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Using labour efficiently in service organisations is an important management objective since labour frequently accounts for more than 70 per cent of total operating costs. While most service organisations can use flexible scheduling rules to improve the utilisation of labour, selecting a good or “best” set of work schedules from a large set of alternatives has proved to be a formidable task. A new modelling procedure is described which allows optimal decisions to be made when the desired level of scheduling flexibility results in a very large population of possible schedules. Flexibility in shift scheduling is increased primarily through increasing the number of different shift lengths and allowing flexible placement of breaks. The power of the new modelling approach was used in an experiment described here to assess the relative impact of shift‐length and break‐placement flexibility on labour utilisation. The results indicated that, while either type of flexibility can improve labour utilisation, there was a rather strong synergistic impact from using both simultaneously.

It is essential to provide drinking water to affected population directly after a disaster. The purpose of this paper is to develop an optimization methodology that helps in the distribution of drinking water in post-disaster situations.

The research was conducted on two phases: phase 1 aims at identifying an appropriate way to deliver drinking water to refugee camps from external sources, considering required drinking water quantities and four possible sources of water with respect to cost and risk assessments. Phase 2 investigates drinking water distribution within a refugee camp using covering models. The MCLP–optimal number of facilities model is proposed to ensure that the water is distributed and delivered to all individuals in a camp with minimum number of water storage tanks required. A control policy is proposed to ensure the fair distribution of water to all targeted individuals.

Al-Za’atari refugee camp, located in northeast of Jordan, was considered as the case study for this research. The result showed that the appropriate way to deliver water to the camp is by using tanker–trucks, and a minimum number of five tanks are required to distribute water to individuals inside the camp with respect to tank locations and the allocation of tank of each area.

The proposed methodology is essential in decision making for the distribution of drinking water in refugee camps in short-term needs. The model adds important value to the literature as the proposed problem has no solution in the literature before.

Like many operations research models, ambulance location models suffer from lack of practical implementation by those who could benefit from them. A major obstacle to adoption of such models is the emphasis their developers place on abstract mathematical principles, which overshadow the functional purpose of the models. This focus leads to impracticality in the presentation and usage of models, owing to such attributes as complex computer interfaces, cumbersome input and output procedures, and non‐intuitive presentation of results. Suggests a solution to these problems, in the form of a graphical interface system called ALIAS (Ambulance Location Identification and Analysis System).

Ambulance response time is an important factor in saving lives and is highly linked with the ambulance location problem. The Maximum Expected Covering Location Problem (MEXCLP), introduced by Daskin (1983), is one of the most used ambulance location models that maximize the probability of stratifying demands for emergency medical service (EMS) centers. Due to huge increase in the operational costs of EMS centers, ambulance location models must consider the cost of coverage and the opportunity to use other companies’ private ambulances to answer emergency calls. The paper aims to discuss these issues.

In this paper, the authors propose to extend the MEXCLP to a bi-objective optimization problem where the cost of satisfying emergency calls is minimized.

The proposed model is tested using data retrieved from the Lebanese Red Cross (LRC) in Beirut capital of Lebanon. The reported findings show significant enhancements in the results where the LRC can fully satisfy the perceived demands from all areas in Beirut within 9 min with an affordable cost.

The model is a first attempt to reduce operational costs of EMS centers while constraining the response time to satisfy emergency calls at an acceptable rate.

The purpose of this paper is to present a case study describing a collaboration with Last Mile Health, a non-governmental organization, to develop a framework to inform its community healthcare networks in remote Liberia.

The authors detail the process of using the unique problem setting and available data to inform modeling and solution approaches.

The authors show how the characteristics of the Liberian setting can be used to develop a two-tier modeling framework. Given the operating constraints and remote setting the authors are able to model the problem as a special case of the location-routing problem that is computationally simple to solve. The results of the models applied to three districts of Liberia are discussed, as well as the collaborative process of the multidisciplinary team.

