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This paper addresses the scheduling problem for cell manufacturing system, considering machine breakdown, job inter-cell movement, and energy consumption for machine idleness, which are common in real production but hard to be handled simultaneously. An integer programming model is established which minimizes the make-span as well as the total cost of energy consumption for machine idleness and repairing of faulty machines during the process. Due to the strong hardness of the problem, the solving approach based on the three-layer wolf pack algorithm combined with discrete event simulation is proposed. Numerical experiments show that the proposed algorithm can find a satisfied solution to this two objectives optimization problem in a reasonable time.

Currently, agricultural machinery (AM) is usually stored in indoor areas, under a shelter and in open equipped areas. However, the indicated storage technologies of AM cannot provide optimal storage conditions and have a low degree of economic efficiency. The purpose of the research is theoretical substantiation and practical implementation of a new method of AM storage in an open area using an experimental design of an active thermal shelter (ATSh). The study presents a comparison of the three most common methods of AM storage, namely storage in an indoor area, under a shelter and in an open equipped area. The analysis showed that a new method of AM storage in an open area with the use of an experimental design of the ATSh requires less investment in arranging the storage place, does not require additional labor and material resources to prepare an AM object for storage, and the total costs for preparation and storage of an AM object are comparable to storing it indoors. The use of the experimental design of ATSh is a more rational way to ensure the reliable safety of the insulated AM object from the impact of the environment for the entire storage period.

The research aims to minimize the total costs and reach an optimal solution in transporting the gasoline product from the main warehouses in Baghdad Governorate to filling stations: by relying on the dataset obtained from the Oil Products Distribution Company of the Iraqi Ministry of Oil, by using the traditional method (linear programming) and the modern method (genetic algorithm), and then compare them to help decision-makers make the right decision. The preference was achieved for the modern method, which was able to make a slight improvement in the final results. This is because the mathematical model of the problem is a linear model, where the cost in the technique of linear programming (1,424,165) dinars and in the technique of the genetic algorithm (1,424,157) dinars.

In present scenario due to rapid growth in population, it is not possible to construct each and every residential building on hard soil. Some time, it is necessary to construct building on soft soil. One of the examples is Newtown, Rajarhat, Kolkata. Rajarhat is a developing area in Kolkata where most of the residential buildings are medium rise, i.e. four or five storied. In that area, soil consists of very soft clay having very low value of SPT value (2–5) up to a depth of 12 m or below, followed by hard strata. In most of the cases, shallow foundation cannot give feasible solution and structural designers prefer pile foundation before adopting other alternatives techniques to improve the soil. Pile foundations are not very cost effective. Cost of the foundation constitutes a significant part of the total cost of the structure. Hence, reduction in cost of the foundation leads to reduction in total cost of the structure. In this study, an effort has been made to recommend feasible cost-effective foundation system by using ground improvement technique (by using stone column) vis-vis pile foundation. A typical medium rise building (G + 4) has been chosen on a typical soft soil profile, typical to Rajarhat area. A comparative study has been made to suggest cost-effective foundation system for medium rise residential building in that area.

In today’s competitive market, inventory management is a difficult job for every business enterprises. Objects are getting deteriorate after some period of time and result into economic loss. Keeping this in mind, this inventory model is for perishable objects where the rate of deterioration is considered to be constant with a constant demand rate. To reflect the real-life situation, the model explores a two-level trade-credit policy, i.e. the supplier offers certain credit period to the retailer and simultaneously the retailer permits a permissible delay in payment to the consumers that helps to increase the demand. If the retailer clears its entire amount during the end of first credit period, then the retailer can utilize it to earn interest. Moreover, if the retailer fails to clear the account by the end of first period, then he/she is allowed to pay off the balance after first credit period or by the end of second credit period. Here, the financial loans can be reduced through constant demand and interest earned. This paper uses a classical optimization method and calculated several numerical examples to elaborate the model. Convexity of cost function is proved through graphs. The objective of the paper is to minimize the total cost with respect to the inventory cycle time. At last, sensitivity analysis is done to study the effects of varying inventory parameters on decision variable and optimal solution.

This chapter deals with a coordinated inventory model by considering supply chain of two stage, single-vendor and single-buyer. An equally sized shipment from vendor to buyer is conducted, and the buyer’s rate of market demand is taken as a constant. Also, in the model, the concept of cost associated with freight discount is undertaken, and formulations are done on the basis of all-weight freight discount model and incremental freight discount model. In this chapter, an estimation of the optimal values of replenishment cycle length, lot size by minimizing the buyer’s total cost, vendor’s total cost as well as total cost of the supply chain inventory system dealing deteriorating items are considered. The increase in the number of production batch lot size factor and number of shipments from the vendor to the buyer will lead to decrease the total cost. Numerical examples are provided to illustrate the theoretical results and convexity of total cost is established. Managerial observations are outlined using sensitivity analysis. The result analysis demonstrates that on imposing freight discount into inventory model results in significant reduction on total cost. Finally, it can be concluded that freight discount model associated with incremental concept gives substantial effects on minimizing the objective of system’s aggregate inventory cost.

