Annals of Operations Research最新文献

Several attempts to dampen the curse of dimensionality problem of the Dynamic Programming approach for solving multistage optimization problems have been investigated. One popular way to address this issue is the Stochastic Dual Dynamic Programming method (Sddp) introduced by Pereira and Pinto (Math Program 52(1–3):359–375. https://doi.org/10.1007/BF01582895). Assuming that the value function is convex (for a minimization problem), one builds a non-decreasing sequence of lower (or outer) convex approximations of the value function. Those convex approximations are constructed as a supremum of affine cuts. On continuous time deterministic optimal control problems, assuming that the value function is semiconvex, Zheng Qu, inspired by the work of McEneaney, introduced in 2013 a stochastic max-plus scheme that builds a non-increasing sequence of upper (or inner) approximations of the value function. In this note, we build a common framework for both the Sddp and a discrete time version of Zheng Qu’s algorithm to solve deterministic multistage optimization problems. Our algorithm generates a monotone sequence of approximations of the value function as a pointwise supremum, or infimum, of basic (affine or quadratic for example) functions which are randomly selected. We give sufficient conditions on the way basic functions are selected in order to ensure almost sure convergence of the approximations to the value function on a set of interest.

Finding the most parsimonious tree inside a phylogenetic network with respect to a given character is an NP-hard combinatorial optimization problem that for many network topologies is essentially inapproximable. In contrast, if the network is a rooted tree, then Fitch’s well-known algorithm calculates an optimal parsimony score for that character in polynomial time. Drawing inspiration from this we here introduce a new extension of Fitch’s algorithm which runs in polynomial time and ensures an approximation factor of 2 on binary, tree-child phylogenetic networks, a popular topologically-restricted subclass of phylogenetic networks in the literature. Specifically, we show that Fitch’s algorithm can be seen as a primal-dual algorithm, how it can be extended to binary, tree-child networks and that the approximation guarantee of this extension is tight. These results for a classic problem in phylogenetics strengthens the link between polyhedral methods and phylogenetics and can aid in the study of other related optimization problems on phylogenetic networks.

Facing consumers’ growing environmental consciousness, retailers need to consider the environmental consequences when choosing between e-commerce and traditional retailing. We develop a game-theoretic model to study the retailers’ channel selection problem in the presence of consumer environmental consciousness in both monopoly and duopoly settings. We find that even though the existence of consumer environmental consciousness drives the retailer toward more eco-friendly channel strategies, there exist conditions under which the retailer’s optimal channel strategy is still misaligned with environmental and social interests. Furthermore, we show that an increase in consumers’ consciousness, though seemingly reducing consumer utility, can in fact be welfare-enhancing. A reduction in the unit environmental impact of a channel (traditional or e-commerce), on the other hand, may lead to lower social welfare due to the retailer’s insubstantial carbon abatement. Finally, contrary to the conventional wisdom that competition tends to increase social welfare, we show that with consumers’ environmental consciousness, retail competition may result in social welfare decline. Our policy implication is that even though the presence of environmentally conscious consumers pushes firms to internalize the environmental consequences of their channel strategies, environmental regulations are still necessary to achieve social welfare maximization.

This paper introduces and details the development of a Long Short-Term Memory (LSTM) model designed to predict Arctic ice thickness, serving as a decision-making tool for maritime navigation. By forecasting ice conditions accurately, the model aims to support safer and more efficient shipping through Arctic waters. The primary objective is to equip shipping companies and decision-makers with a reliable method for estimating ice thickness in the Arctic. This will enable them to assess the level of risk due to ice and make informed decisions regarding vessel navigation, icebreaker assistance, and optimal sailing speeds. We utilized historical ice thickness data from the Copernicus database, covering the period from 1991 to 2019. This dataset was collected and preprocessed to train and validate the LSTM predictive model for accurate ice thickness forecasting. The developed LSTM model demonstrated a high level of accuracy in predicting future ice thickness. Experiments indicated that using daily datasets, the model could forecast daily ice thickness up to 30 days ahead. With monthly datasets, it successfully predicted ice thickness up to six months in advance, with the monthly data generally yielding better performance. In practical terms, this predictive model offers a valuable tool for shipping companies exploring Arctic routes, which can reduce the distance between Asia and Europe by 40%. By providing accurate ice thickness forecasts, the model assists in compliance with the International Maritime Organization’s Polar Code and the Polar Operational Limit Assessment Risk Indexing System. This enhances navigation safety and efficiency in Arctic waters, allowing ships to determine the necessity of icebreaker assistance and optimal speeds, ultimately leading to significant cost savings and risk mitigation in the shipping industry.

