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This paper considers the problem of designing a supply chain assuming routing decisions. The objective is to select a subset of depots to open from a set of candidates, the inventory policies for a two-echelon system, and the set of routes to perform distribution from the upper echelon to the next by a homogeneous fleet of vehicles over a finite planning horizon considering deterministic demand. To solve the problem, a partition is proposed using a Dantzig-Wolfe formulation on the routing variables. A hybridization between column generation, Lagrangian relaxation, and local search is presented within a heuristic procedure. Results demonstrate the capability of the algorithm to compute high quality solutions and empirically estimate the improvement in the cost function of the proposed model at up to 9% compared to the sequential approach. Furthermore, the suggested pricing problem is a new variant of the shortest path problem with applications in urban transportation and telecommunications.

This article presents the findings in the process of evaluating the relationship between perception channels and cognitive styles, from the analysis of conceptions over time and their involvement. Establishing through an experiment, and applying two didactic strategies, the associations with learning. Channels are characterized with VAK, Styles with CHAEA, and Performance with a pre-test/post-test design. It was shown that channels and styles are allies that independently encourage the teaching-learning process. Outcome shows that people with multiple channels and styles develop more skills, achieving better results. Games as ludic activities stimulate all channels, and favor the construction of knowledge, thus improving performance with positive differences in p-values between 0.014 and 0.022.

To improve the effectiveness and the sustainability of logistics, the Physical Internet paradigm proposes disruptive solutions. This implies developing an ecosystem of tech-based logistics solutions and supporting methodologies that enable all players in global trade to cooperate. The purpose of this paper is to investigate systematic literature review (SLR) studies to gain detailed insight into how innovative transport technologies, and digitalization initiatives around the Physical Internet development impact supply chains. This paper presents the results of a tertiary study that systematically identified more than twelve thousand articles and selects to review 74 secondary studies on the application of disruptive technologies and the Physical Internet initiative on supply chains from a management perspective. This is complementary to previous reviews, since no one provides a comprehensive and consolidated approach towards the relationship of these three fields. The five-stage systematic review process proposed by Denyer and Tranfield ( 2009) is followed. As a result, we identify the key activities, knowledge areas and strategies in the supply chain field where the Physical Internet and disruptive technologies interact and are game-changing. Also, we present a conceptual framework that summarises the relationships that exist between relevant disruptive technologies, the physical internet topics, and supply chain key activities. The framework is helpful for researchers and practitioners to find potential technologies to invest in, to assess the potential effects on companies of their implementation, and to support strategic decision-making. The paper concludes with an outlook on future research opportunities from operational, tactical, and strategic perspectives.

Purpose: Prescriptive and predictive analytics and artificial intelligence (AI) provide tools to analyze data with objectivity. In this paper, we provide an overview of how these techniques can improve nursing care, and we detail a quantitative model to afford managerial insights about care management in a Hospital in Colombia. Our main purpose is to provide tools to improve key performance indicators for the care management of inpatients which includes the nurse workload. Methods: The optimal nurse-to-patient assignment problem is addressed using analytics, lean health care, and AI. Also, we propose a new mathematical model to optimize the nurse-to-patient assignment decisions considering several variables about the patient state such as the Barthel index, their risks, the complexity of the care, and the mental state. Findings: Our results show that there are several processes inherent to compassionate nursing care that can be improved using technology. By using data analytics, we can also provide insights about the high variability of the care requirements and, by using models, find nurse-to-patient assignments that are nearly perfectly balanced. Conclusions: We illustrated this improvement with a pilot test that makes the equitable distribution of nursing workload the functionality of this strategy. The findings can be useful in highly complex hospitals in Latin America. Clinical Relevance: The proposed model presents an opportunity to make near perfectly balanced nurse-to-patient assignments according to the number of patients and their health conditions using technology.