A systematic approach in managing operation and maintenance costs of private highways is crucial to obtain sustainable long-term profit of private highways development in Malaysia. Operating and maintenance costs in road projects are a real challenge and form an significant part of the total cost of road growth. This paper is start with examining the process of private highway developments in Malaysia includes all associated funding received from Government of Malaysia since 1982. Cost data from twenty-nine private highway projects are collated and analyzed. The outcome of the research shows that majority of the highways were built with no systematic approach to managing operating and maintenance costs during the concession period agreement. A literature analysis identified five main components and twenty-two elements are contributed to the escalating of operation and maintenance costs. This paper uses a qualitative approach by analyzing secondary data from various industrial report to provide systematic breakdown of highway assets to develop a sustainable cost model. This study has clearly demonstrated the need to develop a sustainable cost model for the management of operations and maintenance of private highways in Malaysia.

Newsvendor problem is an extensively researched topic in inventory management. In this class of inventory problems, shortage and excess costs are considered to be proportional to the quantity lost. But for critical commodities, inventory decision is a typical example where excess or shortage may lead to greater losses than merely the total cost of lost quantity. Such a problem has not been discussed much in the literature. Moreover, majority of the existing literature assumes the demand distribution to be completely known. In this paper, we propose a generalization of the newsvendor problem for critical goods or commodities with higher shortage or excess costs but of same degree. We also assume that, the parameters of the demand distribution are unknown. We also discuss different estimators of the optimal order quantity based on a random sample of demand. In particular, we provide different estimators based on (i) full sample and (ii) broken sample data (i.e., with single order statistic). We also report comparison of the estimators using simulated bias and mean square error (MSE). We have also compared the accuracy of nonlinear cost functions with the linear one in this problem under misspecified power in the cost function.

In this article, an inventory system consisting of two substitutable products is studied. In which, the feature of quantity discount offered to the retailer has been discussed. The inventory of both the products is depleted by their demand which is assumed to be quadratic. When one of them becomes out of stock, partial substitution has been done from the inventory of mutually substitutable product. The unmet demand is assumed to be lost. The mathematical formulation of the model is proposed. The solution procedure is brought forth to obtain the optimal order quantity by minimizing the total cost. Sensitivity analysis has been done to showcase the advantage of substitution over without substitution and hence comprehend the behaviour of the model.

Considering the secretary selection problem as a non-sequential decision problem formulated in terms of a non-linear integer programming exercise, the role of Expected Net Gain due to Sampling has been taken as the objective function to work out a reasonable solution. A generalized version of an earlier formulation involving total cost as the objective function has been indicated and extended to the case of selecting a panel, instead of a single candidate. The case of multiple rankings of the interviewed candidates to arrive at the ‘best’ candidate has been examined in terms of several approaches.

Reduction of excessive carbon emissions (CEs) is an acute challenge faced by manufacturing companies in operation of their Reverse Logistics (RL) networks, specifically in the developing nations. Hence, companies are realizing the importance of examining the carbon footprint across the network and attaining significant reduction through refurbishing, repair and recycling of their end of use and end of life products. This research contributes to the area of modeling reverse logistic network design problems, by developing a bi-objective optimization model under carbon tax scheme, for integrating strategic decision making at the design phase of the RL network. The carbon emissions due to each of the operational activities are considered and their corresponding costs along with investment in technology are also embedded in the model. The objectives of the model are: (1) profit maximization (2) carbon emissions minimization. The RL network design is configured as a mixed-integer programming problem model and fuzzy programming is utilized as solution methodology for obtaining a trade-off solution. A case of an Indian electrical and electronics remanufacturer is considered. The results furnishes valuable managerial and policy insights; (i) impacts of carbon tax on the strategic level of RL from an emerging economy perspective, (ii) optimal carbon price for maximum carbon emissions reductions per dollar increase in the total cost.

This paper describes the EOQ Inventory control of production model in partial backlogging which is evaluated through the Inventory-level variation. In this paper, two methods are explained: (i) Inventory different cost with shortage, back order quantity, maximum Inventory, number of order and total cost are found numerically. These parameters are obtained based on the assumption of Inventory carrying cost (m) and the shortage cost (M). (ii) EOQ cost in without shortage. These two methods can be applied to a small company, which includes sales or delivering any category of product; there is no way to stay away from the necessity to hold an Inventory to make sure timely delivery and avoid no-stock conditions. Conversely, holding Inventory does build extra costs for a company. These costs can happen in several ways, based on the features and size of the company. This helps the company Inventory cost reduce to make effective use of the working capital. These parameters of Inventory costs are estimated by Brownian motion. It is observed to be minimized. The computational results are obtained from numerical examples. The results are shown in Brownian motion. This outcomes because of fluctuations. It is incrementing. Consequently, it is points of increases and random walk. The results are irregular and follows the Brownian motion, and it is Fractals.

