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The requirement of social distancing during the COVID-19 pandemic has presented significant challenges for high-rise buildings, which heavily rely on elevators for vertical transportation. In particular, the need for social distancing has reduced elevator capacity typically by at least two-thirds or as much as over 90% the normal amount. This reduction is a serious concern, as reduced elevator capacities cause large queues to build up in lobbies, which makes social distancing difficult and results in large wait times. The objective of this study is to safely manage the elevator queues by proposing simple, technology-free interventions that drastically reduce the waiting time and length of lobby queues. We use mathematical modeling, epidemiological expertise, and simulation to design and evaluate our interventions. The key idea is to explicitly or implicitly group passengers that are going to the same floor into the same elevator as much as possible. In the Cohorting intervention, we attempt to find passengers going to the same floor as the first person in the queue. In the Queue Splitting intervention, we create a different queue for different groups of floors. Based on simulation and analytical findings, Cohorting and Queue Splitting can significantly reduce queue length and wait time, while also maintaining safety from viral transmission in otherwise crowded elevators, building lobbies, and entrances. These interventions are generally accessible for many buildings since they do not require programming the elevators, and rely on only using signage and/or a queue manager to guide passengers.

Carsharing-facilitating neighbourhood refers to a development scheme to combine carsharing, sustainable transportation-residential planning and housing features to promote less private car use and improve residential environments. Since this concept is new, little is known about residents’ reactions. This study focuses on the individuals who indicated zero-interest in living in such neighbourhoods to discuss the influential factors and possible improvements for the concept. The analysis is based on a stated choice experiment conducted in densely populated areas in the Netherlands. According to the survey results, 25.4% of the respondents indicated zero-interest. A binary logistic regression was applied to understand internal influencing factors on individuals’ zero-interest. Accordingly, respondents’ social-demographics, travel habits and present housing conditions have significant influences on their zero-interest. Particularly, zero-interest residents are more likely to be males, have full-time jobs or not work, have no child, live in medium-sized cities, own large gardens, have no driving licence, often travel by private cars or metros rather than bikes. Besides, based on the unobserved factors indicated by these individuals, several issues can also lead to zero-interest, such as objection to top-down governance, doubt of the necessities to provide shared-cars by neighbourhood management and lack of disability design.

This study explores a tourists’ preference for tour mode bundle, defined as a set of preferable travel modes for multiple trips in a tour, under Mobility-as-a-Service (MaaS). In the context of tourism, MaaS could improve transportation sustainability by providing integrated information about sustainable transportation modes. To develop a systematic understanding of tourists’ preference for tour mode bundle, we design a tour-based stated preference (SP) choice experiment based on bundle configuration of travel modes in MaaS including a shared van as a paratransit mode in combination with the existing bus and taxi services. A web-based SP survey is conducted for 331 individuals who visited Jeju Island in Korea. Two mixed logit models are estimated to reveal differences between the trip- and tour-based mode choice situations. The estimation results reveal that the trip sequence significantly influences the preference for travel mode in a tour mode bundle, which has significant cross effects with individual and travel context attributes. A shared van is the most preferred in both a trip and a tour. Persistence to using the same mode and synergy of using different modes are captured through significant cross effects of sequence-specific travel modes in a tour mode bundle. Based on these findings that can only be identified through tour-based choice situations, MaaS can be more effectively designed in tourism.

Engineering changes (ECs) are new product development activities addressing external or internal challenges, such as market demand, governmental regulations, and competitive reasons. The corresponding EC processes, although perceived as standard, can be very complex and inefficient. There seem to be significant differences between what is the 'officially' documented and the executed process. To better understand this complexity, we propose a data-driven approach, based on advanced text analytics and process and data mining techniques. Our approach sets the first steps toward an automatic analysis, extracting detailed events from an unstructured event log, which is necessary for an in-depth understanding of the EC process. The results show that the predictive accuracy associated with certain EC types is high, which assures the method applicability. The contribution of this article is threefold: 1) a detailed model representation of the actual EC process is developed, revealing problematic process steps (such as bottleneck departments); 2) homogeneous, complexity-based EC types are determined (ranging from 'standard' to 'complex' processes); and 3) process characteristics serving as predictors for EC types are identified (e.g., the sequence of initial process steps determines a 'complex' process). The proposed approach facilitates process and product innovation, and efficient design process management in future projects.

