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Purpose: Automatic grouping of data according to certain characteristics is made possible by clustering algorithms, which makes them an essential tool when working with large datasets. However, although they are unsupervised tools, they generally require the specification of the number of clusters to be formed, k, a task that may be simple for a human, but quite complex to automate. Despite the most commonly used k-value selection techniques offer acceptable results, they are not without shortcomings, suggesting that there is ample room for improvement. This paper briefly introduces clustering techniques, discusses the main shortcomings of conventional k-value selection techniques and examines the advantages and limitations of nine promising alternatives presented in recent years. Design/methodology/approach: An evaluation of the main shortcomings of classic k-value estimation techniques has been carried out, and the newest proposals have been explained and compared. Findings: New k-value estimation indices and methodologies proposed by authors guarantee better results, extending the use of these techniques to large volumes of data, and complex shapes and structures. However, no generical methodology able to overcome all the described shortcomings has still been developed. Research limitations/implications: This research is limited to the newest developed techniques for k-value estimation, including proposals published since 2019. Older proposals have not been considered, as the newest ones overcome the former’s shortcomings. A k-value estimation techniques review published in 2019 is cited in the test as a base reference. Practical implications: Although the examples listed in the text apply to industry, the techniques described and discussed in this review are applicable to any area of science that can benefit from the use of clustering techniques. Originality/value: To date, there has been no paper comparing the new k-value estimation techniques. Although there are literature reviews comparing the classical methods, these methods are nowadays nearly obsolete due to the complexity of the data usually faced.

In the current paradigm for the retail industry, which is experiencing a rapid evolution, especially in textile companies, the generic problem of product allocation in a distribution and supply chain consisting of one main warehouse and several locations, belonging to different sales channels, is a challenge. The omnichannel replenishment process focuses on dynamically optimizing a shop or intermediate warehouse inventory for a wide range of products based on a forecast of sales, in order to fulfill the demand of all of the channels considered. In this context, the aims of this work were (a) to optimize inventory replenishment for multiple channels and products that are not perishable but devalue over time, and (b) to implement a methodology that combines the benefits of the Particle Swarm Optimization metaheuristic and Simulated Annealing. This study was carried out for different sales periods, channels and product configurations by performing a sensitivity analysis between the way new solutions are updated and the degree of intensification used in local search.

The rapid rise of electronic commerce has entailed an increase in logistic complexity, with last-mile logistics being the most critical element in deliveries. Since users prefer goods to be delivered at home, one of the biggest challenges faced by e-commerce is to reduce the number of incidents that occur in the delivery of goods to the homes of customers. In many cases, these deliveries cannot take place because recipients are not at the agreed delivery point, leading to a decrease in the quality of service and an increase in distribution costs. Furthermore, sometimes the delivery policies are not in tune with the customers’ expectations. This work presents a new perspective of the last-mile logistics in the context of multichannel retail, asking customers to provide several delivery locations (at home, at work, at a familiar home, in a shop, in a locker, etc.) associated with different time windows. In addition, the customer could state their preferences about these locations. This work formulates the problem and develops different approaches to solve it. A benchmark is proposed to analyze the performance and limitations. The results reveal that a distribution policy with several locations can improve the efficiency of electronic commerce by reducing delivery costs. The findings of this study have several implications for distribution companies.

Purpose: This study describes the trends and applications of machine learning systems in the management of water supply networks. Machine learning is a field in constant development, and it has a great potential and capability to attain improvements in real industries. The recent tendency of data storage by companies that manage the water supply networks have created a range of possibilities to apply machine learning. One particular case is the prediction of pipe failures based on historical data, which can help to optimally plan the renovation and maintenance tasks. The objective of this work is to define the stages and main characteristics of machine learning systems, focusing on supervised learning methods. Additionally, singularities that are usually found in data from water supply networks are highlighted. Design/methodology/approach: For this purpose, eight studies which contain real cases from around the world are discussed. From the data processing to the model validation, a tour of the methods used in each study is carried out. Moreover, the trendiest models are briefly defined together with the mechanisms that best suit their performance. Findings: As a result of the study, it was found that the imbalanced class problem is typical of data from water supply networks where only a small percentage of pipes fail. Consequently, it is recommended to use sampling methods to train classifiers, however, it is not necessary if we are training a regression system. Additionally, scaling and transformation of variables has generally a positive impact on the model’s performance. Currently, cross-validation is almost a requirement to obtain reliable and representative results. This technique is employed in all revised studies to train and validate their models. Originality/value: The use of machine learning systems to predict pipe failures in water supply networks is still a developing field. This study tries to define the advantages and disadvantages of different methods to process data from water supply networks, as well as to train and validate the models.

The multiple processes taking place on a daily basis at an intermodal container terminal are often considered individually, given the complexity of their joint consideration. Nevertheless, the integrated planning and scheduling of operations in an intermodal terminal, including the arrivals and departures of trains and vessels, is a very relevant topic for terminal managers, which can benefit from the application of Operations Research (OR) techniques to obtain near-optimal solutions without excessive computational cost. Applying the functional integration technique, we present here a mathematical model for this terminal planning process, and solve it using heuristic procedures, given its complexity and size. Details on the benchmark comparison of a genetic algorithm, a simulated annealing routine and a tabu search are provided for different problem instances.