In this paper, we investigate a class of unconstrained multiobjective optimization problems (abbreviated as, MPQs), where the components of the objective function are locally Lipschitz and quasiconvex. To solve MPQs, we introduce an inexact proximal point method with Bregman distances (abbreviated as, IPPMB) via Mordukhovich limiting subdifferentials. We establish the well-definedness of the sequence generated by the IPPMB algorithm. Based on two versions of error criteria, we introduce two variants of IPPMB, namely, IPPMB1 and IPPMB2. Moreover, we establish that the sequences generated by the IPPMB1 and IPPMB2 algorithms converge to the Pareto–Mordukhovich critical point of the problem MPQ. In addition, we derive that if the components of the objective function of MPQ are convex, then the sequences converge to the weak Pareto efficient solution of MPQ. Furthermore, we discuss the linear and superlinear convergence of the sequence generated by the IPPMB2 algorithm. We furnish several non-trivial numerical examples to demonstrate the effectiveness of the proposed algorithms and solve them by employing MATLAB R2023b. To demonstrate the applicability and significance of the IPPMB algorithm, we solve a nonsmooth large-scale sparse quasiconvex multiobjective optimization by employing MATLAB R2023b.

Increasing concerns about climate change and waste management pressure manufacturers to innovate their practices, focusing on the environment and society without impacting the economic returns. Such concerns brought a new realm to manufacturing, sustainable manufacturing, which is a combination of economy, environment and society. Though several studies have attempted to integrate sustainability in manufacturing successfully, very few studies have been reported on sustainable manufacturing practices. Even these existing studies failed to explore the difference between implementing sustainable manufacturing in different geographical contexts. To fill this gap, this study considers four different geographical context Saudi Arabia, India, Peru and China to understand their preferences of achieving sustainable development in manufacturing through sustainable manufacturing practices. At first, common sustainable manufacturing practices were collected from literature and the same has been evaluated with case industries from Saudi Arabia, India, Peru and China. Totally 15 practices under three different dimensions have been considered in this study. From the obtained results, it can be confounded that “Integrate efficient technologies into core manufacturing system” (SP4) has been identified as key influential practices irrespective of the considered geographies. With the enlighten of these obtained results, practitioners and researchers can understood various in-depth insights on how different nations following different strategies for implementing sustainable development in manufacturing sector. Such clarity improves inhouse policy making which leads to better decision making for faster transition towards sustainable manufacturing.

Manufacturing supply chains (SC) shift from traditional make-to-stock systems to make-to-order (MTO) systems in order to coordinate the production and distribution of complex and highly customized products. Despite the need for advanced scheduling approaches when coordinating such MTO-based SCs, there is little research focussing on practical settings that include variable processing speeds, sequence-dependent setup times (SDST) and time-of-use (TOU) electricity prices. However, these settings are important since they influence the energy consumption and the associated electricity costs have an impact on the decision-making process. Furthermore, there is also an increasing concern regarding green production in manufacturing such that the energy consumption cannot be ignored in decision making. In this study, we investigate a bi-objective energy-efficient permutation flow shop scheduling problem in MTO-based SC (EPFSPSC) with the conjoint objectives of minimising the cost of inventory, delivery, tardiness and electricity costs for production. In order to solve this problem, a genetic algorithm-based memetic algorithm is proposed and its effectiveness is demonstrated against a well-known benchmark approach. This research aims to assist production managers in making integrated production and distribution decisions, while simultaneously considering all associated costs and ensuring green manufacturing.

Under the expected total reward criterion, the optimal value of a finite-horizon Markov decision process can be determined by solving the Bellman equations. The equations were extended by White to processes with vector rewards. Using a counterexample, we show that the assumptions underlying this extension fail to guarantee its validity. Analysis of the counterexample enables us to articulate a sufficient condition for White’s functional equations to be valid. The condition is shown to be true when the policy space has been refined to include a special class of non-Markovian policies, when the dynamics of the model are deterministic, and when the decision making horizon does not exceed two time steps. The paper demonstrates that in general, the solutions to White’s equations are sets of Pareto efficient policy returns over the refined policy space. Our results are illustrated with an example.

This paper presents a bi-objective solution approach to address the production scheduling challenge encountered by manufacturers in a shared manufacturing environment. In such scenarios, manufacturers are required to manage orders received through a cloud platform (referred to as cloud orders) while simultaneously fulfilling orders from their long-term and regular clients (local orders). The problem is to efficiently coordinate the production of both types of orders within shared manufacturing facilities. We formulate the problem into a bi-objective mixed integer programming model aimed at simultaneously minimizing the delivery time of cloud orders and mitigating the disruptions to local order production caused by cloud orders. This solution approach comprises three key components: computation of cloud orders’ starting times, construction of available time intervals of manufacturing facilities, and a bi-objective heuristic. This heuristic combines an enhanced hybrid discrete differential evolution with a modified forward–backward earliest starting time algorithm. We introduce an advanced population initialization technique, a novel individual update strategy, and an adaptive local search mechanism based on Pareto-dominance principles to improve the search capabilities of the algorithm towards discovering Pareto non-dominated solutions. Computational results show that the proposed approach outperforms the existing algorithm in most test instances in terms of five common metrics. Insights are discussed, highlighting the practical implications and potential benefits of the proposed approach for shared manufacturing scheduling.