We investigate the benefits of on-site printing at remote geographic locations, where access to spare parts is intermittent and supplies are replenished at fixed intervals. Organizations typically have no solution to spare parts shortages other than expensive expediting orders, or waiting for a part to arrive with the next replenishment. We investigate whether on-site three-dimensional (3D) printing of spare parts can bring relief. Our work extends dual-sourcing literature with fixed order cycles by considering two emergency supply options: expediting regular parts and 3D printing lower quality parts. We model the replenishment and emergency supply decisions as a Markov decision process and find that the optimal inventory control policy consists of two thresholds that control when to expedite, when to print and when to wait for regular parts via the next replenishment. We apply our model to a case study of the Royal Netherlands Army (RNLA) for her United Nations peacekeeping mission in Mali. Our results show that on-site 3D printing, much more so than expediting, leads to large operational cost savings through on-site inventory reductions and increased asset availability, thus increasing the ability of the RNLA to operate in remote locations. These results extend to many other organizations that operate in remote locations, for example, those in the mining and offshore industry.

Traffic congestion is a common phenomenon in road transportation networks, especially during peak hours. More accurate prediction of dynamic traffic flows is very important for traffic control and management. However, disturbances caused by the time-varying origin-destination matrix, dynamic route choices, and disruptions make the modelling of traffic flows difficult. Therefore, this study focuses on modelling the dynamic evolution processes of traffic flows under disturbances and estimating dynamic travel times for arbitrary moment. A revised Lighthill–Whitham–Richards (RLWR) model with non-equilibrium states is presented to describe the dynamic traffic states on individual roads, and the ripple-spreading model (RSM) is integrated to investigate the interactions among several shockwaves from multiple roads. We propose a hybrid RLWR–RSM to model the congestion and congestion-recovery propagations in an entire transportation network. After predicting the dynamic traffic flows by the RLWR–RSM, the road travel times for arbitrary moment were estimated. Theoretical analyses indicated that (1) the RLWR–RSM inherits the advantages of macroscopic traffic flow models and integrates the characteristics of both low- and high-order continuum models, and (2) the RLWR–RSM considers multiple disturbances. From numerical experiments with various inputs, the variation in travel times under disturbances was investigated, and this further demonstrated that (1) the modelled dynamic traffic flows have four basic properties, and (2) the experimental results validate the theoretical analyses. In addition, the RLWR–RSM can explain several distinct traffic phenomena. Finally, the estimated travel times can provide decision supports for vehicle navigation.KeywordsTraffic flow Disturbance Time-varying OD Route choice Network disruption Dynamic travel time

Digital technology is fundamental to experimentation, learning, and the rate of innovation. Digital technology facilitates the rapid distribution of experimental design and debug information. However, we should consider how this fundamentally changes organizational learning and experimentation when managing the rate of product innovation. We address this issue by investigating what drives experimentation‐based learning in high‐tech product innovation and production. The longitudinal dataset in our study consists of 216 projects over a period of almost 5 years, involving thousands of digitally recorded design iterations and design debugs. Based on a time series linear regression analysis, we demonstrate that learning from an accumulation of completed projects drives learning in experimentation more than failure experience in successfully completed design debugs. Furthermore, we show that processing iterations and debugs rapidly enhances the speed of product innovation learning as this allows for short‐loop experimentation that restricts superstitious learning when conditions change over time. The results also show this can be achieved using digital tools as a source of agility.

In the pharmaceutical industry is genres large amounts of waste due to the multiple and complex purification steps needed. This was problem has urged the need for sustainable production pathways in the pharmaceutical industry. In this paper the use of ionic liquids to conduct automated purification is assessed by means of a Life Cycle Assessment. Ionic liquids are regarded as green solvents due to their low vapor pressure and low emission of volatile organic compounds, unlike common organic solvents. They have unique properties that can aid in the separation and recycle steps, which can minimize the need for a complex purification process and have many environmental benefits. Their use can lower the environmental impact; however, they shift the impact towards the early stages. Therefore, its use and implementation in a process need to be carefully evaluated. Two scenarios of ionic liquid-aid separation are developed to assess the environmental impact and identify hot sports before the experimental stages. These scenarios are benchmarked against a batch process and solvent-less process. It was found the impacts for both scenarios were 10-50-fold larger, with respect to the benchmark cases. The scenarios where then optimized to have 100 % automated phase separation and reduce the purification needed. Based on the solubility properties of the ionic liquids it is possible to obtain an automated separation by forming multiphase systems. This optimization showed that under these conditions it is possible to obtain comparable systems with benchmark technologies, and it is possible that further optimization can lead to better process compared to the benchmark cases. It was also found that to maintain a low environmental impact recyclability of the ionic liquid and high concentrated systems are necessary.

