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When approaching a stock‐out situation, a company should be able to actively manage the allocation of available products on the basis of customer requirements and priorities as well as contractual relationships. The purpose of this paper is to describe different order promising mechanisms and analyze how well they can contribute to the effective management of stock‐out situations.

The paper provides a formal description and analysis of alternative order promising mechanism applicable in make to stock systems. Numerical analysis is conducted based on the data of a pharmaceutical company.

The paper clearly points out the potential of alternative order promising mechanisms to alleviate the negative consequences associated with a temporary stock‐out situation.

The paper does not consider implications of inventory pre‐allocation to customer classes. Further research should address the interplay between pre‐allocation and different order allocation mechanisms.

The results obtained from this analysis provide guidelines for manufacturers, retailers, and vendors of supply chain software on how to design and utilize order promising systems.

The paper provides a consistent formal approach to modelling order promising mechanisms, introduce new and innovative order promising mechanisms and provide valuable insight into their performance through numerical analysis.

On one side, the purpose of this paper is to numerically analyze the emergency order allocation mechanism and help managers to understand the relationship between the emergency coefficient, uncertainty and emergency cost in two‐echelon logistics service supply chain. On the other side, the purpose of this paper is to help managers understand how to deal with the problem of order allocation in the two‐echelon logistics service supply chain better in the case of emergency.

The paper presents a multi‐objective planning model for emergency order allocation and then uses numerical methods with LINGO 8.0 software to identify the model's properties. The application of the order allocation model is then presented by means of a case study.

With the augment of uncertainty, the general cost of logistics service integrator (LSI) is increasing, while the total satisfaction of all functional logistics service providers (FLSPs) is decreasing, as well as the capacity reliability; at the same time the emergency cost coefficient is closely correlative with the satisfaction and general penalty intensity of FLSPs; finally, the larger the emergency cost coefficient is, the more satisfaction of FLSPs, but the capacity reliability goes up first and down later.

Management should note that it is not better when emergency cost coefficient is bigger. The general satisfaction degree of FLSP increases with the augment of emergency cost coefficient, but there is an upper limit of the value, i.e. it will not increase indefinitely with the augment of emergency cost coefficient. This paper also has some limitations. The optional emergency cost coefficient only adopted a group of data to analyze while the trend of the reliability of logistics capacity needs to be further discussed. In addition, the algorithm of emergency order allocation model in the case of multi‐objective remains to be solved.

Under emergency conditions, LSIs can adopt this kind of model to manage their FLSPs to obtain the higher logistics performance. But LSIs should be careful selecting emergency cost coefficient. In accordance with different degrees of emergency logistics demand, LSIs can determine reasonable emergency cost coefficient, but not the bigger, the better, on the premise that LSIs acquire maximum capacity guarantee degree and overall satisfaction degree of FLSPs. FLSPs can make contract bargaining of reasonable emergency coefficient with LSIs to make both sides get the best returns and realize the benefit balance.

Many studies have emphasized the capacity allocation of manufactures, order allocation of manufacturing supply chain and scheduling model of emergency resources without monographic study of supply chain order allocation of logistics service. Because the satisfaction degree of FLSPs the cost of integrators needs to be considered in the process of order allocation, and the inventory cost of capacity does not exist, it is different from the issue of capacity allocation planning of manufacture supply chain. Meanwhile, the match of different kinds of logistics service capacity must be considered for the reason of the integrated feature of logistics service. Additionally, cost is not the most important decision objective because of the characteristics of demand uncertainty and weak economy. Accordingly, this paper considers these issues.

The purpose of this paper is to propose a holistic approach for supplier evaluation and purchasing order allocation among the ranked suppliers who meet acceptable levels of economic, environmental and social measures.

A mixed research method of case study and analytical approach is adopted in this research. A fuzzy analytical hierarchical process (FAHP) is applied for ranking of suppliers. Supplier ranks are validated using judgements from multiple decision makers. Purchasing order allocation among the ranked suppliers is determined using cost minimization subject to multiple criteria of economic, environmental and social conditions. A cement manufacturing case example demonstrates and validates the proposed approach.

The research shows that both economic and environmental considerations are significant when suppliers are evaluated for sustainable procurement within the best practice of supply management process. Ranking of suppliers, based on experts’ opinions, indicates varying degrees of importance for each criterion. Adoption of sustainable procurement criteria for evaluating supplier in a cement manufacturing organization is explained by three organizational theories including resource-based, institutional and dynamic capabilities theories. Preferred suppliers from FAHP method are confirmed by judgements from multiple decision-makers. The analysis reveals that purchasing order allocation is different when suppliers are evaluated based on their relative importance and overall ranking.

Currently, individual performance measures and decision-makers are selected from a limited set. The purchasing allocation among ranked suppliers, subjected to cost minimization, incorporates environmental objective of acceptable carbon dioxide emission and social perspective of health and safety of workers, and provides a new approach for dual supplier evaluation and purchasing allocation problem in cement industry. Adopting the proposed supplier evaluation and order allocation approach in practice needs to be guided by the operational principles and an overall methodology which is appropriate for the specific industry with sustainability objectives.

