Multiple Production Lines Research Articles

A note on short-term scheduling of multi-grade polymer plant using DNA computing

Short-term scheduling of batch polymer plant involves the scheduling of different orders in parallel available production lines. The scheduling becomes more challenging due to the presence of sequence-dependent changeover constraints between different orders which lead to combinatorial optimization formulation. Such combinatorial optimization problems have exponential time complexity on the silicon-based computer. DNA computing experiments are found to be promising for such combinatorial optimization problems particularly involving unique feasible optimal solution. However, use of DNA to find a solution to real-life problems involving multiple feasible solutions is an emerging area of research. The present paper illustrates the DNA solutions to the short-term scheduling of a polymer plant involving multiple feasible solutions and parallel production lines. The DNA computer aided with nearest neighbour heuristics and iterative implementation found to be successfully searching the optimal solution in a combinatorial search space for three short-term scheduling problems of multi-grade polymer plant.

On the proposal and evaluation of a benchmark-based threshold derivation method

Software-intensive systems have been growing in both size and complexity. Consequently, developers need better support for measuring and controlling the software quality. In this context, software metrics aim at quantifying different software quality aspects. However, the effectiveness of measurement depends on the definition of reliable metric thresholds, i.e., numbers that characterize a metric value as critical given a quality aspect. In fact, without proper metric thresholds, it might be difficult for developers to indicate problematic software components for correction, for instance. Based on a literature review, we have found several existing methods for deriving metric thresholds and observed their evolution. Such evolution motivated us to propose a new method that incorporates the best of the existing methods. In this paper, we propose a novel benchmark-based method for deriving metric thresholds. We assess our method, called Vale’s method, using a set of metric thresholds derived with the support of our method, aimed at composing detection strategies for two well-known code smells, namely god class and lazy class. For this purpose, we analyze three benchmarks composed of multiple software product lines. In addition, we demonstrate our method in practice by applying it to a benchmark composed of 103 Java open-source software systems. In the evaluation, we compare Vale’s method to two state-of-the-practice threshold derivation methods selected as a baseline, which are Lanza’s method and Alves’ method. Our results suggest that the proposed method provides more realistic and reliable thresholds, with better recall and precision in the code smell detection, when compared to both baseline methods.

AN ANALYSIS ON INFLUENCE OF BEWILDERMENT PHENOMENON ON OPERATION TIME AND LEARNING PERIOD IN MULTI ASSEMBLY PROCESS

In recent years, more and more variety of products are produced also in manual assembly lines like white goods as customer needs become diversified. In response to this, there is a transition in the bases from a traditional mass production line to multiple small production lines for each kind of product. In the small production lines, the process tasks become more complicated than in the mass production lines because a lot of element tasks are involved with a process. As the result, there are some cases where the productivity deteriorates because the tasks which used to be completed in the mass production lines cannot be completed in time. One of the causes is assumed to be occurrence of a bewilderment phenomenon, the phenomenon where movements of human being are frozen or delayed by the freeze or blunting of thought when they handle a lot of tasks. This study, focusing on the bewilderment phenomenon, clarified the types of bewilderment phenomenon which may occur during manual assembly tasks. Then, the relationships between complexity of tasks and occurrence of bewilderment phenomenon were analyzed. Then, it was confirmed that the bewilderment phenomenon cause prolonged operation time and learning periods.

Dynamic response to demand variability for precast production rescheduling with multiple lines

Production scheduling plays a crucial role in the prefabricated construction productivity and on-time delivery of precast components (PCs). However, previous studies mainly focused on the static scheduling of single production line without considering the demand variability in practice. To achieve dynamic production planning, a Two-level Rescheduling Model for Precast Production with multiple production lines is developed to minimise the rescheduling costs based on genetic algorithm, from the two levels of (1) selection of production line and (2) rescheduling of jobs based on PCs’ priority. Further, two scenarios of different and shared mould types are investigated to represent real-world production environments. Finally, a real case study is conducted to test the validity of proposed rescheduling model. 58.1 and 48.5% cost savings are achieved by comparison to no response to changes and heuristic rescheduling methods, respectively. This research contributes to the precast production theory by expanding the insight into dynamic rescheduling with multiple production lines. The methodology will promote the on-time delivery of PCs and enhance the dynamic precast production management.

