Mergers and acquisitions matching for performance improvement: a DEA-based approach

This article proposes a new data envelopment analysis (DEA)-based approach to deal with mergers and acquisitions (M&As) matching. To derive reliable matching degrees between bidder and target firms, we consider both technical efficiency and scale efficiency. Specifically, an inverse DEA model is developed for measuring the technical efficiency, while a conventional DEA model is employed to identify the return of scale of the merged decision-making units (DMUs). Then, an optimization model is formulated to generate matching results to improve DMUs’ performance. An empirical study of M&As matching Turkish energy firms is examined to illustrate the proposed approach. This study shows that both technical efficiency and scale efficiency have impacts on M&As matching practices

Multi-objective optimization matching for one-shot multi-attribute exchanges with quantity discounts in E-brokerage

Electronic brokerages (E-brokerages) are Internet-based organizations that enable buyers and sellers to do business with each other. While E-brokerages have become a significant sector of E-commerce, theory and guidelines for matching the multi-attribute exchange in E-brokerage are sparse. This paper presents an approach to optimize the matching of one-shot multi-attribute exchanges with quantity discounts. Firstly, based on the conception and definition of matching degree and quantity discount, a multi-objective optimization model is proposed to maximize the matching degree and trade volume. This model belongs to a class of multi-objective nonlinear transportation problems and cannot be solved effectively by conventional methods, especially when large-scale problems are involved. Hence, secondly, a novel hybrid multi-objective meta-heuristic algorithm named multi-objective simulated annealing genetic algorithm (MOSAGA) has been developed to solve the proposed model. Finally, the computational results and analyses of some numerical problems are given to illustrate the application and performance of the proposed model and algorithm

A two-sided logistics matching method considering trading psychology and matching effort under a 4PL

As a supply chain integrator, a fourth party logistics (4PL) typically does not have its own logistics facilities, so the 4PL needs to match third party logistics (3PLs) and customers to meet customers' logistics service demands. An effective matching method can not only improve the efficiency of 4PL supply chain management, but also establish more long-term and stable cooperative relationships with customers and 3PLs. Therefore, we propose a novel two-sided logistics matching method considering the trading psychology and matching effort of matching subjects under the 4PL. First, based on considering the trading psychology, the concepts of blocking pair and stable matching are redefined. Then, based on the public values and matching effort of customers and 3PLs, the evaluation values of customers and 3PLs are calculated. And the trading possibilities of customers and 3PLs are calculated by considering the fairness threshold. Next, we consider different stable matching demands of customers and 3PLs and develop a bi-objective matching model to maximize the trading possibilities of both customers and 3PLs. Furthermore, the properties of the proposed method are discussed. Finally, a numerical example and comparison analysis are provided to prove the feasibility and effectiveness of the proposed method