Operations Management Research最新文献

Pallet loading operations support palletisation and truckload optimisation for e-fulfilment processes. Currently, the pallet loading problem is optimised offline using available cargo information, which is advantageous compared to typical freight operations but results in inefficiency when handling fragmented e-commerce orders. This research develops a mixed reality-based online pallet loading system (MROPLS) supported by deep reinforcement learning technology and online algorithms that dynamically decide cargo placements and orientations without prior information for pallet loading operations. The MROPLS proposes a 3-dimensional maximal-rectangle non-guillotine cutting strategy combined with a deep Q-network to increase space utilisation effectively. This approach is achieved using the lookahead algorithm, which predicts upcoming packages in the online pallet loading process and optimises package spatial location and orientation decision-making. We conduct simulation experiments to verify the system’s feasibility and performance by considering SF Express, DHL and Royal Mail package and ISO pallet sizes. The interaction effects between package types, pallet sizes and lookahead values were found and summarised to determine optimal system settings. With the aid of MROPLS, human intelligence in the online pallet loading process can be augmented, resulting in optimal palletisation in warehouse automation.

Climate change, resource efficiency, and global warming pose significant challenges to emerging economies and their small and medium-sized enterprises (SMEs). The concepts of sustainability and Industry 4.0 are intertwined and offer new business opportunities for SMEs. However, it remains unclear whether the adoption of Industry 4.0 technologies has any impact on sustainability at the process, system, and product levels, particularly for SMEs in emerging economies. Manufacturing SMEs currently face pressure from customers and stakeholder for shorter delivery times and environmentally friendly products to remain competitive in global market. While Industry 4.0 adoption is still in its early stages among SMEs, its potential influence on sustainability is anticipated. Consequently, emerging economies are increasingly focused on adopting Industry 4.0 technologies due to investments by multinational corporations. However, SMEs encounter challenges in adopting Industry 4.0 technologies and addressing sustainability issues in their business models. To bridge this gap, the present study conducts an empirical investigation in an emerging economy. A comprehensive review of the literature identifies the main challenges in Industry 4.0, which are validated through an exploratory factor analysis using responses from 233 manufacturing SMEs. The prioritization of challenges is accomplished using a Fuzzy-Analytical Hierarchy Process. The causal interrelationship between the major challenging groups is examined through the Fuzzy-Decision-Making Trial and Evaluation Laboratory approach. The findings highlight “Lack of dedicated Research and development (R&D) teams” and “Data security and privacy issues” as significant challenges faced by SMEs in emerging economies. This study represents an initial attempt to analyse the impact of Industry 4.0 challenges on achieving manufacturing sustainability in SMEs through a large-scale survey in emerging economies, employing a mixed-method approach. The results offer empirical support for addressing sustainability issues in Industry 4.0 for manufacturing SMEs. The framework developed in this study can be utilized by SME managers to effectively tackle sustainability challenges. Additionally, policymakers can leverage the study’s insights to promote sustainability in the manufacturing sector.

Substantial progress has been made toward connecting design with science in operations management (OM) studies. However, this connection appears to be lopsided, with much more emphasis on theory-to-practice than practice-to-theory in applications of design science (DS). This imbalance tends to impede theoretical progress. To mitigate this imbalance, this paper explores the notion of mechanisms as boundary objects between design and science. First, we outline the problem-solving cycle of DS and the research products it generates. Subsequently, we elaborate on mechanisms, mechanism-based explanations, and law-statements, and how these concepts inform the products of DS research. The argument then turns to how causal mechanisms can be inferred in the DS problem-solving cycle. Finally, we propose a framework for closing the self-reinforcing loop between science and design, which allows OM scholars to more effectively produce practical outputs as well as theoretical breakthroughs.

In the last two years, the worldwide outbreak of the COVID-19 pandemic and the resulting heavy casualties have highlighted the importance of further research in healthcare. In addition, the advent of new technologies such as the Internet of Things (IoT) and their applications in preventing and detecting casualty cases has attracted a lot of attention. The IoT is able to help organize medical services by collecting significant amounts of data and information. This paper proposes a novel mathematical model for Emergency Medical Services (EMS) using the IoT. The proposed model is designed in two phases. In the first phase, the data is collected by the IoT, and the demands for ambulances are categorized and prioritized. Then in the second phase, ambulances are allocated to demand areas (patients). Two main objectives of the proposed model are reducing total costs and the mortality risk due to lack of timely service. In addition, demand uncertainty for ambulances is considered with various scenarios at demand levels. Numerical experiments have been conducted on actual data from a case study in Kermanshah, Iran. Due to the NP-hard nature of the mathematical model, three meta-heuristic algorithms Multi-Objective Simulated Annealing (MOSA) algorithm and Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, and L-MOPSO have been used to solve the proposed model on medium and large scales in addition to the exact solution method. The results show that the proposed model significantly reduces mortality risk, in addition to reducing total cost. Data analysis also led to useful managerial insights.