Does Product Platforming Pay Off?

Product platforming—a specific approach to new product development utilizing common technology components or subsystems deployed across multiple products or product lines—has been argued to bring numerous valuable organizational outcomes (e.g., effectiveness of R&D process, superior postlaunch product commercial performance, and ultimately sustained competitive advantage). Yet, large‐scale longitudinal empirical examinations of the mechanisms linking product platforming to firm performance are scarce. Drawing on the concepts of architectural leverage and product life cycle flexibility, the article presents the development and empirical test of a set of hypotheses regarding the commercial outcomes of platforming at the product level using a unique dataset comprising all products developed and sold by a large, global LED lighting manufacturer in 2010–2015. The results suggest that platformed products demonstrate significantly higher sales and gross profit margins aggregated over their product life cycle (PLC), vis‐à‐vis the comparable group of nonplatformed, individually developed products. In addition, the findings demonstrate that a product platforming development approach appears to extend the PLC relative to nonplatformed products based on an integral, nonmodular product architecture.

System reliability for a multi-state manufacturing network with joint buffer stations

This paper studies system reliability for a multi-state manufacturing network with multiple production lines (MSMN-MPL) considering joint buffer station. Joint buffer stations with finite volumes are set into the MSMN-MPL to temporally store blocked outputs from upstream workstations. The usage volume of each joint buffer station is therefore affected by the capacity states of workstations. An adjustment term, defined as the probability that all buffer stations are not blocked, is proposed to analyze volume usage and corresponding probability for joint buffer stations. The system reliability with finite buffer volume is evaluated accurately in terms of the adjustment term. In particular, the relationship between system reliabilities with infinite and finite buffer volumes can be formulated by multiplying the adjustment term. A practical case of footwear manufacturing system is utilized to demonstrate system reliability evaluation with both infinite and finite buffer volumes. The suggested production policy for the case is to produce 144 and 96 products by two production lines because this policy can result a higher reliability. Moreover, experimental results show that the system reliabilities with infinite and finite buffer volume are 87.190% and 83.099%, respectively. This indicates that the assumption of infinite buffer volume overestimates the system reliability in the footwear manufacturing system.

Scale and Scope Economies of Chinese Property-Liability Insurance Industry

Based on the framework of industrial organization, the paper adopts the Translog model to determine the scale and scope economies of the property-liability insurance industry in China. We make use of the data of all insurers in China between 2012 and 2014 and use the insurance claims and premium to measure the output of the industry. The result of the model reveals that when measuring insurance claims as output, all insurers exhibit economies of scale, except for the result of large insurers in 2013. When measuring premium as output, large insurers exhibit diseconomies of scale, while small insurers have economies of scale. In general, operating multiple product line leads to scope economy.