Recent advancements in Information Technology have provided an opportunity to significantly improve the effectiveness of inventory systems. The use of in‐cycle demand information enables faster reaction to demand fluctuations. In particular, for the newsvendor (NV) system, we exploit the newly available data to perform an additional review (AR) of inventory at an endogenously determined, a priori set time during the sales period, and perform an additional replenishment if necessary. We implemented our innovative model at a market‐leading media group. The results of the initial pilot were dramatic, indicating that the proposed model achieves an increase of 4%–24% in profits compared to the policy before implementation. As a result, the company started following the proposed model for all their printed magazines and observed a significant reduction in operational costs. In a generalized setting, we provide a tractable search‐based optimization algorithm, based on the problem's structural properties, for determining the optimal initial quantity, AR timing, and quantity to restock at that time. Based on these theoretical results, we propose a simple heuristic that can be used for many practical situations including our implementation at Yedioth. Through a computational experiment, we show that our algorithm finds the optimal solution quickly and that the proposed heuristic performs well. We also provide additional insights into the problem—for instance, that our system exhibits properties similar to inventory pooling, provided that the demand rate is large enough.

The spatio-temporal imbalance of parking demand and supply results in unwanted on-street cruising-for-parking traffic of conventional vehicles. Autonomous vehicles (AVs) can self-relocate to alleviate the shortage of parking supplies at the trip destinations. The extra floating trips of vacant AVs have adverse impacts on traffic congestion and the parking demand–supply imbalance may still exist when they are not distributed optimally. This paper presents a centralized parking dispatch approach to optimize the distribution of floating AVs and provide regional route guidance. We apply the concept of macroscopic fundamental diagram to represent the evolution of traffic conditions, cruising-for-parking, and dispatched AVs in a congested multi-region network. A model predictive control is suggested to optimize the control inputs. Numerical experiments in a four-region network demonstrate that the proposed parking dispatch and regional route guidance of AVs are effective in reducing intense cruising-for-parking traffic, and the integration of both has the best control performance by regulating the network towards under-saturated conditions. The performance of the proposed schemes is evaluated via simulations with noise in measurement errors and compliance rate prediction. Results show substantial improvements in terms of total time spent, even for low levels of AV market penetration or AV compliance rate to parking dispatch and route guidance.

With the development of vehicle automation and sharing economy, a mix of human-driven and autonomous vehicles can be expected in the next few decades, which means the conventional vehicles, carsharing, autonomous vehicles (AV) and shared AVs will coexist, affecting the way of travel. AVs, especially combined with shared use, may contribute to reduce or solve some of the present most intractable urban problems such as traffic congestion, crashes and wasted use of urban spaces. Given the emerging trend of new mobility options, understanding the decision of people in owning a private human-driven vehicle or private AV versus using a shared AV or joining car sharing would have important indications for policy makers to reduce car ownership in general. Therefore, this paper is devoted to gain insight into individuals’ propensity for purchasing an autonomous vehicle or using shared (autonomous) cars. Based on the data collected using a stated choice experiment, we estimated a random parameter error component model to identify the possible heterogeneity between individuals in the preference and possible correlations between various mobility alternatives. Results show substantial heterogeneity exists among individuals in their preferences. People in general prefer to keep their current car or buy an AV rather than to use shared cars, and shared AVs are more favorable than the traditional car sharing. Regarding the shared AVs, people are not only concerned about the operational costs and hourly rate, but also vehicle availability and access time. Moreover, younger people are more willing to accept these new mobility options.

