Sustainability and digitalization have become the main direction of transformation of enterprises. Building a digital twin platform network can provide enterprises with a comprehensive view of products, manufacture, supply chain, customer experience, and profitability, which is conducive to the construction of a sustainable business model. The purpose of this paper is to study how enterprises use digital twin platform networks to generate economic, social and environmental benefits in various dimensions and their coupling relationships. Based on the literature review, this paper constructs a five-dimensional framework of a sustainable business model, and analyses the coupling relationship between dimensions. Using Haier as a way to verify the five-dimensional framework, it explores the dynamic mechanism of the Haier digital twin platform network, constructs an integrated framework based on coupling perspective and compares it with other two home appliance enterprises. The study shows that, through the digital twin platform network, enterprises can remove the disadvantage of focusing on a single product life cycle, and form a comprehensive network, so as to promote overall sustainable upgrades. This paper draws generic strategies and digital transformation suggestions for enterprises to innovate the sustainable business model. The conclusion enriches the research on sustainable business models both theoretically and practically and provides a feasible reference for the transformation of enterprises in digital economy environment.

Product family design and supplier selection are traditionally separated in two successive stages. First, product development teams determine optimal levels of the attributes of product components for each product variant of a product family, and purchasing departments then choose the qualified suppliers with the lowest cost. However, decoupling the two decision processes may lead to suboptimal solutions with regard to the total market profit of a company. In this article, a unified optimisation model, which integrates product family design with supplier selection, is established with the objective of maximising the total profit of a product family. Consumer purchase behaviour, supplier availability and outsourcing-related cost are considered in the model. A linear programming embedded genetic algorithm was developed to solve the proposed model. A case study is presented to illustrate the feasibility and effectiveness of the proposed model and algorithms.

Motivated by rising concerns regarding global warming and traffic congestion effects, we study the time-dependent green vehicle routing problem with time windows (TDGVRPTW), aiming to minimize carbon emissions. The TDGVRPTW is a variant of the time-dependent vehicle routing problem (TDVRP) in which, in addition to the time window constraints, the minimization of carbon emissions requires determination of the optimal departure times for vehicles, from both the depot and customer location(s). Accordingly, the first exact method based on a branch-cut-and-price (BCP) algorithm is proposed for solving the TDGVRPTW. We introduce the notation of a time-dependent (TD) arc and describe how to identify the nondominated TD arcs in terms of arc departure times. In this way, we reduce infinitely many TD arcs to a finite set of nondominated TD arcs. We design a state-of-the-art BCP algorithm for the TDGVRPTW with labeling and limited memory subset row cuts, together with effective dominance rules for eliminating dominated TD arcs. The exact method is tested on a set of test instances derived from benchmark instances proposed in the literature. The results show the effectiveness of the proposed exact method in solving TDGVRPTW instances involving up to 100 customers. Summary of Contribution: Due to the environmental situation, green vehicle routing problems (GVRPs) aim to consider greenhouse gas emissions reduction, while routing the vehicles, and play a key role in transportation and logistics. Vehicle greenhouse gas emissions strongly depend on the vehicle speeds and traffic conditions which in real life vary continuously over time. To tackle these challenges, we address the time-dependent green vehicle routing problem with time windows (TDGVRPTW) aimed at reducing total carbon emissions under time-dependent travel times and time window constraints. We design an effective exact method for the TDGVRPTW based on a state-of-the-art branch-cut-and-price algorithm. The paper is both of methodological value for researchers and of interest for practitioners. For researchers, the presented algorithm is amenable for various routing constraints and provides a ground for further studies and research. For practitioners, the paper suggests insights on how the carbon emissions change based on different vehicle speed profiles. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This research is supported by the National Natural Science Foundation of China [Grants 71831003, 71831006, 72171043, and 71901180] and the Fundamental Research Funds for the Central Universities [Grants N170405005 and N180704015]. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/ijoc.2022.1195 .

With the increased use of collaborative robots, a new production model of the human-robot collaborative hybrid assembly cell (HRCHAC) is becoming a new trend in customised production. Collaborative assembly between workers and robots in assembly cells can significantly increase productivity and improve the well-being of workers once the distribution of tasks and resources is optimised. This paper proposes a new integrated task allocation model to better utilise human-robot collaboration to increase productivity and improve worker well-being. The developed model enables the skills of both workers and robots to be fully utilised while ensuring economic efficiency and the effective protection of workers’ physiological and psychological health. First, the product assembly process is decomposed into several assembly tasks, and the characteristics of each task are analysed. Second, a bi-objective mixed-integer planning model is developed with the objectives of minimising unit product assembly time and maximising total task matching. The ergonomics-related objectives are considered in terms of both the physiological and psychological fatigue of the worker, and relevant constraints are established. An improved NSGA-II algorithm is developed to determine the final task allocation scheme. Finally, the proposed method is applied to a real industrial case to verify the effectiveness of the approach.