A Hybrid Approach to Sustainable Supplier Selection and Order Allocation Considering Quality Policies and Demand Forecasting: A Real-Life Case Study

Shakibaei H., Farhadi-Ramin M.R., Alipour-Vaezi M., Aghsami A., Rabbani M.

PurposeEvery day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.Design/methodology/approachA new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.FindingsFor this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.Originality/valueObtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

Mohammadnazari Z., Mousapour Mamoudan M., Alipour-Vaezi M., Aghsami A., Jolai F., Yazdani M.

As the destructive impacts of both human-made and natural disasters on societies and built environments are predicted to increase in the future, innovative disaster management strategies to cope with emergency conditions are becoming more crucial. After a disaster, selecting the most critical post-disaster reconstruction projects among available projects is a challenging decision due to resource constraints. There is strong evidence that the success of many post-disaster reconstruction projects is compromised by inappropriate decisions when choosing the most critical projects. Therefore, this study presents an integrated approach based on four multi-criteria decision-making (MCDM) techniques, namely, TOPSIS, ELECTRE III, VIKOR, and PROMETHEE, to aid decision makers in prioritizing post-disaster projects. Furthermore, an aggregation approach (linear assignment) is used to generate the final ranking vector since various methods may provide different outcomes. In the first stage, 21 criteria were determined based on sustainability. To validate the performance of the proposed approach, the obtained results were compared to the results of an artificial neural network (ANN) algorithm, which was applied to predict the projects’ success rates. A case study was used to assess the application of the proposed model. The obtained results show that in the selected case, the most critical criteria in post-disaster project selection are quality, robustness, and customer satisfaction. The findings of this study can contribute to the growing body of knowledge about disaster management strategies and have implications for key stakeholders involved in post-disaster reconstruction projects. Furthermore, this study provides valuable information for national decision makers in countries that have limited experience with disasters and where the destructive consequences of disasters on the built environment are increasing.

Islam S., Amin S.H., Wardley L.J.

Supplier selection and order allocation have significant roles in supply chain management. These processes become major challenges when the demand is uncertain. This research presents a new two-stage solution approach for supplier selection and order allocation planning where a forecasting procedure is integrated with an optimization model. In the first stage, the demand is forecasted to handle the demand vagueness. A novel Relational Regressor Chain method is introduced to determine the future demand, which is compared with the Holt's Linear Trend and the Auto-Regressive Integrated Moving Average methods to ensure the forecasting accuracy. The forecasted demand is then fed to the second stage where a multi-objective programming model is developed to identify suitable suppliers and order quantities from each supplier. Weighted-sum and ε -constraint methods are utilized to obtain the efficient solutions. To our knowledge, this paper is the first study that has integrated demand forecasting with the supplier selection and order allocation planning. A real dataset from a Canadian food supply network is used to examine the results of the forecasting methods and to determine the orders allocated to each supplier. The results of the forecasting methods show that the proposed Relational Regressor Chain method can forecast demand with a higher precision than the other forecasting methods considered in this paper. It is also evident from the results that the selection of the forecasting methods may have impact on both the selection of suppliers and the orders allocated to them. • Integrating demand forecasting and supplier selection and order allocation planning. • Applying two forecasting techniques, and proposing a new forecasting method. • Proposing an optimization model to determine the best suppliers and the orders in Stage 2. • Solving the proposed model using weighted-sum and ε -constraint methods. • Using a real dataset and analyzing the results.

Azani M., Shaerpour M., Yazdani M.A., Aghsami A., Jolai F.

Since food is one of the essential human needs, studies on this topic have always been a global concern. With the advent of COVID-19 and the emergence of many problems in all aspects of the food supply chain (such as production, transportation, distribution), this issue has become doubly important. This paper discusses an MINLP optimization model for handling the impact of the COVID-19 pandemic based on the food supply network through Food Hubs (FHs). In this research, the concept of FH has been used for a more effective and faster connection of consumers to production sites. Due to prevention of the spread of coronavirus and the quarantine conditions, the areas have been divided into two parts (high-risk and low-risk) and two scenarios have been defined for this supply chain. The purpose of this paper is to reduce costs and environmental impacts as much as possible. The proposed model is solved by GAMS software for small- and middle-size test problems, and it is solved with genetic optimization algorithm as a meta-heuristic approach for large-size problems. Also, to solve the developed linear multi-objective model, augmented ɛ-constraint approach is applied, and a real case study from Iran is examined to illustrate the validation of the proposed model. Numerical and computational results are provided to prove the efficiency and feasibility of the presented model. Finally, sensitivity analysis is presented to evaluate the effect of changing some parameters on variables and objective functions.

