Planning robust policing futures: modelling using multimethodology

Purpose – The resourcing of policing activity is characterised by a level of complexity, particularly where evaluating alternative policy options is concerned. In this paper, a case study using multimethodological modelling to compare alternative policy choice in a group context is outlined with respect to response-patrol officer (RPO) deployment within a UK police force. The paper aims to discuss these issues. Design/methodology/approach – The application of a three phase modelling process is illustrated where scenario planning is used to generate the scope of the system elements to be modelled. This is followed by causal mapping to identify the barriers to improving officer resourcing, and system dynamics modelling is used to simulate the impacts of a range of policy options within this policing function. A group model building approach was applied throughout the modelling phases with anexpert group to negotiate a shared view of the structure and dynamics of the resourcing policy challenges. Findings – A fully validated system dynamics model emerged from the multi-phase modelling process which allowed a series of alternative future policy scenarios to be explored and evaluated. Useful policy insights were generated by the system dynamics simulation model which suggested more efficient rules for resource allocation in the police force’s RPO function. Originality/value – The insights from this case study demonstrates that multi-phase modelling has potential application in policy exploration across a range of emergency service providers whoseactions are governed by both variable demand and constrained supply of resourc

Unpacking multimethodology: impacts of a community development intervention

Multimethodology interventions are being increasingly employed by operational researchers to cope with the complexity of real-world problems. In keeping with recent calls for more research into the ‘realised’ impacts of multimethodology, we present a detailed account of an intervention to support the planning of business ideas by a management team working in a community development context. Drawing on the rich steam of data gathered during the intervention, we identify a range of cognitive, task and relational impacts experienced by the management team during the intervention. These impacts are the basis for developing a process model that accounts for the personal, social and material changes reported by those involved in the intervention. The model explains how the intervention's analytic and relational capabilities incentivise the interplay of participants’ decision making efforts and integrative behaviours underpinning reported intervention impacts and change. Our findings add much needed empirical case material to enrich further our understanding of the realised impacts of operational research interventions in general, and of multimethodology interventions in particular

Developing a Systemic Problem Structuring Method for Use in a Problem- Avoiding Culture

This paper presents a Buddhist systems methodology (BSM) designed for use in Taiwanese Buddhist organisations. The authors argue that the BSM has advantages in Taiwanese contexts compared with Western systemic problem structuring methods, which mostly require participants to identify and explore problems or problematic situations. In Taiwanese Buddhist culture, identifying problems is regarded negatively because it could lead to individual blame and threaten organisational harmony. Unlike many Western approaches, the BSM uses Buddhist concepts that are closely associated with the practice of harmonious living. Thus, it reframes systemic problem structuring as the exercise of Buddhist discipline applied to organisational life, which is likely to be viewed as a co-operative and culturally valued endeavour. A BSM intervention is described in which the authors tackled a significant conflict (and issues underlying this) that threatened the future of a large non-governmental Buddhist organisation. An evaluation of the intervention demonstrated significant positive impacts

Developing a diagnostic heuristic for integrated sugarcane supply and processing systems.

Doctoral Degrees. University of KwaZulu-Natal, Pietermaritzburg.Innovation is a valuable asset that gives supply chains a competitive edge. Moreover, the adoption of innovative research recommendations in agricultural value chains and integrated sugarcane supply and processing systems (ISSPS) in particular has been relatively slow when compared with other industries such as electronics and automotive. The slow adoption is attributed to the complex, multidimensional nature of ISSPS and the perceived lack of a holistic approach when dealing with certain issues. Most of the interventions into ISSPS often view the system as characterised by tame problems hence, the widespread application of traditional operations research approaches. Integrated sugarcane supply and processing systems are, nonetheless, also characterised by wicked problems. Interventions into such contexts should therefore, embrace tame and/or wicked issues. Systemic approaches are important and have in the past identified several system-scale opportunities within ISSPS. Such interventions are multidisciplinary and employ a range of methodologies spanning across paradigms. The large number of methodologies available, however, makes choosing the right method or a combination thereof difficult. In this context, a novel overarching diagnostic heuristic for ISSPS was developed in this research. The heuristic will be used todiagnose relatively small, but pertinent ISSPS constraints and opportunities. The heuristic includes a causal model that determines and ranks linkages between the many domains that govern integrated agricultural supply and processing systems (IASPS) viz. biophysical, collaboration, culture, economics, environment, future strategy, information sharing, political forces, and structures. Furthermore, a diagnostic toolkit based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was developed. The toolkit comprises a diagnostic criteria and a suite of systemic tools. The toolkit, in addition, determines thesuitability of each tool to diagnose any of the IASPS domains. Overall, the diagnostic criteria include accessibility, interactiveness, transparency, iterativeness, feedback, cause-and-effect logic, and time delays. The tools considered for the toolkit were current reality trees, fuzzy cognitive maps (FCMs), network analysis approaches, rich pictures (RP), stock and flow diagrams, cause and effect diagrams (CEDs), and causal loop diagrams (CLDs). Results from the causal model indicate that collaboration, structure and information sharing had a high direct leverage over the other domains as these were associated with a larger number of linkages. Collaboration and structure further provided dynamic leverage as these were also part of feedback loops. Political forces and the culture domain in contrast, provided lowleverage as these domains were only directly linked to collaboration. It was further revealed that each tool provides a different facet to complexity hence, the need for methodological pluralism. All the tools except RP could be applied, to a certain extent, across both appreciation and analysis criteria. Rich pictures do not have causal analysis capabilities viz. cause-and-effect logic, time delays and feedback. Stock and flow diagrams and CLDs conversely, met all criteria. All the diagnostic tools in the toolkit could be used across all the system domains except for FCMs. Fuzzy cognitive maps are explicitly subjective and their contribution lies outside the objective world. Caution should therefore be practiced when FCMs areapplied within the biophysical domain. The heuristic is only an aid to decision making. The decision to select a tool or a combination thereof remains with the user(s). Even though the heuristic was demonstrated at Mhlume sugarcane milling area, it is recommended that other areas be considered for future research. The heuristic itself should continuously be updated with criteria, tools and other domain dimensions

