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A Two-Echelon Pharmaceutical Supply Chain Optimization via Genetic Algorithm
Abstract
The two-echelon supply chain model consists of two separate components that have diverse objectives. In this study, a supply chain (SC) is modeled as a two-echelon supply chain consisting of one supplier, one retailer, and one product at a pharmaceutical supply chain (P-SC). The purpose of optimization is to maximize the total profit generated during sales and distribution of the product. Sales take place at the pharmaceutical retailer, and the demand encountered is stochastic, and order periods are determined by the frequency of visits by the drug supplier. Considering this visit frequency, the pharmaceutical retailer follows a periodic review inventory model. For the retailer, the decision variable is the safety factor determined according to the stated order level. It determines the pharmaceutical supplier's profitableness by influencing the sales volume of the P-SC. The problems were optimized with two different scenarios and two different models: the traditional SC model and the two-echelon supply chain model. The heuristic model of these problems provides more profitability for both echelons.
... Genetic algorithm (GA) optimization techniques are inspired by the principles of natural selection and evolutionary biology [38] firstly introduced by Holland in 1975 [39]. These algorithms simulate the process of natural evolution to solve complex optimization problems [40]. The fundamental concept involves representing potential solutions as "chromosomes" and iterative improving these solutions through genetic operators such as selection, crossover, and mutation. ...
Storage operations, order-picking, and product-handling processes have become increasingly important in today’s industrial environment. These operations are a huge burden for businesses in terms of time and cost, but they often do not add direct value to products or services. Therefore, it has become essential to improve the storage operations to the highest quality, reduce the costs arising from storage, and increase customer satisfaction. This study compared genetic algorithm (GA) and simulated annealing (SA) methods with existing real results and operations in order to minimize the distance traveled by the picker in order-picking systems, optimize routes, and increase operational efficiency in the medical textile industry. In the analyses conducted on product-based, list-based, and order-based strategies, real data sets were used to examine the performance of both methods in detail. The study results revealed that GA reduced the total travel distance by 50% and reduced the total number of tours from 51 to 32. In addition, the SA method provided efficient results in certain scenarios, but GA showed superior performance in terms of minimizing the distance and number of tours. While the product-based strategy provided the best results regarding travel distance and number of tours, the list-based approach showed a balanced performance. The study offers significant improvement potential in logistics operations by reducing distances by up to 37% and increasing operational efficiency by up to 50% in order-picking processes.
... Statistical analysis methods (Ghadimi and Aouam, 2021) usually adopt statistical principles to determine a proper result of safety stock. This kind of methods usually works well with stock data that have relatively higher demand and smooth fluctuation, such as the static inventory optimization of pharmaceutical supply chain (Yildirim and Denizhan, 2022). In general, these two kinds of methods are mainly applicable to the business with frequent and stable SPD, but they are not suitable to large manufacturing enterprises whose maintenance logic is rather complex. ...
As a key issue of inventory management for enterprise after-sales service, safety stock is dedicated to ensuring maintenance reliability while keeping low inventory cost. Existing researches of safety stock are mainly designed for frequent and stable spare parts demand (SPD) rather than fluctuating and intermittent demands in practical operation and maintenance of large manufacturing enterprises. Consequently, emergency restocking will be frequently triggered, or an over-safe inventory level with excessive inventory cost will be set. This paper proposes a dynamic safety stock model for after-sales service of large manufacturing enterprise. First, this paper builds a multi-objective safety stock optimization model based on a three-level warehousing architecture. The multi-objective genetic algorithm is adopted to calculate the static values of reorder point (RP) and maximum stock (MS) by simultaneously minimizing excessive inventory cost and shortage cost. Second, this paper proposes a robust forecasting algorithm for multi-variable intermittent time series by integrating the inter-sequence structured information and temporal evolution information of sequence itself. Finally, this paper designs a dynamic information fusion mechanism by integrating the demand prediction values into the static stock. An actual after-sales spare parts dataset from a Chinese large rail transit manufacturing enterprise is introduced for validation. The experimental results show that the proposed model can accurately predict the demand trend of spare parts, and improve the inventory turnover rate as well as requirement coverage. This work is then believed to provide a new idea for inventory management: dynamic information can boost maintenance efficiency and safety, that is, Change is Safer.
In this investigative study, the electro-magneto hydrodynamic (EMHD) infuence on a nano viscous fuid model is scrutinized by
designing an artifcial neural network (ANN) paradigm using a neuro-heuristic approach (NHA) through the combination of GAs
(genetic algorithms) and one of the most efcient locally searching solver SQP (sequential quadratic programming), i.e., NHAGA-SQP. Te fuid fow for the proposed problem is initially interpreted in the form of PDEs and then utilization of suitable
similarity transformation on these PDEs yields in terms of a stif nonlinear system of ODEs. Te numerical results of the suggested
fuidic model based on the variation of its physically existing parameters are calculated through the NHA-GA-SQP solver to detect
the variation in velocity, thermal gradient, and concentration during the fuid fow. A detailed analysis of obtained outcomes
through the NHA-GA-SQP algorithm and their comparison with the reference results estimated via the Adams method are
presented. Te calculation of the proposed solver’s accuracy, stability, and consistency through various statistical operators is also
involved in the current inspection.