Firouzi F., Jadidi O.

• Models a multi-objective supplier selection problem. • Considers fuzzy constraints and fuzzy coefficients in the model. • Develops a weighted additive function to turn the model to a single-objective model. • Applies a resolution method to solve the single-objective model. Disasters, such as Coronavirus pandemic and Japan’s earthquake and tsunami, negatively hits firms and markets. It may drastically increase market demand for some products, or decrease suppliers’ ability to supply them at right quantity, quality and time. This uncertainty can be modeled with the fuzzy set theory that is less data-demanding than the probability theory. When a supplier selection problem (SSP) is formulated by fuzzy mathematical programming technique, we have to address two issues: (1) fuzzy constraints, due to the uncertainty in demand and supply capacity, and (2) fuzzy coefficients, due to the uncertainty in defective and late delivery rates, etc. In this study, we develop a fuzzy multi-objective model for a SSP to address these two issues. We first develop a weighted additive function to transform the fuzzy multi-objective model to a fuzzy single-objective model that can effectively consider the decision makers’ preferences. Then, a resolution method is applied to solve the single-objective model with fuzzy parameters.

Masoumi M., Aghsami A., Alipour-Vaezi M., Jolai F., Esmailifar B.

PurposeDue to the randomness and unpredictability of many disasters, it is essential to be prepared to face difficult conditions after a disaster to reduce human casualties and meet the needs of the people. After the disaster, one of the most essential measures is to deliver relief supplies to those affected by the disaster. Therefore, this paper aims to assign demand points to the warehouses as well as routing their related relief vehicles after a disaster considering convergence in the border warehouses.Design/methodology/approachThis research proposes a multi-objective, multi-commodity and multi-period queueing-inventory-routing problem in which a queuing system has been applied to reduce the congestion in the borders of the affected zones. To show the validity of the proposed model, a small-size problem has been solved using exact methods. Moreover, to deal with the complexity of the problem, a metaheuristic algorithm has been utilized to solve the large dimensions of the problem. Finally, various sensitivity analyses have been performed to determine the effects of different parameters on the optimal response.FindingsAccording to the results, the proposed model can optimize the objective functions simultaneously, in which decision-makers can determine their priority according to the condition by using the sensitivity analysis results.Originality/valueThe focus of the research is on delivering relief items to the affected people on time and at the lowest cost, in addition to preventing long queues at the entrances to the affected areas.

Rezaei A., Aghsami A., Rabbani M.

Supply chain managers have realized that competition between supply chains has replaced competition between companies. In addition, with increasing disruptions and uncertainty in planning, companies need to be able to make informed decisions at risk. Coordination in the resilient supply chain and appropriate selection of suppliers play a key role in risky situations. In previous research, mainly the impact of resilience strategies in the decentralized supply chain has been investigated and ignored the reliability of suppliers in the decision-making process. Therefore, we provide an effective framework for selecting reliable suppliers and order allocation, which increases the supply chain's benefits by considering the risk reduction strategies and coordination between the buyer and the supplier. Thus, we optimized the problem of supplier selection and order allocation in a centralized supply chain using mixed-integer nonlinear programming models and risk reduction strategies. These strategies are protected suppliers, back-up suppliers, reserving additional capacity, emergency stock, and geographical separation. Also, by considering the failure mode and effects analysis technique and the risk priority number constraint, suppliers' reliability has been considered. A numerical example is solved with the exact method. In addition, the application of the proposed models in a case study has been investigated by the Grasshopper optimization algorithm. Based on the sensitivity analysis results, we found that the simultaneous use of risk reduction strategies in the models significantly reduces supply chain costs and increases its benefits. Also, considering the reliability constraints causes supply chain managers to choose suppliers with more desirable reliability.

Khalili Nasr A., Tavana M., Alavi B., Mina H.

Maximizing the value of resources and producing less waste are strategic decisions affecting sustainability and competitive advantage. Sustainable closed-loop supply chains (CLSCs) are designed to minimize waste by circling back (repairing, reselling, or dismantling for parts) previously discarded products into the value chain. This study presents a novel two-stage fuzzy supplier selection and order allocation model in a CLSC. In Stage 1, we use the fuzzy best-worst method (BWM) to select the most suitable suppliers according to economic, environmental, social, and circular criteria. In Stage 2, we use a multi-objective mixed-integer linear programming (MOMILP) model to design a multi-product, multi-period, CLSC network, and inventory-location-routing, vehicle scheduling, and quantity discounts considerations. In the proposed MOMILP, the total network costs, the undesired environmental effects, and the lost sales are minimized while job opportunities and sustainable supplier purchases are maximized. A fuzzy goal programming approach is proposed to transform the MOMILP into a single objective model. We present a case study to demonstrate the applicability of the proposed method in the garment manufacturing and distribution industry.