In this article, the warehouse of the sugar industry system was created for the growing commodity demand and demand with an increase in dual warehouses using different variables. Warelo its warehouse (OW) has a capacity of W units; the warehouse (RW) has unlimited capacity. At this point, we think the sugar industry is holding higher RW records than at EW using different evolution. Sugar companies are allowed to quit, and the sugar industry will get worse in the near future, fluctuating when they make various changes. There is also an effect of an increase in the various costs associated with the marketing of sugar systems using different variables. Numerical symbols are also used to study the behavior of the model using different variables. Reduction costs are used to obtain a statement of total costs in other areas using a different evolution method.

Vehicle logistics refers to the whole process of rapid delivery of vehicles distributed by logistics company according to customer orders. Auto-carriers are used as the transportation tool, which efficiency and loading rate directly determine the logistics cost. Compared with traditional vehicle routing problems, route planning of auto-carriers also needs to consider how to load the vehicles, which has higher requirements for the model formulation. In this paper we provide a three-dimensional loading constraint and build a multi-objective mixed integer programming model based on the minimum total cost, the minimum number of auto-carriers and the shortest total driving distance. We use Gurobi as a solver to pre-process multiple constraints and solve the model. The experimental results show that the model can find the optimal solution efficiently and the algorithm can give an accurate solution in a limited time, which indicates that the method designed in this paper is feasible and even optimal for certain problem scales.

Resource allocation (RA) for cloud-based big data systems mainly consists of assigning the tasks to suitable resources. A dynamic unbalanced load in individual virtual machines (VMs) causes the complete inefficient utilization of resources. Apart from this, high execution cost and lower performance are the other issues of RA. A predictive cost-efficient resource allocation (PCRA) scheme for big data applications is developed. In this scheme, the user specifies the number of executors, cores, memory, etc., for the required application. Then, the task accomplishment period of a solicitation is predicted centered on the number of initiators and the specific properties of the application. Then, VMs are determined for the task requests, such that the number of executors is sufficient to minimize the task completion time and total cost. Some idle executors are released to return some resources to the hosts which could be revoked in the future if needed.

This chapter examines the factors that influence the imbalance of rural long tail public services. The imbalance of rural long tail public services is caused by a series of complex factors, including many aspects of economy, society, culture and the institutional environment, with multi-subjectivity and dimensions. This chapter first describes the imbalance from both sides of supply and demand. Then, according to the different fields of rural long tail public services, empirical research is conducted from the perspectives of rural special education, special health, elderly care and finance. With regard to the demand side factors, the overall reason for the imbalance of rural long tail public service is that rural residents’ public demand is irrational, discrete, fragmented and atomized. The irrationality of rural residents’ long tail demand refers to the irrationality based on personal preferences and cognition when choosing the required public services. In particular, there is often an irrational side in the selection of the types of public services with information cost and human capital requirements. The indifference utility curve of farmers in terms of public service demand always tends to be extended, without considering the decision-making cost. The decision is related to the non-economic factors existing in the demand selection, which means the over-marginal cost expenditure of the supply curve is unable to measure the total cost, and this makes the demand cost deviate from the expected income. Of course, the irrational demand is related to the rational factors behind that demand, such as the decentralized management mode of farmers, as well as social system obstacles (such as the household registration system) in the marketization process. With regard to the factors that affect the supply side of rural long tail public services, this section intends to analyze the supply reasons and influencing factors of the imbalance from the following perspectives: government financial constraints, government rational choice deviation, immature NGOs, and the lack of a supply “market”. After summarizing the overall reasons for the imbalance in this section, the next three sections will show the results of an empirical regression analysis of specific rural long tail public services, including special education, special health, special elderly care, and special finance. Field-based reasoning and deduction will also be conducted with regard to the above-mentioned influencing factors.

this paper presents Biogeography-based optimization (BBO) techniques for the excellent location to achieve minimum transmission active power loss, optimal cost, and optimal bus voltage. The proposed methods are authentic in the IEEE-30 bus system with power flow equation using a Newton-Raphson algorithm. The execution of IEEE-30 bus system is computed on different reactive loading condition. Here we are present the BBO and PSO’s comparative result using distinct optimization techniques. In the end, BBO is best for the transmission of active power loss minimization and total cost of the system at the various loading for the IEEE-30 bus system as compared to PSO (Particle Swarm optimization). Biogeography-base optimization is the best solution, is to reduce active power loss, system operating cost including the cost of FACTS devices and congestion in the transmission network.

One of the most challenging tasks in managing a supply chain is to select an efficient inventory ordering policy, i.e., deciding on ‘when to order’ and ‘how much to order’ while minimizing the total cost and maximizing the service level. Classical inventory policies based on periodic and continuous-review are generally implemented in practice. In our work, we attempt to combine the characteristics of both periodic-review order-up-to (R, S) policy and continuous-review (s, Q)/(s, S) policies to propose two new hybrid ordering policies, namely continuous-review (s, Q*) and continuous-review (\({s,\mathrm{O}\mathrm{Q}}^{*})\) hybrid policies. We further develop mixed integer linear programming (MILP) models to obtain the optimal policy parameters by considering a single-stage and two-stage supply chain with discrete, deterministic demand over a finite planning horizon. The proposed policies are benchmarked against the existing order policies, namely (R, S), (s, Q), (s, S) and hybrid (R, S, Qmin) policies. From results, we observe that the performance of the proposed hybrid policies outperforms the existing classical and hybrid policies in terms of total supply chain cost.

