Supplier Selection Using Multi‐objective Programming: A Decision Support System Approach

Optimization model for multi-products multi-periods multi-suppliers raw-material selection and composition, and order quantity problem with one-year minimum order quantity contract

This paper concerns the optimization model for a multi-product multi-period raw-material selection and composition, and order quantity problem faced by a beverage company.•There are some criteria in raw material selection, which we accommodate all the criteria in the objective function. There are several suppliers, and one of the decision criteria is a one-year minimum order quantity contract between the company and the suppliers. The actual one-year demand for raw materials may deviate significantly from the one-year minimum order quantities.•We derive a function that can be regarded as a penalty function to maintain the total order quantities in one year to fulfill the minimum one-year order quantity contracts. This penalty function is a part of the objective function and can be relaxed once the one-year minimum order quantity contracts are fulfilled.•We performed several numerical experiments to check the optimal solutions for various demands and for various objective functions. These experiments show our MILP (Mixed Integer Linear Programming) gives the desired optimal solutions and show the influence of decision criteria on the optimal solution.

A dynamic approach to supply chain reconfiguration and ripple effect analysis in an epidemic

The COVID-19 pandemic has illustrated the unprecedented challenges of ensuring the continuity of operations in a supply chain as suppliers' and their suppliers stop producing due the spread of infection, leading to a degradation of downstream customer service levels in a ripple effect. In this paper, we contextualize a dynamic approach and propose an optimal control model for supply chain reconfiguration and ripple effect analysis integrated with an epidemic dynamics model. We provide supply chain managers with the optimal choice over a planning horizon among subsets of interchangeable suppliers and corresponding orders; this will maximize demand satisfaction given their prices, lead times, exposure to infection, and upstream suppliers' risk exposure. Numerical illustrations show that our prescriptive forward-looking model can help reconfigure a supply chain and mitigate the ripple effect due to reduced production because of suppliers' infected workers. A risk aversion factor incorporates a measure of supplier risk exposure at the upstream echelons. We examine three scenarios: (a) infection limits the capacity of suppliers, (b) the pandemic recedes but not at the same pace for all suppliers, and (c) infection waves affect the capacity of some suppliers, while others are in a recovery phase. We illustrate through a case study how our model can be immediately deployed in manufacturing or retail supply chains since the data are readily accessible from suppliers and health authorities. This work opens new avenues for prescriptive models in operations management and the study of viable supply chains by combining optimal control and epidemiological models.

An integrated mathematical model based on grey optimal ranking for supplier selection considering pandemic situation

Optimal selection and allocation of suppliers are crucial decisions for an organization and it becomes more critical when the firm faces disruptive events. The recent outbreak of COVID-19 has led to massive supply disruptions in a supply chain. This paper aims to address the supplier selection and allocation problem of manufacturing firms under pandemic environment. In this study, a novel Mixed Integer Linear Programming (MILP) model integrated with grey optimal ranking of suppliers considering factors related to pandemic situation is proposed. The methodology is implemented in two subsequent stages. In the first stage, Grey Relational Analysis is adopted to determine the grey possibility scoring, and in the second stage, a supplier selection model is proposed to integrate the grey scoring to a MILP model to determine optimal allocation of suppliers. The paper presents a numerical study to demonstrate the proposed model and sensitivity analysis is conducted to deduce key managerial insights regarding the factors affecting the allocation under pandemic situation. Further, the illustration demonstrates how the proposed method integrates the expert ranking based approach and the cost minimization approach. The study is generic in nature and provides useful directions for practitioners involved in supplier selection in manufacturing organizations.

Gresilient supplier selection through Fuzzy Ordinal Priority Approach: decision-making in post-COVID era

Because of the COVID-19 outbreak, the supply of both essential and non-essential goods and services has been unprecedentedly disrupted. In the absence of any playbook, the need for innovative technologies to aid recovery from the Supply Chain (SC) disruptions quickly and effectively becomes persistent. The study aims to develop an innovative decision-making technology to handle the supplier selection problems arising from the frequent impreciseness and incompleteness in the nowadays' SC reports within the framework of the gresilient supply chain management. Through the integration of green and resilience aspects of the SCs, the supply chain 'gresilience' has been conceptualized. In light of this construct and based on the data collected from a manufacturing firm, the study deploys a two-fold decomposition of the core algorithm of the Ordinal Priority Approach (OPA), one for attributes and other for alternatives. It extends the OPA to the Fuzzy OPA (OPA-F) for solving the supplier selection problems. The study illustrates how green and resilience aspects of the SC can be integrated to better understand the gresilient suppliers in the wake of the SC disruptions. A novel construct of SC Gresilience is also furnished along with a novel definition of SC Gresilience. It also provides an innovative SC decision-making technology to help procurement managers to evaluate their suppliers. The resultant framework can help them better prepare for the COVID-19 like disruptions in the future.

