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The purpose of this paper is to present a novel and applied method for optimum designing of plate-finned heat exchanger network. Considering the total annual cost as the objective function, a network of plate-finned heat exchanger is designed and optimized.

Accurate evaluation of plate-finned heat exchanger networks depends on different fin types with 10 different geometrical parameters of heat exchangers. In this study, fin numbers are considered as the main decision variables and geometrical parameters of fins are considered as the secondary decision variables. The algorithm applies heat transfer and pressure drop coefficients correction method and differential evolution (DE) algorithm to obtain the optimum results. In this paper, optimization and minimization of the total annual cost of heat exchanger network is considered as the objective function.

In this study, a novel and applied method for optimum designing of plate-finned heat exchanger network is presented. The comprehensive algorithm is applied into a case study and the results are obtained for both counter-flow and cross-flow plate-finned heat exchangers. The total annual cost and total area of the network with counter-flow heat exchangers were 12.5% and 23.27%, respectively, smaller than the corresponding values of the network with cross-flow heat exchanger.

In this paper, a reliable method is used to design, optimize parameters and the economic optimization of heat exchanger network. Taking into account the importance of plate-finned heat exchangers in industrial applications and the complexity in their geometry, the DE methodology is adopted to obtain an optimal geometric configuration. The total annual cost is chosen as the objective function. Applying this technique to a case study illustrates its capability to accurate design plate-finned heat exchangers to improve the objective function of the heat exchanger network from the economic viewpoint with the design of details.

The purpose of this paper is to present the application of an adaptive bacterial foraging (BF) algorithm for the design optimization of an energy efficient induction motor.

The induction motor design problem is formulated as a mixed integer nonlinear optimization problem. A set of nine independent variables is selected, and to make the machine feasible and practically acceptable, six constraints are imposed on the design. Two different objective functions are considered, namely, the annual active material cost, and the sum of the annual active material cost, annual cost of the active power loss of the motor and annual energy cost required to supply such power loss. A new adaptive BF algorithm is used for solving the optimization problem. A generic penalty function method, which does not require any penalty coefficient, is employed for constraint handling.

The adaptive BF algorithm is validated for two sample motors and benchmarked with the genetic algorithm, particle swarm optimization, simple BF algorithm, and conventional design methods. The results show that the proposed algorithm outperforms the other methods in both the solution quality and convergence rate. The annual cost of the induction motor is remarkably reduced when designed on the basis of minimizing its annual total cost, instead of minimizing its material cost only.

To the best of the knowledge, none of the existing work has applied the BF algorithms for electrical machine design problems. Therefore, the solution to this problem constitutes the main contribution of the paper. According to the huge number of induction motors operating all over the world, the BF techniques used in their design, on minimum annual cost basis, will lead to a tremendous saving in global energy consumption.

We study changes in age-specific diabetes-related mortality and annual health care utilization. We find that half of the estimated 16% increase of diabetic mortality falls within employable age groups. We estimate that disease combination-specific increase in case fatality has resulted in premature diabetic mortality costing $3.2 billion annually. The estimated annual direct cost of treating high-risk diabetics reaches $36 billion, of which Medicare and Other Federal Programs compensate 54%. Respiratory conditions among diabetics comprise the same proportion of high-risk diabetics as do the disease combinations including coronary heart diseases. Treating of general diabetic conditions has become more efficient as indicated by the estimated declines in per unit health care costs.

To develop just‐in‐time (JIT) purchasing threshold value (JPTV) models for ready mixed concrete (RMC) suppliers to decide whether or not to switch from an economic order quantity (EOQ) approach to a JIT purchasing approach for the purchase of their raw materials, when a price discount is offered.

The existing economic order quantity (EOQ) with a price discount versus the JIT purchasing cost comparative models neglect some important cost components under the inventory management systems, for example, the out‐of‐stock costs and the impact of inventory policy on product quality and production flexibility. In addition, these models do not empirically study the capability of an inventory facility to hold the EOQ‐JIT cost indifference point's amount of inventory. These models suggest that the JIT purchasing approach is always preferred to the EOQ approach when the JIT purchasing approach can capitalize on physical plant space reduction. The JPTV models developed in this study overcome the two limitations of the existing EOQ and JIT purchasing cost comparative models.

By developing the JPTV models, this study suggests that the theoretical advantages of JIT purchasing may have been overstated.

The field studies conducted in the RMC industries in Chongqing, China and Singapore supported the propositions in this study. The JPTV models, if adopted, would help to enhance performance in the RMC industries in other cities as well.

Recent models comparing inventory costs under just‐in‐time (JIT) purchasing plans and economic order quantity (EOQ) purchasing plans have tended to favor EOQ purchasing in situations where annual demand of inventory is moderately large. Contends that these cost models are lacking dynamic cost components inherent in virtually all JIT purchasing plans. Presents a series of inventory purchasing cost models that extend prior methodology by Fazel by including relevant physical distribution cost savings. Additional comparative models are presented to further demonstrate how other relevant costs factors can be included in a comparative EOQ/JIT model. A cost comparison with an existing problem from the literature is used to illustrate the informational efficacy of new models.

Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.

The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.

For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.

The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

Current studies indicate that buyers can improve lead‐time performance and reduce total inventory‐system costs by splitting orders between two suppliers. These studies, however, treat transportation only implicitly as an element of the cost of placing an order. This is an important limitation, because shipping costs increase disproportionately as the size of shipment decreases and typically comprise a sizeable portion of total logistics cost. Investigates the role of transportation in the decision to procure from either one or two suppliers. A state‐of‐the art model was first modified to treat transportation costs explicitly and then used to conduct 54 experiments to measure the gains or losses in total logistics costs under a variety of representative conditions.

The conventional Economic Order Quantity (EOQ) lot‐size model ignores the effects of damage costs. While producing an EOQ lot, the inventory may go “out of control”. An extension of the EOQ production model based on damage costs is presented and the relevant costs of damage and their effects on stockholding costs are discussed. A new EOQ formula including damage costs is developed and a comparison between the conventional concept and the new concept based on annual total cost is made. The analysis shows that, if the costs of damage to stock are taken into account, the computed EOQ is smaller, and the conventional EOQ is not the optimum solution.

Inventory management is an extremely important function to any business, since inadequacies in control can result in serious problems. If inventories are managed in an inefficient manner, it is likely that delays in production, dissatisfied customers, or curtailment of working capital will result.