. The definition of bottleneck presented in the paper has the potential to not only reduce ambiguity regarding the meaning of the term but also open a new window to the formulation and analysis of a rich set of problems faced by managers.

DNA SEPARATION AT A STRETCH AND MULTISTAGE MAGNETIC SEPARATION OF MICROSPHERES

This thesis consists of two parts. The first part focuses on development of a novel DNA separation technology by tethering DNA strands to a solid surface and then stretching the DNA with an electric field. The anchor is such designed that the critical force to detach a DNA is independent of its size. Because the stretching force is proportional to the DNA net charge, a gradual increase of the electric field leads to size-based removal of the DNA from the surface and thus DNA separation. This strategy may provide a convenient, low-cost, and high-speed alternative to existing methods for DNA separation, because sieving matrices are not required, separated DNA can be readily recovered, and in principle, there is no upper limit on the length of DNA that can be separated. Using this method, we have demonstrated (i) efficient separation of lambda double-stranded DNA (dsDNA) (48,502 bp) from human genomic dsDNA (>100 kbp) in a dc electric field applied between two parallel plates, (ii) separation of short single-stranded DNA (ssDNA) with less than 100 nucleotides (nt) at 10-nt resolution by tethering and stretching DNA in microfluidic channels filled with a low conductivity buffer, and (iii) separation of short ssDNA by taking the advantage of the strong yet evolving non-uniform electric field near the charged Au surface in contact with an electrolyte. The second part of my thesis focuses on development of a multistage separation technology to circumvent the challenge caused by non-specific interactions in current single-stage magnetic separation techniques. The key idea is to allow the magnetic particles (MNPs) to reversibly capture and release the targets by manipulating the hydrophobic interaction between the MNPs and the targets. This will be enabled by attaching temperature-responsive polymers to both the MNPs and the targets. Through temperature cycling, which triggers the reversible hydrophilic-to-hydrophobic phase transition of the polymers, the targets can be reversibly captured and released by the MNPs (due to hydrophobic interaction) at a higher efficiency than the non-targets which may also be captured and released by the MNPs due to non-specific interactions. The difference in the capture-and-release efficiencies of targets versus non-targets in a single cycle will be amplified by multiple separation stages, following a similar concept to the distillation process. As a proof-of-concept demonstration, we have demonstrated efficient separation of poly(N-isopropylacrylamide) (PNIPAM, a temperature responsive polymer)-functionalized polystyrene (PS) microspheres from bare PS microspheres by using PNIPAM-functionalized MNPs. The overall enrichment factor is observed to significantly increase with the number of separation stages, and reaches as high as 1.87 E+5 after 5 stages

Optimal Configuration of Inspection and Rework Stations in a Multistage Flexible Flowline

Inspection and rework are two important issues of quality control. In this research, an N-stage flowline is considered to make decisions on these two issues. When defective items are detected at the inspection station the items are either scrapped or reworked. A reworkable item may be repaired at the regular defect-creating workstation or at a dedicated off-line rework station. Two problems (end-of-line and multistage inspections) are considered here to deal with this situation. The end-of-line inspection (ELI) problem considers an inspection station located at the end of the line while the multistage inspection (MSI) problem deals with multiple in-line inspection stations that partition the flowline into multiple flexible lines. Models for unit cost of production are developed for both problems. The ELI problem is formulated for determining the best decision among alternative policies for dealing with defective items. For an MSI problem a unit cost function is developed for determining the number and locations of in-line inspection stations along with the alternative decisions on each type of defects. Both of the problems are formulated as fractional mixed-integer nonlinear programming (f-MINLP) to minimize the unit cost of production. After several transformations the f-MINLP becomes a mixed-integer linear programming (MILP) problem. A construction heuristic, coined as Inspection Station Assignment (ISA) heuristic is developed to determine a sub-optimal location of inspection and rework stations in order to achieve minimum unit cost of production. A hybrid of Ant-Colony Optimization-based metaheuristic (ACOR) and ISA is devised to efficiently solve large instances of MSI problems. Numerical examples are presented to show the solution procedure of ELI problems with branch and bound (B&B) method. Empirical studies on a production line with large number of workstations are presented to show the quality and efficiency of the solution processes involved in both ELI and MSI problems. Computational results present that the hybrid heuristic ISA+ACOR shows better performance in terms of solution quality and efficiency. These approaches are applicable to many discrete product manufacturing systems including garments industry

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF