Setup Cost Reduction Research Articles

A waste reduction strategy through autonomation under a closed-loop supply chain management

Currently, apart from manufacturing processes, the remanufacturing of products is considerably important. Appropriate remanufacturing requires the operation of long-run manufacturing systems. However, in long-run processes, the production system may convert to an out-of-control state due to machine breakdowns. Then, defective products are frequently produced; this increases wastage and disrupts environmental sustainability. In this model, a smart autonomation policy is deliberated for an error-free inspection in separating defective products during production. The autonomation policy facilitates waste reduction through remanufacturing. This paper concentrates on customer awareness and service-dependent demand, which directly improves the overall profitability of the system. A discrete investment to reduce setup cost, continuous investment to collect used goods, and cap-and-trade strategy to limit carbon emission are considered to obtain a more realistic model. Classical optimization method is applied for global maximum profit test of the profit function with respect to cycle length, customer awareness, service investment, discrete investment to reduce setup cost, number of shipments, and container capacities. Numerical testing, sensitivity to total profit in different cost parameters, and comparisons with previous research are explained. Some special scenarios including graphical representations are discussed to prove that a large investment is more beneficial than the cost of specific setup and collection.

A Sustainable Supply Chain Model with a Setup Cost Reduction Policy for Imperfect Items under Learning in a Cloudy Fuzzy Environment

The present paper deals with an integrated sustainable supply chain model with the effect of learning for an imperfect production system under a cloudy fuzzy environment where the demand rate is treated as a cloudy triangular fuzzy (imprecise) number, which means that the demand rate of the items is not constant, and shortages and a warranty policy are allowed. The vendor governs the manufacturing process to serve the demand of the buyer. When the vendor supplies the demanded lot after the production of items, it is also considered that the delivery lots have some defective items that follow an S-shape learning curve. After receiving the lot, the buyer inspects the whole lot, and the buyer classifies the whole lot into two categories: one is the defective-quality items and the other is the imperfect-quality items. The buyer returns the defective-quality items to the seller after a screening process, for which a warranty cost is included. During the transportation of the items, a lot of carbon units are emitted from the transportation, damaging the quality of the environment. The seller includes carbon emission costs to achieve sustainability as per considerations. A one-time discrete investment is also included for the minimizing of the setup cost of the seller for the next cycles. We developed models for the scenario of the separate decision and for the integrated decision of the players (seller/buyer) under the model’s consideration. Our aim is to jointly optimize the integrated total fuzzy cost under a cloudy fuzzy environment sustained by the seller and buyer. Numerical examples, sensitivity, analysis limitations, future scope and conclusions have been provided for the justification of the proposed model, and the impact of the input parameters on the decision variables and integrated total fuzzy cost for the supply chain are provided for the validity and robustness of this proposed model. The effect of learning in a cloudy fuzzy environment was positive for this proposed model.

CryptoMaze: Privacy-Preserving Splitting of Off-Chain Payments

Payment protocols developed to realize off-chain transactions in Payment channel network (PCN) assumes the underlying routing algorithm transfers the payment via a single path. However, a path may not have sufficient capacity to route a transaction. It is inevitable to split the payment across multiple paths. If we run independent instances of the protocol on each path, the execution may fail in some of the paths, leading to partial transfer of funds. A payer has to reattempt the entire process for the residual amount. We propose a secure and privacy-preserving payment protocol, CryptoMaze. Instead of independent paths, the funds are transferred from sender to receiver across several payment channels responsible for routing, in a breadth-first fashion. Payments are resolved faster at reduced setup cost, compared to existing state-of-the-art. Correlation among the partial payments is captured, guaranteeing atomicity. Further, two party ECDSA signature is used for establishing scriptless locks among parties involved in the payment. It reduces space overhead by leveraging on core Bitcoin scripts. We provide a formal model in the Universal Composability framework and state the privacy goals achieved by CryptoMaze. We compare the performance of our protocol with the existing single path based payment protocol, Multi-hop HTLC, applied iteratively on one path at a time on several instances. It is observed that CryptoMaze requires less communication overhead and low execution time, demonstrating efficiency and scalability.

