A law clinic typically involves staff and students in a range of complex processes that are highly resource-intensive and which have the potential to detract from core value-adding activities. This paper aims to highlight the challenges associated with resourcing a university law clinic, and evaluate the extent to which lean management is able to provide solutions. It is submitted that proactive and deliberate application of lean management philosophies to law clinic process design has the potential to both reduce resource intensity and enhance value. A literature review was conducted in order to identify lean management principles and methodologies that might be applicable. A case study approach was then used to evaluate key resourcing challenges faced by a UK university law clinic and to explore the extent to which lean thinking might help to overcome them. There is very little literature which discusses the application of lean thinking in the higher education sector, and none which considers the university law clinic context specifically. This paper will provide law school leaders with a resource that will enable them to evaluate and design their clinic processes more effectively, improving the wellbeing of clinic staff and enhancing the pedagogical value of clinic work for students. It will also contribute to the emerging body of literature which highlights the benefits of lean thinking within the higher education sector.
In this paper, we try reducing the moral hazard of profit misreporting in Profit and Loss Sharing Contract (PLS). In this kind of contracts , the corporate manager has a temptation to misreport profits which can lead to either project failing or to financiers receiving an unfair allocation of profits. To help in solving this problem we propose a new model that includes a real option that gives the corporate manager (agent) the right, but not the obligation, to gradually buy shares in the corporation from the financier/bank. We compare our results with the standard case of PLS without real options. We show, using a multi-agent simulation (Netlogo) that embedding real options in the PLS contract can reduce the profit misreporting case. The fact that PLS contracts are riskier compared to other forms of financing such as debt, provides an incentive for the creation of models that reduce their risk to capital providers. Given the results obtained from our real options model, the latter could prove to be of practical use to financial institutions willing to engage in PLS financing.
In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method.
The project selection problem is considered as one of the most imperative decisions for investor organizations. Due to non-deterministic nature of some criteria in the real world projects in this paper, a new model for project selection problem is proposed in which some parameters are assumed probabilistic. This model is formulated as a non-linear, multi-objective, multi-period, zero-one programming model. Then the epsilon constraint method and an algorithm are applied to check the Pareto front and to find optimal solutions. A case study is conducted to illustrate the applicability and effectiveness of the approach, with the results presented and analysed. Since the proposed model is more compatible with real world problems, the results are more tangible and trustable compared with deterministic cases. Implications of the proposed approach are discussed and suggestions for further work are outlined.Keywords: Project selection, multi objective programming, chance constraint, epsilon constraint method. INTRODUCTIONDecision making about selection or rejection of a project is considerable from both theoretical and practical aspects and it depends on satisfaction of financial and nonfinancial constraints of the problem. In the real world project selection problems there are normally more than one objective function. This makes the solution algorithm more complicated and time consuming. The purpose of this paper is decision making about selection of N projects in T periods of time such that the profit gets maximum and total cost of equipment, human resources and used raw materials become minimum. In real world problems many of parameters are unlikely to be deterministic and ignoring the stochastic model can lead to unreliable results. In this paper a new framework is proposed for modeling a project selection problem relying on the concept of risk and by applying a linear approximation on probabilistic constraints. The mentioned model is called "Mean-Risk" model which the main idea of it is maximizing the expected profit of selecting a project such that risk curve of this selection always be below of the confidence curve. Here without loss of generality and just for simplification, the confidence is assumed a linear function. Since some parameters are probabilistic, the constraints include these parameters are also probabilistic constraints. Chance constrained programming was developed as a means of describing constraints in the form of probability levels of attainment. Consideration of chance constraints allows the decision maker to consider objectives in terms of their attainment probability. This approach changes constraints with stochastic parameters to constraints with a confidence level as the threshold of decision maker, using variance-covariance matrix. If α is a predetermined confidence level desired by the decision maker, the implication is that a constraint will have a probability of satisfaction of α. After transforming the problem from probabilistic mode to deterministic mode, the resulting model i...
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