In this article, an EOQ mathematical model has been developed for perishable items with two level trade-credit financing policy and preservation technology under learning effect. Learning effect minimizes the holding cost and ordering cost because the holding cost and ordering cost follow the effect of the learning. Trade credit policy is very effective tool for seller to increase his sales. Furthers, seller gives a fixed credit period the buyer to increase his profit and buyer gives same policy to his customer. Some useful results determined when learning rate increases, cycle length almost fixed and retailer’s total cost decreases and if trade-credit period increases then cycle length and retailer’s total cost decreases due to the learning and credit financing. Finally, the total inventory cost minimizes with respect to cycle length. The numerical example describes the applicability of the present model.

Nowadays entrepreneurs need to examine the different ways to grow and survive in this competitive environment. Coordination among the players and the use of appropriate promotional tools can reduce the total cost of supply cost. In this we have studied the effect of quantity discount on independent as well as integrated scenario. Here we have considered time-dependent demand for time-dependent deteriorating items with a fixed lifetime under shortages. A numerical example is presented to support this inventory model. The proposed model is useful to the decision-making of the supply chain associated with Drugs, Cosmetics, FMCGs, etc.

All along the food supply chain, managers face the challenge of making important cost-optimized decisions relating to transportation and storage conditions. An efficient management of food products requires the consideration of their perishable nature to solve the safety problem, and logistics efforts in minimizing the total cost while maintaining the quality of food products above acceptable levels. This paper addresses a methodology to resolve a capacitated model for food supply chain (FSC). The model is a constrained mixed integer nonlinear programming problem (MINLP) that computes the cost of quality internally for a three echelon FSC, to minimize the total cost under overall quality level constraints, due to the perishable nature of the products, and the other constraints of demand, capacity, flow balance, and cost. The practical application of the model is demonstrated using two approaches: an exact method based on the Branch & Bound technique, and a Genetic Algorithm solution method. Then, we propose a comparison of different GA selection strategies, such as Tournament, Stochastic Sampling without Replacement, and Boltzmann tournament selection; the performance of each selection method is studied using paired “T-Test” of statistical analysis. The results of computational testing are reported and discussed, and implications and insights for managers are provided by studying instances of practical and realistic size. It was evident that the tournament selection is more likely to produce a better performance than the other selection strategies.

An inventory model for a single deteriorating item under fuzzy environment has been presented in this paper. Here demand rate is considered to be constant for some time period, post which the same is a linear function of time. This situation is common during the time of a new product launch in the market. As the product becomes popular, its demand increases with time although it remains constant during the initial days. Cycle time is considered to be constant in most of the models. However, practically it has been observed that it is difficult to pro-actively predict the cycle time. Because of this problem, cycle time has been considered as uncertain and has been further described as Symmetric Triangular Fuzzy number. The Signed Distance method has been used for defuzzification of the total cost function. For illustration of the process for finding the total optimal cost and the cycle time, numerical examples have been considered. The effects of changing parameter values on the optimal solution of the system have been demonstrated through Sensitivity Analysis.

In response to the call of Chinese government, accelerate the development of multimodal transport system. This article aims to the key layout planning problem as study object, takes container piggyback transportation as the background and bases on the idea of hub-and-spoke network. Aiming at minimizing the total cost of network operation, establishes the location model of piggyback transport node based on the hub-and-spoke network, designs the heuristic algorithm and combines the current regional cargo traffic and transportation network status in China and then calculate the transportation network hub location scheme and the total operating cost of the hybrid hub-and-spoke network, compared with the pure-axis spoke network and the shortest-circuit network.

In recent years, the centralized interconnection of renewable energy sources has led to a surge in the reserve demand of power grid. However, relying solely on conventional reserve for risk management and control obviously cannot meet the operational requirements of grid . In view of the shortcomings of the existing optimal configuration of reserve, this paper studies the optimal configuration of unconventional reserve, namely emergency reserve, which can participate in short-term regulation of power system and has additional configuration or regulation cost. Aiming at the problem that the existing optimal configuration of reserve is mostly limited to a single region, this paper is on the basis of risk method. With the goal of minimizing the total cost of global risk control, the cross-regional optimal configuration model and algorithm of emergency reserve are established. Through simulation analysis of examples, it is verified that the algorithm can better coordinate the global emergency reserve resources, and play the role of reducing costs, controlling risks and improving efficiency while sharing resources.

Modern multimedia platform design becomes more complex. In a single device, a multimedia system (MMS) requires the support of many applications. Increasingly, processors are integrated to achieve high peak performance in systems. Due to the application domain and digital hardware, MMS heterogeneity has also increased since time algorithms were supported. To achieve system performance, limited resources must be shared. Interference occurs when applications run dynamically across shared resources. Why don't you run these applications? Because adding dimensions to the design problems. Most companies spend 65–75% of the total cost of product development on the verification of use cases. Only analytical techniques can reduce development costs. Analysis in product design is not feasible as applications are added to the system during runtime. Therefore, the article includes a run-time practice for multimedia design's performance that can predict the performance of multiple applications before they run accurately and quickly in the system.