This paper addresses the transportation of valuable goods in which security carriers are interested to optimize the operating costs and the vehicle routes’ security. In real operations, route planning and scheduling are normally performed manually based on individual experience. Hereupon, an optimizing approach should be developed to target the robbery risk reduction and the distribution network’ operational costs. Facing the dangerous nature of operations, some considerations play an important role, including demand fluctuations and traffic congestion of the urban environment. To handle these issues, we respectively benefit from the robust optimization theory and considering time-dependent travel speeds with satisfying the “first-in-first-out” property. This paper proposes a rich vehicle routing problem denoted by the secure time-dependent vehicle routing problem with time windows including pickup and delivery with uncertain demands (S-TD-VRPTWPD-UD). A mathematical formulation and an efficient solution approach combining the greedy randomized adaptive search procedure (GRASP) and the iterated local search (ILS) are developed to minimize the predictability of route plans using an integrated risk index, besides the travel costs. Extensive computational experiments on this problem are performed to analyze the impact of the demand uncertainty and the speed time-dependency and to show the efficiency of the GRASP × ILS implementation. The results show the significant improvements due to the time-dependency as well as the extra cost of protecting the model against the worst-case scenario of demand requests by deriving the robust counterpart of the S-TD-VRPTWPD-UD.

Purpose The transaction process of an office building is known to be time consuming and inefficient, in part due to the lack of market transparency. The purpose of this paper is to focus on the development of a blockchain application that can improve the transaction process of office buildings in the Netherlands. Design/methodology/approach Conducting design science research, the current transaction process of an office building and status quo of blockchain technology in real estate is investigated. Subsequently, multiple parties are interviewed to define major pain points within the process. The interview findings are used to design a blockchain solution which overcomes the aforementioned pain points. After designing, the interviewees are asked again to pragmatically validate the proposed model. Findings One of the major pain points identified concerning the transaction process of an office building is that it is difficult to define the characteristics of a property, due to lack of data structure and quality. The proposed application improves the way specific assets are understood by structuring physical and contractual information in one place and guarantees the quality of the data by using the blockchain mechanisms. Practical implications A blockchain application is proposed, which can improve the transaction process of an office building. Originality/value Recent studies indicate that blockchain technology could lead to improvements in efficiency, transparency and therefore trust within the transaction process. Therefore, the proposed application is of value for the future of real estate data management and the transaction process.

The sixth generation (6G) mobile systems will be the ultimate technology to support any networking requirement. 6G along with fog computing mobile nodes can support any application and use case within the supply chain delivery, regardless of the systems of systems complexity. In this paper, a holistic end-to-end framework platform based on the integration and leverage of 6G and fog mobile systems for physically-entangled systems is proposed. The framework platform, which relies on 6G fog enabled-delivery drones, terahertz communication, and artificial intelligence technologies, is described. The framework can be applied for highly cooperative, autonomous, and dynamic applications and systems such as supply chain’s last mile delivery of goods, harbour cranes, and maritime ports to help increasing access to goods in general, reduce traffic congestion and energy comsumption, and ease CO2 emissions from transportation. The proposed framework paves the way towards next generation supply chain management including last mile delivery.

Design science (DS) approaches have been emerging in engineering, management and other disciplines operating at the interface between design research and the natural or social sciences. Research informed by DS is challenging because it involves “mixing oil with water”, using a famous phrase of Herbert Simon. A key challenge here is the dual role of theory: one can develop a “theory of” any empirical phenomenon to explain its characteristics and outcomes, or alternatively, develop a “theory for” generating this phenomenon, focused on solving problems and enlarging possibilities. To clearly distinguish these two perspectives, we talk about theorizing in relation to theory-of and framing related to theory-for. A state-of-the-art review of how DS is applied by management researchers results in two main findings. First, explicit (re)framing efforts appear to be highly instrumental in challenging a given theoretical paradigm and thereby reduce the risk of being constrained to it; these findings confirm the generative nature of design activity. Moreover, many studies reviewed draw on knowledge formats that synthesize descriptive-explanatory and prescriptive-normative knowledge. Our main findings are subsequently integrated into a DS methodology, which may especially be of interest to design-oriented disciplines that tend to adopt a rather intuitive (undefined) notion of theory.

