Effects of Customized Skin Diagnosis Service on Participation-based Consumer Segmentation

Purpose: This study aimed to verify the possibility of consumer segmentation of customized cosmetic skin diagnosis services by identifying the needs of potential customers related to their consumption value, participation level, and pursuit benefits for each type of customized cosmetic service.Methods: An online survey was conducted for 13 days in September 2020 on individuals aged at least 20 years, living in Seoul and its metropolitan area. Among 483 received responses, only 393 were used for an analysis; insufficient responses and those written by residents living outside of the included region were excluded. Data were analyzed as per frequency analysis, factor analysis, reliability analysis, one-way ANOVA, post-hoc analysis, correlation analysis, and regression analysis.Results: Findings confirmed that consumer segmentation is possible in the customized cosmetic skin diagnosis services and customized ingredients services. After analyzing the average difference of consumption value, participation level, and pursuit benefits, the monthly average purchasing cost in cosmetics showed significant differences. Participation level, consumption value, and benefits were positively correlated. Participation level affects the consumption value.Conclusion: The customized cosmetic skin diagnosis service can serve as the foundation for the skin cosmetic industry development and as part of systematic and secure skincare.

Customer demand-driven low-carbon vehicles combined strategy and route optimisation integrated decision

With the optimal operating cost and optimal carbon emission target of the chemical logistics companies, a low-carbon routing optimisation with a multi-energy type vehicle combined problem is proposed by considering the concept of the logistics companies’ low-carbon behaviour. An integrated decision-making of multi-energy type vehicles combined strategy and route optimisation based on customer demand is presented, and an improved genetic algorithm is designed. A case study is then applied based on the data collected from the case research. The effectiveness of the improved genetic algorithm is tested. The two joint objectives of operating cost and carbon emission are examined through the cost analysis of environmental energy vehicles and traditional energy vehicles in different combination scenarios. The case analysis shows that a rational multi-energy type vehicle combination with route optimisation has a significant correlation with the operating cost and carbon emissions, while the environmental vehicle purchasing cost reduction and subsidy policy affect the operating cost.

Minimizing the Cost of Automotive Accidents by Optimizing the Design of Advanced Driver Assist Systems: An Empirical Study Based on a Full-Size Light-Duty Pickup Truck

Advanced Driver Assistance Systems (ADAS) have become increasingly common in vehicles in the last decade. The majority of studies has focused on smaller vehicles with gross vehicle weight rating (GVWR) under 5,000lbs, predominantly sedans, for their ADAS evaluations. While it is sensible to use this style of vehicle because it is ubiquitous worldwide for a typical vehicle body style, these studies neglect full-size light-duty pickup trucks (FSLDPTs), GVWR 5,000 – 10,000lbs, which are abundant on the roads in the United States, 18% of vehicles. The increase in mass, higher center of gravity, and utilitarianism of the vehicles allows for unique conditions for studying the effects of ADAS. This work determines the best and worst location to be hit in a full-size light-duty pickup truck based on data for the industry sales leader in this class of vehicles. The objective is to use these results for future designs of ADAS technologies and their placement on the FSLDPT. While these methods could be applied to any vehicle, the FSLDPT sales leader will be investigated as it represents about 9% of registered vehicles in the United States. The results will be optimized with respect to cost in terms of initial up-front purchasing cost and post-accident vehicle repair cost.

Production/Inventory Policies for a Two-Echelon System with Credit Period Incentives

Trade credit is a crucial source of capital particularly for small businesses with limited financing opportunities. Inventory models considering trade credit financing have been widely studied. However, while there is extensive research on the single-vendor single-buyer inventory model allowing delays in payments, the systems where the vendor supplies to more than one buyer have received less attention. In this paper, we analyze a two-echelon inventory system where a single vendor supplies an item to two buyers who face a constant deterministic demand. The vendor produces the items at a finite rate and offers the buyers a delay payment period. That is, the buyers can delay the payment for the purchased items until the end of the credit period. Therefore, during such a period, the buyers sell the items and use the sales revenue to earn interest. At the end of the credit period, the buyers should pay the purchasing cost to the vendor for which external funding may be necessary. It is widely accepted that, in general, centralized policies reduce the total cost of the supply chain. Therefore, we first deal with an integrated model assuming that the vendor and the buyers make decisions jointly. However, in some cases, the buyers are not willing to collaborate, and the management of the supply chain has to be carried out in a decentralized manner. Hence, we also address the problem under a non-cooperative setting. Numerical examples are presented to illustrate both models. Additionally, we perform a computational experiment to compare both strategies, and a sensitivity analysis of the parameters is also carried out. From the results, we derived that, in general, it was more profitable to follow the integrated policy excepting when the replenishment costs for the buyers were high. Finally, in order to validate the computational results, a statistical analysis is performed.