In this paper, we present a new approach to solve the Electric Vehicle (EV) Charging Station (CS) Placement problem. It aims to find the most suitable sites with the adequate type of CS. Using a meta-heuristic approach to tackle this issue seems appropriate since it is defined as an NP-hard problem. Therefore, we have introduced an improved genetic algorithm adapted to our problem by developing a new heuristic to generate initial solutions. Moreover, we have proposed modified crossover and mutation operators to enhance generated solutions. Throughout this work, we have aimed to minimize the total costs consisting of: travel, investment, and maintenance costs. However, we have to respect two major constraints: budget limitation and charging station capacity. The results provided by experimentation show that the proposed algorithm provides better results compared to the most efficient algorithms in the literature.

To find the optimal solution, the idea of LPP plays a key role and queuing models possess a major part in determining the probable number of people in the system and an optimal number of servers. It is impossible to decide on performance measures and optimal expected total cost of the multi-server queuing system when the arrival rate, service rate and the number of servers not given. But in this research article, the necessary number of servers and a probable number of customers in the system of multi-server queuing model determined using linear programming problem model, namely big-M method, when the arrival rate, service rate and several servers of the multi-server queuing model not known.

Software testing is an important but time-consuming part of the software development life cycle. In fact, it takes almost more than fifty percent of the total cost of development. Organizations spend too much time on testing, so research in this field is necessary to minimize these times and improve the quality of the software projects. In this paper, my colleagues and me present an algorithm to reduce the initial set of test cases and minimize that time cost. Our algorithm eliminates redundant test cases without losing effectiveness applying mutant coverage and genetic algorithms. The results show that with this approach we obtain a minimum test suite with the same effectiveness as the original test suite.

In order to reconcile the contradiction between the economy and efficiency of photovoltaic module operation and maintenance in photovoltaic power plant and improve the investment efficiency, a new planning method of intelligent operation and maintenance robots based on the nondominated sorting genetic algorithm II (NSGA-II) is proposed. Based on the basic pattern of photovoltaic power plant, a track that connects the photovoltaic operation and maintenance system is designed. The total cost and time of operation with multi robots are taken as the planning objective. Considering the number and path of robots, a Pareto optimal solution set is obtained by the NSGA-II algorithm. The simulation results prove the effectiveness of the method, which can provide a variety of decision optimization schemes to meet the preferences of different decision makers.

This research studies the performance and outcomes of the Vendor Managed Inventory (VMI) system with comparison to the traditional Retailer Managed Inventory (RMI) system. In the proposed model of the VMI system, we integrate the role of the VMI system in the location-inventory assignment problem with the replenishment policy assignment for a chain of hospitals. The healthcare supply chain network under a study composed of a single vendor provides multiple commodities of medical implants to multiple hospitals with a VMI system under a deterministic demand environment. The vendor needs to determine the location of VMI, the number of products, and the number of orders in a way to have a minimal total cost of transportation, inventory, and other associated operating costs. In this study, we compare the total cost of the VMI system with traditional RMI systems for a chain of hospitals. We consider the total cost for each system as a tool of performance measure between VMI and RMI systems. In each case, we developed a mathematical model as a mixed integer non-linear programming for the VMI and RMI with continuous review policies. The two models were solved using GAMS, and the computational results and sensitivity analysis are provided. The results for this case study show that the application of the VMI system is more justified and works better in terms of the cost, same being lower than the cost of traditional RMI systems.

Nowadays, the world has depended on fossil fuels for energy supply. The worldwide consumption of fossil fuels (coal, gas and oil) is still increasing in spite of the growing global awareness of the environmental impact of fossil fuel consumption and of limited fossil fuel reserves. Presently, increase in the price of oil has been a most important cause of economical problem in the world. The production of oil level in the world will dissipate in less than 50 years. So, we have strong motivation on our technologies to develop and harvest the abundant solar energy into clean renewable energy for the ultimate replacement of fossil fuels. The primary obstacle in the growth of photovoltaics is the high total cost of photovoltaic installations. In contrast, coal is cheap and abundant in key consuming countries such as India and China, making coal the world’s rapidly growing fuel. Consequently, the main aim of this research is making photovoltaic cells more efficient for converting solar energy into electricity and by reducing production cost. In recent years, the photovoltaic industry has experienced an average growth of 30%.

A model of economic production quantity plays a crucial role in the field of manufacturing and service products. In the modern era, it is very important to study the management and safeguarding of deteriorating items in production inventory. In the proposed study, we introduce an EPQ model with three different production rates, and there is demand as a decreasing function of time. For the fuzzy model, the cost of production, the cost of ordering, the cost of carrying, and the cost of shortage are taken as in the form of a trapezoidal fuzzy number. Total cost is defuzzify by using signed distance technique. The paper’s aim is to reduce the overall the overall cost to the manufacturer. A comparative study of the model in a crisp and fuzzy environment has done through a numerical example and sensitivity analysis demonstrates the effects of the model’s parameters on the optimum result of the crisp and fuzzy model.