Coordination supply chain management in flexible production system and service level constraint: A Nash bargaining model

The interest conflicts significantly impact the supply chain (SC) functioning. Hence, it is imperative to establish a suitable coordination mechanism to eliminate the SC conflicts. This paper focuses on coordination in a two-member SC with a flexible production system under buyer’s service level constraint and stochastic demand. The study adopts a multiple inspection policy and utilizes a discrete investment function for setup cost reductions. Bargaining is the fundamental subject of any business presently within the SC players to profit from a cooperative centralized model. The authors develop three SC models, a buyer Stackelberg model, a centralized decision model, and a Nash bargaining model. The cost allocation ratio with different bargaining powers gives a better representation of the cost-sharing scenario—Study attempt to associate bargaining with centralization and decentralization. Discrete investment helps to reduce the total cost further in the presence of a cost allocation ratio. A controllable lead time is used to relate the cost allocation ratio within the SC model. The model is solved analytically by a game-theoretic approach. Numerical example and sensitivity analysis are included to validate the proposed models. The results show the centralized cooperative model based on Nash bargaining significantly improves the overall profit of the SC. Therefore, when vendor and buyer cooperate to form a centralized SC, the SC can be optimized to achieve the dual objectives of increasing profit and improving customers’ service.

An efficient sustainable smart approach to biofuel production with emphasizing the environmental and energy aspects

By using renewable biomass, the advancement in biofuel production through a smart inspection production framework is a crucial substitute to fossil fuels which support minimizing environmental pollution and zero carbon emissions. Traditional biofuel production can be converted into a sustainable smart production system by utilizing smart machines, skilled labor, and reduced energy consumption. The prime purpose of this study is to produce pure biofuel with less energy consumption and carbon emissions through a smart production framework of biofuel. The work-in-process inventory is calculated for different costs, including air handling, lighting, and carbon emissions, in each stage of the study. Although smart variable production is utilized, still impure biofuel is produced with a random production rate. The impure biofuel is purified to make it pure. A multi-delivery technique is used such that a fixed amount of biofuel of n segments of the pure biofuel is transported to the market as per the market’s demand. The total profit of this study is maximized globally with the help of the classical optimization technique. Four numerical examples are observed to validate the model. The applicability and efficiency of the proposed model are finally demonstrated through a real case study in India. Two special cases prove the necessity of smart production and discrete investment for setup cost reduction. The sensitivity analysis and the graphical representation give each parameter’s effect on the model’s total profit. Numerical results prove that if no discrete investment for setup cost reduction and fixed production rate is taken, the common production cycle time is increased remarkably. It is seen that the total profit of this study rises 4.30% from the traditional production system. Moreover, numerical results prove a major effect on renewable energy in a sustainable smart production system.

The role of humans in flexible smart factories

In the smart factory of production automation, the setup between different products is the key to realizing flexibility. Compared with workers in traditional factories responsible for production and setup, workers in smart factories are only tasked with setup. Under this task variation, we model the optimal setup of a flexible smart factory, assuming that setup time is a convex function of the human scale. The model considers tradeoffs between machine production, setup, and mismatch costs. We find that the optimal scale of setup workforce increases as technological progress leads to a decrease in the production cost of a smart factory. Therefore, different from the general perception, there will be no dark factory for flexible smart factories. Heterogeneous orders in smaller lot size lead to a more significant role for human beings in flexible smart factories. If mismatch costs increase, more investment is required to reduce setup costs. There is a square-root relationship between the optimal scale of workforce and setup cost, product cost, and productivity under the strongest individual demand conditions. Our model enables a flexible smart factory to make more rational workforce planning and investment decisions.

Two-stage variable-fidelity modeling of antennas with domain confinement

Surrogate modeling has become the method of choice in solving an increasing number of antenna design tasks, especially those involving expensive full-wave electromagnetic (EM) simulations. Notwithstanding, the curse of dimensionality considerably affects conventional metamodeling methods, and their capability to efficiently handle nonlinear antenna characteristics over broad ranges of the system parameters is limited. Performance-driven (or constrained) modeling frameworks may be employed to mitigate these issues by considering a construction of surrogates from the standpoint of the antenna performance figures rather than directly geometry parameters. This permits a significant reduction of the model setup cost without restricting its design utility. This paper proposes a novel modeling framework, which capitalizes on the domain confinement concepts and also incorporates variable-fidelity EM simulations, both at the surrogate domain definition stage, and when rendering the final surrogate. The latter employs co-kriging as a method of blending simulation data of different fidelities. The presented approach has been validated using three microstrip antennas, and demonstrated to yield reliable models at remarkably low CPU costs, as compared to both conventional and performance-driven modeling procedures.