The share of intermodal transportation, which is often considered as a sustainable transportation alternative, is rather low compared to road transportation. There are several reasons for this situation, including the increased need for coordination of scheduled transport services and the reduced reliability of intermodal transport chains in case of disruptions. In this regard, developing an advanced algorithmic approach can help to handle real-time data during the execution of transportation and react adequately to detected unexpected events. In this way the reliability of intermodal transport can be increased, which might help to increase its usage and to minimize the negative externalities of freight transportation. This paper proposes a novel real-time decision support system based on a hybrid simulation-optimization approach for intermodal transportation which combines offline planning with online re-planning based on real-time data about unexpected events in the transportation network. For each detected disruption, the affected services and orders are identified and the best re-planning policy is applied. The proposed decision support system is successfully tested on real-life scenarios and is capable of delivering fast and reasonably good solutions in an online environment. This research might be of particular benefit to the transport industry for using advanced solution methodologies and give advice to transportation planners about the optimal policies that can be used in case of disruptions.

A multi-agent path finding (MAPF) problem is concerned with finding paths for multiple agents such that certain objectives, such as minimizing makespan, are reached in a conflict-free manner. In this paper, we solve a practical MAPF problem with automated guided vehicles (AGVs) for the conveying of luggage in segment-based layouts (MAPF-SL).Most existing algorithms for MAPF are mainly focused on grid environments. However, the conflict prevention problem is more challenging with segment-based layouts in which software is constrained to oversee that vehicles remain on predefined travel paths. Hence, the existing multi-agent path finding algorithms cannot be applied directly to solve MAPF-SL. In this paper, we propose an algorithm, called WHCA*S-RL, that combines Deep Reinforcement Learning (DRL) with a heuristic approach for solving MAPF-SL. DRL is used for determining travel directions while the heuristic approach oversees the planning in a segment-based layout. Our experiment results show that the proposed WHCA*S-RL approach can be successfully used for making path plans in which traffic congestion is both avoided and relieved. In this way, individual vehicles are found to reach goal destinations faster than the approach with search only.

Closed-loop supply chains (CLSCs) are seen as one of the circular economy’s leading approaches for reducing our natural environment load. Many CLSC models require collaboration among different parties. Game theory (GT) offers a way to consider the profits of all parties in a CLSC, providing insight into the costs and benefits to the involved parties in an objective and quantitative way. Presently, available reviews on the use of GT, in the context of CLSC, are quite limited and consider only a few relevant elements. Here, we present a new and more extensive framework, focusing on the collaboration structure of CLSCs. It contains a content-based analysis of 230 papers based on a four-step systematic literature review process. The characteristics studied are channels for collection, reprocessing and selling, the planning horizon, and the types of games. The structures found are graphically reviewed, leading to 196 different structures. The results show that, so far, most attention has been paid to the dual-channel collection, where collection by two retailers (dual-retailer) is the most studied case. With respect to selling, most attention has been paid to situations with two selling channels (dual-selling), i.e., one channel managed by a manufacturer and one channel managed by a remanufacturer. Studies have prioritized the role of manufacturers as that of the leader and collector. Finally, a number of directions for further research are pointed out.

Freight distribution with electric vehicles (EVs) is a promising alternative to reduce the carbon footprint associated with city logistics. Algorithms for planning routes for EVs should take into account their relatively short driving range and the effects of traffic congestion on the battery consumption. This paper proposes new methodology and illustrates how it can be applied to solve an electric vehicle routing problem with stochastic and time-dependent travel times where battery recharging along routes is not allowed. First, a new method for generating network-consistent (correlated in time and space) and time-dependent speed scenarios is introduced. Second, a new technique for applying branch and price on instances defined on real street networks is developed. Computational experiments demonstrate the effectiveness of the approach for finding optimal or near-optimal solutions in instances with up to 133 customers and almost 1,500 road links. With a high probability, the routes in the obtained solutions can be performed by EVs without requiring intermediate recharging stops. An execution time control policy to further reduce the chances of stranded EVs is also presented. In addition, we measure the cost of independence, which is the impact on solution feasibility when travel times are assumed statistically independent. Last, we give directions on how to extend the proposed framework to handle recourse actions.

