Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world applications. However, most MOEAs based on Pareto-dominance handle many-objective problems (MaOPs) poorly due to a high proportion of incomparable and thus mutually nondominated solutions. Recently, a number of many-objective evolutionary algorithms (MaOEAs) have been proposed to deal with this scalability issue. In this article, a survey of MaOEAs is reported. According to the key ideas used, MaOEAs are categorized into seven classes: relaxed dominance based, diversity-based, aggregation-based, indicator-based, reference set based, preference-based, and dimensionality reduction approaches. Several future research directions in this field are also discussed.

https://dl.acm.org/doi/pdf/10.1145/2792984

Many-Objective Evolutionary Algorithms: A Survey

This paper reviews the breadth of complexity of the design process, products, manufacturing, and business. Manufacturing is facing unprecedented challenges due to increased variety, market volatility and distributed global manufacturing. A fundamental residue of globalization and market uncertainty is the increasing complexity of manufacturing, technological and economic systems. The nature and sources of complexity in these areas are reviewed and complexity modeling and management approaches are discussed. Enterprises that can mitigate the negative aspects of complexity while managing its positives should thrive on the continuous change and increasing complexity. To reap these benefits in the future, manufacturing companies need to not only adopt flexible technical solutions but must also effectively innovate and manage complex socio-technical systems.

Complexity in engineering design and manufacturing

The paper reviews applications of data mining in manufacturing engineering, in particular production processes, operations, fault detection, maintenance, decision support, and product quality improvement. Customer relationship management, information integration aspects, and standardization are also briefly discussed. This review is focused on demonstrating the relevancy of data mining to manufacturing industry, rather than discussing the data mining domain in general. The volume of general data mining literature makes it difficult to gain a precise view of a target area such as manufacturing engineering, which has its own particular needs and requirements for mining applications. This review reveals progressive applications in addition to existing gaps and less considered areas such as manufacturing planning and shop floor control.

Data Mining in Manufacturing: A Review

http://www-personal.umich.edu/~ykoren/uploads/Assembly_System_Design_and_Operations.pdf

Assembly system design and operations for product variety

https://isiarticles.com/bundles/Article/pre/pdf/631.pdf

Dynamic capabilities, environmental dynamism, and competitive advantage: Evidence from China

This paper studies static complexity in manufacturing systems. We enumerate factors influencing static complexity, and define a static complexity measure in terms of the processing requirements of parts to be produced and machine capabilities. The measure suggested for static complexity in manufacturing systems needs only the information available from production orders and process plans. The variation in static complexity is studied with respect to part similarity, system size, and product design changes. Finally, we present relationships between the static complexity measure and system performance.

Complexity in manufacturing systems, Part 1: Analysis of static complexity

There is a need to develop user-oriented math programming techniques for resolution of decision problems in which several objectives must be considered. One approach, the Geoffrion-Dyer-Feinberg algorithm, allows interaction between the computer and the decision maker during the solution process. The interactive approach is adopted in this paper. However, our approach focuses on reducing the feasible region of the decision space rather than improving the stored image of the overall preference function. In so doing, the problem is reduced to a series of pairwise tradeoffs between the objectives. This obviates the need for any type of choice among vectors on the part of the decision maker and stays reasonably within his capability to supply necessary information for problem solution.

A Tradeoff Cut Approach to Multiple Objective Optimization

Real-time scheduling in flexible manufacturing systems (FMS) is a difficult problem because of the complexities and dynamic behaviour of FMS. In this paper, a transient-based real-time scheduling algorithm which selects a dispatching rule dynamically for a next short time period in order to respond to changes of system state is proposed. In this algorithm, the scheduling interval associated with the selected dispatching rule is defined based on the system transient state as predicted by discrete event simulation. Simulation is also applied to evaluate the performance of the candidate dispatching rules for the next scheduling interval.

A transient-based real-time scheduling algorithm in FMS

A decision rule for real-time dispatching of parts, each of which may have alternative processing possibilities, has been developed and tested in a simulated flexible manufacturing system. A part, upon completion of an operation, is not routed to a specific machine, but is, in effect, sent to a general queue. Thus, a machine has a global option for choosing parts which in turn may be processed on alternative machines. For effective use of the system's routeing flexibility under these circumstances, the machine needs an intelligent part-selection strategy (rather than shallow heuristics represented by the conventional dispatching rules) that takes into account the current state and trends of the system. The proposed intelligent reasoning procedure has been found to achieve better shop performance than some of the popular dispatching rules, the improved performance being due to the ability to respond to changing circumstances.

Joseph J. Talavage

Papers

Complexity in manufacturing systems, Part 1: Analysis of static complexity

A Tradeoff Cut Approach to Multiple Objective Optimization

A transient-based real-time scheduling algorithm in FMS

Intelligent dispatching for flexible manufacturing

The strategic value of flexibility in sequential decision making