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Successful implementations of scheduling techniques in practice are scarce. Not only do daily disturbances lead to a gap between theory and practice, but also the extent to which a scheduling technique can adequately model the processes on the shopfloor, and the extent to which the optimization goal of a technique matches the organizational goal are not great enough. Further, the schedulers’ actions may play an important role in the fulfilment of the generated schedules. The organizational structure with its different responsibilities and conflicting goals may also result in the poor performance of scheduling techniques. Besides these, there is the problem of measuring the quality of a schedule. Discusses the causes for these gaps and illustrates the solutions to improve scheduling by way of a case study.

Job shop scheduling has been the focus of much research. Heuristic rules to assist in this endeavour abound in the literature. However, rules leading to the optimum schedule have been elusive. The predominant scheduling methods now used are specifically tailored to the type of job shop. Generally, various rules are tried and those giving the best result are used as a starting point. The human expert sifts the schedule through his experience filter, negotiates with affected parties, and finalizes a schedule. Expert systems are beginning to impact in this area. By assuming some of the filtering and negotiating roles of the human expert, they can allow schedulers to look at more alternatives and/or produce more timely schedules. Contains an extensive review of the literature pertaining to expert systems and job shop scheduling. Conclusions are drawn in terms of the advantages and disadvantages of these applications. Finally, recommendations for future directions are given.

The purpose of this paper is to statistically assess the effects of the design factors including usage of data-driven decision support tool (DST), classification of patients (triage), prioritization based on vital scores of patients, number of ambulances and hospital selection rules, on the casualty transportation system’s performance in large-scale disasters. Besides, a data-driven DST for casualty transportation is proposed to enhance the casualty survival and ambulance transportation times during the disaster response stage.

In this study, the authors applied simulation and statistical analysis to evaluate the effects of usage of data-driven DST, classification of patients (triage), prioritization of the patients based on vital scores, number of ambulances and hospital selection rules, on the patient survival and transportation time of the casualty transportation system. An experimental design was made, and 16 scenarios were formulated. Simulation models were developed for all scenarios. The number of unrecoverable casualties and time-spent by the casualties until arriving at the hospital was observed. Then, a statistical analysis was applied to the simulation results, and significant factors were determined.

Utilization of the proposed DST was found to improve the casualty transportation and coordination performance. All main effects of the design factors were found statistically significant for the number of unrecoverable casualties. Besides, for the Time spent Until Arrival of T1-Type Casualty at the Hospital, all of the main factors are significant except the number of ambulances. Respiratory rate, pulse rate, motor response score priority and hospital selection rule based on available hospital capacities must be considered to reduce the number of unrecoverable casualties and time spent until arrival of the casualties at the hospitals.

In this study, the factors that significantly affect the performance of the casualty transportation system were revealed, by simulation and statistical analysis, based on an expected earthquake case, in a metropolitan city. Besides, it was shown that using a data-driven DST that tracks victims and intends to support disaster coordination centers and medical staff performing casualty transportation significantly improves survival rate of the victims and time to deliver the casualties. This research considers the whole systems’ components, contributes to developing the response stage operations by filling gaps between using the data-driven DST and casualty transportation processes.

While it is well known that managers make scheduling decisions based on multiple objectives, the majority of sequencing research is directed towards single criterion assessments of scheduling performance. A preference value decision model approach for the selection of job shop sequencing rules using multiple performance criteria is presented. The model is general in nature and may be applied to any of a number of sequencing rules and performance measures. A case example is provided to illustrate the procedure.

The overall functioning of this system is based on two approaches: construction and supervision. The first is conducted entirely by the machine, and the second requires the intervention of the designer to collaborate with the machine. The morphological translation of urban rules is sometimes contradictory and may require additional external relevance to urban rules. Designer arbitration assists the artificial intelligence (AI) in accomplishing this task and solving the problem.

This paper provides a method of computational design in generating the optimal authorized bounding volume which uses the best target values of morphological urban rules. It examines an intelligent system, adopting the multi-agent approach, which aims to control and increase urban densification by optimizing morphological urban rules. The process of the system is interactive and iterative. It allows collaboration and exchange between the machine and the designer. This paper is adopting and developing a new approach to resolve the distributed constraint optimization problem in generating the authorized bounding volume. The resolution is not limited to an automatic volume generation from urban rules, but also involves the production of multiple optimal-solutions conditioned both by urban constraints and relevance chosen by the designer. The overall functioning of this system is based on two approaches: construction and supervision. The first is conducted entirely by the machine and the second requires the intervention of the designer to collaborate with the machine. The morphological translation of urban rules is sometimes contradictory and may require additional external relevance to urban rules. Designer arbitration assists the AI in accomplishing this task and solving the problem. The human-computer collaboration is achieved at the appropriate time and relies on the degree of constraint satisfaction. This paper shows and analyses interactions with the machine during the building generation process. It presents different cases of application and discusses the relationship between relevance and constraints satisfaction. This topic can inform a chosen urban densification strategy by assisting a typology of the optimal authorized bounding volume.

