Inspection Allocation Research Articles

Inspection Allocation Optimization with Resource Constraints Based on Modified NSGA-II in Flexible Manufacturing Systems

With the development of smart manufacturing, quality has become an indispensable issue in the manufacturing process. Although there is increasing publication about inspection allocation problems, inspection allocation optimization research considering resource capability is scarce. This paper focuses on the inspection allocation problem with resource constraints in the flexible manufacturing system. Combined with the inspection resource capability model, a bi-objective model is developed to minimize the cost and balance loads of the inspection station. A modified NSGA-II algorithm with adaptive mutation operators is suggested to deal with the proposed model. Finally, a simulation experiment is conducted to test the performance of the modified algorithm and the results demonstrate that modified NSGA-II can obtain acceptable inspection solutions.

Machine learning for environmental monitoring

Public agencies aiming to enforce environmental regulation have limited resources to achieve their objectives. We demonstrate how machine-learning methods can inform the efficient use of these limited resources while accounting for real-world concerns, such as gaming the system and institutional constraints. Here, we predict the likelihood of a facility failing a water-pollution inspection and propose alternative inspection allocations that would target high-risk facilities. Implementing such a data-driven inspection allocation could detect over seven times the expected number of violations than current practices. When we impose constraints, such as maintaining a minimum probability of inspection for all facilities and accounting for state-level differences in inspection budgets, our reallocation regimes double the number of violations detected through inspections. Leveraging increasing amounts of electronic data can help public agencies to enhance their regulatory effectiveness and remedy environmental harms. Although employing algorithm-based resource allocation rules requires care to avoid manipulation and unintentional error propagation, the principled use of predictive analytics can extend the beneficial reach of limited resources. Machine learning using big data can enhance environmental law monitoring. Applied to the US Clean Water Act, such methods can help public agencies to increase the likelihood of inspecting non-compliant facilities up to sevenfold.

Design of an integrated quality assurance strategy in production systems

High-quality of products is a critical issue for manufacturers to maintain their competitiveness in global markets. For this reason, more attention has been paid by operations managers and academics to the design of quality assurance strategies, acceptance sampling plans and inspection allocation problems. In the last decades, international research has studied and introduced several models and approaches to investigate these issues. The purpose of this paper is to provide a new methodology for designing and selecting correct integrated quality assurance strategies, defining cost models for acceptance policies and inspection station configurations. Generally, high-quality of items is guaranteed by avoiding defects, mainly caused by non-conforming components, resulting from instantaneous and standard infant mortality. Thus, an optimal acceptance policy is defined in order to reduce the instantaneous infant mortality defects. A closed-form equation has been introduced to determine easily and quickly the optimal percentage of checked items. Furthermore, a more convenient inspection station configuration is determined in order to minimise the expected total cost, composed of testing, inspection and penalty cost elements. The innovative concept of defect rate as an inspection time variable dependent has been introduced. The impact of different economic and survival parameters on designing inspection policies is also investigated. Finally, a real-life case study demonstrates the applicability of this methodology in real production systems and several considerations are reported about the future research, that the authors will carry out.

A genetic algorithm with tournament selection for optimising inspection allocation in multiproduct multistage production systems

Multiproduct multistage production systems have been widely implemented in recent years. One of the important considerations in such systems is identifying the location of inspection stations. This paper considers a multiproduct setting where part types compete with each other for common production resources. In this environment, it is important to consider factors such as throughput time variability and to include the corresponding queuing aspects into the model. Each workstation is modelled as a GI/G/c queue. The optimal allocation is determined by using a genetic algorithm with tournament selection, one-point crossover and uniform mutation.

Optimal Inspection Allocation for Workstations of Attribute Data with Multi-Characteristics in Multi-Station Systems

. The study models multi-characteristics inspection for inspection allocation problems with workstations of attribute data in serial production systems. Either 100% or 0% inspection is performed and Type I and Type II errors are considered. In addition, this study considers three possibilities of treatment of detected nonconforming units, namely, repair, rework and scrap. With the above considerations, a profit model is developed for optimally allocating inspections. Moreover, a genetic algorithm is used to solve the problem and it is proved to have much less computation time, compared with an optimization method based on complete enumeration, especially when number of workstations and characteristics becomes more.

The Implementation of Sampling Plan for Optimal Inspection Allocation in Multi-Station Systems with Consideration of Rework

This study explores sampling plans for optimal inspection allocation in multi-station systems with consideration of rework. After components have processed from each workstation in a serial production system, we can apply different sampling plans for inspection in order to maintain the outgoing quality level. The defective components that are detected at each sampling plan will return to the specific workstation to rework. This study proposes a minimum total cost model with a proper sampling plan after each workstation, which can help people to manager production better.

Optimal Inspection Allocation Decision with Production Line Balance in Multi-Station Assembly Systems

This paper explores the inspection allocation problem with considering production line balance at the same time for multi-station assembly systems. A mathematical model is developed in order to find the minimal total cost. When the number of parts increases, the time spent increases dramatically with the enumerative method. Therefore, an approach to genetic algorithm is developed and it takes much less time with near-optimal solutions.

