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The aim of this paper is to solve a multi-period supplier selection and inventory lot-sizing problem with multiple products in a serial supply chain. Compared to previous models proposed in the literature, our research incorporates a richer cost structure involving joint replenishment costs for raw material replenishment and production, and a more...
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To contribute to the mitigation of climate change, a significant number of businesses from a wide range of sectors increasingly commit to science-based carbon reduction targets. However, to integrate their ambition into strategic and operational business processes, companies often merely rely on historically determined emission data. These data pro...
Citations
... For the study of optimal replenishment decisions, Ventura, José A. [2] et al. developed a multiproduct dynamic supply chain inventory model that takes into account supplier selection, joint replenishment, and transportation costs; Goedhart Joost [3]et al. modeled the rationing and ordering decisions as a Markov decision-making problem that maximizes the expected profit; Adeinat Hamza [4]et al. used a mixed-integer nonlinear programming model with the objective of maximizing total profit per unit time to confirm that pricing and inventory replenishment decisions are more sensitive to the parameters of the demand function; Zhao Nenggui [5]et al. drew relevant conclusions by investigating the single and joint effects of the reference effect ( RE ) and the quick replenishment ( QR ) strategies on customers' purchasing behaviors, retailer's optimal decisions, and total profit in both periods; Wan Guangyu [6]et al. investigated a novel joint replenishment strategy controlled by a sales threshold; Castellano Davide [7]et al. solved a stochastic joint replenishment problem by assuming stochastic demand with a controllable lead time and imposing a fill-rate constraint on each item in the context of a joint replenishment problem under a class of recurring strategies; Carlos Otero-Palencia [8]et al. proposed the stochastic collaborative joint replenishment problem (S-CJRP) approach to solve the stochastic joint replenishment problem with transportation and warehouse constraints; Ji Seong Noh [9]et al. developed a mathematical model describing the replenishment activities of a single type of item from the buyer's point of view, and proposed an algorithm to find a low-cost base period, order interval multiplier, and a safety factor; Jing Lu [10] The first-order linear partial differential equations about time and product freshness are used to portray the inventory dynamic system of fresh agricultural products, construct a system optimization model, and solve the optimal pricing strategy of the enterprise by using the theory of optimal control of distribution parameters. ...
... The following conclusions are obtained by combining the examples: (1) the selling price and cost can be calculated in terms of categories to get the proportion of cost-plus pricing, and then the optimisation model is constructed, from which the optimal replenishment quantity and pricing strategy are solved. (2) In the case of the total number of single products and the minimum display quantity are restricted, the replenishment quantity and pricing strategy for the coming week ...
In fresh food superstores, because vegetable commodities are characterized by perishability and high value loss, superstores usually replenish daily commodities based on the historical sales and demand of various commodities. In order to make the optimal replenishment decision, this paper firstly firstly uses Matlab fitting toolbox to get the functional relationship between the total sales volume and the cost-plus pricing, so as to analyze the relationship between the total sales volume and the cost-plus pricing of each vegetable category, and then takes the category as a unit, and then constructs the optimization model, and solves the optimal replenishment volume and pricing strategy. The replenishment decision is obtained by filtering out the most profitable single product among the factors of unit price, sales volume, and total replenishment volume through objective planning under the constraints of considering the single product category and minimum display volume.
... Schmelzle and Mukandwal [40] investigated the impact of supply chain relationship configurations on supplier performance by analyzing buyer-supplier relationships in the aerospace industry, which identified the key dimensions of relationship quality, including trust, communication, and coordination, and they showed that high-quality relationships lead to improved supplier performance in terms of cost, quality, and delivery. Ventura et al. [41] studied a multi-period dynamic supplier selection and inventory lot-sizing problem with multiple products in a serial supply chain which incorporated a richer cost structure involving joint replenishment costs for raw material replenishment and production, and an accurate representation of the transportation costs using a vector of full-truck load costs for different size trucks. They proposed two mixedinteger linear programming formulations: integrated and sequential. ...
... industry, which identified the key dimensions of relationship quality, including trust, communication, and coordination, and they showed that high-quality relationships lead to improved supplier performance in terms of cost, quality, and delivery. Ventura et al. [41] studied a multi-period dynamic supplier selection and inventory lot-sizing problem with multiple products in a serial supply chain which incorporated a richer cost structure involving joint replenishment costs for raw material replenishment and production, and an accurate representation of the transportation costs using a vector of full-truck load costs for different size trucks. They proposed two mixed-integer linear programming formulations: integrated and sequential. ...
... In addition, our model is based on a single-buyer multi-vendor coordination framework with a single product. Thus, analyzing a more realistic supply chain that includes multiple buyers, several suppliers, and multiple products to obtain results that can be applied to more complex real-world situations is another area of great interest [4,41]. Finally, the profit-sharing mechanism can benefit all members of the supply chain system in many cases [1]. ...
This paper analyzes different lot-sizing policies for the supplier selection and order allocation problem in a two-stage supply chain. The supply chain consists of multiple candidate suppliers and a single buyer. In this system, selected suppliers produce a product in batches at finite production rates, ship it to the buyer, and the buyer sells it to the market at a constant demand rate. Our goal is to evaluate two lot-sizing policies and select the one that optimizes the supply chain by minimizing the total cost and maximizing supplier efficiency. A bi-objective mixed-integer nonlinear programming (BOMINLP) model is proposed. The first objective consists of the development of a coordination mechanism for supplier selection and order allocation that minimizes the entire supply chain cost, and the second objective comprises a data envelopment analysis (DEA) approach to evaluate the overall performance of suppliers to optimize supplier efficiency. Then, the lot-for-lot and order frequency policies are applied to the BOMINLP model separately to determine the set of selected suppliers as well as the corresponding order quantities and number of orders allocated to each selected supplier per replenishment cycle. Numerical examples that illustrate the solution approach and compare the two lot-sizing policies are provided.