A population of rapid growth, a place of the booming economy, whistle-stop urbanization along with disease escalation requires persistent improvement in healthcare services. However, delays, medical errors, measurement errors and variability weaken the coherence of healthcare, which increases the pressure in redesigning the process for quality improvement. In this paper, Six sigma is implemented for selecting the parameters in health care management. Then optimization based on nonlinear programming is performed for minimizing the total cost in bed scheduling, where the constraints depend on the budget spent, types of doctors, the technicians, the medical equipment, etc. It also aims to minimize the total cost associated with packaging medications and emergency department allocation. Optimized results showed that the emergency department has to be taken care in order to gain a superlative performance of healthcare maintenance. Hence the usage of probability models based on Poisson regression has been imposed on bed occupancy of the emergency department by considering the number of arrivals and the number of departures of patients on specific days. The results are then examined using DMAIC techniques and are found to be under control using an exponentially weighted moving average chart. The computational behavior, the efficiency of the proposed model and the practical applicability is exemplified by a case study. This novel methodology of integrating six sigma with optimization techniques and probability models has gained a strong potential success related to cost reduction, increase in patient satisfaction and enables continuous monitoring of health care service, thereby promoting economic sustainability.

Presently, the development of grid-interactive homes (GIH) is becoming an additional wing for the extension of power system. The optimal coordination of residential grid-interactive solar photovoltaic (PV) and electric vehicle (EV) are presented in this paper. Optimal operation of local generation is framed as mixed-integer linear programming (MILP). Owners of GIH can utilize their local generation smartly, to get more benefits economically by reducing the operational cost of GIH in order to adequate the energy demand. A pioneer work of this paper is to utilize demand-side management (DSM) with real-time pricing (RTP) so that GIH has suitable decisions for energy planning and can find optimal capacity for PV with EV. The integrated PV output is formulated by considering different real parameters, so that accurate results can be achieved. Moreover, the optimal charge/discharge of EV can give additional economic support to owners of GIH. Reduction in the total cost of GIH can be noticed from the obtained results due to optimal operations of PV, EV and grid.

The construction industry is on the forefront for the growth of any nation. In olden days, shelter was merely a means for survival but in the present age infrastructure symbolises a position of power. In order to build infrastructure, there is a need of material, most importantly building material. Building materials make up for 75% of the total cost of construction. As the years go on, there is a need for innovative and sustainable building materials using industrial, agricultural and natural wastes. Bricks are the most frequently used building constituents in the construction world. For lining furnaces in metallurgy and glass industries, bricks are used. Green building material has attracted attention because of sustainability factor. Using the waste as substitute raw material in manufacturing of building products is an innovative way of waste utilisation. A large quantity of waste is produced in every sector of life. In this paper we have given a thrust on the use of waste material such as lateritic soil, human hair along with other raw materials to manufacture a masonry unit. These materials are used in different proportions, and several trials are conducted to obtain a product with better competencies than a traditional brick, creating an alternative and sustainable masonry unit.

In this study; a reinforced concrete retaining wall was dimensioned under static and dynamic loads at optimum cost using the Jaya algorithm, which is one of the metaheuristic algorithms. As an objective function, the total cost for the unit length of the retaining wall and the cross-sectional dimensions are defined as design variables. Thanks to the single phase of the Jaya algorithm, the solution was reached quickly, and the best design variables were obtained with the minimal solution in the objective function compared to regular calculations. In addition to achieving optimum dimensioning results in terms of safety and cost, the relationship between earthquake and cost has been examined with the optimization method used as a result of the reinforced concrete design made by applying the regulations on the Buildings to be built in Earthquake Zones (DBYBHY 2007).

Manufacturers can reduce environmental impact and costs by introducing remanufacturing. In remanufacturing, inventory control of remanufactured and newly manufactured products should be considered for production planning. It is indispensable for predicting the demand quantity of products and the collecting quantity of used products to minimize the total cost incurred by remanufacturing and newly manufacturing. The prediction varies depending on the product characteristics and the time elapsed since shipment. In this paper, we study a remanufacturing system minimizing the total cost by a life-cycle simulation method. In the flow of products, there are two types of inventory: one is a remanufactured and newly manufactured product inventory before shipment to the market, and the other is a collected product inventory for used products. Depending on the time elapsed since launch, products pass through an introductory stage, a growth stage, a maturity stage, and a decline stage in a usual manner. We analyze the impact on the total cost of the prediction accuracy of the product demand and the collected used products to each stage. When the ratio of an ordering cost for newly manufactured products to an inventory holding cost for the finished products has a significant value, or as the life-cycle stage is approaching the decline stage, the predicting accuracy of collection of the used products and the product demand has a high impact on the total cost.