This research enables decision-makers to incorporate sustainability analysis in the supplier evaluation as the basis for best practice with an industry-friendly holistic approach. Using organizational theories, the research re-enforces the importance of not only the energy consumption and environmental management systems of environmental dimension as driving forces/factors from Institutional theory perspective, but also pollution controls and prevention as purchasing capabilities from resource-based theory perspective. The proposed approach is expected to motivate decision-makers to consider sustainable perspectives in supplier evaluation and order allocation processes in a global supply chain and can become a benchmarking tool.

Suppliers’ information on health and safety of their truck drivers are used in order allocation, thus emphasizing the importance of social dimension and encouraging better conditions and benchmarking for delivery drivers.

This paper extends the contribution to the literature by providing guidelines for managers to set strategies, benchmarks and policies within broader sustainable supply chain practices and demonstrates the applicability of the approach using a cement-manufacturing scenario in an emerging economy.

This paper focuses on multi-objective order allocation with product substitution for the vaccine supply chain under uncertainty.

The weighted-sum minimization approach is used to find a compromised solution between three objectives of minimizing inefficiently vaccinated people, postponed vaccinations, and purchasing costs. A mixed-integer formulation with substitution quantities is proposed, subject to capacity and demand constraints. The substitution ratios between vaccines are assumed to be exogenous. Besides, uncertainty in supplier reliability is formulated using optimistic, most likely, and pessimistic scenarios in the proposed optimization model.

Covid-19 vaccine supply chain process is studied for one government and three vaccine suppliers as an illustrative example. The results provide essential insights for the governments to have proper vaccine allocation and support governments to manage the Covid-19 pandemic.

This paper considers the minimization of postponement in vaccination plans and inefficient vaccination and purchasing costs for order allocation among different vaccine types. To the best of the authors’ knowledge, there is no study in the literature on order allocation of vaccine types with substitution. The analytical hierarchy process structure of the Covid-19 pandemic also contributes to the literature.

The paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.

First, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.

The paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.

The paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.

Supplier selection problem is the key process in decision making of supply chain management. An effective selection of vendors is heavily responsible for the success of any organization. Vendor selection problem (VSP) reflects a more practical view when the decision makers involved in the problem are present on different levels. Moreover, vendor selection consists of various random parameters to be dealt with in real life. The purpose of this paper is to present a decentralized bi-level VSP where demand and supply are normal random variables and objectives are fuzzy in nature. Decision makers are present at two levels and are called as leader and follower. As the next purpose, this paper extends and presents a solution approach for fuzzy bi-level multi-objective decision-making model with stochastic constraints. Different scenarios have been developed within a real-life case study based on different sets of controlling factors under the control of leader.

This study uses chance-constrained programming and fuzzy set theory to generate the results. Stochastic constraints are converted into deterministic constraints using chance-constrained programming. Decision variables in the bi-level VSP are partitioned between the two levels and considered as controlling factors. Membership functions based on fuzzy set theory are created for the goals and controlling factors and are used to obtain the overall satisfactory solutions. The model is tested on a real-life case study of a textile industry and different scenarios are constructed based on the choice of leader’s controlling factors.

Results showed that the approach is quite helpful as it generates efficient results producing a good level of satisfaction for the decision makers of both the levels. Results showed that on choosing the vendors that are associated with worst values in terms of associated costs, vendor ratings and quota flexibilities as controlling factors by the leaders, the level of satisfaction achieved is highest. The level of satisfaction of solution is lowest for the scenario when the leader chooses to control the decision variables associated with vendors that are profiled with minimum vendor ratings. Results also showed that higher availability of materials and budget with vendors proved helpful in obtaining quota allocations. Different scenarios generate different results along with different values of satisfaction degrees and objective values which shows the flexible feature of the approach based on leader’s choice of controlling factors. Numerical results showed that the leader’s control can be effectively incorporated maintaining satisfaction levels of the followers under various scenarios or conditions.

The paper makes a certain contribution toward the study of vendor selection existing in a hierarchical manner under uncertain environment. A wide set of data of different factors is needed which can be seen as a limitation when the available time is short for the supplier selection process.

VSP which is generally adopted by most of the large organizations is characterized with hierarchical decision making. Moreover, dealing with the real-life concern, the data available for some of the parameters are not complete, representing an uncertainty of parameters. This study is quite helpful for decentralized VSP under uncertain environment to reduce the costs, improve profit margins and to create long-term relationships with selected vendors. The proposed model also provides an avenue to explore the decision making when the leader has control over some of the decision variables.

Reviewing the literature available, this is the first attempt to present a multi-objective VSP where the decision makers are at hierarchical levels considering uncertain parameters such as demand and supply as per the best knowledge of authors. This research further provides an approach to construct scenarios or different cases based on the choice of leader’s choice of controlling factors.

The strategic importance of sourcing is inherent in the positioning of the purchasing operation in a supply chain and supplier evaluation – a crucial step in sourcing – is a complex multicriteria decision making (MCDM) problem. The purpose of this paper is to provide a meta‐model for supplier evaluation and order quantity allocation, based on a MCDM method, namely the Analytic Network Process (ANP) and a multiobjective mathematical programming method (MOMP), the AUGMECON.