Ventura J.A., Bunn K.A., Venegas B.B., Duan L.

We study a supplier selection and order quantity allocation problem in a two-stage supply chain, which is composed of a set of potential suppliers and a single buyer/retailer trading one product. The demand at the buyer's stage is price-sensitive and suppliers have finite production rates. Within this framework, the optimization of the supply chain is evaluated by considering the performance of both stages and guaranteeing their profitability through a coordination mechanism based on profit-sharing. Two mixed integer nonlinear programming models are presented under different lot-sizing policies to determine the optimal set of selected suppliers, retail price, and order quantity and number of orders per order cycle for each selected supplier. The structure of the models' solutions is examined by deriving properties of the optimal supplier selection and order quantity allocation scheme. Numerical examples are also provided to illustrate the applicability of the proposed models.

Aktepe A., Yanık E., Ersöz S.

Demand forecasts are used as input to planning activities and play an important role in the management of fundamental operations. Accurate demand forecasting is an important information for many organizations. It provides information for each stage of inventory management. In this study, multiple linear regression analysis, multiple nonlinear regression analysis, artificial neural networks and support vector regression were applied in a production facility that produces spare parts of construction machinery. The aim of the study is to forecast the number of spare parts requested in the future period by the customer as close as possible. As the input variables in the developed models, the sales amounts of the past years belonging to the manifold product group, which is one of the important spare parts of the construction machinery, number of construction machines sold in the world, USD exchange rate and monthly impact rate are used as input variables. The inputs of the model are designed according to construction machinery sector. In the model, monthly impact rate enables us to create more robust model. In addition, the estimation results have high accuracy by systematic parameter design of artificial intelligence methods. The data of the 9 years (from 2010 to 2018) were used in the application. Demand forecasts were conducted for 2018 to compare actual values. In forecasts, artificial neural network and support vector regression produced better results than regression methods. In addition, it was found that support vector regression forecasting produced better results in comparison to artificial neural network. __________________________________________________________________________________________

Kaur H., Prakash Singh S.

In recent times, Supply chains are required to undergo the structural changes in order to adapt to the positive events such as Industry 4.0 and negative events such as natural and man-made disasters. Both positive and negative events tend to cause disruptions and affect business operations continuity. Supplier selection, being the critical and foremost activity must ensure that selected suppliers are capable of supporting the organizations against disruptions caused by these events. Hence, supplier selection and order allocation must be restructured considering the dynamics of Industry 4.0 and disaster events to ensure undisrupted flow of materials across supply chain. The paper proposes a multi stage hybrid model for integrated supplier segmentation, selection and order allocation considering risks and disruptions. The suppliers are then evaluated based on set of criteria suitable in Industry 4.0 environment using Data Envelopment Analysis (DEA) and are further prioritized using Fuzzy Analytical Hierarchical Process and Technique for Order of Preference by Similarity to Ideal Solution (FAHP-TOPSIS). The risk associated with each supplier is computed. The paper also proposes a Mixed Integer Program (MIP) as to optimize multi-period, multi item order allocation to suppliers in such a way that overall cost and risk of disruption is simultaneously minimized. In event of any disruption either from supply or demand side, the multi-stage hybrid model tends to reduce its economic impact by allocating emergency orders, thus, ensuring business operations continuity. The proposed multi-stage hybrid model is illustrated using a case of an automobile company.

Liu Y., Eckert C.M., Earl C.