The rise of e-commerce has increased the demands placed on pickup and delivery operations, as well as customer expectations regarding the quality of services provided by those operations. One strategy a logistics provider can employ for meeting these increases in demands and expectations is to complement and coordinate its fleet operations with those of for-hire, third-party logistics providers. Herein, we study an optimization problem for coordinating these operations: the time-dependent profitable pickup and delivery problem with time windows. In this problem, the logistics provider has the opportunity to use its fleet of capacitated vehicles to transport shipment requests, for a profit, from pickup to delivery locations. Owing to demographic and market trends, we focus on an urban setting, wherein road congestion is a factor. As a result, the problem explicitly recognizes that travel times may be time-dependent. The logistics provider seeks to maximize its profits from serving transportation requests, which we compute as the difference between the profits associated with transported requests and transportation costs. To solve this problem, we propose an adaptive large neighborhood search algorithm. The results of our extensive computational study show that the proposed algorithm can find high-quality solutions quickly on instances with up to 75 transportation requests. Furthermore, we study its impact on profits when explicitly recognizing traffic congestion during planning operations.

Many technology-intensive (TI) firms find it challenging to leverage customisation and achieve sustainable innovation. Although some firms use modularity to tackle this challenge, mixed effects on sustainable innovation have been reported. This study uses organisational learning and ambidexterity theory to provide insights into how TI firms can achieve ‘win-win’ situations where sustainable innovation is increased through customisation. First, we argue that customisation should be viewed two-dimensionally and identify both modularity and solution space freedom as important dimensions. We argue that modularity reflects knowledge specialisation and solution space freedom reflects knowledge variety. Both of these dimensions affect organisational learning and, in turn, sustainable innovation. Second, we argue that the relationship between customisation and organisational learning is affected by supplier characteristics, specifically supplier sophistication. Survey data from 166 managers were used to empirically test the conceptual model and hypotheses. Polynomial response surface analysis confirms that customising by balancing high degrees of both modularity and solution space freedom results in superior organisational learning. High levels of supplier sophistication do not strengthen these effects. Rather, our results show that combining high degrees of modularity with constrained solution spaces increases learning for TI firms working with less sophisticated suppliers. In addition, organisational learning fully mediates the effect of customisation on sustainable product and process innovation.

We propose a metaheuristic for the Time-Dependent Pollution-Routing Problem, which consists of routing a number of vehicles to serve a set of customers and determining their speed on each route segment with the objective of minimizing the cost of driver’s wage and greenhouse gases emissions. The vehicles face traffic congestion which, at peak periods, significantly restricts vehicle speeds and leads to increased emissions. Our algorithm is based on an adaptive large neighborhood search heuristic and uses new removal and insertion operators which significantly improve the quality of the solution. A previously developed departure time and speed optimization procedure is used as a subroutine to optimize departure times and vehicle speeds. Results from extensive computational experiments demonstrate the effectiveness of our algorithm.

Since the beginning of the industrial age, material performance and design have been in the midst of innovation of many disruptive technologies. Today’s electronics, space, medical, transportation, and other industries are enriched by development, design and deployment of composite, heterogeneous and multifunctional materials. As a result, materials innovation is now considerably outpaced by other aspects from component design to product cycle. In this article, we review predictive nonlinear theories for multiscale modeling of heterogeneous materials. Deeper attention is given to multiscale modeling in space and to computational homogenization in addressing challenging materials science questions. Moreover, we discuss a state-of-the-art platform in predictive image-based, multiscale modeling with co-designed simulations and experiments that executes on the world’s largest supercomputers. Such a modeling framework consists of experimental tools, computational methods, and digital data strategies. Once fully completed, this collaborative and interdisciplinary framework can be the basis of Virtual Materials Testing standards and aids in the development of new material formulations. Moreover, it will decrease the time to market of innovative products.

We investigate the impact of random deviations in planned arrival times on user equilibrium in an extension of Vickrey's celebrated bottleneck model. The model is motivated by the fact that in real life, users can not exactly plan the time at which they depart from home, nor the delay they experience before they join the congestion bottleneck under investigation. We show that the arrival density advocated by the Nash equilibrium in Vickrey's model, is not a user equilibrium in the model with random uncertainty. We then investigate the existence of a user equilibrium for the latter and show that in general such an equilibrium can neither be a pure Nash equilibrium, nor a mixed equilibrium with a continuous density. Our results imply that when random distortions influence user decisions, the dynamics of standard bottleneck models are inadequate to describe such more complex situations. We illustrate with numerical analysis how the mechanics of a bottleneck with delayed arrivals are unstable for any continuous arrival strategy, thus shedding more light on the non-existence result.

