Optimizing the Joint Replenishment and Delivery Scheduling Problem under Fuzzy Environment Using Inverse Weight Fuzzy Nonlinear Programming Method

In reality, decision-makers are always in front of imprecise and vague operational conditions. We propose a practical multiobjective joint replenishment and delivery scheduling (JRD) model with deterministic demand and fuzzy cost. This model minimizes the total cost defuzzified by the signed distance method and maximizes the credibility that the total cost does not exceed the budget level. Then, an inverse weight fuzzy nonlinear programming (IWFNLP) method is adopted to formulate the proposed model. This method embeds the idea of inverse weights into the Max-Min fuzzy model. Thirdly, the fuzzy simulation approach and differential evolution algorithm (DE) are utilized to solve this problem. Results show that solutions derived from the IWFNLP method satisfy the decision-maker’s desirable achievement level of the cost objective and credibility objective. It is an effective decision tool since it can really reflect the relative importance of each fuzzy component. Our study also shows that the improved DE outperforms DE with a faster convergence speed.

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Modeling and Optimization of Stochastic Joint Replenishment and Delivery Scheduling Problem with Uncertain Costs

Discrete Dynamics in Nature and Society ◽

10.1155/2013/657465 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 18

Author(s):

Lin Wang ◽

Hui Qu ◽

Yanhui Li ◽

Jing He

Keyword(s):

Inventory Management ◽

Differential Evolution Algorithm ◽

Joint Replenishment ◽

Total Cost ◽

Operational Conditions ◽

Long Run ◽

Holding Cost ◽

Fuzzy Inventory ◽

Inventory Holding ◽

Delivery Scheduling

The stochastic joint replenishment and delivery scheduling (JRD) problem is a key issue in supply chain management and is a major concern for companies. So far, all of the work on stochastic JRDs is under explicit environment. However, the decision makers often have to face vague operational conditions. We develop a practical JRD model with stochastic demand under fuzzy backlogging cost, fuzzy minor ordering cost, and fuzzy inventory holding cost. The problem is to determine procedures for inventory management and vehicle routing simultaneously so that the warehouse may satisfy demand at a minimum long-run average cost. Subsequently, the fuzzy total cost is defuzzified by the graded mean integration representation and centroid approaches to rank fuzzy numbers. To find optimal coordinated decisions, a modified adaptive differential evolution algorithm (MADE) is utilized to find the minimum long-run average total cost. Results of numerical examples indicate that the proposed JRD model can be used to simulate fuzzy environment efficiently, and the MADE outperforms genetic algorithm with a lower total cost and higher convergence rate. The proposed methods can be applied to many industries and can help obtaining optimal decisions under uncertain environment.

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Fuzzy Nonlinear Optimization Model to Improve Intermittent Demand Forecasting

Exploring Innovative and Successful Applications of Soft Computing - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-4785-5.ch010 ◽

2014 ◽

pp. 181-198

Author(s):

Raúl Poler ◽

Josefa Mula ◽

Manuel Díaz-Madroñero ◽

Mariano Jiménez

Keyword(s):

Nonlinear Programming ◽

Fuzzy Numbers ◽

Programming Model ◽

Demand Forecasting ◽

Fuzzy Model ◽

Fuzzy Decision ◽

Intermittent Demand ◽

Decision Variables ◽

Nonlinear Programming Model ◽

Fuzzy Nonlinear Programming

This chapter proposes a fuzzy nonlinear programming model for intermittent demand forecasting purposes. The authors formulated the Syntetos and Boylan (SB) forecasting method as a crisp nonlinear programming model. They also attempted to improve it with a new fuzzy nonlinear programming formulation. This fuzzy model is based on fuzzy decision variables, which represent fuzzy triangular numbers. The authors applied fuzzy arithmetic operations, such as addition and subtraction of fuzzy numbers, fuzzy decision variables. They carried out the defuzzification of the fuzzy decision variables through the possibilistic mean value of fuzzy numbers. Finally, the authors validated and tested it by comparing it with the deterministic nonlinear programming model that they adopted as the basis of this work. The computational studies show that fuzzy model performance is consistently better than the SB nonlinear programming model, especially when intermittency is high.