Software cost prediction is the technique of accurately evaluating the amount while developing the software. Estimation involves the total time required for the completion of the software, effort required that is measured in terms of person per month (PM), and the total cost to complete the activity. Accuracy and duration are the two desirable criteria in the software estimation process. In software estimation process, there are several inputs that are being fed to the system and these inputs are used for the generation or calculation of the set of outputs. The important work of the software project managers in the present scenario is the computation of cost or effort before the absolute advancement of any particular software. There are several methods applied for software cost estimation but we will focus on the fuzzy logic which is a soft-computing method. We feel that model which is based on fuzzy logic for the software cost estimation should be able to give the uncertain values rather than other models which give precision and certain values and the results based on fuzzy logic model will be more accurate than other models. In this research paper, we have developed the fuzzy logic model by taking several inputs and membership functions, also the fuzzy rules have been deduced by expert-knowledge with the help of MATLAB’s Fuzzy logic toolbox and the results are then compared with the multiple regression model using SPSS Tool upon desharnais data set which gives less accuracy than fuzzy-based model.

Supplier evaluation and selection on economic, social, and environmental dimensions are crucial for sustaining the pressure of a competitive global supply chain. In this work, a mixed-integer linear programming for supplier selection and order allocation in a single period, multi-supplier, multi-item environment with a prime consideration to the selection of transportation alternatives while delivering items is developed. To capture the real-world situation, the proposed model incorporates no discount and all quantity discount situations considering the bad quality and late delivery disruptions in the supply chain. To reflect a wide variety of operational conditions, two scenarios with two cases have been developed to demonstrate the effect of disruptions and discounts over demand and procurement cost. A real-life case of the automotive sector in central India is studied to validate the proposed model. Also, sensitivity analysis has been performed to understand the trade-offs between different sustainability criteria and the total cost of purchase.

Since many years, energy poverty and climate change are the pivotal issues faced by societies. Harnessing offshore wind energy would be a reliable solution to both of these issues. Offshore wind energy is an attractive source of green energy, because of abundant of wind resources in the sea. In compression to the onshore wind turbine, visual and noise impact no more an issue in case offshore wind turbine. Developing offshore wind turbines 15–30 km away from shore experience hostile environmental loads such as ocean currents, storm wave loading, ice loads, and potential ship impact loads significantly contributes the high cost in offshore structure. The turbine blade is an important constituent of the wind turbine. It costs around 22% of the total cost of a wind turbine. In this paper, an approximate cost equation for turbine blade was derived. This cost equation is sensitive to the quantity of various materials used in turbine blade and diameter of the wind turbine. This cost equation can be used to compute the material cost for a wind turbine up to a diameter of 80 m.

Accommodation establishments represent a complex sector with many elements, including demand uncertainty, customer-oriented management, complex production constraints, unified long raw material procurement processes, and adaptation to changing economic conditions. In such a sector, it is not possible to minimize the costs of the business and to maximize the efficiency of the service by controlling only one of the largest cost items. To create a competitive advantage in a customer-oriented market, many elements, such as manpower, energy, water, maintenance, and stocks, need to be properly managed. Planning for as much communication as possible in supply chain management is also an important factor. Planning is the creation of a common consensus regarding the future objectives of business management. There are two basic elements to making the plans concrete. The first element is the estimates that embody the vision of the management of the business. Estimates may vary depending on the size of the vision. Some businesses connect their vision to standards, while some businesses base their vision on more than one estimate. The second element is the operating budget. Operating budgets are an important guide for determining the extent to which business management visions are realized. This guide allows for the evaluation of the success of the entire business, as well as the success of each department and the managers. The main aim of the study is to measure the success of the hotel business. Supply chain management and standard costing techniques are used to measure this success. To achieve this goal, a five-star hotel has four years of data. In the study, electricity consumption, the total cost of electricity, and the fixed and variable costs are first estimated and actualized for the four-year data of the hotel, which represents the activity period of the hotel. Using this data set, the performance is assessed using data about the rooms and customers per night. In the second part of the study, electricity consumption, total cost of electricity, and fixed and variable costs are determined. Performance evaluation can be conducted at the level of rooms and customers per night. The degree to which the performance evaluation of each part of the hotel affects the overall performance of the hotel can therefore be analyzed.

The study explores the impact of delay in getting appropriate treatment for mental illness through analysing cost. There was a significant correlation between the appraisal delay and total cost (r = 0.396, p < 0.01), which means higher appraisal delay is related to higher treatment cost.

