Vivek Balaraman

Towards Better Crisis Management in Support Services Organizations Using Fine Grained Agent Based Simulation

Critical support service operations have to run 24 × 7 and 365 days a year. Support operations therefore do contingency planning to continue operations during a crisis. In this paper we explore the use of fine-grained agent-based simulation models, which factor in human-behavioral dimensions such as stress, as a means to do better people planning for such situations. We believe the use of this approach may allow support operations managers to do more nuanced planning leading to higher resilience, and quicker return to normalcy. We model a prototypical support operation, which runs into different crisis severity levels, and show for each case, a reasonable size of the crisis team that would be required. We identify two contributions in this paper: First, emergency planning using agent based simulations have mostly focused, naturally, on societal communities such as urban populations. There has not been much attention paid to study crisis responses within support services organizations and our work is an attempt to address this deficit. Second, our use of grounded behavioral elements in our agent models allows us to build complex human behavior into the agents without sacrificing validity.

Can a Buffering Strategy Reduce Workload Related Stress

The support services industry remains a competitive business operating on very stringent budgets, metrics, and milestones. Given the heavy and varying workloads that are characteristic in this business, stress is a common all-too-common phenomenon and emerges from different sources. In this context, we use an agent based approach to examine whether using a workload buffering strategy based loosely on the Leaky Bucket Algorithm can help to manage work-load-related stress. We use our workplace stress model to see the implications of such a buffering approach on workload related stress at an individual level and on macro indicators such as turn-around time (TAT). The experiments show that the strategy can help not only reduce the stress but also provide knobs to the operations managers to manage workload and to ensure compliance. We conclude with implications for future research and practice.

Case Based Reasoning

Case-Based Reasoning (CBR) is the model of human problem solving using prior experiences (calledcases). A case memory is a learning environment where new cases are being injected, existing cases getting purged and yet others adapted to fit situations in response to environmental pressures. As experience accumulates, case memories ideally progress incrementally towards expertise. At present however there is the lack of a framework to understand the nature of this progression and how various factors influence it. In this essay, using the analogy with natural selection, we cast case memories as evolutionary systems to see whether the perspective affords us any insights. We examine case memory processes in the light of their evolutionary role and enquire into the nature of search in evolutionary case memories (ECM). We show that while there are several unresolved questions, the perspective affords us interesting speculations and observations. As an extended application of the ECM perspective we examine whether the evolution of cases can lead to abstract knowledge levels. There is evidence that experiential knowledge may not suffice to achieve higher levels of task expertise but may require abstract knowledge structures such as schema. However, little is currently known about how schema come into existence. We explore the intriguing possibility that schema may evolve from cases as an evolutionary operation. The essay also raises a number of research problems that can be attempted by both CBR and Evolutionary System communities.