Samir Elhedhli

Bio: Samir Elhedhli is an academic researcher from University of Waterloo. The author has contributed to research in topics: Facility location problem & Lagrangian relaxation. The author has an hindex of 24, co-authored 59 publications receiving 1765 citations.

Green supply chain network design to reduce carbon emissions

Abstract: We consider a supply chain network design problem that takes CO2 emissions into account. Emission costs are considered alongside fixed and variable location and production costs. The relationship between CO2 emissions and vehicle weight is modeled using a concave function leading to a concave minimization problem. As the direct solution of the resulting model is not possible, Lagrangian relaxation is used to decompose the problem into a capacitated facility location problem with single sourcing and a concave knapsack problem that can be solved easily. A Lagrangian heuristic based on the solution of the subproblem is proposed. When evaluated on a number of problems with varying capacity and cost characteristics, the proposed algorithm achieves solutions within 1% of the optimal. The test results indicate that considering emission costs can change the optimal configuration of the supply chain, confirming that emission costs should be considered when designing supply chains in jurisdictions with carbon costs.

The Effectiveness of Simple Decision Heuristics: Forecasting Commercial Success for Early-Stage Ventures

Abstract: We investigate the decision heuristics used by experts to forecast that early-stage ventures are subsequently commercialized. Experts evaluate 37 project characteristics and subjectively combine data on all cues by examining both critical flaws and positive factors to arrive at a forecast. A conjunctive model is used to describe their process, which sums good and bad cue counts separately. This model achieves a 91.8 forecasting accuracy of the experts correct forecasts. The model correctly predicts 86.0 of outcomes in out-of-sample, out-of-time tests. Results indicate that reasonably simple decision heuristics can perform well in a natural and very difficult decision-making context.

Heuristic Decision Making

Abstract: As reflected in the amount of controversy, few areas in psychology have undergone such dramatic conceptual changes in the past decade as the emerging science of heuristics. Heuristics are efficient cognitive processes, conscious or unconscious, that ignore part of the information. Because using heuristics saves effort, the classical view has been that heuristic decisions imply greater errors than do “rational” decisions as defined by logic or statistical models. However, for many decisions, the assumptions of rational models are not met, and it is an empirical rather than an a priori issue how well cognitive heuristics function in an uncertain world. To answer both the descriptive question (“Which heuristics do people use in which situations?”) and the prescriptive question (“When should people rely on a given heuristic rather than a complex strategy to make better judgments?”), formal models are indispensable. We review research that tests formal models of heuristic inference, including in business organizations, health care, and legal institutions. This research indicates that (a) individuals and organizations often rely on simple heuristics in an adaptive way, and (b) ignoring part of the information can lead to more accurate judgments than weighting and adding all information, for instance for low predictability and small samples. The big future challenge is to develop a systematic theory of the building blocks of heuristics as well as the core capacities and environmental structures these exploit.

Optimal demand response based on utility maximization in power networks

Abstract: Demand side management will be a key component of future smart grid that can help reduce peak load and adapt elastic demand to fluctuating generations. In this paper, we consider households that operate different appliances including PHEVs and batteries and propose a demand response approach based on utility maximization. Each appliance provides a certain benefit depending on the pattern or volume of power it consumes. Each household wishes to optimally schedule its power consumption so as to maximize its individual net benefit subject to various consumption and power flow constraints. We show that there exist time-varying prices that can align individual optimality with social optimality, i.e., under such prices, when the households selfishly optimize their own benefits, they automatically also maximize the social welfare. The utility company can thus use dynamic pricing to coordinate demand responses to the benefit of the overall system. We propose a distributed algorithm for the utility company and the customers to jointly compute this optimal prices and demand schedules. Finally, we present simulation results that illustrate several interesting properties of the proposed scheme.

Selected Topics in Column Generation

Abstract: Dantzig-Wolfe decomposition and column generation, devised for linear programs, is a success story in large-scale integer programming. We outline and relate the approaches, and survey mainly recent contributions, not yet found in textbooks. We emphasize the growing understanding of the dual point of view, which has brought considerable progress to the column generation theory and practice. It stimulated careful initializations, sophisticated solution techniques for the restricted master problem and subproblem, as well as better overall performance. Thus, the dual perspective is an ever recurring concept in our "selected topics."

Venturing for Others with Heart and Head: How Compassion Encourages Social Entrepreneurship

Abstract: Social entrepreneurship has emerged as a complex yet promising organizational form in which market-based methods are used to address seemingly intractable social issues, but its motivations remain undertheorized. Research asserts that compassion may supplement traditional self-oriented motivations in encouraging social entrepreneurship. We draw on research on compassion and prosocial motivation to build a model of three mechanisms (integrative thinking, prosocial cost-benefit analysis, and commitment to alleviating others' suffering) that transform compassion into social entrepreneurship, and we identify the institutional conditions under which they are most likely to do so. We conclude by discussing the model's contribution to and implications for the positive organizational scholarship literature, entrepreneurship literature, and social entrepreneurship literature.