Fuzzy Optimization Model for Decision-Making in Supply Chain Management

Choosing a supplier is a complex decision-making process that can reduce the total cost of production inputs and increase profits without increasing the price or sacrificing product quality. However, supplier selection processes usually involve multiple quantitative and qualitative criteria which increase the complexity of the problem and may decrease the accuracy and effectiveness of the process. Such complex decision-making problems can be supported by using multicriteria decision-making (MCDM) models. While there have been multiple MCDM models to support supplier selection processes in different industries and sectors, only a few are developed to support the supplier selection processes in the garment industry, especially under uncertain decision-making environment. This paper presents an integrated mathematical model under a fuzzy environment and applies it to the supplier selection process in the garment industry. In this research, the authors utilize the Buckley extension based fuzzy Analytical Hierarchical Process (FAHP) method in combination with linear normalization based fuzzy Grey Relational Analysis (F-GRA) method to develop a MCDM approach to the supplier selection process under a fuzzy environment. As a result, supplier 08 (SA08) is the optimal supplier. The contribution of this work is to propose an MCDM model for ranking potential suppliers in the garment industry under a fuzzy environment. The proposed approach can also be applied to support complex decision-making processes under a fuzzy environment in different industries.

Efficient team formation from pool of talent: comparing AHP-LP and TOPSIS-LP approach

Purpose

This research focuses on suggesting an optimized model for selecting best employees using advanced multi-criteria decision making method to a supply chain firm, who is planning to start a new cold chain business vertical.

Design/methodology/approach

Study has been conducted in a supply chain firm in North India, who wants to expand its business with the help of efficient team members. In total 38 applicants were considered for the study, as selected by the firm after initial screening from pool of talent. AHP-LP and TOPSIS-LP integrated approach were applied separately for evaluation and implementation of personnel selection model. Further, both the approaches were compared to find the best fit and optimized model.

Findings

As per the findings, both AHP and TOPSIS can be used to select the best candidate among the alternatives available. TOPSIS was found easier to implement as it involves ranking of applicants with respect to each skills required for respective job profile only once, whereas AHP involves pair-wise comparison among candidates with respect to each skills required for respective job profile and normalization of each comparison, resulting in the formation of number of comparison matrices. However, AHP is more reliable as it considers consistency check for each level of pair-wise comparison. Hence, there is a chance to avoid or revise the human judgment error. Integrated ranking and optimization approach minimizes the cost by suggesting the relevant positions to be filed to make an efficient team.

Research limitations/implications

Group of interviewers are involved in the decision-making process, hence there are chances of biasness in ranking method which can influence the group decision. Research is limited to a particular geography of North India therefore needs to be tested for other regions also in order to generalize. The research will help the third party logistics (3PL) and other related firms in efficient team selection.

Originality/value

The researcher focuses on formalizing a method for potential candidate selection by considering the constraints of the organization. It has been observed that limited researches have been done on the application of AHP-LP or TOPSIS-LP integrated approach for selection process. Hence, this research proposes two integrated ranking-optimization method and suggests the best fit by comparing both the approaches.

A new weighted fuzzy programming model for supplier selection and order allocation in the food industry

Purpose

The purpose of this paper is to develop a combined intuitionistic fuzzy analytic hierarchy process (IFAHP) and fuzzy multi-objective optimization approach to select suppliers and allocate the orders to them in the bottled water production context.

Design/methodology/approach

First, the primary weights of criteria associated with the supplier selection problem are calculated using the IFAHP technique. Then a fuzzy multi-objective optimization model is developed to allocate the appropriate amount of orders to each supplier.

Findings

The proposed methodology has been successfully implemented in the case of an Iranian food company in its bottled water factory. Results demonstrate our model is capable of practically handling the uncertainty in DMs’ preference that leads to effective and efficient supplier selection and order allocation decisions.

Originality/value

The authors develop a novel hybrid decision-making tool to tackle the uncertainty in decision-makers’ opinions with a demonstrated applicability and some promising outcomes in efficiently allocating the order quantity to suppliers in the area of bottled water production.

Factors influencing the selection of a picture archiving and communication system: A qualitative study

BACKGROUND

Picture Archiving and Communication System (PACS) is an evolving technology in health care domains that is used for storage, management, retrieval, transfer, and delivery of medical images. Some medical centers in Iran have installed the PACS in recent years but have not used it appropriately. One of the problems in implementing this system is inability to select appropriate PACS. Several factors are involved in the selection process. The objective of this study was to determine the factors that influence PACS selection.

