Designing a single-vendor and multiple-buyers' integrated production inventory model for interval type-2 fuzzy demand and fuzzy rule based deterioration

In this paper, a single-vendor and multiple-buyers' integrated production inventory model is investigated where deterioration rate of the item is assumed to change in accordance with the weather conditions of a particular region. It relies upon the values of certain attributes that have a direct influence on the extent of deterioration. These parameter values are easily forecasted and thereby can be utilized to determine the item depletion rate, which is executed here using Mamdani fuzzy inference scheme. Besides, a nearest interval approximation formula for the defuzzification of interval type-2 fuzzy number (IT2FN) is developed. Its application in the proposed model is brought off by considering imprecise demand patterns at the buyers' locations which are in the form of IT2FNs. The model optimizes the total number of shipments to be made to the buyers within a complete cycle so as to minimize the overall integrated cost incurred. An optimization problem with interval objective function is formulated. A detailed illustration of the theoretical results is further demonstrated with the help of numerical example, followed by sensitivity analysis which provides insights into better decision making.

Implications of Price Matching in Supply Chain Negotiation

Problem definition: Few papers have explored the impact of price matching negotiation (PM), in which a channel matches its price with the resulting wholesale price bargained by another channel, on firms’ performances, consumer welfare, and social welfare, with and without supply chain coordination. Academic/practical relevance: Negotiation has been widely seen in determining both uniform and discriminatory wholesale prices, which affect outcomes of competitive supply chain practices. Methodology: To characterize the PM mechanism, we use game theory and Nash bargaining theory to compare PM with simultaneous negotiation (SN) through a common-seller two-buyer differentiated Bertrand competition model. Results: Our analysis reveals that PM can benefit the seller but hurt all buyers, which is at odds with some fair wholesale pricing clauses intending to protect buyers. Under coordination with side payments, however, all firms can conditionally benefit more from PM than from SN. Despite firms’ gains, PM leads to less consumer utility and social welfare compared with SN, unless the second buyer in PM is considerably less powerful than the first buyer. Coordination further worsens PM’s negative impact on consumer utility and social welfare. Moreover, the existence of a spot market can increase the wholesale price in PM, hurting buyers, consumers, and society. Furthermore, the qualitative results about PM remain robust under an alternative disagreement point for PM, multiple buyers, and other extensions. Managerial implications: This paper delivers insights on when price matching in supply chain wholesale price negotiation can benefit a seller, buyers, consumers, and society in a variety of scenarios. It advocates how managers can use PM to their own advantages and provides rationale to decision makers for policy regulations regarding wholesale pricing.

Supply chain models with consignment stock agreement, payment schemes and delay-in-payments

Supply chain management (SCM) has shown to be a successful strategy to manage the flow of goods, materials, information and services between multiple entities in one organization or multiple businesses working together to provide final customers with final products or services with the objective of improving and enhancing the performance of the chain and maximizing its profit. Inventory management (IM) is one element of the SCM that has shown researchers’ interests as it plays a major role in increasing supply chain profits and satisfying customers. Different coordination mechanisms have been developed to improve the collaboration and the integration of supply chain players. Consignment stock (CS) is one of the coordination mechanisms that is extensively studied by researchers to reflect its benefits, drawbacks, and the proper techniques of implementing it between two or more players in the chain. The studies of the CS still have some gaps that can be covered by researchers such as studying its effect in a three-level supply chain or when a delay-in-payment exists. Optimizing the number of payments or studying a three-level supply chain system with multiple suppliers and multiple buyers has not been developed. This thesis covers these gaps and considers different scenarios where a CS, a traditional policy (TP) or a combination between both of them might exist in case a system consists of three players. The main findings are optimizing the number of payments and incorporating a delay-in-payment increase the profit of the chain. In addition, a combination of a TP between the upstream players and a CS between the downstream players has shown to be better than adopting the same policy between all players. Some results of adopting a CS by all players have shown to be very close to the best scenario which could be the best option when demands highly fluctuate.

Supply chain models with consignment stock agreement, payment schemes and delay-in-payments

Supply chain management (SCM) has shown to be a successful strategy to manage the flow of goods, materials, information and services between multiple entities in one organization or multiple businesses working together to provide final customers with final products or services with the objective of improving and enhancing the performance of the chain and maximizing its profit. Inventory management (IM) is one element of the SCM that has shown researchers’ interests as it plays a major role in increasing supply chain profits and satisfying customers. Different coordination mechanisms have been developed to improve the collaboration and the integration of supply chain players. Consignment stock (CS) is one of the coordination mechanisms that is extensively studied by researchers to reflect its benefits, drawbacks, and the proper techniques of implementing it between two or more players in the chain. The studies of the CS still have some gaps that can be covered by researchers such as studying its effect in a three-level supply chain or when a delay-in-payment exists. Optimizing the number of payments or studying a three-level supply chain system with multiple suppliers and multiple buyers has not been developed. This thesis covers these gaps and considers different scenarios where a CS, a traditional policy (TP) or a combination between both of them might exist in case a system consists of three players. The main findings are optimizing the number of payments and incorporating a delay-in-payment increase the profit of the chain. In addition, a combination of a TP between the upstream players and a CS between the downstream players has shown to be better than adopting the same policy between all players. Some results of adopting a CS by all players have shown to be very close to the best scenario which could be the best option when demands highly fluctuate.