The present research-oriented study provides the application of a renowned numerical-based neuro-evolution heuristic technique by the utilization of feed-forward neural networks (FFNNs) trained with a hybrid composition of genetic algorithms (GAs) and sequential quadratic programming (SQP) i.e. FFNNs-GAs-SQP to provide a descriptive analysis on the dynamics of Prandtl-Eyring nanofluid flow over the stretchable sheet under the influence of heat and viscous dissipation. FFNNs-GAs-SQP approach is used to calculate numerical results based on the variation of temperature-difference parameter, material-parameter, stretching-parameter, heterogeneous-reactionparameter along with Biot-number, Prandtlnumber as well as the Schmidt-number. The effect of these parameters on velocity profile, temperature and nanofluid concentration is expressed in form of tables/graphs. Furthermore, uplifting the values of the temperature difference parameter and the stretching parameter enhances the nanofluid velocity. The larger values of the material parameter and Prandtl number diminish the temperature of nanofluid but this effect is reversed in the case of the Biot number and temperature difference parameter. It is also observed that a boost in the values of heterogeneous reaction parameter, Schmidt number and temperature difference parameter minimize the nanofluid concentration however this effect is reversed in Biot number and Prandtl number cases.
This study presents an extension of the integrated Pharmaceutical Supply Chain design with maximum expected coverage, where different hospitals with specific reliability value for different pharmaceutical substances are considered. A four-layer multi-period supply chain including manufacturers, Distribution Centers, hospitals and patients is considered in this research. To increase the supply chain reliability, the pharmaceutical substance flow in each individual layer is allowed. Pharmaceutical substance’s priority for each hospital varies according to its patients types. A reliability index for pharmaceutical substances is proposed to consider substances priority and improve the service level of hospitals for patients. A multi-objective model that increases demands’ coverage and minimizes the total costs is developed. Centroid method is applied to handle demand uncertainty and a Multi-objective Non-dominated Ranked Genetic Algorithm is proposed to solve the model. This algorithm can generate a Pareto front with higher quality and better diversity when compared with Multiple Objective Particle Swarm Optimization and Non-dominated Sorting Genetic Algorithm II. As a case study, the proposed model is applied for the medical sector in the Fars province of Iran. Results show that when different reliability level for various pharmaceutical substances in different hospitals is considered, significantly better results in terms of unsatisfied demand without a significant increase in costs are obtained. In addition, the proposed approach is able to provide solutions to improve the supply chain reliability in a practical pharmaceutical supply network.
Product quality and pricing, as the important competitive tools, play a key role in attracting consumers. In a supply chain, the decisions on product quality and pricing are usually interlinked and would influence the cooperation relation between the members, especially when they are fairness-concerned and have different bargaining power. However, linking the quality and pricing decisions to the decision-makers’ behavioral factors such as fairness concern draws a little attention in the literature of supply chain management. This paper incorporates the members’ fairness preference and bargaining power into the product quality and pricing decisions in a two-echelon supply chain, where the supplier offers core components with a certain quality level to the downstream manufacturer, who subsequently sells the final products in the end market. Both the supplier and the manufacturer are assumed to be fairness-concerned by adopting Nash bargaining solutions as their fairness reference points. We use game-theoretic models to analyze the equilibrium product quality and pricing strategies under the setting of integrated and decentralized supply chain, respectively. Detailed comparisons and sensitivity analysis are further conducted to examine the impacts of members’ strengths of fairness concern, bargaining power, and decision structure on their equilibrium product quality and pricing strategies and corresponding payoffs.
In this paper, the coordination of pricing and periodic review inventory decisions in a supplier-retailer supply chain (SC) is proposed. In the investigated SC, the retailer faces a stochastic price dependent demand and determines the review period, order-up-to-level, and retail price. On the other hand, the supplier decides on the replenishment multiplier. Firstly, the decentralized and centralized decision making models are established. Afterwards, a quantity discount contract as an incentive scheme is developed to coordinate the pricing and periodic review replenishment decisions simultaneously. The minimum and maximum discount factors, which are acceptable to both SC members, are determined. In addition, a set of numerical examples is conducted to demonstrate the performance of the proposed coordination model. The results demonstrate that the proposed coordination mechanism can improve the profitability of SC along with both the SC members in comparison with the decentralized model. In addition, the results revealed that the proposed incentive scheme is able to achieve channel coordination. Moreover, the coordination model can fairly share the surplus profits between SC members based on their bargaining power.