Optimal policies for a deterministic continuous-time inventory model with several suppliers: a hyper-generalized (s,S) policy

A deterministic continuous-time continuous-state inventory model is studied. In the absence of intervention, the level of stock evolves by a process governed by a differential equation. The inventory level is monitored continuously, and can be adjusted upwards at any time. The decision maker can order from several suppliers, each of which charges a different ordering and purchasing cost. The problem of selecting the supplier and the size of the order to minimize the total inventory cost over an infinite planning horizon is formulated as the solution of a quasi-variational inequality (QVI). It is shown that the QVI has a unique solution. This corresponds to a generalized $(s,S)$ policy under amenable conditions, which have been characterized in an earlier work by the present authors. Under the complementary conditions a new type of optimal control policy emerges. This leads to the concept of a hyper-generalized (s,S) policy. The theory behind a policy of this type is exposed.

An Inventory Model with Stock-Dependent Demand Rate and Maximization of the Return on Investment

This work presents an inventory model for a single item where the demand rate is stock-dependent. Three fixed costs are considered in the model: purchasing cost, ordering cost and holding cost. A new approach focused on maximizing the return on investment (ROI) is used to determine the optimal policy. It is proved that maximizing profitability is equivalent to minimizing the average inventory cost per item. The global optimum of the objective function is obtained, proving that the zero ending policy at the final of a cycle is optimal. Closed expressions for the lot size and the maximum ROI are determined. The optimal policy for minimizing the inventory cost per unit time is also obtained with a zero-order point, but the optimal lot size is different. Both solutions are not equal to the one that provides the maximum profit per unit time. The optimal lot size for the maximum ROI policy does not change if the purchasing cost or the selling price vary. A sensitivity analysis for the optimal values regarding the initial parameters is performed by using partial derivatives. The maximum ROI is more sensitive regarding the selling price or the purchasing cost than regarding the other parameters. Some useful managerial insights are deduced for decision-makers. Numerical examples are solved to illustrate the obtained results.

A profit jump inventory model for imperfect quality items with receiving reparative batch and order overlapping in dense fuzzy environment

This paper presents an economic order quantity (EOQ) inventory model for imperfect quality items with receiving a reparative batch and order overlapping in a dense fuzzy environment Here, the imperfect items are identified by screening and are divided into either scrap or reworkable items. The reworkable items are kept in store until the next items are received. Afterwards, the items are returned to the supplier to be reworked. Also, discount on the purchasing cost is employed as an offer of cooperation from a supplier to a buyer to compensate for all additional holding costs incurred to the buyer. The rework process is error free. An order overlapping scheme is employed so that the vendor is allowed to use the previous shipment to meet the demand by the inspection period. However, we assume the total monthly demand quantity as the dense fuzzy number because of learning effect. Moreover, first of all a profit maximization deterministic model is developed and solve by classical method. Fuzzifying the final optimized function via dense fuzzy demand quantity we have employed extended ranking index rule for its defuzzification. During the process of defuzzification we make an extensive study on the paradoxical unit square of the left and right deviations of dense fuzzy numbers. A comparative study is made after splitting the model into general fuzzy and dense fuzzy environment. Finally numerical and graphical illustrations and sensitivity analysis have been made for its global justifications.

Zinc ore supplier evaluation and recommendation method based on nonlinear adaptive online transfer learning

<p style='text-indent:20px;'>Purchasing decisions determine the purchasing cost, which is the largest section of the production cost of zinc smelting enterprise(ZSE). An excellent supplier recommendation is significant for ZSE to reduce the cost. However, during the supplier recommendation process, the nonlinear demand feature of purchasing department varies with the production environment, and there are wrong samples that can affect the supplier recommendation effect. To handle these problems, the recommendation strategy based on a multiple-layer perceptron adaptive online transfer learning algorithm(AOTLMLP) are proposed. In this method, the original prediction function is modified based on MLP nonlinear projective function and adaptive loss function, which enables the AOTLMLP algorithm to tackle the nonlinear classification problems and efficiently follow the demand change of purchasing department, thereby improving the result of the recommendation. The performance of the AOTLMO algorithm is evaluated through a common dataset and a purchasing dataset from a zinc smelter that generated by a supplier evaluation model. It can be assumed that AOTLMLP can ignore the influence of wrong samples and provide an effective recommendation confronting the characteristic of zinc ore purchasing.</p>