Genetic algorithm modeling for the inspection allocation in reentrant production systems

One of the most important characteristics of reentrant production systems is that the products are manufactured layer-by-layer, so it is difficult to inspect some defects after they are covered by the next layer. This study proposes a genetic algorithm (GA) approach that is very suitable for solving the inspection allocation problem, because the codes used in the chromosome of the GA approach are exactly the same as the representation of the inspection allocation policy for workstations in the production system. Meanwhile, this study shows better performance than the researches done in the literature and is very much closer to the optimization method based on complete enumeration. In addition, a discussion regarding GA parameters is performed to suggest proper parameters used for various production systems. The result obtained in this study is highly practical, extensible and applicable, so it can serve as a production planning tool to solve the inspection allocation problem in reentrant production systems.

Costs and benefits of inspection systems and optimal inspection allocation for uniform defect propensity

PurposeThe objective of this paper is to demonstrate basic economic principles underlying the use of inspection systems and to derive a basis for comparing inspection costs with the alternative costs of quality improvement.Design/methodology/approachFor this purpose, the impact of final, rectifying and sequential inspection on unit production costs is assessed using a production process model with uniform defect propensity in the single process steps. Based on this model, an objective function with the objective of minimizing unit production costs including inspection costs is formulated and a genetic algorithm method used to optimize it.FindingsTwo distinct patterns of optimal inspection allocation could be detected for changing defect rate, processed volume, fixed inspection costs and variable input costs. These allocation patterns highlight the basic economic relations of an inspection approach and verify the assumption that sequential inspection schemes are cost‐optimal. However, the benefits of quality improvement are found to be superior to any inspection scheme for a majority of cases.Research limitations/implicationsThe findings are derived from a theoretical examination. Further limitations stem from the cost‐centred approach which only covers the internal component of an economic assessment of quality. However, the economic perspective advanced in this paper can in future be subjected to empirical testing and be elaborated by subsequent research.Practical implicationsAlthough built on simplifying assumptions, the process model can provide important insights into basic economic relations and demonstrate that inspection is an inferior way of dealing with quality problems. It can thereby help to promote a better economic understanding of quality.Originality/valueThis paper provides an economic assessment of an important aspect of quality management, which has so far not been advanced.

Layer modelling for the inspection allocation problem in re-entrant production systems

In the past two decades, re-entrant production systems have received a great deal of attention. One of the most important characteristics of re-entrant production systems is that the work units are manufactured layer-by-layer, so that some defects are more difficult to inspect after they are covered by the next layer. In this research, a mathematical model considering layered fabrication is developed to find the optimal solution for allocating inspections in re-entrant production systems. Workstations of variables data and inspections of quality characteristics measurement are modelled. In addition, this study considers three possibilities for the treatment of detected non-conforming units, namely repair, rework and scrap. Moreover, a heuristic algorithm is proposed in order to improve the optimization method based on complete enumeration, which suffers from a large amount of computation time, especially as the number of workstations increases. The model is highly extensible and applicable, so it can serve as a production-planning tool to solve the inspection allocation problem in re-entrant production systems.

Inspection allocation planning with two types of workstation: WVD and WAD

Allocating inspection stations is a very important issue for controlling quality and saving cost. The study considers inspection allocation problems for serial production systems with two types of workstations, workstation of attribute data and workstation of variable data, which better represents real practice. In addition, this study considers three possibilities for the treatment of detected nonconforming units, namely, repair, rework and scrap. With the above considerations, a profit model is developed for optimally allocating inspection stations. Moreover, a heuristic solution method is developed and it is proved to have much less computation time, compared with an optimization method based on complete enumeration, especially as the number of workstations increases. The model is highly extensible and applicable. Therefore, it can serve as a production planning guideline to solve the inspection allocation problem for serial production systems .

Allocating solder-paste printing inspection in high-volume electronics manufacturing

The paper describes an optimization model for allocating solder-paste printing inspection that explicitly considers the economic tradeoff between board yield and inspection accuracy. The paper also shows that the use of a heuristic solution method for solving the post-printing inspection allocation model is effective and efficient for high-volume electronics manufacturing. The model has been developed using real production and visual inspection data provided by a high-volume electronics manufacturer located in Huntsville, AL. In addition, randomly generated problems are used to evaluate the performance of the proposed heuristic. Results from a large case study show that when compared with a full post-printing inspection, the heuristic approach provides a solution that can increase the total expected gains by 15%.

An optimization model for selective inspection in serial manufacturing systems

This paper describes an optimization model for allocating inspection efforts at each stage in a serial multi-stage assembly line. The model explicitly considers the economic tradeoff between product yield and inspection accuracy. The paper also shows that the use of a heuristic solution method, simulated annealing, is effective and efficient for solving the inspection allocation model. Problem instances have been developed using real production and visual inspection data provided by a local high-volume electronics manufacturer. In addition, randomly generated problems are used to evaluate the performance of the proposed heuristic.