This paper proposes heuristic methods to determine an ordering policy with a minimum inventory cost while satisfying the expected service level constraint in a divergent two-echelon inventory system. A periodic-review base-stock policy with an echelon stock concept was applied at all locations. Seasonal demand with a short cycle was considered at the level of the retailers. A two-phase heuristic method was then proposed to reduce the computational time required to determine a proper policy compared to the mixed-integer programming models. The first-phase algorithm was used to determine the initial ordering policy for each location, while the second-phase algorithm was used to determine the safety stock. Both algorithms could find solutions that are as good as the solutions from the mixed-integer programming models in testing instances but they substantially reduced the computational time. The solutions were tested with other instances having a 10,000-period horizon, where the average loss was within the expected service level and the number of periods with an unsatisfied expected service level was less than 1% of the planning horizon.
The proper supply of end-of-life (EOL) items plays a critical role in the success of remanufacturing systems. In many real-world practices, a remanufacturer has multiple links with independent third party collectors, while these links should be simultaneously coordinated. However, most previous studies mainly focus on reverse supply chain (RSC) coordination in a one-to-one setting (single link). Moreover, the competition among third party collectors affects the acquisition of EOL products and this is an important issue. In this paper, an RSC consisting of a single remanufacturer and duopolistic competing third party collectors is explored and a new mechanism is proposed to achieve channel coordination across multiple links. To be specific, we consider the case when the collectors compete on the acquisition prices offered to the consumers and the remanufacturer decides on the pricing and environmental effort decisions with two considerations: 1) increasing return of EOL items and 2) boosting demand of remanufactured products. The optimal decisions of RSC members and performance of RSC are analyzed under different behaviors of competing collectors. Moreover, a multilateral two-part tariff contract is proposed to coordinate the decisions across multiple links. The results indicate that the proposed contract improves not only profits of the remanufacturer and competing collectors but also the collection quantity of EOL items and demand of remanufactured products, which helps improve the environment.
The problem is to determine a review period and stocking policy that are mutually beneficial to a producer and a retailer. In our model, the retailer uses a periodic review, base stock policy for ordering the item from the producer's Distribution Center (DC). Excess customer demand is assumed to be lost. A make-to-order production system supplies to the DC. We show that given a review period, unless the manufacturer agrees to share the cost of carrying a fraction of the safety stocks at the retailer, the two will not agree upon the level of stocks to be carried in the store. We prove that there is an equilibrium value for this fraction, such that the retailer and the manufacturer are always in agreement with regard to the stocking level. We then show that complete coordination on the stocking level as well as the review period can be achieved solely through carrying out negotiations on credit terms. These theoretical results are used to construct an algorithm for calculating the optimal policy parameters for a supply chain. As part of the analysis we suggest a modification of the base stock policy for the positive lag lost sales case of periodic review inventory models that consistently outperforms the base stock policy in our numerical studies.
This paper analyzes the effects of corporate social responsibility (CSR) and explores channel coordination in a socially responsible manufacturer-retailer closed-loop supply chain (CLSC) by considering two areas - profit maximization and social responsibility through product recycling. The manufacturer is socially responsible and exhibits it by recycling of used product that it collects through the retailer using the reverse channel. It is found that the channel's non-profit maximizing motive through CSR practice generates higher profit margin than the profit maximizing objective and revenue sharing contract resolves channel conflict. Recycling is a key factor for the wholesale price and hence pure profit of the manufacturer because variation of it may lead to decrement or increment even negative wholesale price. Thus, there must be a limit of recycling for optimal benefit. For best channel performance the manufacturer provides the retailer all revenues that it generates through recycling in the form of reward. Thus, CSR is purely a costly endeavor to the manufacturer. Moreover, unlike the pure profit maximizing supply chain CSR has the ability to vary surplus profit share.
In this paper, simultaneous coordination of visit interval and service level in a two-echelon pharmaceutical supply chain (PSC) with stochastic demand is studied. In the investigated model, a single pharmaceutical supplier (pharma-supplier) visits a single pharma-retailer at constant intervals and subsequently delivers the order after a deterministic lead-time. The pharma-retailer employs a periodic review inventory system and decides on the PSC service level, which impacts on the pharma-supplier's profitability. On the other hand, the pharma-supplier has full authority over the visit interval which determines order delivery frequency and consequently impacts on the pharma-retailer's inventory costs. Firstly, both members' decisions under decentralized and centralized decision-making structures are investigated, and afterward collaboration models are proposed in two different scenarios: (1) economic collaborative decision-making and (2) social collaborative decision-making. Finally, a set of numerical examples is carried out to compare the performance of different decision-making structures. The results indicate that collaborative decision-making on visit interval and service level could be of great benefit, both socially and economically.