... They demonstrated how JRP may assist small cross-border e-commerce businesses in reducing costs, shortening replenishment cycles, and accelerating product turnover. Ventura et al. (2022) addressed a more sophisticated cost structure with joint replenishment costs for raw resources. ...
In the increasingly competitive market, supply chain decision-makers are making efforts to improve operational efficiency and reduce costs by joint replenishment approach. Recognizing the value of joint replenishment strategy in the supply chain, we are motivated to write a review on the importance of joint replenishment strategy. Despite the vast literature on the joint replenishment problem (JRP), a comprehensive study survey for recent years is lacking. The goal of this study is to review and synthesize research on JRP from 2006 to 2022. Details of JRP are introduced first. Literature selection and an overview of the extant literature are then discussed. Recent research on JRP with relaxed assumptions is summarized, including stochastic demand, dynamic demand, and resource constraints. In addition, recent research on other JRPs and the joint replenishment and delivery (JRD) problem is summarized. The observations and insights of these studies can guide academics and practitioners to implement joint replenishment strategies in different aspects of supply chain management.
... Heuristic algorithm may be an effective method for supply chain ordering decision-making, but Firouz Mohammad, Keskin B. and Melouk S. [3] found in 2017 that using heuristic algorithm based on decomposition to solve the problem of multi-supplier and single product would get poor decision-making. The latest work of Jos é A. Ventura et al. [4] (2022) provided two methods to solve the problem of multi-product and multi-cycle supplier selection and inventory lot size in serial supply chainmixed integer linear programming method and sequence method, which have achieved good results, but they have not systematically studied the supplier's evaluation screening, historical prediction and optimization of the whole system. ...
... The work in this paper is compared with some previous work. Taking the work of Jos é A. Ventura et al [4]. Mentioned in the introduction as an example, they put forward the integration method and sequence method of mixed integer linear programming model, both of which achieved good results. ...
... The solution effect is good and the solution speed is high. Although there are differences in details between the specific work of Jos é A. Ventura et al [4], from the performance of the results, it can also be seen that the supplier decision system of screening-forecasting-optimization proposed in this paper has the characteristics of stable model and excellent solving ability. ...
Ordering and transportation optimization of raw material is a classic problem in the field of optimization. Based on the analysis, evaluation and prediction of previous data, this paper fully considers the actual situation of ordering, transshipment and storage, and establishes a multistage and large-scale mixed integer linear programming model for ordering and transporting raw materials. Combined with extensive data background, select appropriate indicators, and establish supplier evaluation systems by using entropy weight-CRITIC and RTOPSIS method. GM (1,1) is used to predict the general trend, the ARIMA model is used to predict the random fluctuation items, and a grey time series prediction model is constructed to obtain the predicted values of the data of the supply and loss rate in the next cycle. The prediction result are introduced into the planning model as parameters, and the evaluation score are used to construct a satisfaction function. The final goal of mixed integer linear programming model, as well as the three goals of sorting, transferring and storing, are obtained by reduction process. Finally, this paper uses Gurobi to solve practical problems, and obtains the ordering scheme and transshipment scheme that are superior to the historical schemes in ordering cost, transshipment loss and storage cost.
Purpose
This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.
Design/methodology/approach
To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.
Findings
Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.
Originality/value
Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.
The complexity of decision-making for companies buying transportation services has increased due to the presence of more options and pricing schedules for transportation. Many companies make transportation and inventory decisions in an uncoordinated way and select only one transportation mode, missing opportunities for logistics cost savings. The experimental study in this paper is based on a real-world decision problem faced by a Scandinavian company that distributes fast-moving consumer goods and wants to determine its transportation strategy. We propose a novel multi-mode lot-sizing model with dynamic deterministic demand to illustrate the cost impact of accurately modelling piecewise-linear transportation costs and allowing a more flexible usage of transportation modes when planning order replenishments. We compare three transportation strategies with increasing degrees of flexibility: two single mode strategies, where one strategy is more flexible than the other, and a multi-mode strategy. We conclude that managers can significantly reduce costs by increasing the flexibility of mode selection in transportation strategies.
This paper discusses the challenging problem of jointly optimizing safety stock placement and supplier selection in a multi-layered distribution network, under the guaranteed service model (GSM) approach and single-supplier strategy for the customers. The original GSM-based model is extended to include optimal echelon (R, Q) policies. We propose an approximation model, which determines the optimal configuration flow of orders, order quantities, reorder points, safety stock amounts, inbound and outbound service times at each node. A two-stage heuristic is developed for this complex model that optimizes the flow configuration and then determines the other variables. Current experimental results on a few test networks indicate a promising computational time performance.
Due to the tremendous increases in the cost of providing health care systems, it is crucial importance to control the health spending. This study aims to explore the optimization of centralized drug procurement based on a capacitated joint replenishment and delivery strategy. A mixed integer nonlinear programming model is proposed to describe the practical operation process in China. The objective is to find a replenishment and delivery schedule, which operates in such a way that the total cost (including setup costs, storage costs, freight costs and cost of capital) is minimized under the constraints of production and distribution capacity. A novel hybrid optimization, composed of an ingenious memetic algorithm and a well-designed bound heuristic, is developed to solve the proposed model. The memetic algorithm includes a modified genetic operation and a local search procedure, and the bound heuristic is used to reduce the decision space substantially. The experimental results demonstrated that the proposed optimization can effectively obtain high-quality solutions, as illustrated by a comparative numerical study.