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A QFD-Based Quality and Capability Design Method for Transboundary Services

Mathematical Problems in Engineering ◽

10.1155/2020/9478083 ◽

2020 ◽

Vol 2020 ◽

pp. 1-18 ◽

Cited By ~ 1

Author(s):

Chao Ma ◽

Wei Dong Liu ◽

Zhi Ying Tu ◽

Zhong Jie Wang ◽

Xiao Fei Xu

Keyword(s):

Nonlinear Programming ◽

Quality Function Deployment ◽

Design Method ◽

Quality Parameters ◽

Optimal Configuration ◽

Two Phase ◽

Internet Service ◽

Fuzzy Nonlinear Programming ◽

Nonlinear Programming Methods ◽

Multiple Domains

The “transboundary”, an emerging phenomenon in the Internet service ecosystem, is leading to the flourishing of innovative services. A transboundary service incorporates services, resources, and technologies from multiple domains into its business to create a particular competitive advantage and unique user experiences. It is difficult to comprehensively consider all the constraints from multiple domains to precisely design the nonfunctional characteristics of transboundary services, such as quality attributes and capability attributes. We propose a two-phase quality design method for transboundary services called value quality deployment-quality capability deployment (VQD-QCD) based on quality function deployment (QFD). Given the restrictions of transboundary services, VQD-QCD translates the value expectations of multiple stakeholders into an optimal configuration for global quality parameters (GQPs), local quality parameters, and capability parameters. Details of VQD are illustrated. Considering the inherent vagueness and uncertainty of relationships between value expectations and GQPs, and among GQPs, fuzzy least absolute regression and fuzzy nonlinear programming methods are incorporated into QFD to identify the quantitative relations between value indicators and GQPs, and among GQPs, and obtain an optimal configuration scheme for GQPs. Usability of the proposed method is validated through a case study on the “DiDi mobile transportation service”, which is a representative transboundary service in China. Compared with the current method, which is inaccurate and inefficient because its translation between value expectations and relevant quality and capability parameters is artificial and subjective, the proposed method integrates fuzzy least absolute regression and fuzzy nonlinear programming methods into QFD, which facilitate transboundary service designers to precisely and efficiently design the quality and capability characteristics of innovative services in the manner of semiautomatisation, which promotes the innovative design of transboundary services.

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Sustainable optimal ordering quantity for non-instantaneous deteriorating items under joint replenishment with substitution and carbon emission

Kybernetes ◽

10.1108/k-10-2020-0714 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Ranu Singh ◽

Vinod Kumar Mishra

Keyword(s):

Cost Function ◽

Carbon Emission ◽

Inventory Model ◽

Deteriorating Items ◽

Joint Replenishment ◽

Content Type ◽

Total Cost ◽

Optimal Ordering ◽

Ordering Quantity ◽

Total Cost Function

Purpose Carbon emission is a significant issue for the current business market and global warming. Nowadays, most countries have focused to reduce the environmental impact of business with durable financial benefits. The purpose of this study is to optimize the entire cost functions with carbon emission and to find the sustainable optimal ordering quantity for retailers. Design/methodology/approach This paper illustrates a sustainable inventory model having a set of two non-instantaneous substitutable deteriorating items under joint replenishment with carbon emission. In this model demand and deterioration rate are considered as deterministic, constant and triangular fuzzy numbers. The objective is to find the optimal ordering quantity for retailers and to minimize the total cost function per unit time with carbon emission. The model is then solved with the help of Maple software. Findings This paper presents a solution method and also develop an algorithm to determine the order quantities which optimize the total cost function. A numerical experiment illustrates the improvement in optimal total cost of the inventory model with substitution over without substitution. The graphical results show the convexity of the cost function. Finally, sensitivity analysis is given to get the impact of parameters and validity of the model. Originality/value This study considers a set of two non-instantaneous substitutable deteriorating items under joint replenishment with carbon emission. From the literature review, in the authors’ knowledge no researcher has undergone this kind of study.

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Inventory Model with Demand Dependant on Unit Cost- Input Parameters as Triangular Fuzzy Numbers

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i2.1911 ◽

2021 ◽

Vol 12 (2) ◽

Author(s):

R. Kasthuri, Et. al.

Keyword(s):

Fuzzy Numbers ◽

Unit Cost ◽

Inventory Model ◽

Average Total Cost ◽

Triangular Fuzzy Numbers ◽

Lot Size ◽

Distance Method ◽

Total Cost ◽

Signed Distance ◽

Fuzzy Environment

This paper considers an inventory model in which the shortages are backlogged and the demand is dependent on unit cost. An optimum value for average total cost is calculated by considering various input costs, lot size and maximum inventory under fuzzy environment. The process of defuzzification is done by using the signed distance method. Numerical example and sensitivity analysis is given for calculating both crisp and fuzzy values of the total cost.

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