Quick decision-making support for inspection allocation planning with rapidly changing customer requirements

Manufacturing products with multiple quality characteristics is always one of the main concerns for an advanced manufacturing system. To assure product quality, finite automatic inspection systems should be available and employed. Inspection planning to allocate inspection stations should then be performed to manage limited inspection resources.

Inspection Allocation Planning for a Multiple Quality Characteristic Advanced Manufacturing System

Producing products with multiple quality characteristics is always one of the concerns for an advanced manufacturing system. To assure product quality, finite automatic inspection systems should be used. Inspection planning to allocate inspection stations should then be performed to manage the limited inspection resource. Except for finite inspection station classes, in this work, the limited number of inspection stations, of each inspection station class, is considered for solving the inspection allocation problem in a multiple quality characteristic advanced manufacturing system. Since the product variety in batch production or job shop production increases to satisfy the changing requirements of the various customers, the tolerances specified will vary from time to time. This inspection allocation problem is solved using a unit cost model in which the manufacturing capability, inspection capability, and tolerance specified are concurrently considered for a multiple quality characteristic product. The situation of unbalanced tolerance design is also considered. The inspection allocation problem can then be solved according to customer requirements. Since determining the optimal inspection allocation plan seems to be impractical, as the problem size becomes large, two decision criteria (i.e. sequence order of workstation and tolerance interval) are employed separately to develop two different heuristic solution methods in this work. The performance of each method is measured in comparison with the enumeration method that generates the optimal solution. The result shows that a feasible inspection allocation plan can be determined efficiently.

Inspection allocation for multistage deteriorating production systems

The paper deals with the layout and operation of an inspection system used for detecting malfunctioning processors in a multistage production system. This problem involves three inter-related decisions: (i) the overall inspection capacity; (ii) the assignment of inspection tasks to inspectors; and (iii) the scheduling of the inspector's tasks. These decisions require a trade-off between the cost of inspectors and the loss associated with non-conforming products. A hierarchical heuristic solution procedure is proposed to support these three related decisions. Numerical experiments demonstrate the performance of the heuristic, showing that solution criteria are very close to their lower bounds. Although we use production terminology, the results might be applicable to any organization, which inspects and maintains a variety of characteristics of its branches or activities.

Inspection resource assignment in a multistage manufacturing system with an inspection error model

Producing high-quality products at low cost is always one concern for a multi-stage manufacturing system. That is, production costs and inspection efficiency should receive equal importance. Inspection planning to allocate inspection stations should then be performed to manage limited inspection resources during process planning. Product quality and the possible costs can then be concurrently considered when evaluating a manufacturing plan. Except for finite inspection station classes, the limited number of inspection stations of each inspection station class is considered to solve the inspection allocation problem in this research. Rather than utilizing a constant inspection error or a specified inspection error probability distribution determined by previous observations, the inspection allocation problem is solved using relative cost models in which the inspection error model is embedded. The inspection allocation problem can then be solved by practically reflecting the inspection error when tolerances are rapidly changed to satisfy customer requirements. Since determining the optimal inspection allocation plan seems impractical as the problem size becomes quite large, two heuristic methods have been developed by considering the defective rate, manufacturing cost and earliest stage priority in this research. The performance of each method is measured in comparison with the enumeration method that generates the optimal solution. A feasible manufacturing plan can then be determined and confirmed during process planning by concurrently solving the inspection allocation problem.

Information-based inspection allocation for real-time inspection systems

Information-based inspection allocation for real-time inspection systems

Inspection allocation for multistage deteriorating production systems

The paper deals with the layout and operation of an inspection system used for detecting malfunctioning processors in a multistage production system. This problem involves three inter-related decisions: (i) the overall inspection capacity; (ii) the assignment of inspection tasks to inspectors; and (iii) the scheduling of the inspector's tasks. These decisions require a trade-off between the cost of inspectors and the loss associated with non-conforming products. A hierarchical heuristic solution procedure is proposed to support these three related decisions. Numerical experiments demonstrate the performance of the heuristic, showing that solution criteria are very close to their lower bounds. Although we use production terminology, the results might be applicable to any organization, which inspects and maintains a variety of characteristics of its branches or activities.

Information-based inspection allocation for real-time inspection systems

The use of real-time Automated Visual Inspection (AVI) in the electronics assembly industry is subject to cycle time constraints determined by the manufacturing system. These constraints must be met to maintain the nominal production rate. A problem thus created is determining which components to inspect at each one of the AVI stations such that the inspection time available is used in an optimal way. This paper presents a real-time inspection allocation that is based on the information gained by inspecting one additional component. The selection of which components to inspect is modeled as an information maximization problem. A modified Knapsack greedy heuristic is used to find near-optimal solutions to this optimization problem within the required time constraints.