The effect of social capital on the career choice of entrepreneurship or employment in a closed ecosystem

Restricted by China’s “hukou” system, the population in any given area of the country is relatively constant, and its employability and workability are mutually determined, as in a closed ecosystem. Social capital (or “guanxi” in Chinese society) in China has the effects of reducing set-up costs for entrepreneurship or securing the job-seeking for employment. This paper uses equilibrium analysis and makes some modifications to Kihlstrom and Laffont’s model to explore career choice mechanisms in China’s context. It was found that when social capital only reduces the set-up costs for entrepreneurship, there exists one equilibrium point; individuals with more social capital will choose entrepreneurship, and those with less social capital will be workers or unemployed. When social capital simultaneously reduces the set-up costs for entrepreneurship and secures employment, four equilibrium points appear along with the strength of social capital, and the career options occur in the order of entrepreneurship, employment, entrepreneurship, and unemployment. The findings fill the gap that career choice is mainly determined by an individual’s risk-aversion and contribute specifically to China’s entrepreneurship and employment selection.

IoTaaS: Drone-Based Internet of Things as a Service Framework for Smart Cities

The Internet of Things (IoT) offers new services in the context of smart cities through digital devices embedded with sensing, computation, and communication capabilities. The IoT devices enhance the smart city vision by employing advanced communication and computation technologies for smart city administrations. The IoT-based smart city applications require many IoT devices and gateways to be deployed at different city points. Heterogeneous sensing devices, placing smart devices in a constrained or physically inaccessible area, and large urban areas to monitor together make IoT node deployment and sensing management tasks difficult, time-consuming, and expensive. Additionally, certain tasks may require smart devices to be deployed for a very short period of time to sense and report contextual information, making it economically infeasible to purchase the devices. In this regard, we propose a drone-based IoT as a Service (IoTaaS) framework that enables the dynamic provisioning or deployment of IoT devices using drones. IoTaaS allows IoT devices and gateways to be mounted on drones and provides a distributed cloud service by placing the IoT devices in an area according to the requirements specified by a user. We also provide an economic analysis for operating such drone-based IoT services. A proof-of-concept implementation of IoTaaS for smart agriculture and air pollution monitoring applications shows that IoTaaS can reduce setup costs and increase the usage of IoT devices.

Carbon-emission and waste reduction of a manufacturing-remanufacturing system using green technology and autonomated inspection

Environmental-friendly technology helps to reduce waste and carbon emissions of an imperfect production system. In general, the defective products generated during the “out-of-control” state are treated as waste. The single-stage manufacturing-remanufacturing system effectively depletes such defective spare parts within the same cycle but causes a tremendous amount of carbon. In such a circumstance, green technology to reduce carbon emissions is highly recommended. Also, the autonomated inspection makes defective detection more reliable and is ultimately helpful for waste reduction. Hence, in this study, we optimize the production plan along with the investments for applying green technology and autonomated inspection in an assembled product manufacturing-remanufacturing system. The numerical result shows that the appropriate green technology decreases carbon emissions up to 2.81% and autonomated inspection reduces the waste up to 2.37%, along with a reduction of entire production cycle cost up to 18.26%. In addition, the setup cost reduction is considered due to the characteristics of assembled product production.

Controlling defective items in a complex multi-phase manufacturing system

In manufacturing systems, defective items are produced for machine drift and error. Usually, an imperfect production rate is random, and if the items are not reworked, these are considered trash and harm the environment. The proposed model aims to reduce waste by reworking defective products and maximizing profit. For profit maximization or overall cost minimization of the manufacturing system, setup cost has significant. A discrete investment for each phase is introduced with an inequality investment constraint for reducing the setup cost. Selling price-dependent demand is trained for more generalized applications for various industries. The proposed model is a multi-phase manufacturing system with optimum batch size, selling price, and investment with an irregular, imperfect production rate. Defects are detected at the first inspection, and the reworked items are checked if the reworked items are all non-defective in the second inspection. The model conducts a two-stage inspection. One is for detecting defective items, and another is for checking if all items are not defective after reworking. The model is solved with the Karush–Kuhn–Tucker (KKT) method, and the global maximum profit is obtained. The model shows that all investments should be assigned to maximize the profit and the optimal solution. Reducing setup cost with the investment is better than a constant setup cost.

