Arbitrage-Free Conditions and Hedging Strategies for Markets with Penalty Costs on Short Positions

We consider a discrete-time financial model in a general sample space with penalty costs on short positions. We consider a friction market closely related to the standard one except that withdrawals from the portfolio value proportional to short positions are made. We provide necessary and sufficient conditions for the nonexistence of arbitrages in this situation and for a self-financing strategy to replicate a contingent claim. For the finite-sample space case, this result leads to an explicit and constructive procedure for obtaining perfect hedging strategies.

Optimal periodic preventive maintenance policy for leased equipment

For leased equipment the lessor incurs penalty costs for failures occurring over the lease period and for not rectifying such failures within a specified time limit. Through preventive maintenance actions the penalty costs can be reduced but this is achieved at the expense of increased maintenance costs. The paper looks at a periodic preventive maintenance policy which achieves a tradeoff between the penalty and maintenance costs. (c) 2005 Elsevier Ltd. All rights reserved.

Optimal preventive maintenance of leased equipment with corrective minimal repairs

For leased equipment, the lessor carries out the maintenance of the equipment. Usually, the contract of lease specifies the penalty for equipment failures and for repairs not being carried out within specified time limits. This implies that optimal preventive maintenance policies must take these penalty costs into account and properly traded against the cost of preventive maintenance actions. The costs associated with failures are high as unplanned corrective maintenance actions are costly and the resulting penalties due to lease contract terms being violated. The paper develops a model to determine the optimal parameters of a preventive maintenance policy that takes into account all these costs to minimize the total expected cost to the lessor for new item lease. The parameters of the policy are (i) the number of preventive maintenance actions to be carried out over the lease period, (ii) the time instants for such actions, and (iii) the level of action. (c) 2005 Elsevier B.V. All rights reserved.

Maintenance Strategies for used equipment under lease

Abstract: Purpose – The aim of this research is to determine the optimal upgrade and preventive maintenance actions that minimize the total expected cost (maintenance costs+penalty costs). Design/methodology/approach – The problem is a four-parameter optimization with two parameters being k-dimensional. The optimal solution is obtained by using a four-stage approach where at each stage a one-parameter optimization is solved. Findings – Upgrading action is an extra option before the lease of used equipment, in addition to preventive maintenance action. Upgrading action makes equipment younger and preventive maintenance action lowers the ROCOF. Practical implications – There is a growing trend towards leasing equipment rather than owning it. The lease contract contains penalties if the equipment fails often and repairs are done within reasonable time period. This implies that the lessor needs to look at optimal preventive maintenance strategies in the case of new equipment lease, and upgrade actions plus preventive maintenance in the case of used equipment lease. The paper deals with this topic and is of great significant to business involved with leasing equipment. Originality/value – Nowadays many organizations are interested in leasing equipment and outsourcing maintenance. The model in this paper addresses the preventive maintenance problem for leased equipment. It provides an approach to dealing with this problem.

Feasibility study and flow scheduling optimization of a multi-product pipeline system

The first goal of this study is to analyse a real-world multiproduct onshore pipeline system in order to verify its hydraulic configuration and operational feasibility by constructing a simulation model step by step from its elementary building blocks that permits to copy the operation of the real system as precisely as possible. The second goal is to develop this simulation model into a user-friendly tool that one could use to find an “optimal” or “best” product batch schedule for a one year time period. Such a batch schedule could change dynamically as perturbations occur during operation that influence the behaviour of the entire system. The result of the simulation, the ‘best’ batch schedule is the one that minimizes the operational costs in the system. The costs involved in the simulation are inventory costs, interface costs, pumping costs, and penalty costs assigned to any unforeseen situations. The key factor to determine the performance of the simulation model is the way time is represented. In our model an event based discrete time representation is selected as most appropriate for our purposes. This means that the time horizon is divided into intervals of unequal lengths based on events that change the state of the system. These events are the arrival/departure of the tanker ships, the openings and closures of loading/unloading valves of storage tanks at both terminals, and the arrivals/departures of trains/trucks at the Delivery Terminal. In the feasibility study we analyse the system’s operational performance with different Head Terminal storage capacity configurations. For these alternative configurations we evaluated the effect of different tanker ship delay magnitudes on the number of critical events and product interfaces generated, on the duration of pipeline stoppages, the satisfaction of the product demand and on the operative costs. Based on the results and the bottlenecks identified, we propose modifications in the original setup.

An evaluation of the costs of making specific secondary metabolites: does the yield penalty incurred by host plant resistance to insects due to competition for resources?

The presumption that the synthesis of 'defence' compounds in plants must incur some 'trade-off' or penalty in terms of annual crop yields has been used to explain observed inverse correlations between resistance to herbivores and rates of growth or photosynthesis. An analysis of the cost of making secondary compounds suggests that this accounts for only a small part of the overall carbon budget of annual crop plants. Even the highest reported amounts of secondary metabolites found in different crop species (flavonoids, allylisothiocyanates, hydroxamic acids, 2-tridecanone) represent a carbon demand that can be satisfied by less than an hour's photosynthesis. Similar considerations apply to secondary compounds containing nitrogen or sulphur, which are unlikely to represent a major investment compared to the cost of making proteins, the major demand for these elements. Decreases in growth and photosynthesis in response to stress are more likely the result of programmed down-regulation. Observed correlations between yield and low contents of unpalatable or toxic compounds may be the result of parallel selection during the refinement of crop species by humans.

A penalty-based grouping genetic algorithm for multiple composite SaaS components clustering in Cloud

Software as a Service (SaaS) in Cloud is getting more and more significant among software users and providers recently. A SaaS that is delivered as composite application has many benefits including reduced delivery costs, flexible offers of the SaaS functions and decreased subscription cost for users. However, this approach has introduced a new problem in managing the resources allocated to the composite SaaS. The resource allocation that has been done at the initial stage may be overloaded or wasted due to the dynamic environment of a Cloud. A typical data center resource management usually triggers a placement reconfiguration for the SaaS in order to maintain its performance as well as to minimize the resource used. Existing approaches for this problem often ignore the underlying dependencies between SaaS components. In addition, the reconfiguration also has to comply with SaaS constraints in terms of its resource requirements, placement requirement as well as its SLA. To tackle the problem, this paper proposes a penalty-based Grouping Genetic Algorithm for multiple composite SaaS components clustering in Cloud. The main objective is to minimize the resource used by the SaaS by clustering its component without violating any constraint. Experimental results demonstrate the feasibility and the scalability of the proposed algorithm.

Exposure to pesticides, ill health and averting behaviour : costs and determining the relationship

Farmers' exposure to pesticides is high in developing countries. As a result many farmers suffer from ill-health, both short and long term. Deaths are not uncommon. This paper addresses this issue. Field survey data from Sri Lanka are used to estimate farmers' expenditure on defensive behavior (DE) and to determine factors that influence DE. The avertive behavior approach is used to estimate costs. Tobit regression analysis is used to determine factors that influence DE. Field survey data show that farmers' expenditures on DE are low. This is inversely related to high incidence of ill health among farmers using pesticides.