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A survey of multi-objective scheduling techniques on the job shop problem is offered in this chapter. The survey traces the development of techniques from Integer programming to genetic algorithms that take advantage of the power of recent computing technology. Applications are in areas as diverse as job scheduling, nurse scheduling, and groundwater monitoring.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

Climate change requires society to focus more strongly on sustainability. This requires an adjustment both on the demand side and on the supply side. Consumers must be given incentives to optimise their consumption according to sustainability aspects. In the supply of capital goods and consumer goods, firms must do their part to ensure that environmental savings are made possible and cost-efficiency. However, there must be doubts that a more resource-efficient production technology leads to the desired environmental effects. Policymakers ignore the Jevon’s paradox. The Jevon’s paradox states that an improved technology that leads to resource savings disproportionately increases the intensity of use. In absolute terms, there is a higher consumption of resources after the technology is introduced. This effect is currently ignored, for example, by all forecasts on demand for lithium for electromobility. Regardless of this, it is fundamentally better to optimise the technologies. However, this raises the question of whether the Jevon’s paradox cannot be undermined by artificial intelligence. Artificial intelligence applied to production promises the possibility to replace partial optimisations with total optimisations. By pursuing an absolute maximum (maximum maximorum), the intensity of use is limited. Therefore, this chapter is concerned with understanding the primary effects of artificial intelligence in production and highlighting the potential effects on sustainability.

Purpose: Increasing the sustainability in industrial production is getting more and more important. Furthermore, the technology of artificial intelligence is getting more and more important as well. For this reason, it is time to understand how artificial intelligence and sustainability are linked with one another in the context of production.

Need for the study: This chapter aims to deliver a solid argumentation regarding the prospects and the relevance of the usage of artificial intelligence in the context of production. Moreover, it specifically aims to show how artificial intelligence affects the sustainability of production.

Method: Literature analysis.

Findings: The findings are that artificial intelligence does enforce cooperative action within the industry via the effects on productivity variables, transaction costs, and production elasticities. Furthermore, the Jevon’s paradox does not seem to apply to artificial intelligence. Therefore, it is suggested that more empirical research has to focus on this topic.

Practical Implications: This chapter highlights the importance of artificial intelligence for the topic of sustainability.

The paper provides an overview of research published in the innovation and operations management (IOM) literature on 15 methods for cost management in new product development, and it provides a comparison to an earlier review of the management accounting (MA) literature (Wouters & Morales, 2014).

This structured literature search covers papers published in 23 journals in IOM in the period 1990–2014.

The search yielded a sample of 208 unique papers with 275 results (one paper could refer to multiple cost management methods). The top 3 methods are modular design, component commonality, and product platforms, with 115 results (42%) together. In the MA literature, these three methods accounted for 29%, but target costing was the most researched cost management method by far (26%). Simulation is the most frequently used research method in the IOM literature, whereas this was averagely used in the MA literature; qualitative studies were the most frequently used research method in the MA literature, whereas this was averagely used in the IOM literature. We found a lot of papers presenting practical approaches or decision models as a further development of a particular cost management method, which is a clear difference from the MA literature.

This review focused on the same cost management methods, and future research could also consider other cost management methods which are likely to be more important in the IOM literature compared to the MA literature. Future research could also investigate innovative cost management practices in more detail through longitudinal case studies.

This review of research on methods for cost management published outside the MA literature provides an overview for MA researchers. It highlights key differences between both literatures in their research of the same cost management methods.

There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.

Universities face incredibly difficult, complex decisions concerning the degree to which they participate in the process of commercializing research. The U.S. government has made an explicit policy decision to allow funded entities to obtain patents and thereby has encouraged participation in the commercialization of federally funded research. The Bayh-Dole Act enables universities to participate in the commercialization process, but it does not obligate or constrain them to pursue any particular strategy with respect to federally funded research. Universities remain in the driver's seat and must decide carefully the extent to which they wish to participate in the commercialization process.The conventional view of the role of patents in the university research context is that patent-enabled exclusivity improves the supply-side functioning of markets for university research results as well as those markets further downstream for derivative commercial end-products. Both the reward and commercialization theories of patent law take patent-enabled exclusivity as the relevant means for fixing a supply-side problem – essentially, the undersupply of private investment in the production of patentable subject matter or in the development and commercialization of patentable subject matter that would occur in the absence of patent-enabled exclusivity.While the supply-side view of the role of patents in the university research context is important, a view from the demand side is needed to fully appreciate the role of patents in the university research context and to fully inform university decisions about the extent to which they wish to participate in the commercialization process. Introducing patents into the university research system, along with a host of other initiatives aimed at tightening the relationship between universities and industry, is also (if not primarily) about increasing connectivity between university science and technology research systems and the demands of industry for both university research outputs (research results and human capital) and upstream infrastructural capital necessary to produce such outputs.In this chapter, I explore how university science and technology research systems perform economically as infrastructural capital and explain how these systems generate social value. I explain how the availability of patents, coupled with decreased government funding, may lead to a slow and subtle shift in the allocation of infrastructure resources.