International Transactions in Operational Research最新文献

This paper develops a three-party game model comprising a supplier, an online retailer, and an offline retailer, where the supplier wholesales the product to retailers. Considering webrooming (i.e., consumers experience and purchase offline after evaluating products online) and information leakage, we examine the supplier encroachment channel (i.e., online or offline) and retailers' information sharing strategies. We find that the channel selection of encroachment depends on the cost difference between online and offline encroachment. Information sharing by a retailer can result in another retailer not sharing. Webrooming influences encroachment strategy by altering cost thresholds, prompting the supplier to encroach on the offline channel as webrooming increases. However, a strong preference for online shopping will lead the supplier to forgo encroachment despite webrooming. We identify a “win-win-win” situation where all stakeholders benefit from webrooming. Finally, we discuss consumer surplus and social welfare and extend the model based on differential pricing and information confidentiality.

Index tracking aims to replicate the performance of a selected index by constructing a portfolio. Tracking portfolio decisions rely on minimizing tracking error or the linear relationships among assets, neglecting the impact of complex nonlinear asset dependency structures on portfolio performance. In this paper, the network-based index tracking model without adaptive adjustment (ITN) and the network-based index tracking model with adaptive adjustment (ITNA) are proposed by utilizing asset dependency structures. Specifically, we construct networks based on the correlations among assets and express community structure and asset centrality, which reflect the dependency structure, as community structure constraints and centrality constraints, thus forming ITN. This model allows the portfolio to replicate the structure of the index, leading to improved tracking performance. To continuously benefit from asset dependency structures, it is necessary to adjust the portfolio when it significantly diverges from the new dependency structure. A structural consistency constraint, which allows for adjustments to the portfolio in response to variations in the dependency structure, is incorporated into ITN, resulting in ITNA. Empirical tests are conducted using data from six stock market indices. Compared to general index tracking, ITN achieves higher adjusted returns with reasonable tracking error. Additionally, ITNA achieves lower tracking errors and higher adjusted returns in large indices compared to the periodic adjustment strategy.

The traveling salesman problem with job times (TSPJ) involves a traveler visiting a set of vertices, ensuring one visit to each of them while he initiates a job. The time of each job depends on the vertex where it is performed. Once started, the traveler moves to the next vertex, and the job continues autonomously. The aim is to minimize the maximum completion time. Since the problem is NP-hard, existing mixed-integer linear programming (MILP) models are unable to solve large instances efficiently. Therefore, this paper proposes to enhance the existing MILP model and introduces a new MILP model for the TSPJ, which incorporates valid lower and upper bounds to strengthen them. Moreover, we propose two branch-and-cut (B&C) algorithms based on the improved existing model and the new one. These algorithms integrate strengthened exponential-size formulations that explicitly incorporate subtour elimination constraints and blossom inequalities. B&C algorithms are tested on instances from the literature, comprising four sets of instances with sizes ranging from 17 to 1200 vertices. Computational results show that the proposed B&C algorithms outperform the state-of-the-art MILP models in all instances. Since no formulation achieves optimality within the given time limit for large instances, only three medium instances with up to 454 vertices have reached optimality. Consequently, we propose a fifth set of instances, ranging from 100 to 390 vertices, to further assess performance limits. B&C algorithms demonstrate improved performance with lower gap values in all instances, and faster computing times while optimally solving instances with up to 386 vertices.

This paper addresses an optimization problem concerning the purchase and subsequent material processing in a meat processing company. Unlike the majority of existing papers, we do not concentrate on how this problem concerns supply chain management, but we focus on the production stage, primarily the meat cutting. Specifically, we study the operational level of production management, where the company responds to fluctuations in demand and controls the flow of materials, as this level of management significantly affects its profit. The problem involves the concept of alternative ways of material processing, stock of material with different expiration dates, and extra constraints widely neglected in the current literature addressing meat cutting and processing, namely, the minimum order quantity and the minimum percentage in alternatives. We prove that each of these two constraints makes the problem $�\mathrm{NP}$-hard, and we design an exact iterative approach based on integer linear programming that allows us to solve real-life instances even using an open-source integer linear programming solver. Another advantage of this approach is that it mitigates numerical issues, caused by the extensive range of data values, we experienced with a commercial solver. The results obtained using real data from the meat processing company showed that our algorithm can find the optimum solution in a few seconds for all considered use cases.

In the competitive landscape of plastic product manufacturing, optimizing production scheduling is crucial for enhancing efficiency and reducing costs. This paper presents an integrated approach to address the unrelated parallel machine scheduling problem (UPMSP) with a focus on minimizing setup costs and tardiness penalties, while also considering the necessity of preventive maintenance. We incorporate practical constraints such as release time, machine eligibility, and delivery due dates into our model to ensure its applicability to real-world manufacturing scenarios. We propose a mixed-integer linear programming model designed to minimize the total costs associated with setup and tardiness. To solve this complex problem, we develop a constructive heuristic and an adaptive large neighborhood search (ALNS) algorithm, which introduces novel destroy and repair operators. These algorithms are tailored to the specific structure of the UPMSP and are tested through a case study involving a real plastic product manufacturer. Results from the case study demonstrate that our proposed solution method significantly outperforms the company's current planning approach, achieving a reduction in total costs by 73.11%. Furthermore, the ALNS algorithm's performance is validated through numerical experiments on a range of randomly generated instances, showcasing its ability to provide high-quality solutions within practical computation times.

In post-disaster situations, time-critical decision-making is essential. Due to high demand and limited resources, visiting all affected locations is often infeasible. This study presents a novel variant of the covering tour problem that addresses these constraints by integrating location selection and vehicle assignment decisions. In the proposed problem, vehicles depart from selected relief centers, visit a subset of victim locations, and are allowed to complete their tours at any center. The demands of unvisited locations are satisfied through demand transfers from nearby visited nodes, with associated transfer times included in the total operation time. A mixed integer linear programming (MILP) model is formulated to minimize total operation time, incorporating both travel and demand transfer times. Scenario-based analyses are performed to evaluate the model's performance under various operational conditions, including transfer time sensitivity, route flexibility, demand coverage constraints, time-based covering radius, and partial fulfillment policies. To address scalability, a two-stage clustering-based heuristic is developed, offering a practical and computationally efficient solution method. From a humanitarian logistics perspective, the findings emphasize the importance of flexible routing, strategic placement of relief centers, and careful management of coverage thresholds. Additionally, the simplicity and adaptability of the proposed heuristic make it well suited for real-time decision-making in post-disaster response operations.