Soft situational strategic planning (SSSP): a method and case study of its application in a Brazilian municipality

Municipal government planning is challenging in the extreme being characterised by ill-structured and messy problems, the complexity of which is compounded by often conflicting views and priorities of multiple stakeholders. In South America, Situational Strategic Planning (SSP) is a wide spread method of such planning. The purpose of this paper is to explore the use of a proposed multi-methodological approach, Soft Situational Strategic Planning (SSSP) in a South American municipal government. SSSP is a variant of SSP enhanced with elements of Soft Systems Methodology (SSM) and Strategic Choice Approach (SCA). Through an action research case study in a Brazilian municipality, we implemented SSSP through a strategic planning cycle. The findings suggest that SSSP complement the SSP process regarding the implementation and monitoring of strategy. The application also indicated that SSSP has the potential to make government planning processes more structured for policy makers

Towards facilitated optimisation

Optimisation modelling in healthcare has addressed a diverse range of challenges inherent to decision-making and supports decision-makers in determining the best solution under a variety of constraints. In contrast, optimisation models addressing planning and service delivery issues in mental healthcare have received limited attention. Mental healthcare services in England are routinely facing issues relative to scarcity of available resources, inequities in their distribution, and inefficiencies in their use. Optimisation modelling has the potential to support decision making and inform the efficient utilisation of scare resources. Mental healthcare services are a combination of several subsystems and partnerships comprising of numerous stakeholders with a diversity of interests. However, in optimisation literature, the lack of stakeholder involvement in the development process of optimisation models is increasingly identified as a missed opportunity impacting the practical applicability of the models and their results. This thesis argues that simulation modelling literature offers alternative modelling approaches that can be adapted to optimisation modelling to address the shortcoming highlighted. In this study, we adapt PartiSim, a multi-methodology framework to support facilitated simulation modelling in healthcare, towards facilitated optimisation modelling and test it using a real case study in mental healthcare. The case study is concerned with a Primary Care Mental Healthcare (PCMH) service that deploys clinicians with different skills to several General Practice (GP) clinics. The service wanted support to help satisfy increasing demand for appointments and explore the possibility of expanding their workforce. This research puts forward a novel multimethodology framework for participatory optimisation, called PartiOpt. It explores the adaptation and customisation of the and PartiSim framework at each stage of the optimisation modelling lifecycle. The research demonstrates the applicability and relevance of a 'conceptual model' to optimisation modelling, highlighting the potential of facilitated optimisation as a methodology. This thesis argues for the inclusion of conceptual modelling in optimisation when dealing with real world practice-based problems. The thesis proposes an analytics-driven optimisation approach that integrates descriptive, predictive, and prescriptive analytics stages. This approach is utilised to construct a novel multi-skill multi-location optimisation model. By applying the analytics-driven optimisation approach to the case study, previously untapped resource potential is uncovered, leading to the identification of various strategies to improving service efficiency. The successful conceptualisation of an optimisation model and the quantitative decision support requirements that emerged in the initial stages of the study drive the analytics-driven optimisation. Additionally, this research also presents a facilitative approach for stakeholder participation in the validation, experimentation, and implementation of a mathematical optimisation model. Reflecting on the adaptation and subsequent amendments to the modelling stages, the final PartiOpt framework is proposed. It is argued that this framework could reduce the gap between theory and practice for optimisation modelling and offers guidance to optimisation modellers on involving stakeholders in addressing real world problems

A review of simheuristics: extending metaheuristics to deal with stochastic combinatorial optimization problems

Many combinatorial optimization problems (COPs) encountered in real-world logistics, transportation, production, healthcare, financial, telecommunication, and computing applications are NP-hard in nature. These real-life COPs are frequently characterized by their large-scale sizes and the need for obtaining high-quality solutions in short computing times, thus requiring the use of metaheuristic algorithms. Metaheuristics benefit from different random-search and parallelization paradigms, but they frequently assume that the problem inputs, the underlying objective function, and the set of optimization constraints are deterministic. However, uncertainty is all around us, which often makes deterministic models oversimplified versions of real-life systems. After completing an extensive review of related work, this paper describes a general methodology that allows for extending metaheuristics through simulation to solve stochastic COPs. ‘Simheuristics’ allow modelers for dealing with real-life uncertainty in a natural way by integrating simulation (in any of its variants) into a metaheuristic-driven framework. These optimization-driven algorithms rely on the fact that efficient metaheuristics already exist for the deterministic version of the corresponding COP. Simheuristics also facilitate the introduction of risk and/or reliability analysis criteria during the assessment of alternative high-quality solutions to stochastic COPs. Several examples of applications in different fields illustrate the potential of the proposed methodology.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (grant TRA2013-48180-C3-P), FEDER, and the Ibero-American Programme for Science and Technology for Development (CYTED2014-515RT0489). Likewise we want to acknowledge the support received by the Department of Universities, Research & Information Society of the Catalan Government (Grant 2014-CTP-00001) and the CAN Foundation (Navarre, Spain) (Grant 3CAN2014-3758)