Analytic Hierarchy Process (AHP) is a broadly applied multi-criteria decision-making method to determine the weights of criteria and priorities of alternatives in a structured manner based on pairwise comparison. As subjective judgments during comparison might be imprecise, fuzzy sets have been combined with AHP. This is referred to as fuzzy AHP or FAHP. An increasing amount of papers are published which describe different ways to derive the weights/priorities from a fuzzy comparison matrix, but seldomly set out the relative benefits of each approach so that the choice of the approach seems arbitrary. A review of various fuzzy AHP techniques is required to guide both academic and industrial experts to choose suitable techniques for a specific practical context. This paper reviews the literature published since 2008 where fuzzy AHP is applied to decision-making problems in industry, particularly the various selection problems. The techniques are categorised by the four aspects of developing a fuzzy AHP model: (i) representation of the relative importance for pairwise comparison, (ii) aggregation of fuzzy sets for group decisions and weights/priorities, (iii) defuzzification of a fuzzy set to a crisp value for final comparison, and (iv) consistency measurement of the judgements. These techniques are discussed in terms of their underlying principles, origins, strengths and weakness. Summary tables and specification charts are provided to guide the selection of suitable techniques. Tips for building a fuzzy AHP model are also included and six open questions are posed for future work.

Dobos I., Vörösmarty G.

This paper offers a solution to the supplier selection problem and the problem of lot‐sizing decisions. Data Envelopment Analysis (DEA) is seldom applied to help solve multilevel supplier selection problems. The order allocation problem is reformulated as a linear programming problem in a novel way. First, a Data Envelopment Analysis/common weights model (DEA/CWA) is introduced to help define the supplier pool of capable suppliers. Selection criteria include management and green criteria. In a second phase, a nonlinear mathematical programing model is offered to determine capacity distribution among selected suppliers. A numerical example is provided to support the theoretical model.

Sontake A., Jain N., Singh A.R.

Supplier evaluation and selection on economic, social, and environmental dimensions are crucial for sustaining the pressure of a competitive global supply chain. In this work, a mixed-integer linear programming for supplier selection and order allocation in a single period, multi-supplier, multi-item environment with a prime consideration to the selection of transportation alternatives while delivering items is developed. To capture the real-world situation, the proposed model incorporates no discount and all quantity discount situations considering the bad quality and late delivery disruptions in the supply chain. To reflect a wide variety of operational conditions, two scenarios with two cases have been developed to demonstrate the effect of disruptions and discounts over demand and procurement cost. A real-life case of the automotive sector in central India is studied to validate the proposed model. Also, sensitivity analysis has been performed to understand the trade-offs between different sustainability criteria and the total cost of purchase.

Li F., Wu C., Zhou L., Xu G., Liu Y., Tsai S.

Environmental factors are increasingly being considered in supply chain risk management (SCRM), which itself represents a growing trend. Accordingly, dynamic supplier selection and order allocation have become very important. Both qualitative and quantitative factors should be considered in the selection of eligible suppliers. In this study, a novel two-stage comprehensive mathematical model is developed for selecting a set of suppliers and assigning an order quantity to each supplier. The first stage involves a primary selection of alternative suppliers according to the risk value, which is determined using qualitative and quantitative methods based on the best–worst method, and the second stage involves establishing a multiobjective mathematical model for dealing with dynamic supplier selection and order allocation. The proposed approach, which helps enterprises manage uncertainty in SCRM, is applied in this study to the new energy vehicles industry.

Gabellini M., Mak S., Schoepf S., Brintrup A., Regattieri A.

Belghand M., Asadi A., Alipour-Vaezi M., Jolai F., Aghsami A.

Purpose The purpose of this study is developing a new buy-back coordination contract in the symbiotic supply chain. In this new contract, the goal of the supply chain members (profit maximization) is realized. Design/methodology/approach This paper encourages the manufacturer to order products optimally by presenting a new buy-back coordination contract, and in return, the supplier undertakes to buy the unsold products from the manufacturer at the buy-back price. By using data-driven decision-making and multiobjective decision-making and considering the existing conditions in the symbiosis industry, a contract has been presented that guarantees the profits of supply chain members. Findings In this paper, it was found out how the authors can determine the order quantity, buy-back price and wholesale price in a symbiotic supply chain in such a way that it makes a profit for both the supplier and the manufacturer. In other words, how to determine these variables to encourage the manufacturer to order more quantity to the supplier so that both will benefit. Originality/value To the best of the authors’ knowledge, this is the first paper that defines a new buy-back coordination contract in the symbiotic supply chain by considering uncertain demand and a multiobjective model. Due to the importance of environmental issues, the sharing of resources by companies and organizations with each other, and the necessity of their cooperation, industries are moving toward a symbiosis industry.

Faraji N., Mohammadnazari Z., Rabbani M., Aghsami A.