The human-computer collaboration is achieved at the appropriate time and relies on the degree of constraint satisfaction with fitness function.

The resolution of the distributed constraint optimization problem is not limited to an automatic generation of urban rules, but involves also the production of multiple optimal ABV conditioned both by urban constraints as well as relevance, chosen by the designer.

Recently, there has been considerable interest in the EEC in overtime working. In these times of high unemployment, some commentators have suggested that a reduction in overtime working could lead to job creation. This study attempts to look at reasons for overtime working as seen by management and to assess the potential for a reduction in overtime working in a policy of work sharing. It is in two parts, the first gives a summary of the results of a national level sample survey among managers of firms in the Republic of Ireland while the second part describes the use of a simulation model to test decisions relating to overtime working in an engineering job shop in the United Kingdom. The two sections complement one another and give insights into the behaviour of managers in relation to overtime working. It should be noted that the views of workers or of legislators are not reported. The views of workers would, of course, be very important in relation to the possibility of the successful implementation of a policy of reduced overtime working.

The purpose of this paper is to present a system for recognition of location names in ancient books written in languages, such as Chinese, in which proper names are not signaled by an initial capital letter.

Rule-based and statistical methods were combined to develop a set of rules for identification of product-related location names in the local chronicles of Guangdong. A name recognition system, with functions of document management, information extraction and storage, rule management, location name recognition, and inquiry and statistics, was developed using Microsoft's .NET framework, SQL Server 2005, ADO.NET and XML. The system was evaluated with precision ratio, recall ratio and the comprehensive index, F.

The system was quite successful at recognizing product-related location names (F was 71.8 percent), demonstrating the potential for application of automatic named entity recognition techniques in digital collation of ancient books such as local chronicles.

Results suffered from limitations in initial digitization of the text. Statistical methods, such as the hidden Markov model, should be combined with an extended set of recognition rules to improve recognition scores and system efficiency.

Electronic access to local chronicles by location name saves time for chorographers and provides researchers with new opportunities.

Named entity recognition brings previously isolated ancient documents together in a knowledge base of scholarly and cultural value.

Automatic name recognition can be implemented in information extraction from ancient books in languages other than English. The system described here can also be adapted to modern texts and other named entities.

Cultural heritage archiving is experiencing an increase in digitalisations of artefacts in the last 15 years. The reason behind this trend is a demand for providing information about the artefact in a more accessible way to the audience, for example, through online delivery or virtual reality. Other reasons might be to simplify and automate the management of artefacts. Having a ‘digital copy’ of artefacts, allows one to search an archive and plan its storage and dissemination in a comprehensive manner. With the increased digitalisation comes an increased use of artificial intelligence [AI] applications. AI can be very beneficial in classifying artefacts automatically through machine learning [ML] and natural language processing [NLP]. For example, an algorithm can identify the source and age of artefacts based on an image and can do this much faster for a large collection of photos than a human. Although AI provides many benefits, it also presents challenges: Sophisticated AI techniques require certain insights on how they work, need specialists to customise a solution, and require an existing large dataset to train an algorithm. Another challenge is that typical AI techniques are regarded as black boxes, which means they decide, but it is not obvious why a decision has been made. This chapter describes a project in collaboration with the South Australian Museum [SAM] on the application of AI to extract material lists from a description of artefacts. A large dataset to train an algorithm did not exist, and hence, a customised approach was required. The outcome of the project was the application of NLP in combination with easy-to-customise rules that can be applied by non-IT specialists. The resulting prototype achieved the extraction of materials from a large list of artefacts within seconds and a flexible solution that can be applied on other collections in the future.

The present study intends to foster understanding of how a traditional manufacturer can utilize the “simple rules” approach of managerial heuristics to facilitate its smart solution development (SSD) process.

The study uses an in-depth single case research strategy and 25 senior manager interviews to understand the application of simple rules in smart solution development.

The findings reveal process, boundary, preference, schedule, and stop rules as the dominant managerial heuristics in the case and identify how the manufacturer applies these rules during the innovation process phases of ideation, incubation, transformation, and industrialization for attaining project outcomes.

The study contributes to the new service development (NSD) literature by shedding light on simple rules and how managers may apply them to facilitate SSD. The main limitations stem from applying the qualitative case study approach and the interpretative nature of the study, which produces novel insights but prevents direct generalization to other empirical cases.

The resulting framework provides guidelines for managers on how to establish formal and clear simple rules that enable industrial solution providers to approach decision-making in smart solution development in a more agile manner.

The study comprises one of the first attempts to investigate managerial heuristics in the context of SSD and puts forward a plea for further NSD research applying psychological conceptualizations to enrich the simple rules perspective.

Scheduling is defined by Baker as, “the allocation of resources over time to perform a collection of tasks”. The term facilities is often used instead of resources and the tasks to be performed may involve a variety of different operations.