Hierarchical Combinatorial Auction in Computing Resource Allocation for Mobile Blockchain

The mobile blockchain has been recognized as an emerging solution to address the security and privacy issues in a mobile application system. The mining process in mobile blockchain requires high computing resources which could overwhelm that which mobile devices can offer. In this case, mobile edge computing servers (MESs) can be involved to offer computing services to miners in mobile blockchain. Note that the resources of MESs are also limited; MESs could further request resources from the cloud computing server (CCS). Accordingly, the issue of hierarchical computing resource allocation arises. In this paper, we first consider a simple case with single-seller multiple buyers and a hierarchical single-seller multibuyer combinatorial auction model is proposed to solve this problem, based on which efficient and truthful frameworks are provided. We then extend the model to consider multiple CCSPs and propose a hierarchical multiple-seller multiple-buyer combinatorial auction model. For both models, the winner determination problems are formulated and computationally tractable algorithms are proposed. Also, pricing schemes are proposed to ensure the property of incentive compatibility and individual rationality. Finally, we evaluate the proposed schemes via simulations.

Strongly Budget Balanced Auctions for Multi-Sided Markets

In two-sided markets, Myerson and Satterthwaite's impossibility theorem states that one can not maximize the gain-from-trade while also satisfying truthfulness, individual-rationality and no deficit. Attempts have been made to circumvent Myerson and Satterthwaite's result by attaining approximately-maximum gain-from-trade: the double-sided auctions of McAfee (1992) is truthful and has no deficit, and the one by Segal-Halevi et al. (2016) additionally has no surplus — it is strongly-budget-balanced. They consider two categories of agents — buyers and sellers, where each trade set is composed of a single buyer and a single seller.The practical complexity of applications such as supply chain require one to look beyond two-sided markets. Common requirements are for: buyers trading with multiple sellers of different or identical items, buyers trading with sellers through transporters and mediators, and sellers trading with multiple buyers. We attempt to address these settings.We generalize Segal-Halevi et al. (2016)'s strongly-budget-balanced double-sided auction setting to a multilateral market where each trade set is composed of any number of agent categories. Our generalization refines the notion of competition in multi-sided auctions by introducing the concepts of external competition and trade reduction. We also show an obviously-truthful implementation of our auction using multiple ascending prices.Full version, including omitted proofs and simulation experiments, is available at https://arxiv.org/abs/1911.08094.

Optimal Auction Based Automated Negotiation in Realistic Decentralised Market Environments

Automated negotiations based on learning models have been widely applied in different domains of negotiation. Specifically, for resource allocation in decentralised open market environments with multiple vendors and multiple buyers. In such open market environments, there exists dynamically changing supply and demand of resources, with dynamic arrival of buyers in the market. Besides, each buyer has their own set of constraints, such as budget constraints, time constraints, etc. In this context, efficient negotiation policies should be capable of maintaining the equilibrium between the utilities of both the vendors and the buyers. In this research, we aim to design a mechanism for an optimal auction paradigm, considering the existence of interdependent undisclosed preferences of both, buyers and vendors. Therefore, learning-based negotiation models are immensely appropriate for such open market environments; wherein, self-interested autonomous vendors and buyers cooperate/compete to maximize their utilities based on their undisclosed preferences. Toward this end, we present our current proposal, the two-stage learning-based resource allocation mechanism, wherein utilities of vendors and buyers are optimised at each stage. We are aiming to compare our proposed learning-based resource allocation mechanism with two state-of-the-art bidding-based resource allocation mechanism, which are based on, fixed bidding policy (Samimi, Teimouri, and Mukhtar 2016) and demand-based bidding policy (Kong, Zhang, and Ye 2015). The comparison is to be done based on the overall performance of the open market environment and also based on the individual performances of vendors and buyers.

The Single-Vendor Multibuyer Integrated Production-Delivery Model with Production Capacity under Stochastic Lead Time Demand

In this paper, we consider an integrated production-delivery model in which a vendor supplies the same product to multiple buyers. Unlike existing study, in this proposed model, we assume that the sum of all buyers’ demand rates is larger than the vendor’s production rate under normal work, but less than that under overtime. All buyers are independent of each other. For each buyer, the lead time demand is stochastic and the shortage during lead time is permitted. The main objective of this model is to determine the optimal production and delivery policies and the optimal overtime strategy, which minimize the joint expected annual cost of the system. Based on the genetic algorithm, we develop a solution procedure to find the optimal production, delivery, and overtime decision of this model. Computational experiments show the error rate between the objective values obtained by the proposed solution procedure and the solutions solved by the exhaustive method. The results indicate that the proposed mixed genetic algorithm is more effective and adoptable in comparison with the exhaustive method as it can be able to calculate the optimal solutions for at least 96% for the instances. Ultimately, an adequate numerical example is given to show the detailed process of the solution procedure, and sensitivity analysis of main parameters with managerial implication is discussed.