International Transactions in Operational Research最新文献

This paper introduces the operational fleet composition problem with stochastic demands (OFCP-SD), which is commonly faced by e-commerce companies in the context of short-term capacity planning for freight transportation. We propose a two-stage stochastic programming formulation, in which the planned fleet size and mix decisions constitute the first stage, while recourse actions are taken in the second stage to hire extra vehicles or to make vehicle cancellations according to the observed demands. Hiring extra vehicles incurs additional costs, while vehicle cancellations involve financial restitutions. The objective is to minimize the fleet overall cost, which comprises the first-stage planned cost as well as the expected cost or restitution stemming from the fleet adjustments made in the second stage. A scenario generation procedure is devised, and a variable-fixing matheuristic is suggested for the OFCP-SD. A case study conducted within the Brazilian middle-mile operation of the leading e-commerce company in Latin America shows the advantages of explicitly modeling the stochastic demands and underlines the benefits of the proposed approach for the business. Compared to a simplified deterministic approach, the use of the OFCP-SD demonstrated a yearly potential cost avoidance of more than USD 2.5 million as well as an annual reduction of more than 20 thousand pallets transported by means of extra vehicles.

The selection of investment alternatives is a decision problem mostly solved by considering three criteria: liquidity, return, and risk. This article proposes a wider investment analysis, considering environmental and social criteria. Therefore, environmental, social, and governance (ESG) principles are considered in investments' multiple criteria decision analysis (MCDA). There are several MCDA methods, so choosing a method is a prior problem in decision-making. In this article, the problem of selecting investment alternatives is divided into sorting and ranking alternatives. The Multi-Attribute Utility Theory (MAUT) was applied for sorting, and the Analytic Hierarchy Process (AHP) was applied for ranking. Identifying these two methods as effective for the problem in question is an initial contribution of this work. The article also presents an ESG multicriteria hierarchical model for analyzing investment alternatives. Finally, the model is tested by analyzing five cooperative companies producing walnuts in the Brazilian Amazon. With the MAUT application, three companies were considered attractive for investment, while the other two were considered unattractive. With the AHP, the three attractive alternatives were ranked from best to worst for investment. Although ESG and MCDA are two fertile research fields, no document was found in the literature review combining ESG and MCDA in investment analysis. This literature gap raises a practical question: When something has not been applied, how can it be applied first? This is the main question answered by this article.

The facility management (FM) unit of an organization struggles with the sustainable selection of contractors for the maintenance of critical facilities. This paper aimed to optimally allocate maintenance contractors to public buildings by incorporating cost and other criteria into a matching objective that ensures sustainability. Sustainability criteria related to contractors and buildings were identified through an extensive literature review and expert opinion. These criteria were analyzed using integrated AHP and TOPSIS techniques, and the result was then utilized as input for a bi-objective optimization model designed for contractor allocation to mechanical, electrical, and plumbing and Fire assets. The optimization model reduces costs, increases matching between buildings and contractors, and respects FM strategy to limit the number of contractors. The quality of the solutions obtained and their contribution to achieving the United Nations Sustainable Development Goals (SDGs) is discussed. A case study is presented based on real data from the FM department responsible for maintaining 146 governmental buildings in the United Arab Emirates. The results show that the proposed approach outperforms the current solution, potentially reducing the number of contractors to three, reducing maintenance costs by 23.01%, and significantly improving sustainability by 50.02%. The study demonstrates the practical application of integrating SDGs into FM operations, contributing to achieving global sustainability targets. To show the robustness of the proposed approach, the sensitivity analyses of AHP weights on the ranking of contractors and buildings, as well as the sensitivity analysis of the number of buildings and contractors on the efficiency of the optimization model, have been conducted.

The study of the performance of primary and secondary educational institutions has gained popularity in recent years, but cross-country evaluations of higher education (HE) systems remain rare. This study aims to fill this gap by assessing the technical efficiency (TE) of 87 national HE systems using stochastic production frontier models. The empirical model considers, for each country, the number of universities in the 2023 SCImago Institutions Ranking (SIR) by knowledge areas as the dependent variable, and GDP, R&D expenditures, and national English proficiency as explanatory variables. To offer a comprehensive analysis, the model is applied across all SIR ranking levels. Results show that Western countries generally lead in TE, but three performance profiles emerge: countries that concentrate their resources on a few HE institutions (efficient at the top SIR levels), others with homogeneous HE institutions (high TE across all levels), and low-performance HE systems. Policy implications are discussed.

The number of asylum applications in the European Union has significantly increased in recent years, highlighting the need for effective decision-making in the resettlement process. This research focuses on the refugee status and the resettlement process that transfers refugees from an asylum country to a receiving country. Integration plays a crucial role in the success of resettlement, benefiting both refugees and receiving countries. The priorities of receiving countries and the preferences of refugees also influence the assignment process. However, no comprehensive optimization framework exists that combines these dimensions for decision support. This study proposes a decision-making system that incorporates integration potential, receiving countries' priorities, and refugees' preferences, considering quotas, and analyzing different scenarios. The system aims to aid organizations in making informed decisions and could be integrated into existing information systems. The research contributes to addressing the complex challenges of refugee resettlement and has potential societal impact.