Integrating freight flows with scheduled public transportation services creates attractive business opportunities as the same transportation needs can be met with fewer operating costs. The pickup and delivery problem with time windows and scheduled lines (PDPTW-SL) aims at routing a given set of vehicles to transport freight requests from their origins to their corresponding destinations, where the requests can use scheduled passenger transportation services as a part of their journeys. We describe the PDPTW-SL as an arc-based mixed-integer program. Computational results on a set of small-size instances provide a clear understanding of the benefits of using scheduled line services as a part of freight's journey.

Shared parking allows the effective use of undersupplied parking spaces and contributes to the alleviation of urban parking problems, traffic congestion, environmental pollution, and other negative externalities of traffic. However, little is known about the acceptance of shared parking by consumers of a different socio-demographic profile. To understand the feasibility and potential success of shared parking, this paper develops a stated choice experiment with three choice options: fixed mode shared parking, flexible mode shared parking and not interested, to investigate parking space owners’ propensity to engage in shared parking under varying conditions. Because the demand for shared parking is uncertain, the revenues owners may generate are uncertain. As one of the most popular theories of decision making under uncertainty, the cumulative prospect theory is incorporated into a multinomial logit model to capture the decision problem in which some variables are uncertain and others are not. The revenue that owners expect shared parking can bring is used as the reference point to differentiate between gains and losses. Gains refer to outcomes that exceed the reference point, while losses refer to outcomes that fall short. To examine unobserved heterogeneity, a random parameter version of the model is specified to estimate the distribution of decision weights across the sample. Results show that socio-demographic characteristics, context variables, revenues and psychological concerns are all important factors in explaining parking space owners’ propensity to engage in platform-based shared parking schemes. Incorporating unobserved heterogeneous improves the overall goodness-of-fit of the model. Understanding parking space owners’ propensity to share their parking spaces in relation to their psychological concerns and uncertain conditions is critical to improve shared parking policies. The results of this paper may help designers and planners in the delivery of shared parking services and promote the success and future growth of the shared parking industry.

We present a shortest-path algorithm for the departure time and speed optimization problem under traffic congestion. The objective of the problem is to determine an optimal schedule for a vehicle visiting a fixed sequence of customer locations to minimize a total cost function encompassing emissions cost and labor cost. We account for the presence of traffic congestion, which limits the vehicle speed during peak hours. We show how to cast this problem as a shortest-path problem by exploiting some structural results of the optimal solution. We illustrate the solution method and discuss some properties of the problem. The online appendix is available at https://doi.org/10.1287/trsc.2018.0820 .

To achieve a complex value proposition, innovating firms often need to rely on other actors in their innovation ecosystem. This raises many new challenges for the managers of these firms. However, there is not yet a comprehensive approach that would support managers in the process of analysis and decision making on ecosystem strategy. In this paper, we develop a strategy tool to map, analyze and design (i.e., model) innovation ecosystems. From the scholarly literature, we distill the constructs and relationships that capture how actors in an ecosystem interact in creating and capturing value. We embed these elements in a visual strategy tool coined the Ecosystem Pie Model (EPM) that is accompanied by extensive application guidelines. We then illustrate how the EPM can be used, and conclude by exploring the multiple affordances of the EPM tool as a boundary object between research and practice.

Despite the prevalent discussions on automated vehicles, little research has been conducted with a focus on inclusiveness of traditionally excluded populations from driving. Even though we may envision a future where everyone can drive with perfect automation, the problem will not be that simple. As with any other problem domains, we need to scrutinize all the design considerations - not only each population's characteristics (capabilities and limitations), but also the entire system, technological limitations, and task environments. To this end, the present paper explores challenges and opportunities of automated vehicles for multiple populations, including people with various difficulties/disabilities, older adults, and children. This paper brings up some controversial points and is expected to promote lively discussions at the conference.