Importantly, the authors describe how the problem setting can enable the development of a properly scoped model that is implementable in practice. Thus the authors provide a case study that bridges the gap between theory and practice.

The purpose of this paper is to benchmark and measure the comparative efficiency of emergency medical services (EMS) in major US cities (populations greater than 100,000). In so doing, this paper aims to develop a benchmark that can be emulated by cities lagging in EMS efficiency. Also, it seeks to develop a profile of cities that are successful in providing highly efficient EMS as benchmarks.

Data envelopment analysis is used to measure the EMS efficiency of 127 selected large cities in the USA under the premise of a constant‐return to scales method of service delivery. In addition, to identify factors influencing the US cities' EMS efficiency and then to predict their efficiency scores, a Tobit regression analysis is employed, which tended to result in a smaller standard error, a smaller bias, and a smaller mean squared error than ordinary least squares.

This paper examines whether more densely settled and populated areas have greater efficiency in delivering EMS. After controlling variables such as weather and climate, income, population growth, the age of a residential home, and geographic size of a city in land area, It is found that more densely settled, geographically large, and high income cities show more efficient provision of EMS.

This paper is the first to develop in a comprehensive way EMS benchmark performance standards for municipal governments and elaborate on a host of factors which are associated with the success of EMS deliveries. By setting such standards and identifying factors affecting EMS efficiency, this paper helps municipal governments to continuously improve their EMS and develop more efficient public policy.

The purpose of the study is to modify the plan of a network of heliport for HEMS, civil protection and transport proposed by the regional government through the combined use of linear programming and GIS techniques.

The methodology used for the evaluation is based on the application of a set covering model.

An optimal solution was obtained that allows savings in relation to the plan proposed by the regional government by ensuring the best coverage of the whole territory.

This research is limited by the field of application and the weaknesses of the linear programming techniques. However, it could be the first step for further works that will include more accurate models.

The study suggests the reorganization of the network of heliports; this would be a fundamental step to improve the services supplied by the helicopters, such as HEMS, civil protection, SAR, aerial work, firefighting and transport, especially in a region characterized by a poor accessibility.

This is the first study on the application of the combined use of open-source GIS software and OR methods for planning a heliport network.

Emergency relief supply chain (ERSC) design is an important strategic decision that significantly affects the overall performance of emergency management activities. The performance of an ERSC can be measured by several performance measures some of which may conflict with each other. The purpose of this paper is to propose an ERSC design framework by simultaneously taking total logistics cost (TLC), risk level, and amount of demands covered in an ERSC into consideration.

The study considers TLC of an ERSC as the sum of logistics cost from distribution warehouses (DWHs) to Break of Bulbs (BOBs) and from BOBs to affected neighborhoods. The risk level of an ERSC is measured by estimating the expected number of disrupted relief items (EDI) distributed from DWHs through BOBs to neighborhoods. The covered demand (CDM) is defined as total populations that are supported in case of an emergency, the populations within the maximal coverage distance (MCD) from relief facilities. Based on these performance measures, the authors formulate a Goal Programming (GP) model to distribute emergency relief items to affected locations. Ideal values of these performance measures are decided, and the GP model seeks to minimize the weighted sum of the percentage deviations of those performance measures from the ideal values. The relationships among performance measures have been thoroughly analyzed through detailed trade-off studies under two realistic case studies by changing weights of each performance measure.

Three performance measures are interdependent over specific values of weights. TLC and EDI have a trade-off relationship when the weight on each measure increases. TLC and CDM also have a trade-off relationship when the weight on EDI increases. However, this relationship becomes less apparent when the MCD increases. EDI and CDM also have the same trade-off relationship when the weight on TLC changes. Therefore, decision makers should thoroughly analyze these trade-off relationships when they design ERSCs. Overall, the study identified that an ERSC with higher MCD outperforms one with lower MCD in terms of TLC, EDI, and CDM.