Definition of simplified fatigue tests using numerical optimization

“Virtual prototyping” has been established as an important part of the development process for technical components and technical systems. Fatigue simulation is a main discipline in virtual prototyping, but it is a quite complex field, especially for structures being exposed to time-varying multi-channel loads. Thus, at the end of the development process, the series approval for technical components is often based on extensive fatigue tests, particularly with regard to automotive chassis parts.Currently, test-based fatigue qualification gathers interest in other process phases, for instance• to assure fatigue safety just for non-series / prototype parts• to make quality checks during series parts production on the supplier’s site• to validate fatigue simulation methods just within the first phases of the development processMain requirements concerning these fatigue tests are a simplified test set-up (cost reduction) as well as a short test duration (time reduction). These requirements are interpretable as constraints of a kind of “fatigue test optimization”. Reproduction of the results of the “full” fatigue test is the main objective of this optimization task.Some meaningful aspects of fatigue test simplification being essential for “classic” (manual) optimization are clarified using the main structural part of a double wishbone wheel suspension exemplarily. A very new method of numerical optimization to be presented overcomes limits and restrictions of manual optimization. It’s software implementation promises efficient fatigue test optimization by providing high quality results.

Multi-echelon green supply chain model with random defectives, remanufacturing and rework under setup cost reduction and variable transportation cost

Multi-echelon green supply chain model with random defectives, remanufacturing and rework under setup cost reduction and variable transportation cost

Efficient algorithms for the joint replenishment problem with minimum order quantities

Suppliers often impose a minimum order quantity (MOQ) to ensure that production runs and shipping quantities of each specific item are economically viable. Additional economies of scale may arise as various items share high joint costs (e.g. the cost of an overseas container shipment) and require coordination of their replenishment policies. The buyer needs to find the joint ordering interval and replenishment policies for each of the individual items to minimize the system-wide ordering and inventory costs while satisfying the quantity restrictions. We focus on the case of constant demand without backlogging. Given a fixed joint reorder interval, we characterize the optimal inventory ordering strategy for each item, which we refer to as Multi-Replenishment (MR) inventory ordering policy, and derive a closed-form expression for the optimal average inventory costs per unit of time. In contrast to the previous literature, the MR inventory ordering policy does not follow the zero-inventory-ordering (ZIO) rule and allows for orders of different sizes over time to optimally accommodate the MOQ restriction. A numerical approach is used to determine the optimal joint reorder interval, and is later extended to account for the presence of empty replenishments. An extensive computational study shows 1) the total inventory and setup cost reduction associated with the MR inventory policies and empty replenishments; 2) the impact of various parameters on cost and policy performance; and 3) the loss associated with discretizing time at different levels of granularity (weeks, days, hours, minutes, seconds) to accommodate restrictions in the timing of item deliveries.

Involvement of controllable lead time and variable demand for a smart manufacturing system under a supply chain management

The concept of controllable lead time and variance is critical issues for the smart supply chain management. This study concerns about variable lead time and variance under controllable production rate and advertise-dependent demand. Managers of any supply chain always improve their performance by reducing lead time and its variance. This paper explores and quantifies these benefits of such lead time reduction for commonly used lot size quantity, production rate, safety factor, reorder point, advertisement cost, vendor’s setup cost. Instead of expected total cost equations, this study provides an exact total cost equation built on an inherent relationship between on-hand inventory and backorder. The marginal value analysis on lead time and its variance achieve more accurate results. The analytical results show that the total supply chain cost is a convex function of both lead time and variance. In other words, the cost savings on both lead time and its variance reduction decrease when lead time becomes larger. Two continuous investments are implied to reduce setup costs and improve the reliability of the production process. The expected backorder and inventory for the buyer uniformly distributed throughout reorder point. Moreover, a smart production process is developed under stochastic demand and flexible production rates. The global optimality of the cost function and decision variables are validated through classical optimization. The numerical examples confirm analytical results and sensitivity analysis is provided for different parameters. Some special cases along with graphical representations are given to validate the model.

A novel approach to high-pressure gas releases simulations

Risk-relevant plants like Oil & Gas (O&G) or nuclear ones are subjected to strict safety regulations. Risk Assessment is mandatory for these plants, and the damage quantification is a crucial step which has to be carefully addressed.Nowadays the state of practice for consequences estimation entails the use of semi-empirical methods which permit a fast evaluation of the large number of accidental scenarios needed for a Quantitative Risk Assessment (QRA).However, in case of large and congested industrial environments like offshore platforms or equipment inside nuclear primary containment buildings, the aforementioned methods usually lead to an overestimation of the damage areas, for example because they neglect the space congestion which highly affects the accident evolution. A more accurate analysis can be performed using the Computational Fluid Dynamics (CFD), nonetheless its high computational cost represents an important drawback.In this work a CFD approach, called Source Box Accident Model (SBAM), is presented. It models high-pressure gas releases (>10 bar) in congested environments guaranteeing a good computational cost-accuracy compromise. The aim of SBAM is to permit a fast consequences estimation (evaluation of flammable/toxic areas, etc.) via CFD, in order to have a simulations time compatible with the plants design schedule. The long-term objective is to integrate the CFD contribution in a safety driven design process.SBAM is based on the splitting of the multi-physics and multiscale phenomena characterizing the accident: the initial supersonic compressible release and the successive low speed dispersion. The first one is simulated in a small domain called Source Box (SB) and the second one in the case study domain.The two simulations are coupled in a suitable way, through proper parameters which are extensively discussed. This work presents a detailed description of SBAM and two different analyses: a sensitivity study on the coupling parameters and a numerical benchmark which uses a standard CFD simulation as reference. The sensitivity analysis shows that the coupling is a crucial step of the method and the coupling parameters must be treated in the most accurate way. The numerical benchmark shows that SBAM is not introducing significant errors with respect to a standard CFD simulation and, in addition, permits a relevant simplification in the simulation setup and computational cost reduction.