The high volume of traffic originates two well-known problems in many cities: congestion and pollution. In recent years, a social phenomenon is emerging cooperation. This work is aimed at evaluating the circumstances under which transport cooperation is possible between different stakeholders operating in the same geographical area. To this end, a double survey process was conducted in a marketplace situated in the Seville City (Spain) centre. The first survey was designed to know the characteristics of the retailers and their preferences with respect to cooperation and regulations. A relational analysis between retailer features and their willingness to cooperate was carried out. After analysing the motivations for non-cooperation, a mixed proposal was designed and surveyed. Although the research was limited to a marketplace, the relevant data gathered from this double survey process highlights some implications: (a) the importance of personal relations in retailer cooperation; (b) a high volume of freight and the use of vans as on-street warehouses appear as significant motivations for non-cooperation; (c) forcing changes in the statu quo encourages cooperation.

The application of the principles of sustainability to the implementation of urban freight policies requires the estimation of all the costs and externalities involved. We focus here on the case of access time windows, which ban the access of freight vehicles to central urban areas in many European cities. Even though this measure seeks to reduce congestion and emissions in the most crowded periods of the day, it also imposes additional costs for carriers and results in higher emissions and energy consumption. We present here a mathematical model for the Vehicle Routing Problem with Access Time Windows, a variant of the VRP suitable for planning delivery routes in a city subject to this type of accessibility restriction. We use the model to find exact solutions to small problem instances based on a case study and then compare the performance over larger instances of a modified savings algorithm, a genetic algorithm, and a tabu search procedure, with the results showing no clear prevalence of any of them, but confirming the significance of those additional costs and externalities.

The presented work is done for a company that currently operates twenty lines of passenger transport in the metropolitan area of Seville. The planning of these was originally carried out manually, building routes and shifts in an Excel spreadsheet. In order to automate the process as much as possible. It was designed and implemented by a scheduling algorithm that would be much simple than other algorithms in the literature and that, in addition, would make it possible to allow mixing vehicles and drivers between the lines. The objective was, firstly, to employ the minimum number of drivers; then, it is trying to use the least possible number of vehicles; finally, the study tries to reduce as much as possible the amount of split shifts. In addition, restrictions on the design of routes and shifts were added. At all times the service frequencies remained above the set limit. To allow the possibility of unexpected demand peaks, was established in the capacity of each route some slack.

Highlights • We describe a new development of a tracking/tracing/management information system • The system is designed for containers in an intermodal corridor • The paper links the impacts of the system with the supply chain requirements • Benefits for the different stakeholders involved are described and discussed Purpose Intermodal transport chains often appear as “black boxes” to the cargo owners and their clients, who lose track of the container until it arrives at the final end of the chain. The paper discusses why a tracking and tracing system should represent an important added value for them. Design/methodology/approach We describe here the configuration and features of a novel low-cost system to track and trace containers in an intermodal supply chain, provide information to shippers regarding delays and other unexpected events, and assist terminal operations accordingly. We then analyze the positive impacts of such a system over the entire supply chain, identifying the requirements of the main chain actors regarding the availability of information and how the proposed system contributes to the fulfillment of those requirements. Findings The visibility of containers throughout the entire supply chain provides multiple benefits for shippers, terminals and transport providers. Research limitations/implications The operational and economic benefits of the system are clear, but the lack of historical data does not allow for a quantitative estimation of those foreseen benefits. Practical implications Intermodal terminals might see this type of information systems as an opportunity to generate added value for their customers, including shippers and carriers. In a scenario with high competition levels between terminals, this added value could represent the competitive advantage necessary to guarantee the growth of a terminal without requiring expensive investments and costs. Originality/value The paper describes the benefits provided by the system to the main stakeholders of the intermodal supply chain and provides some conclusions with respect to those benefits and to future developments. Keywords: Containers; tracking and tracing; supply chain; logistics; port

Designing a Zero Energy Building (ZEB) requires an optimal choice of the materials of a building envelope. Different material properties and window areas could be selected to generate a set of possibilities of the design of a building, being the demand defined by its thermal characteristics. The energy demand of a building could be produced with renewable systems such as photovoltaic. Moreover, the HVAC (Heating, Ventilation, and Air Conditioning) systems could be selected considering the system cost. The present methodology focuses on finding a balance between investment and low energy consumption for a building, based on an integrated optimization method. Such methodology applies a Tabu search algorithm and a simplified model to select the passive design. Afterwards, active elements of the design, as photovoltaic systems, are selected. Therefore, the methodology faces the problem of estimating the annual energy demand and the life cycle cost. The goal is the design of a building with a large amount of energy generated by renewable energy, to have a ZEB, and in the worst case, a nearly Zero Energy Building (nZEB). This methodology reduces investment, reduces the energy demand and selects the best construction materials, renewable energy, and air conditioning system. The present paper analyzes a set of case studies considering different climatic zones in Spain. The results conclude that the methodology could help builders in the design stage, to find a new design that allows a ZEB with the optimal life cycle cost.

The ex-ante evaluation of urban freight solutions is a complex task, due to the interference of different stakeholder groups with different views and objectives. The multi-actor multi-criteria methods have developed as a response to this scenario, but the determination of the weights required by them remains an unclear and controversial task. We propose the use of discrete choice methods as a powerful tool to confront these multi-faced evaluation problems, since the resulting surveys are flexible and easy to respond, and do not give away the final quantitative results. We have applied this methodology to the selection of urban freight solutions in the city of Seville, in Spain, followed by the determination of the relative weights associated to different objectives, both analyses carried out from the side of the carriers stakeholder group.