Present Value Of Total Cost Research Articles

An in-depth metaheuristic approach to design a sustainable closed-loop agri-food supply chain network

As an essential component of human life, agricultural products play a very important role in guaranteeing that individuals get all the essential nutrition. Governments and industries spend great financial resources, define short- and long-term goals, and organize their policies to develop a steady agri-food supply chain and provide fresh, healthy products to their societies. This work proposes a new mixed-integer linear programming model to propose an agri-food supply chain network design for the coconut industry under sustainable terms. This study mainly aims to solve a multi-objective closed-loop supply chain, considering both forward and reverse product movements. The model attempts to manage the net present value of total cost for specific planning horizons while monitoring environmental pollution and job opportunities within the network. Given the NP-hard nature of the network, the solution approach embraces a set of recently developed metaheuristics to overcome its complexity effectively. To this end, six multi-objective optimizers and three hybrid algorithms are utilized, among which the multi-objective artificial rabbit optimizer is first developed and applied in this study. Hence, the model's compatibility with real conditions is investigated using fifteen practical tests. The results of interval plots and the Friedman statistical test emphasize that optimizers can solve all sizes of problems. However, the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) outperforms solving practical tests according to both the results of statistical tests and the novel hybrid Multi-Criteria Decision Making (MCDM) framework.

An optimal network constraint-based joint expansion planning model for modern distribution networks with multi-types intermittent RERs

Currently, distribution systems are continuously evolving towards modern and flexible structures while integrating promising renewable energy resources (RERs). In this regard, an optimal network constraint-based expansion planning model combined with an optimal integration framework of intermittent RERs is proposed in this work to improve the topological flexibility in modern distribution networks (DNs). Specifically, the best investment locations and times of substations, lines, and RER-based distributed generations (DGs) are jointly taken into consideration. Additionally, the uncertainty-based multiple scenarios are modeled by probability distribution functions to strengthen the robustness and reliability of DNs influenced by the stochastic of renewable energy and load behavior. The novel network constraint is combined with three levels, where the first level introduces the graph theory to guarantee the radiality topology of modern DNs. In the second level of the network constraint, graph theory and fictitious load theory are collaboratively applied to ensure that each subsystem has a reserve connection interconnected to other subsystems. The third level is modifying the conventional fictitious load theory to ensure each subsystem is linked with at least one DG. The proposed planning model is driven by the minimum present value of total cost, including investment cost of branches, DGs, and substations, cost of substations operation, the electricity purchasing cost of substations and DGs, power losses cost, and environmental penalty cost of conventional generators. Numerical results are presented to verify that a more flexible and resilient topology of the DN system is obtained, and criteria evaluation is introduced to validate its higher performance with respect to existing procedures from power supplied quality, environmental burden, and supplied flexibility three aspects.

The Effect of Risk of Maturation Failure and Access Type on Arteriovenous Access-Related Costs among Hemodialysis Patients.

Several studies report lower costs associated with attaining and maintaining patency for arteriovenous (AV) fistulas as compared to AV grafts among patients receiving hemodialysis. However, these costs may vary according to the AV access's risk of failure to mature (FTM). The aim of this study was to examine the effect of AV access type and risk of FTM on the total costs of attaining and maintaining AV access patency over 1, 3, and 5 years postcreation, among incident accesses. All first AV access creations (January 1, 2002-January 1, 2018), revisions/resections, and interventions from a single academic institution were prospectively captured. The units costs (from 2011 in CA$) were estimated primarily through the provincial patient Ontario Case Costing Initiative database. The present value of total vascular access-related costs from a third-party payer perspective was calculated by multiplying specific unit costs by the number of AV access creations, revisions/resections, and interventions from the date of creation to 1, 3, and 5 years post creation. The potential associations of AV access type and FTM risk stratum with AV access cost were examined using log-linear models and generalized estimating equations. A total of 906 patients were included in the study, of which 696 had fistulas and 210 had grafts. The median present value of total costs to attain and maintain AV access over 1, 3, and 5 years was positively associated with the highest FTM risk stratum in all models. It was not associated with AV access type when the interaction between AV access type and FTM risk stratum was considered. The costs of attaining and maintaining AV access were increased among patients with high/very high FTM risk. Risk of FTM, related interventions, and costs should be considered when choosing vascular access type for an individual patient.