Economic Assessment of APC and RTO Using Option to Expand

This paper aims to develop a new economic assessment (EA) method using the option to expand for Advanced Process Control (APC) and Real Time Optimization (RTO). The new EA criteria for investment decision for APC and RTO employ net present value of APC and call option of RTO. Calculation of call option adapts arithmetic measurement method to compute annualized volatility. The new EA applies scenario analysis to take appropriate action. There are four scenarios and their corresponding actions, namely, (1) safe scenario - invest only APC, (2) value-added scenario and (3) risky scenario - invest in APC and RTO, (4) gamble scenario - reject APC. Furthermore, early exercise criterion for RTO investment uses American option method. Applying new EA method to VCM plant demonstrates the effectiveness of the option to expand. The results show that when NPV of APC is negative and the sum of NPV of APC and Call of RTO is positive, APC project is risky scenario. We recommend to invest in APC and RTO. In comparison to conventional NPV and Payback Period (PB) methods, APC is not feasible since NPV is negative and PB is not available due to negative expected profit. In the case study, volatility calculation addresses only one product line in chemical industry which is VCM. Real production comprises of multiple product lines and their volatility is larger than that of one product. With the new EA method, management has comprehensive and flexible tool to assess APC/RTO benefits. Moreover, the new EA provides the timing to invest RTO. Profit margin, expiration period and yield are key parameters that affect early exercise. The new EA is the first method to apply real options to APC and RTO which evaluates the benefit not only APC but also the integrated APC and RTO. The early exercise criterion can facilitate the decision maker to invest in the most beneficial period.

Optimized flowshop scheduling of multiple production lines for precast production

The current approach in practice to produce the flowshop schedules of precast production is the dispatching rule technique, which does not guarantee optimal schedules. Some researchers have developed models for a single production line to optimize the scheduling, in which Genetic Algorithm (GA) is used to obtain the solution. However the models cannot be used for multiple production lines. Moreover, some optimization constraints and objectives were missing in the models, such as avoiding frequent type change of precast components during production. Both issues hinder their work to be applied to real precast plants. To overcome the problem, this paper proposes a Flowshop Scheduling Model of Multiple production lines for Precast production (MP-FSM) and develops a corresponding optimization approach to facilitate optimized scheduling by using GA. The approach was validated preliminarily by comparing with traditional scheduling approaches. The results demonstrated that optimized schedules can be obtained by using the proposed approach.

Maintenance Scheduling Optimization in a Multiple Production Line Considering Human Error

An analytical multiobjective maintenance planning model that maximizes reliability while minimizing cost and human error is proposed. In order to incorporate human error, the model minimizes the maximum human error over the planning horizon. Human Error Assessment and Reduction Technique HEART is used to quantify the human error. Maintenance activities include adjustment and replacement activities, in which each of them consumes a certain amount of human resource, spare parts, and budget and brings about a specified level of reliability and human error. Economic dependence is also considered, in which grouping maintenance activities reduces total cost. However, this may increase human error probability due to operator fatigue or time pressure. The main purpose is to investigate the relationship between human factors and maintenance activities to find the preferred maintenance plan. A multiple production line is considered as a case study. A sensitivity analysis is performed, and the effects of grouping and human factors on the preferred maintenance plan are discussed. It is shown how human proficiency may affect reliability and cost.

Optimal scheduling of manufacturing processes across multiple production lines by polynomial optimization and bagged bounded binary knapsack

Key objectives of ‘Industry 4.0’ methodologies in manufacturing include improved quality control, predictive maintenance and tracking. We consider whether a further objective that can easily and cost-effectively be achieved is the implementation of aggressive optimization of production schedules across multiple manufacturing lines. The present contribution describes how this objective can be achieved by exploiting operations research techniques for the supporting of plant operation scheduling. We focus on methodologies for optimizing energy and time consumption in particular. The data acquisition from networked sensors ends with the aggregation and insertion of measures in a relational database of multi-variate sample records representing the state of separate production lines. Starting from these records, production plans can be generated automatically. Plans can be represented as tables reporting sequences of the numbers of pieces of a specific type to be produced by each line. We describe schedule optimization methodologies in detail, reporting in particular on the costs of their computation. We finally discuss issues related to the design of the end user interface, which can usefully be based on a web service-oriented architecture meant to allow communicating the obtained results to human operators, finally allowing them to monitor and implement the resulting schedules. Results are evaluated on real data acquired by sensors installed in a metal injection molding plant in Bizkaia, Spain.