METHODS

This qualitative study aimed to identify factors influencing the PACS selection. Data were collected through semistructured interviews with 10 experts in three educational hospitals and in the position to make decision for the purchase of PACS. Data were analyzed by the conventional qualitative content analysis method proposed by Lundman and Graneheim.

RESULTS

Analyses achieved 11 subcategories in two specific and general categories that influence PACS selection. The specific category of this study included six subcategories, and the general category included five subcategories.

CONCLUSION

The results of this study determined that usability was the most important factor from the perspective of participants. Since the main users of a system have a critical role in adoption or rejection of a system, ease of use (usability) is significant and must be considered in system selection as a significant factor.

Multi-criteria supplier selection model using the analytic hierarchy process approach

Purpose

– The purpose of this paper is to propose a supplier selection model for an automobile company using analytic hierarchy process (AHP) approach. Supplier selection is now growing to be an important but complex issue, as it involves a large number of factors and decisions. These factors have complicated operational and financial implications.

Design/methodology/approach

– A case study of an Indian automobile company is used to illustrate and propose a supplier selection model using AHP approach.

Findings

– Key criteria of the supplier selection problem are identified based on the literature review and case study. Then, a model is proposed for supplier selection using AHP approach followed by a detailed sensitivity analysis.

Practical implications

– This paper contributes to supplier selection process, and points out the importance of supplier selection. It proposes a supplier selection model for an automobile industry which often faces heterogeneous supply environments. The proposed model provides key criteria for supplier selection in Indian context. This model may have high acceptability, where a large number of suppliers are available to supply the materials or provide the services.

Originality/value

– The proposed model can provide guidelines and directions for managers involved in purchasing function to effectively select suppliers on the basis of the identified criteria in the heterogeneous manufacturing environment.

Multi-period demand allocation among supplier in a supply chain

Purpose

– This paper aims to address the problem of optimal allocation of demand of items among candidate suppliers to maximize the purchase value of items. The purchase value of the items directly relates to cost and quality of raw materials purchased from the supplier. In an increasingly competitive environment, firms are paying more attention to selecting the right suppliers for procurement of raw materials and component parts for their products. The present research work focuses on this issue of supply chain management.

Design/methodology/approach

– This present research work devises a hybrid algorithm for multi-period demand allocation among the suppliers. The customer demand is allocated by using a hybrid algorithm based on the technique for order preference by similarity to ideal solution (TOPSIS), fuzzy set theory and the mixed linear integer programming (MILP) approaches.

Findings

– The supply chain network is witnessing a changing business environment due to government policies aimed at promoting new small manufacturing enterprises (small and medium-sized enterprises) for intermediate parts and components. Hence, the managers have an option to select a new group of suppliers and allocate the optimal multi-period demand among the new group of suppliers to maximize their purchase value. In this context, the proposed hybrid model would be beneficial for the managers to operate their supply chain effectively and efficiently. The present research work will be helpful for the managers who are interested to reconfigure their supply chain under the failure of any supply chain partner or in a changing business environment. The model provides flexibility to the managers for evaluation of the different available alternatives to take a decision of optimal demand allocation among the suppliers. The proposed hybrid (fuzzy, TOPSIS and MILP) model provides more objective information for supplier evaluation and demand allocation among suppliers in a supply chain. The managers can use the proposed model to the analysis of other management decision-making problems.

Originality/value

– This present research work devises a hybrid algorithm for multi-period demand allocation among the suppliers. This hybrid algorithm prioritizes the suppliers and then allocates the multi-period demand among the suppliers. The customer demand is allocated by using a hybrid algorithm based on the technique for order preference by similarity to ideal solution (TOPSIS), fuzzy set theory and the mixed linear integer programming (MILP) approaches.

Singh R, Issar S, Varkey J. Evaluation of vendors ranking by EATWOS approach

Purpose

– In order to improve the supply chain performance, organizations need to improve the efficiency of vendors. Although many techniques are used for vendor efficiency measurement, there is a lack of research about defining satisficing level to analyze improvement potential of vendors. The purpose of this paper is to incorporate satisficing level of some outputs to determine efficiency improvement potential of existing vendors.

Design/methodology/approach

– Method used in this paper is an extension of Data Envelopment Analysis, which has been used frequently for efficiency measurement of Decision Making Units. This method is known as Efficiency Analysis Technique with Output Satisficing (EATWOS), which has been mainly used in the field of economics for efficiency measurement.