Laboratory-Scale Research of Non-Catalyzed Supercritical Alcohol Process for Continuous Biodiesel Production

This work investigates the non-catalyzed supercritical methanol (SCM) process for continuous biodiesel production. The lab-scale setup was designed and used for biodiesel production in the temperature range of 520–650 K and 83–380 bar with an oil-to-methanol molar ratio ranging from 1:5 to 1:45. The experiments were performed in the coiled plug flow tubular reactor. The volumetric flow rate of the methanol/oil ranged from 0.1–10 mL/min. This work examines a new reactor technology involving preheating and pre-mixing of the methanol/oil mixture to reduce setup cost and increase biodiesel yield under the same reaction conditions. Work performed showed that FAME’s yield increased rapidly with temperature and pressure above the methanol critical points (i.e., 513 K and 79.5 bar). The best methyl-ester yield using this reaction technology was 91% at 590 K temperature and 351 bars with an oil-to-methanol ratio of 39 and a 15-min residence time. Furthermore, the kinetics of the free catalyst transesterification process was studied in supercritical methanol under different reaction conditions.

A Multi-Item Sustainable Manufacturing Model with Discrete Setup Cost and Carbon Emission Reduction Under Deterministic and Trapezoidal Fuzzy Demand

A Multi-Item Sustainable Manufacturing Model with Discrete Setup Cost and Carbon Emission Reduction Under Deterministic and Trapezoidal Fuzzy Demand

Price Competition and Setup Cost

Few studies analyze the endogenous emergence of price competition in a new product market. This paper analyzes two differentiated products, an existing product and a newly introduced substitutable product, and investigates conditions under which a price competition endogenously emerges in a new product market in the context of a choice between engaging in price competition and holding price leadership. We demonstrate that Bertrand price competition emerges when the setup cost for the new product is high enough. This result implies that government policies reducing setup costs such as subsidies could change the type of competition to price leadership in a new product market.

Delay-Sensitive Multi-Source Multicast Resource Optimization in NFV-Enabled Networks: A Column Generation Approach

Telecommunication networks are currently realizing more-huge-than-ever data demands from subscribers all over the world. Due to the ongoing pandemic, nearly all businesses have adapted working models with remote operations. People engaged with major industries, e.g., academia, health and municipalities are utilizing online platforms to carryout their routine tasks. This indeed shifts the attention from one-to-one (unicast) communication to one-to-many (multicast) and many-to-many (multi-source multi-destination) communications. Network operators are facing increased pressure to provide quick responses in order to satisfy the bandwidth hungry and time sensitive user demands. This can only be done by enhancing deployability as well as manageability of the services. Network Function Virtualization (NFV) provides a transformation of traditional proprietary network designs to a more agile and software based environment in order to achieve flexible deployments, reduced setup costs and less-time-to-market for the new services which is very much needed in the current scenarios. Previous studies on NFV-enabled multicast problem either proposed Integer Linear Program (ILP) models, that are pretty unscalable, or heuristic-based techniques that do not guarantee good quality of the solutions obtained. In this article, we propose an NFV multicast resource optimization model exploiting the use of multiple sources and considering the end-to-end delay and bandwidth requirements. Herein, we propose a novel Dantzig-Wolfe (DW) decomposition model that tackles the complexity of the problem by breaking it down into a master problem and several pricing problems. We compare the DW approach with the ILP and heuristic methods and demonstrate that our approach achieves near to optimal solution (in comparison to heuristic based methods) much faster than ILP. We also study the dynamic admission of NFV-enabled multicast requests by solving the problem in an online manner using the batch processing of requests. We then evaluate the performance of the proposed algorithms through extensive simulations and demonstrate that proposed algorithms are promising and outperform existing solutions.