The product-mix problem for multiple production lines in sequenced stages: a case study in the steel industry

The product-mix problem is common and widely applicable in many industries. This paper formulates the product-mix problem for multiple production lines in sequenced stages where the output of the upstream stage is the input for other downstream operations. The objective is to maximize the upstream production (throughput per time) by which throughput of the whole system including the downstream operations will be maximized to satisfy production constraints: productivity, capacity, and sales requirements. The problem is formulated using an integer linear programming (ILP) model and demonstrated in a steel plant in Saudi Arabia. The 480-MT steel plant has six production lines, namely push-pickling line, cold-reversing mill, batch-annealing facility, temper mill, continuous galvanizing line, and color-coating line. The plant intended to leverage its annual production capacity. Hence, a recent expansion was done on the batch annealing facility. After the expansion, the plant had several production interruptions and stoppages in the downstream facilities. The model provided the maximum throughput for all production stages and avoids production interruptions.

Structuring automotive product lines and feature models: an exploratory study at Opel

Automotive systems are highly complex and customized systems containing a vast amount of variability. Feature modeling plays a key role in customization. Empirical evidence through industry application, and in particular methodological guidance of how to structure automotive product lines and their feature models is needed. The overall aim of this work is to provide guidance to practitioners how to structure automotive product lines and their feature models, understanding strengths and weaknesses of alternative structures. The research was conducted in three phases. In the first phase, the context situation was understood using interviews and workshops. In the second phase, possible structures of product lines and feature models were evaluated based on industry feedback collected in workshops. In the third phase, the structures were implemented in the tool GEARS and practitioner feedback was collected. One key challenge was the unavailability of structuring guidelines, which was the focus of this research. The structures considered most suitable for the automotive product line were multiple product lines with modular decomposition. The structures most suitable for the feature model were functional decomposition, using context variability, models corresponding to assets, and feature categories. Other structures have been discarded, and the rationales have been presented. It was possible to support the most suitable structures with the commercial tool GEARS. The implementation in GEARS and the feedback from the practitioners provide early indications for the potential usefulness of the structures and the tool implementation.

Analysis of dynamic pricing scenarios for multiple-generation product lines

In technology-intensive markets, it is a common strategy for companies to develop long-term multiple generation product lines instead of releasing consecutive single products. Even though this strategy is more profitable than sequentially introducing single product generations, it can also result in inter-product line cannibalization. Cannibalization of multiple-generation product lines is a complex problem that needs to be taken into account at the early product line planning stage in order to sustain long-term profitability. In this paper, we propose an agent-based model that can simulate the potential cannibalization scenarios within a multiple-generation product line. We view a multiple-generation product line (MGPL) as complex adaptive system where each product generation in the MGPL adjusts its sales price over time based on the shifts in the market demand. The proposed model provides insights into how various pricing strategies impact the overall lifecycle profitability of MGPL and can be used to assist companies in developing appropriate dynamic pricing strategies at the early product line planning stages.

Multi-product Bertrand Oligopoly with Exogenous and Endogenous Consumer Heterogeneity

AbstractWe develop a spatial model in which consumers receive firm-specific location shocks and firms endogenously determine both franchise/product locations and prices. Remarkably, firms fail to profit from endogenous product-specific heterogeneity alone: while ex-post consumer heterogeneity ensures positive gross profits, competition for market share results in socially excessive product lines and zero net profits. With added exogenous taste heterogeneity, endogenous spatial heterogeneity drives profits below their levels with only taste heterogeneity. Finally, we introduce multiple product lines and show that when product costs differ across lines, firms earn positive profits as long as consumer preferences over lines are imperfectly correlated.

Predecessor-set technique for reliability evaluation of a stochastic manufacturing system

This paper studies the reliability evaluation of a stochastic manufacturing system with multiple production lines in parallel. Multiple repairs and different failure rates, never simultaneously addressed in earlier works, are taken into account. First, a revised graphical methodology integrating transformation and decomposition is utilized to construct the stochastic manufacturing system as a multi-state manufacturing network (MSMN). In particular, a “predecessor-set” technique is proposed to deal with multiple repairs. An algorithm is proposed to generate the lowest capacity vectors (LCVs) that stations should provide to satisfy the workloads. Subsequently, the system reliability of the MSMN, which is defined as the probability of demand satisfaction, is calculated in terms of the LCVs. A real case of a printed circuit board manufacturing system is utilized to demonstrate how the system reliability can be evaluated. A further decision making issue is addressed based on the derived system reliability.