Optimal Allocation of Renewable Energy Hybrid Distributed Generations for Small-Signal Stability Enhancement

This paper solves the allocation planning problem of integrating large scale renewable energy hybrid distributed generations and capacitor banks into the distribution systems. Extraordinarily, the integration of renewable energy hybrid distributed generations such as solar photovoltaic, wind, and biomass takes into consideration the impact assessment of variable generations from PV and wind on the distribution networks’ long term dynamic voltage and small-signal stabilities. Unlike other renewable distributed generations, the variability of power from solar PV and wind generations causes small-signal instabilities if they are sub-optimally allocated in the distribution network. Hence, the variables related to small-signal stability are included and constrained in the model, unlike what is obtainable in the current works on the planning of optimal allocation of renewable distributed generations. Thus, the model is motivated to maximize the penetration of renewable powers by minimizing the net present value of total cost, which includes investment, maintenance, energy, and emission costs. Consequently, the optimization problem is formulated as a stochastic mixed integer linear program, which ensures limited convergence to optimality. Numerical results of the proposed model demonstrate a significant reduction in electricity and emission costs, enhancement of system dynamic voltage and small-signal stabilities, as well as improvement in welfare costs and environmental goodness.

Selecting and Scheduling Link and Intersection Improvements in Urban Networks

Deciding which projects, alternatives or investments to implement is a complex and important problem not only in transportation engineering, but in management, operations research and economics. Projects are interrelated if their benefits or costs depend on which other projects are implemented. Furthermore, in the network development problem analyzed here, the timing of projects also affects the benefits and costs of other projects. This paper presents a method for optimizing the selection and scheduling of interrelated improvements in road networks that explicitly considers intersections. The Frank Wolfe algorithm, which is modified here to consider intersections, is used for evaluating network improvements as well as for traffic assignment. Intersections are modelled with pseudo-links whose delays are estimated with Akcelik’s generalized model. The objective is to minimize the present value of total costs (including user time) by determining which projects should be selected and when they should be completed. A genetic algorithm is used for optimizing the sequence and schedule of projects.

Inventory model with variable demand rate under stochastic inflation for deteriorating and ameliorating items with permissible delay in payment

In this paper, an inventory policy for the production units is presented with stochastic inflation, variable demand rate, deteriorating and ameliorating items in warehouse and permissible delay in payment. Shortages, backlogging and lost sale, are allowable. Also, inflation is constantly changing due to economic, political, social and cultural issues in the real world and we assume it uniform distribution. The demand rate is depended on selling price and permissible delay in payment and increases as delay in payment increases, however, decreases as selling price increases. The objective function considers optimal pricing policy, the number of production cycles and time fraction without shortage. So, the purpose is to derive the optimal replenishment policy that minimises the net present value of total cost per unit time. The study shows that the optimal solution not only exists but also is unique. Finally, some numerical examples and sensitivity analysis on parameters are made to validate the results of the proposed inventory system.

Fuzzy rough supply chain model under inflation and credit period with stock dependent consumption rate and partial backlogging shortages via genetic algorithm

In many inventory situations, purchasers are allowed a period to pay back for the goods bought without paying any interest. Depending on the length of that payment period, the purchaser can earn interest on the sales of the inventory. This paper develops a model to determine an optimal ordering policy for deteriorating items under inflation, stock dependent demand, permissible delay of payment, allowable shortage and unfinished demand is partially backlogged in fuzzy rough environments. The present value of total cost incurred in this inventory system is developed first, then an optimal order quantity and maximum allowable shortage are obtained by using a search procedure. The effect of inflation and time value of money was investigated under given sets of inflation and discount rates. This study shows that the optimal order quantity and maximum allowable shortage vary with the difference between inflation and time discount. Computational results provide some interesting policy implications. The genetic algorithm GA is used to make decision for above inventory model. The model is illustrated with some numerical data.

An EPQ model of ramp type demand with Weibull deterioration under inflation and finite horizon in crisp and fuzzy environment

This paper deals with an economic production model for deteriorating items with ramp type demand rate. A single item is produced and the finite rate of production is proportional to the demand rate. The deterioration rate of the item is taken as two parameter Weibull׳s distribution. We also have considered the effect of inflation when there is no shortage in the stock and the model is under finite time horizon. The inventory model is solved under crisp and fuzzy environment to evaluate the optimum solution of the model in different cases. We illustrate the model with an example by taking various number of replenishment. We present sensitivity analysis to illustrate the example under crisp and fuzziness. Thus the objective of the paper is to find the optimal solution of production time so as to minimize the present value of total cost of the whole replenishment cycle and to check which environment (crisp or fuzzy) is desirable.