Findings

– A case illustration of a National Capital Region of Delhi, India, based manufacturer and distributor of packaged drinking water is depicted to portray the practical application of EATWOS in vendor efficiency evaluation and their relative ranking. This method has helped in evaluating the improvement potential in performance of given vendors and helping in strategic decision making for the given organization

Research limitations/implications

– In case of more variables, this method becomes more complex to get the solution. Second, sometimes difference in vendors ranking without and with satisficing concept is very less, as in this case. Therefore, recording and analysis of output data of vendors should be done very carefully.

Practical implications

– Major implications of this study is that while selecting vendors, organizations should also try to understand improvement potential for given vendors by applying satisficing concept as given in this research. This approach helps in analyzing improvement potentials of suppliers.

Originality/value

– This paper explores an innovative approach to rank vendors on basis of certain criteria after considering the satisficing concept and improvement potentials of vendors.

Land resources allocation strategies in an urban area involving uncertainty: a case study of Suzhou, in the Yangtze River Delta of China

A large number of mathematical models have been developed to support land resource allocation decisions and land management needs; however, few of them can address various uncertainties that exist in relation to many factors presented in such decisions (e.g., land resource availabilities, land demands, land-use patterns, and social demands, as well as ecological requirements). In this study, a multi-objective interval-stochastic land resource allocation model (MOISLAM) was developed for tackling uncertainty that presents as discrete intervals and/or probability distributions. The developed model improves upon the existing multi-objective programming and inexact optimization approaches. The MOISLAM not only considers economic factors, but also involves food security and eco-environmental constraints; it can, therefore, effectively reflect various interrelations among different aspects in a land resource management system. Moreover, the model can also help examine the reliability of satisfying (or the risk of violating) system constraints under uncertainty. In this study, the MOISLAM was applied to a real case of long-term urban land resource allocation planning in Suzhou, in the Yangtze River Delta of China. Interval solutions associated with different risk levels of constraint violation were obtained. The results are considered useful for generating a range of decision alternatives under various system conditions, and thus helping decision makers to identify a desirable land resource allocation strategy under uncertainty.

Helping the decision maker effectively promote various experts' views into various optimal solutions to China's institutional problem of health care provider selection through the organization of a pilot health care provider research system

BACKGROUND

The main aim of China's Health Care System Reform was to help the decision maker find the optimal solution to China's institutional problem of health care provider selection. A pilot health care provider research system was recently organized in China's health care system, and it could efficiently collect the data for determining the optimal solution to China's institutional problem of health care provider selection from various experts, then the purpose of this study was to apply the optimal implementation methodology to help the decision maker effectively promote various experts' views into various optimal solutions to this problem under the support of this pilot system.

METHODS

After the general framework of China's institutional problem of health care provider selection was established, this study collaborated with the National Bureau of Statistics of China to commission a large-scale 2009 to 2010 national expert survey (n = 3,914) through the organization of a pilot health care provider research system for the first time in China, and the analytic network process (ANP) implementation methodology was adopted to analyze the dataset from this survey.

RESULTS

The market-oriented health care provider approach was the optimal solution to China's institutional problem of health care provider selection from the doctors' point of view; the traditional government's regulation-oriented health care provider approach was the optimal solution to China's institutional problem of health care provider selection from the pharmacists' point of view, the hospital administrators' point of view, and the point of view of health officials in health administration departments; the public private partnership (PPP) approach was the optimal solution to China's institutional problem of health care provider selection from the nurses' point of view, the point of view of officials in medical insurance agencies, and the health care researchers' point of view.

CONCLUSIONS

The data collected through a pilot health care provider research system in the 2009 to 2010 national expert survey could help the decision maker effectively promote various experts' views into various optimal solutions to China's institutional problem of health care provider selection.

A Multi-Criteria Intuitionistic Fuzzy Group Decision Making Method for Supplier Selection with VIKOR Method

Supplier selection, the process of finding the right suppliers who are able to provide the buyer with the right quality products and/or services at the right price, at the right time and in the right quantities, is one of the most critical activities for establishing an effective supply chain, and is typically a multi-criteria group decision problem. In many practical situations, there usually exists incomplete and uncertain information, and the decision makers cannot easily express their judgments on the candidates with exact and crisp values. Therefore, in this paper an extended VIKOR method for group decision making with intuitionistic fuzzy numbers is proposed to solve the supplier selection problem under incomplete and uncertain information environment. In other researches in this area, the weights of each decision makers and in many of them the weights of criteria are pre-determined, but these weights have been calculated in this paper by using the decision matrix of each decision maker. Also, normalized Hamming distance is proposed to calculate the distance between intuitionistic fuzzy numbers. Finally, a numerical example for supplier selection is given to clarify the main results developed in this paper.