Demand satisfaction and decision-making for a PCB manufacturing system with production lines in parallel

This paper studies demand satisfaction and decision-making for a printed circuit board (PCB) manufacturing system. A performance indicator, the system reliability, is proposed to evaluate the demand satisfaction. In particular, two important characteristics, (i) multiple production lines in parallel and (ii) multiple repair actions, are considered in this study. The PCB manufacturing system is modelled as a stochastic-flow manufacturing network (SFMN) by the graphical transformation and decomposition techniques. Based on the network-structured SFMN, this paper develops an algorithm to generate all minimal capacity vectors that workstations should provide to satisfy a given demand. In terms of all minimal capacity vectors, the system reliability is derived. In addition, the reliable production policy is determined based on each single minimal capacity vector.

Supporting Incremental Product Development using Multiple Product Line Architecture

Software product line engineering (SPLE) has been successfully applied in various application domains to support systematic reuse. Besides of its benefits it is also acknowledged that the SPLE process can in practice be considered too time consuming and heavyweight due to the required planning and development of the asset base. For this reason more lightweight SPLE processes are required that can be integrated in the ongoing product development of the organization. In this context, the authors share their experiences in adopting a multiple product line architecture to support the incremental product development of Aselsan REHIS, a leading high technology company in Turkey. The authors first discuss the important business needs for defining a more lightweight multiple product line engineering (MPLE) process. Then they discuss the multiple product line architecture and how it has been used to guide the incremental product development.

Energy efficiency optimization of air supply system in a water bottle manufacturing system

Abstract This paper presents the use of green manufacturing methods for optimization of energy efficiency of air compressors. The method developed during this research is applied to one industry problem in a facility that fills polyethylene terephthalate (PET) bottles with mineral water. Bottle manufacturing systems in large beverage production companies often have multiple PET bottle production lines. A common issue in these lines is failure of one of the compressors used for a machine in such manufacturing systems. These equipment failures prevent companies from manufacturing their products until the compressor and its line get repaired. In this way, companies are losing significant financial resources. Therefore, in this paper, one possible solution for such a problem is given. The main goal of the proposed optimization solution is focused on the improvement of system reliability and higher energy efficiency. This was achieved by the replacement of previous, multiple compressors of various capacities with one multiple compressor station with a larger, optimized capacity, customized for a specific solution. In addition, another goal was to establish a platform for a design retrofit in a company with a limited budget. As a result of this research, a suggestion was given that high-pressure compressors, linked to four individual lines for blowing PET bottles, were linked to one high-pressure tank. In this way, the reliability of the system was increased. During this research, discrete event simulation methods were used to verify a proposed solution and to assist researchers in determining an optimal design of an air supply system. The solution given was based on a suitable combination of PET bottle blowers work cycles, which would better meet the total demand for compressed air based on methods related to robust design and Design of Experiments (DoE).

Object-oriented Bayesian networks for complex quality management problems

Quality management and customer satisfaction evaluation can be difficult tasks to perform when processes involve multiple production lines or provide multichannel services. As a consequence, the top management needs to analyse the problem from different perspectives, to evaluate possible improvement strategies at several levels and to take appropriate decisions. To this aim, we propose to use object-oriented Bayesian networks by which different quality aspects and evaluations can be integrated in a unique framework allowing to analyse improvement strategies in real time. We show, by an application to an internal-customer satisfaction survey, how to combine the perceived quality of different production areas and how to evaluate the impact on the global quality of improvement actions developed in one or more areas.