Cost Analysis of PET and Comprehensive Lifestyle Modification for the Reversal of Atherosclerosis

We present a preliminary cost analysis of a combination intervention using PET and comprehensive lifestyle modification to reverse atherosclerosis. With a sensitivity of 92%-95% and specificity of 85%-95%, PET is an essential tool for high-precision diagnosis of coronary artery disease, accurately guiding optimal treatment for both symptomatic and asymptomatic patients. PET imaging provides a powerful visual and educational aid for helping patients identify and adopt appropriate treatments. However, little is known about the operational cost of using the technology for this purpose. The analysis was done in the context of the Century Health Study for Cardiovascular Medicine (Century Trial), a 1,300-patient, randomized study combining PET imaging with lifestyle changes. Our methodology included a microcosting and time study focusing on estimating average direct and indirect costs. The total cost of the Century Trial in present-value terms is $9.2 million, which is equal to $7,058 per patient. Sensitivity analysis indicates that the present value of total costs is likely to range between $8.8 and $9.7 million, which is equivalent to $6,655-$7,606 per patient. The clinical relevance of the Century Trial is significant since it is, to our knowledge, the first randomized controlled trial to combine high-precision imaging with lifestyle strategies. The Century Trial is in its second year of a 5-y protocol, and we present preliminary findings. The results of this cost study, however, provide policy makers with an early estimate of the costs of implementing, at large scale, a combined intervention such as the Century Trial. Further, we believe that imaging-guided lifestyle management may have considerable potential for improving outcomes and reducing health-care costs by eliminating unnecessary invasive procedures.

An inventory control problem for deteriorating items with back-ordering and financial considerations

This paper investigates the effects of time value of money and inflation on the optimal ordering policy in an inventory control system. We proposed an economic order quantity model to manage a perishable item over the finite horizon planning under which back-ordering and delayed payment are assumed. The demand and deterioration rates are constant. The present value of total cost during the planning horizon in this inventory system is modeled first, then a three phases solution procedure is proposed to derive the optimal order and shortage quantities, and the number of replenishment during the planning horizon. Finally, the proposed model is illustrated through numerical examples and the sensitivity analysis is reported to find some managerial insights.

Learning effect on an inventory model with two-level storage and partial backlogging under inflation

In this paper, we study the effect of learning on the optimal replenishment inventory policy for non-instantaneous deteriorating items with two-level storage. Here, we assumed that the ordering cost is partly constant and partly decreasing in each cycle due to learning effect. Here, shortages are allowed and partially backlogged. In addition, the effects of inflation and time value of money on replenishment policy under instantaneous replenishment are also considered. An algorithm is presented to derive optimal replenishment policy when the present value of total cost is minimised. Numerical examples are presented to demonstrate the developed model and to illustrate the procedure. In addition, the sensitivity analysis of the optimal solution with respect to various parameters of the system is carried out by using MATHEMATICA–5.2 for the feasibility and applicability of our model.

Costs of Expanding the Network of Protected Areas as a Response to Climate Change in the Cape Floristic Region

The expansion of protected areas is a critical component of strategies to promote the continued existence of biodiversity (i.e., life at all levels of biological organization) as climate changes, but scientific, social, and economic uncertainties associated with climate change are some of the major obstacles preventing such expansion. New models of climate change and species distribution and new methods of conservation planning now make it possible to explore the uncertainties associated with climate changes and species responses. Yet few reliable estimates of the costs of expanding protected areas and methods for determining these costs exist, largely because of the many (and uncertain) determinants of these costs. We developed a cost-accounting model to estimate the range in costs of various options for expanding protected areas and to explore the variables that drive these costs. Model development was informed by an existing plan to expand protected areas in the Cape Floristic Region of South Africa to address species conservation under a scenario of climate change. The 50-year present value of total costs varied from US$260 million ($1077/ha) for an off-reserve option that involves agreements with landowners and no compensation of forgone production and associated revenue to $1020 million ($4228/ha) for an on-reserve option that involves land acquisition and protection. The costs of acquiring land or compensating landowners for forgone production and development opportunities were the major drivers of the total costs across all options because most of the area identified in the protected-area expansion plan consisted of urban and high-quality agricultural lands. Total costs were also affected by changes in protected area extent and discount rate. Model-generated outputs such as these may be useful for informing implementation strategies and the allocation of future efforts in monitoring, data collection, and model development.