Considering that an online retail platform system exists not only a product supply chain (manufacturer and e-tailer) but also a traffic supply chain (platform and e-tailer) in the era of the digital economy, this paper mainly aims to answer the following question: How to integrate a platform system under considering these two heterogeneous supply chains? To this end, we analyze the decisions of the platform system under the decentralized decision (DD model), the integrated traffic supply chain (TF model), the integrated product supply chain (TP model), and the centralized decision (CD model). By comparing the decisions, we explore how to integrate the platform system under the TF model, the TP model, and the DD model. Our main findings include the following. First, eliminating the double marginalization of the product (or traffic) supply chain will lead to an increase in the double marginalization of the traffic (or product) supply chain. Second, integrating product or traffic supply chains separately can improve the platform system's profit, but the trade-off of double marginalization will reduce the increase of the platform system's profit. Even if the traffic supply chain is integrated separately, the profit of the traffic supply chain will decrease. Third, we design a revenue-sharing (RS) contract, a quantity discount (QD) contract, and a combination QD-RS contract under the TP model, the TF model, and the DD model, respectively, to achieve the platform system integration.

An equitable distribution of workload is essential when deploying vehicle routing solutions in practice. For this reason, previous studies have formulated vehicle routing problems with workload-balance objectives or constraints, leading to trade-off solutions between routing costs and workload equity. These methods consider a single planning period; however, in practice, equity is often sought over several days. In this work, we show that workload equity over multiple periods can be achieved without impact on transportation costs when the planning horizon is sufficiently large. This is demonstrated in the context of a generic multiperiod vehicle routing problem, using a simple two-phase method. In the first phase, solutions of minimal distance are produced for each period. Next, the resulting routes are allocated to drivers to obtain equitable workloads over the planning horizon. We conducted extensive numerical experiments to measure the performance of the proposed approach and the level of workload equity achieved for different planning-horizon lengths. For horizons of five days or more, we observed that quasi-optimal workload equity and optimal routing costs can be jointly achievable.

In this study, a filtered beam search (FBS) algorithm is proposed to address the two-dimensional rectangle packing problem, whose objective is to minimize the height of a rectangular sheet of fixed width in which n rectangular items can be packed without overlapping. The corner-occupying placement (COP) strategy is introduced in the packing process, which packs each rectangle into the sheet so that it occupies a corner position formed by the sheet's edges and the edges of already packed rectangles. Two evaluation criteria, namely, local evaluation and global evaluation, are proposed to evaluate the benefit of a candidate COP and are seamlessly integrated into the FBS framework in order to maintain a better balance between the solution quality and computation time. Extensive testing on four sets of 112 zero-waste test instances indicated that the proposed method competes favourably with the state-of-the-art reference methods. Furthermore, the proposed method is modified to handle a variant of the rectangle packing problem where item rotations are not allowed. The modified algorithm is competitive, compared with the existing top algorithms, according to computational results on widely used benchmarks.

Tabu search is one of the most commonly used local search-based metaheuristics. A key component of the search is the tabu criterion, which controls whether it should be prohibited to make certain moves relative to the current solution. While attribute-based tabu criteria have been dominant for decades, an alternative has received increased attention lately. Using a set of hash functions to map solutions onto hash values, a solution-based tabu criterion has been used successfully, in particular for optimization problems involving binary variables. This paper compares the performance of solution-based and attribute-based tabu search on the general binary integer programming problem. According to computational experiments conducted on a sample of 160 benchmark instances, there is no statistically significant difference between the performances of solution-based tabu search and attribute-based tabu search.

Public services around the world face growing demands, limited budgets, and changing populations. As a result, analyzing efficiency is more important than ever. Techniques like data envelopment analysis, stochastic frontier analysis, and machine learning techniques offer valuable tools for improving how public services are managed. This special issue highlights current research on measuring efficiency in education, healthcare, finance, real estate, and more. It is divided into four main areas: new ways to measure efficiency, factors affecting how well educational institutions perform, improvements in public services such as health and social care, and insights into how businesses use efficiency measures. The findings show how policy settings, governance, and data quality all affect how we measure and improve efficiency. Moreover, the contributions show how combining traditional methods with innovative techniques can provide a better analysis of complex systems and long-term outcomes. Building on the insights from the papers in the special issue, we propose a future research agenda that encourages collaboration between experts in different fields, uses new data tools, and supports evidence-based public service delivery. By connecting theory to practical use, this special issue aims to improve how public services operate, making them more efficient, fair, and based on real data to help decision-makers.