The proposed approach consists of two parts. The former develops and applies the ANP method in order to evaluate the suppliers in qualitative terms. The latter implements the AUGMECON method in order to find the Pareto optimal solutions for the allocation of order quantities in a multiple sourcing environment. The integrated meta‐model is exposed through an illustrative case concerning the parapharmaceutical enterprise cluster in Greece.

The proposed meta‐model constitutes an efficient method that enables managers to actively participate in the decision making process and exploit the “qualitative value” of their suppliers, while minimizing the costs and the mean delivery times. In addition, it is proved to be suitable for the enterprise clusters, as it adapts a multiple sourcing strategy and enhances the partnership among the members.

The outcome of the model is highly dependent on the inputs provided by the decision maker. Moreover, the ANP method is computational intensive, but this limitation can be alleviated by appropriate software tools.

The proposed meta‐model is an innovative approach for decision making in the area of multiple sourcing and order allocation.

This paper aims to minimize the mean-risk cost of sustainable and resilient supplier selection, order allocation and production scheduling (SS,OA&PS) problem under uncertainty of disruptions. The authors use conditional value at risk (CVaR) as a risk measure in optimizing the combined objective function of the total expected value and CVaR cost. A sustainable supply chain can create significant competitive advantages for companies through social justice, human rights and environmental progress. To control disruptions, the authors applied (proactive and reactive) resilient strategies. In this study, the authors combine resilience and social responsibility issues that lead to synergy in supply chain activities.

The present paper proposes a risk-averse two-stage mixed-integer stochastic programming model for sustainable and resilient SS,OA&PS problem under supply disruptions. In this decision-making process, determining the primary supplier portfolio according to the minimum sustainable-resilient score establishes the first-stage decisions. The recourse or second-stage decisions are: determining the amount of order allocation and scheduling of parts by each supplier, determining the reactive risk management strategies, determining the amount of order allocation and scheduling by each of reaction strategies and determining the number of products and scheduling of products on the planning time horizon. Uncertain parameters of this study are the start time of disruption, remaining capacity rate of suppliers and lead times associated with each reactive strategy.

In this paper, several numerical examples along with different sensitivity analyses (on risk parameters, minimum sustainable-resilience score of suppliers and shortage costs) were presented to evaluate the applicability of the proposed model. The results showed that the two-stage risk-averse stochastic mixed-integer programming model for designing the SS,OA&PS problem by considering economic and social aspects and resilience strategies is an effective and flexible tool and leads to optimal decisions with the least cost. In addition, the managerial insights obtained from this study are extracted and stated in Section 4.6.

This work proposes a risk-averse stochastic programming approach for a new multi-product sustainable and resilient SS,OA&PS problem. The planning horizon includes three periods before the disruption, during the disruption period and the recovery period. Other contributions of this work are: selecting the main supply portfolio based on the minimum score of sustainable-resilient criteria of suppliers, allocating and scheduling suppliers orders before and after disruptions, considering the balance constraint in receiving parts and using proactive and reactive risk management strategies simultaneously. Also, the scheduling of reactive strategies in different investment modes is applied to this problem.

With the increasing competition among the industries, they remain under pressure as how to select the best set of suppliers for the competitive edge. Often, it has been challenging to develop an effective set of suppliers due to varied and asymmetric mode of criteria. The purpose of this paper is to develop a responsive chain under original equipment manufacturer (OEM).

This study proposes a responsive chain under a two-echelon system (TES) of OEM, which needs to collaborate with a set of suppliers at each echelon through an integrated methodology of AHP and TOPSIS. According to the OEM’s criteria, demands and suppliers’ capacity vary with time, therefore they are not static for a longer period. Hence, supplier selection (SS) problem possesses dynamicity in real practice. For this, MILP is used for finding optimal order quantities based on the optimal ranking at each echelon in the multi-period scenario. Subsequently, sensitivity analysis (SA) is conducted through Taguchi method of parameter design (TMPD) to achieve an optimal ranking in the TES.

This study suggests optimal criteria’s weight, percentage contribution, and flexibility for the suppliers and manufacturers involving through maximum demand strategy at each echelon of OEM. It also provides robust group of suppliers and manufacturers in the TES through optimal ranking and simultaneously in the order allocations. Furthermore, it restricts the number of suppliers and manufactures at each echelon through proposed methodology to obtain the solution in a very short running time.

To validate this model, a real data set for the case of chain conveyor company is used. This adopted methodology can suggest the organization that how the approach should be implemented.

Acknowledges that the effect of displayed inventory on retail sales is widely recognized in the logistics, marketing and operations management literature and has been empirically verified. However, neither the marketing literature (shelf‐space allocation models) nor the operations management literature (inventory control models) has appropriately modeled this effect. The displayed‐inventory news‐vendor problem is developed and analyzed, utilizing a simple model to illustrate the interdependencies between the inventory and space‐allocation decisions. The model is then extended to the multi‐item case, which can be incorporated as part of a comprehensive shelf‐management system.