Residential Water Scarcity in Cyprus: Impact of Climate Change and Policy Options

This paper presents an assessment of the cost of water scarcity in Cyprus, today and in the next 20 years, taking into account the effect of projected climate change in the region. It focuses on the residential sector, accounting also for tourism and industry. Using a simple demand function, total scarcity costs in Cyprus are computed for the period 2010–2030, and three scenarios of future water demand are presented. The central estimate shows that the present value of total costs due to water shortages will amount to 72 million Euros (at 2009 prices), and, if future water demand increases a little faster, these costs may reach 200 million Euros. Using forecasts of regional climate models, costs are found to be about 20% higher in a “climate change” scenario. Compared to the loss of consumer surplus due to water shortages, desalination is found to be a costly solution, even if environmental damage costs from the operation of desalination plants are not accounted for. Finally, dynamic constrained optimization is employed and shows that efficient residential water prices should include a scarcity price of about 40 Eurocents per cubic meter at 2009 prices; this would constitute a 30–100% increase in current prices faced by residential consumers. Reductions in rainfall due to climate change would raise this price by another 2-3 Eurocents. Such a pricing policy would provide a clear long-term signal to consumers and firms and could substantially contribute to a sustainable use of water resources in the island.

Optimizing Maintenance Activities Using HGA and Monte Carlo Simulation

The present industrial environment needs proper maintenance for effective functioning of the system underlining the need for an optimal maintenance planning. Maintenance planning is a complex and an inherently stochastic process. This paper presents maintenance planning problem for a process industry. The problem is formulated to determine which of the possible actions viz. maintenance or replacement is to be carried out for the subsystems during the planning period. Maintenance is carried out by analyzing improvement in the parameters (viz. MTBF & MTTR) during the planning period. The objective is to minimize the present value of total costs that are incurred by the decision taken during the planning period. The problem is effectively solved by hybrid genetic algorithm (HGA) technique.

A Cash Flow Oriented EOQ Model with Deteriorating Items Under Permissible Delay in Payments

This paper develops an inventory model to determine an optimal ordering policy for deteriorating items with delayed payments permitted by the supplier under inflation and time discounting. This study applies the discounted cash flows (DCF) approach for problem analysis. Mathematical models have been derived for obtaining the optimal cycle time and optimal payment time for item so that the annual total relevant cost is minimized. The present value of the annual total relevant cost in this inventory system is developed first, then an optimal number of replenishment, cycle time, and order quantity are obtained by a solution procedure. The effect of credit period, inflation and time value of money was investigated under givens sets of above parameters. This study shows that the optimal order quantity, cycle time and associated the present value of total costs vary with difference among credit period, deterioration rate and net discount rate of inflation. Computational results can help managers provide some interesting policy implications to determine the optimal the present value of total costs.

Genetic Algorithm for Selecting and Scheduling Interdependent Projects

Investment planning for systems with interdependent queues, such as a series of interacting waterway locks, is a very challenging problem. Under budget constraints, it must be determined which projects should be selected and when they should be funded to minimize the present value of total cost over the planning horizon. Due to interdependence among service stations (e.g., locks), investment planning for such systems cannot be analyzed separately for each station. Published models on investment planning for interdependent projects are rare and difficult to apply to a series of queuing systems. A genetic algorithm approach is presented in this paper for solving project sequencing and scheduling problems for such systems. A numerical example is designed to investigate the proposed algorithm. Results indicate that the proposed approach is quite efficient in finding very good solutions.

Supply chain models for perishable products under inflation and permissible delay in payment

In many inventory situations, purchasers are allowed a period to pay back for the goods bought without paying any interest. Depending on the length of that payment period, the purchaser can earn interest on the sales of the inventory. This paper develops a model to determine an optimal ordering policy for deteriorating items under inflation, permissible delay of payment and allowable shortage. The present value of total cost incurred in this inventory system is developed first, then an optimal order quantity and maximum allowable shortage are obtained by using a search procedure. The effect of inflation and time value of money was investigated under given sets of inflation and discount rates. This study shows that the optimal order quantity and maximum allowable shortage vary with the difference between inflation and time discount. Computational results provide some interesting policy implications. Scope and purpose A significant part of the manufacturing goods are usually kept in the supply chain, especially either in a manufacturer’s/wholesaler’s inventory or in a retailer’s storage. This paper addresses the ordering policy by a retailer of a perishable product, where the decision is influenced by the time value of money and inflation, and the retailer is allowed a delay period to pay back the dues for the products purchased. The purpose of this research is to aid the retailers in economically stocking the inventory (i.e., ascertaining the ordering quantity) under the influence of different decision criteria such as time value of money, inflation rates, purchase price of the product, and deterioration rate.