The horticulture industry has a special role in the health of society due to its direct impact on the food security in society, so it is one of the important industries that affect the lives of all people. Also, due to the increase in the growth of the world population, the need for food is increasing day by day, so it is necessary to pay special attention to the horticulture industry to reduce the rate of hunger in the world, which causes various problems. This study focuses on the routing of vehicles to carry different kinds of horticultural products considering their shelf life to keep the stability and quality of these products at the highest level. This aim has been achieved through presenting a comprehensive multi-period MILP model for different types of horticultural products and various types of vehicles in the horticultural supply chain. A case study in European Union is also presented to analyze the efficacy of model. The outcome of this research could be proliferative for managers, decision makers, and policy designers of horticulture supply chain since it proffers avenues of sustainable production enhancement.

Husna A.U., Ghasempoor A., Amin S.H.

The process of selecting the most suitable suppliers and allocating orders to them is critical in supply chain management. This research proposes a new framework to address the challenges of Supplier Selection and Order Allocation (SS&OA) by introducing a two-phase combined approach. In the first phase, three Machine Learning (ML) clustering techniques (i.e., K-means clustering, Gaussian Mixture Model, and Balance Iterative Reducing and Clustering using Hierarchies) are employed to identify a small group of suitable suppliers from a large pool of potential suppliers. The accuracy of the developed clustering models is assessed using Silhouette score technique. In the second phase, we focus on one of the clusters based on the results of Phase 1. In this phase, a new multi-objective model is developed for SS&OA that considers multi-source, multi-period, and multi-product scenarios. Compromise method is utilized to obtain efficient solutions. The framework is applied to extensive, real historical contract data from Canada's Public Works and Government Services Canada (PWGSC) on behalf of federal departments and agencies. The results indicate that K-means clustering model is the most accurate among the models examined for this dataset, and the choice of ML clustering techniques has a significant impact on SS&OA.

Regattieri A., Gabellini M., Calabrese F., Civolani L., Galizia F.G.

The strategic selection of suppliers and the allocation of orders across multiple periods have long been recognized as critical aspects influencing company expenditure and resilience. Leveraging the enhanced predictive capabilities afforded by machine learning models, direct lookahead models—linear programming models that optimize future decisions based on forecasts generated by external predictive modules—have emerged as viable alternatives to traditional deterministic and stochastic programming methodologies to solve related problems. However, despite these advancements, approaches implementing direct lookahead models typically lack mechanisms for updating forecasts over time. Yet, in practice, suppliers often exhibit dynamic behaviours, and failing to update forecasts can lead to suboptimal decision-making. This study introduces a novel approach based on parametrized direct lookahead models to address this gap. The approach explicitly addresses the hidden trade-offs associated with incorporating forecast updates. Recognizing that forecasts can only be updated by acquiring new data and that the primary means of acquiring supplier-related data is through order allocation, this study investigates the trade-offs between data acquisition benefits and order allocation costs. An experimental design utilizing real-world automotive sector data is employed to assess the potential of the proposed approach against various benchmarks. These benchmarks include decision scenarios representing perfect foresight, no data acquisition benefits, and consistently positive benefits. Empirical findings demonstrate that the proposed approach achieves performance levels comparable to those of decision-makers with perfect foresight while consistently outperforming benchmarks not balancing order allocation costs and data acquisition benefits.

Jafarnejad E., Makui A., Hafezalkotob A., Aghsami A.

Environmental pollution and social welfare have become significant issues for governments and policy-makers in both developed and developing countries. That’s why there is an essential need to develop a comprehensive model for investigating the impact of government interventions on the production quantities of refineries considering competition between bio and oil fuels. Although some papers have focused on the tariff policy as a subsidy or a tax scheme, there is still a lack of models taking the government’s role as an independent player in the competitive market of refineries into account. Also, previous studies have not modeled the government and refineries competition as a competitive game in which they are the leader and follower, respectively. Moreover, no study has discussed the issue based on the sustainability goals of the government in the contexts of economic, environmental, and social aspects considering the selection of the tariff or investment strategy. To fill these gaps, this paper develops a bi-level multi-objective mathematical model incorporating two policies of tariff and investment on production capacity as environmental governance policy in refineries competition. The first level presents government problems under sustainability considerations. In the second level, the competition of bio and oil refineries is formulated using the Cournot competition game model. The transformation method is proposed by applying KKT conditions to obtain the best responses of refineries in the corresponding game. In addition, the revised multi-choice goal programming approach is used to solve proposed multi-objective model. A case study is presented to show the applicability of the model and the sensitivity analysis of the critical parameters is conducted. The findings show that government intervention policies on fuel production and consumption can be positively and directly related to reducing pollution and increasing social welfare.