With the great popularity of the public transit which is a kind of green transportation, peak-only bus lane is gradually implemented on the corridors of large cities to make the bus runs a privilege to go through the bottleneck, then the bus runs can keep a faster speed which will definitely attract more potential commuters. And thus this will alleviate the traffic congestion caused by private cars and reduce the energy consumption and emissions to some degree. In this paper, we investigate the impact of the peak-only bus lane on alleviating traffic congestion and reducing energy consumption by using bottleneck model with auto and bus modes. The peak-only bus lane will occupy the bottleneck’s capacity by a fixed amount just within a fixed peak hour period. While the capacity of the bottleneck for auto mode commuters is time-varying within the whole commuting period. It is assumed that the mode choice and the departure time choice are governed by the user equilibrium criterion and nobody can decrease his/her total cost by adjusting the mode or the departure time in the equilibrium state. The departure rates for both bus and auto modes are derived analytically. The travel cost and energy consumption are analyzed with different bus dispatch frequencies and bus lane capacities. The numerical results showed that the setting of the peak-only bus lane will descend the number of commuters who choose the auto mode, and thus decrease the system’s total travel cost and energy consumption to certain degree. The optimal setting of the road resources and the frequency for peak-only bus lane was also investigated. We believe that the results are helpful to the planning and operating of peak-only bus lane, and it’s useful to alleviate traffic congestion, reduce the energy consumption and protect the environment.

Increasing population, rapid urbanization and hasty industrialization are assisting gradual shift in human residence from rural to urban. Any city trying to become a smart and sustainable one must provide a better quality of life for its citizens and address the problem of degrading air quality and increasing pollutant emissions. The solution for these problems requires selection and decision-making among a number of candidate emission-control-alternatives that need to satiate a number of technical constraints, policy criteria and regulations. For a given pollutant and emission source, a number of control technologies are available; hence, it is coveted to choose the best combination among them to reduce emissions to a desired standard. Because of its applicability, flexibility and ease of computation in solving large-scale practical problems, dynamic programming-based approach appears to be the appropriate and feasible choice for optimal air pollution control technology selection for an urban metropolitan area. Current study develops a dynamic programming (DP) model that determines the optimal selection strategy, after defining different parameters, at a minimized total cost. The usage and applicability of the proposed model were illustrated with a representative case study of a simulated city with three major sources of pollution. It is inferred that DP is ideal for the ‘multiple sources—multiple control technologies—single air pollutant’ optimization problem.

At present, “parts-to-picker” picking mode is one of the hotspots in the field of logistics warehousing. In this paper, the problem of multi-logistics robot task assignment is studied. It is proposed that the load balancing of a single robot is based on minimizing the total cost of robot execution tasks, and an improved auction algorithm is set to solve the problem. Firstly, an optimization model that comprehensively considers the total cost of the robot to perform tasks and the load balancing of a single robot is established, so that the result of task assignment is more in line with the actual situation. Then consider the correlation between tasks, using the improved auction algorithm for the task solving of the robot. The experimental results show that compared with the traditional auction algorithm, under the condition of ensuring that the total cost of executing multi-logistics robot is minimized, the cost of each robot to perform tasks can be balanced, and the task assignment is more reasonable.

Green transportation has always been the focus of international attention. With the proposal of “One Belt And One Road” and the emphasis on logistics efficiency and cost at home and abroad, multimodal transport, as an advanced form of transport organization, has been developing continuously. However, there are few studies on carbon emission of multimodal transport in domestic and foreign literatures. In recent years, high-speed railway has become an indispensable way for Chinese tourists to travel, and the use of high-speed railway in the field of logistics also arises at the historic moment. Order is proposed in this paper for a batch of goods, by air, high-speed rail and highway combination of three kinds of transport mode, build the satisfying path capacity constraints, hard time window to minimize the total transportation cost under the restriction of the mathematical model of the total cost including transportation cost of transport costs, transport costs and carbon emissions, and using ant colony algorithm to solve the model, then use different local optimization strategy for processing, finally it is concluded that the optimization results are verified through the calculation example.

Nowadays, electrical energy is playing vital role in human life. The economic operation of the power system is always desirable and this can be achieved by economic load dispatch. The economic load dispatch means allocating the power to different generating units to minimize or reduce the total fuel cost satisfying the different power system constraints. The main purpose of economic load dispatch (ELD) is to allocate the total power at generating units to meet total load demand with minimum operational cost fulfilling all operational obstacles (constraints). For optimal dispatch of power, different evolutionary optimization techniques are being used like CSO, PSO, etc. In this paper, PSO has been implemented on MATLAB to minimize the total cost for 3-unit and 6-unit systems and the results are compared with CSO and other evolutionary optimization techniques for different power demands.

Transfer pricing is the price between two related parties. Multinational companies have a tendency to shift their profits from the country where they operate to their home countries. The objective of this study is to examine the effects of transfer pricing on profit shifting by multinational enterprises in Rwanda. The study tests transfer pricing’s factors such as finance costs, intra-group transactions/services costs, and royalty expenses on profit shifting by multinational companies in Rwanda. The study is guided by the theory of optimal transfer prices, agency theory, and accounting theory. The study adopts a quantitative research design with a target population of 72 Multinational Enterprises registered in the large taxpayer’s offices. It collected data from financial statements and uses inferential statistics to ascertain the effects of transfer pricing on profit shifting. The study finds that multinational companies shift profits through royalties, finance costs, and intra-group services. The study concludes that a unit change in the independent variables influences total costs as well as taxable incomes. The study recommends that the Rwanda Revenue Authority (RRA) should come up with a clear law or legislation on transfer pricing.