Efes Beverage Group Makes Location and Distribution Decisions for Its Malt Plants

We conducted a study for Efes Beverage Group to evaluate various sites as potential locations for new malt plants. We performed an economic analysis that showed the inferiority of some alternatives. To evaluate the remaining alternatives, we developed a mixed-integer-programming model that considers both the location of new malt plants and the distribution of barley and malt. It considers the long-run effects of the decisions and minimizes the present value of total costs. Sakarya, Izmir, and Ankara turned out to be the best locations for new malt plants. Efes is currently using the model for distribution decisions. Based on our results and new developments that occurred since then, top managers are currently debating where and when to locate the new malt plants.

An Economic Analysis of Water and Sanitation Infrastructure Improvements in the Colonias of El Paso County, Texas.

The authors conducted a cost-benefit analysis of a project to improve water supply and sanitation infrastructure in a rural community (colonia) along the United States-Mexico border. The present value of total costs in the scenario without improvements was $42,937,507, compared with net costs with improvements of $34,600,800. It is estimated that improved access to safe drinking water and solid waste sanitation prevented 155 cases of hepatitis A and 5,165 cases of gastrointestinal illness over 26 years and saved $846,541 in associated health care costs, approximately 10% of the total estimated savings. The majority of benefits was in the form of time savings and increased land values. The authors did not consider the value of improving environmental conditions for future generations and preventing communicable disease epidemics associated with poor sanitation. Accurate value estimates for these benefits might strengthen the argument for undertaking similar infrastructure-improvement projects in the colonias and in other underdeveloped rural areas of the United States. cost-benefit analysis; water supply and sanitation; health care costs; disease-prevention effectiveness Hanley & Belfus, Inc. Int J Occup Environ Health 1077-3525 2 3 1996 July/September Decreases in Subpopulations of T Lymphocytes and Natural Killer Cells in the Blood of Retired Chromate Workers 222 225 EN Takeshi Tanigawa Shunichi Araki Department of Public Health, Faculty of Medicine, University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113, Japan. Akinori Nakata Takaharu Araki Susumu Sakurai To examine the effects of past exposure to chromates on lymphocyte subpopulations in man, the authors measured subpopulations of T lymphocytes and natural killer (NK) cells by flow cytometry together with B lymphocytes in blood samples from 27 male workers and 22 healthy controls. All subjects were nonsmokers. The numbers of CD4+CD45RA+ (suppressor-inducer), CD8+ (suppressor/cytotoxic) and total T lymphocytes, CD16+CD57+ NK cells, and total lymphocytes in the blood of chromate workers were significantly smaller than those in the blood of healthy controls (analysis of covariance with age as a covariate). It is suggested that suppressor-inducer and suppressor/cytotoxic T lymphocytes and NK cells with intermediate NK cell activity are most susceptible to the effects of past exposure to chromates as compared with other lymphocyte subpopulations such as B and CD4+CD29+ T lymphocytes.

On incorporating business risk into continuous review inventory models

This paper analyzes the effect of risk on the lot size and reorder point decisions of a firm, when both these decisions are considered simultaneously. It considers the ( Q, r) inventory model with setup, inventory, and backorder costs with the objective of minimizing the present value of total cost. The capital asset pricing model is used to value the uncertain cash flows from inventory decisions. Optimality conditions for the lot size and the reorder point are derived. Numerical analysis suggest several conclusions. First, an increase in demand riskiness, measured by the covariance of demand with the market return, leads to lower reorder point and lower lot size when the replenishment lead time is small. When the replenishment lead time is large an increase in demand riskiness decreases the reorder point, but may result in a larger lot size. Second, the average inventory in either case is a strictly decreasing function of demand riskiness. Third, comparison with the cost minimization model which uses a fixed opportunity cost of capital shows that large penalties in cost can result if the opportunity cost of capital is not adjusted for the risk of the cash flows from inventory decisions.