The study presents a design framework to generate more balanced ERSCs by simultaneously taking three conflicting performance measures into consideration, and demonstrated the feasibility of the framework through realistic case studies. The trade-off analysis provides useful insights and theoretical knowledge to researchers and practitioners in the discipline of emergency logistics management. The results from this study are expected to contribute to the development of more balanced ERSCs.

This paper aims to serve two main purposes. In the first instance it aims to it provide an overview addressing the state‐of‐the‐art in the area of activity recognition, in particular, in the area of object‐based activity recognition. This will provide the necessary material to inform relevant research communities of the latest developments in this area in addition to providing a reference for researchers and system developers who ware working towards the design and development of activity‐based context aware applications. In the second instance this paper introduces a novel approach to activity recognition based on the use of ontological modeling, representation and reasoning, aiming to consolidate and improve existing approaches in terms of scalability, applicability and easy‐of‐use.

The paper initially reviews the existing approaches and algorithms, which have been used for activity recognition in a number of related areas. From each of these, their strengths and weaknesses are discussed with particular emphasis being placed on the application domain of sensor enabled intelligent pervasive environments. Based on an analysis of existing solutions, the paper then proposes an integrated ontology‐based approach to activity recognition. The proposed approach adopts ontologies for modeling sensors, objects and activities, and exploits logical semantic reasoning for the purposes of activity recognition. This enables incremental progressive activity recognition at both coarse‐grained and fine‐grained levels. The approach has been considered within the realms of a real world activity recognition scenario in the context of assisted living within Smart Home environments.

Existing activity recognition methods are mainly based on probabilistic reasoning, which inherently suffer from a number of limitations such as ad hoc static models, data scarcity and scalability. Analysis of the state‐of‐the‐art has helped to identify a major gap between existing approaches and the need for novel recognition approaches posed by the emerging multimodal sensor technologies and context‐aware personalised activity‐based applications in intelligent pervasive environments. The proposed ontology based approach to activity recognition is believed to be the first of its kind, which provides an integrated framework‐based on the unified conceptual backbone, i.e. activity ontologies, addressing the lifecycle of activity recognition. The approach allows easy incorporation of domain knowledge and machine understandability, which facilitates interoperability, reusability and intelligent processing at a higher level of automation.

The comprehensive overview and critiques on existing work on activity recognition provide a valuable reference for researchers and system developers in related research communities. The proposed ontology‐based approach to activity recognition, in particular the recognition algorithm has been built on description logic based semantic reasoning and offers a promising alternative to traditional probabilistic methods. In addition, activities of daily living (ADL) activity ontologies in the context of smart homes have not been, to the best of one's knowledge, been produced elsewhere.

The increasing number of natural disasters has increased the attention on emergency plans aimed at providing fast support to affected communities. In this context, inventory pre-positioning management, which involves positioning the materials required to meet the affected community's needs early, has been increasingly acknowledged, but many challenges persist. The purpose of the paper is to provide a decision support system for the optimal quantification and location of humanitarian aid, trying to enhance and extend the existing literature on this topic.

The paper develops a numerical model for inventory pre-positioning of humanitarian aid to reduce both emergency response times and costs connected to goods procurement for seismic events. By examining the characteristics of the territory and the affected population, the model defines the optimal stock levels for four basic need items (hygienic sanitary kits, beds, blankets and camp tents) to be pre-allocated in the territory.

The model was validated using data obtained from the two severe earthquakes that occurred in Italy. The case study showed how the simulated outputs differ from the real case data and the economic benefits of adopting inventory pre-positioning considering the cost reductions (purchase, storage, transport and fulfilment of requirements).

The proposed decision support system allows the pre-positioning of emergency supplies in local areas in order to reduce response times and increase the speed of intervention in the event of seismic events, exploiting the advantages of a simulation model. Numerical and graphical results can easily support improvements in humanitarian logistics, providing those affected with rapid, cost-effective and better-adapted responses.