Industry 4.0 is expected to deliver significant gains in productivity by assimilating several technological advancements including cloud computing, the Internet-of-Things, and smart devices. However, it is unclear how these technologies should be leveraged together to deliver the promised benefits. We present the architecture design of an information system that integrates these technologies to support hybrid manufacturing processes, i.e., processes in which human and robotic workers collaborate. We show how well-structured architecture design is the basis for a modular, complex cyber-physical system that provides horizontal, cross-functional manufacturing process management and vertical control of heterogenous work cells. The modular nature allows the extensible cloud support enhancing its accessibility to small and medium enterprises. The information system is designed as part of the HORSE Project: a five-year research and innovation project aimed at making recent technological advancements more accessible to small and medium manufacturing enterprises. The project consortium includes 10 factories to represent the typical problems encountered on the factory floor and provide real-world environments to test and evaluate the developed information system. The resulting information system architecture model is proposed as a reference architecture for a manufacturing operations management system for Industry 4.0. As a reference architecture, it serves two purposes: (1) it frames the scientific inquiry and advancement of information systems for Industry 4.0 and (2) it can be used as a template to develop commercial-grade manufacturing applications for Industry 4.0.

Assigning and scheduling vehicle routes in a stochastic time-dependent environment is a crucial management problem. The assumption that in a real-life environment everything goes according to an a priori determined static schedule is unrealistic. Our methodology builds on earlier work in which the traffic congestion is captured in an analytical way using queueing theory. The congestion is then applied to the VRP problem. In this paper, we introduce the variability in traffic flows into the model. This allows for an evaluation of the routes based on the uncertainty involved. Different experiments show that the risk taking behavior of the planner can be taken into account during optimization. As more weight is given to the variability component, the resulting optimal route will take a slightly longer travel time, but will be more reliable. We propose a powerful objective function that is easily implemented and that captures the trade-off between the average travel time and its variance. The evaluation of the solution is done in terms of the 95th-percentile of the travel time distribution (assumed to be lognormal), which reflects well the quality of the solution in this stochastic time-dependent environment

Many enterprises have recently been pursuing process innovation or improvement to attain their performance goals. To align a business process with enterprise performances, this study proposes a two-stage process analysis for process (re)design that combines the process-based performance measurement framework (PPMF) and business process simulation (BPS). The two-stage analysis consists of macro and micro analyses of business processes. At the early stage of business process analysis (BPA), macro process analysis is conducted to identify the influence of a business process on a target key performance indicator (KPI) or the contribution of a target KPI to other KPIs. If target business processes that need improvement are identified through the macro process analysis and to-be processes are newly designed, micro process analysis using simulation is conducted to predict the performance. The proposed method is validated by application to a real business process within the setting of a large Korean company. By using the proposed, two-stage process analysis, company staff involved in process innovation projects can determine the processes with the greatest influence on enterprise strategy, and can systematically evaluate the performance prediction of the newly designed process.

International audience; Transportation is an important component of supply chain competitiveness since it plays a major role in the inbound, inter-facility, and outbound logistics. In this context, assigning and scheduling vehicle routes is a crucial management problem. In this paper, a vehicle routing problem with dynamic travel times due to potential traffic congestion is considered. The approach developed introduces mainly the traffic congestion component based on queueing theory. This is an innovative modeling scheme to capture travel times. The queueing approach is compared with other approaches and its potential benefits are described and quantified. Moreover, the optimization of the starting times of a route at the distribution center is evaluated. Finally, the trade-off between solution quality and calculation time is discussed. Numerous test instances are used, both to illustrate the appropriateness of the approach as well as to show that time-independent solutions are often unrealistic within a congested traffic environment, which is usually the case on European road networks.

As part of process redesign initiatives, substantial time is spent on the systematic description and analysis of the as-is process. By contrast, to-be scenarios are often generated in a less rigorous way. Only one or a few workshops are organized for this purpose, which rely on the use of techniques that are susceptible to bias and incompleteness, e.g. brainstorming. In this paper, we evaluate a new technique for generating process improvement ideas: the RePro (Rethinking of Processes) technique. Its backbone is formed by process improvement principles that guide practitioners in a systematic and comprehensive exploration of the solution space. An experiment was conducted to compare the performance of the RePro technique with traditional brainstorming. Results confirm the potential for using a more advanced technique during process redesign workshops, but also show that the way such a technique is used strongly affects its performance. Keywords: Process redesign Process innovation Improvement principles Controlled experiment RePro

Companies, scientific communities, and individual scientists with varying requirements for their compute-intensive applications may want to use public Infrastructure-as-a-Service clouds to increase the capacity of the resources they have access to. To enable such access, resource managers that currently act as gateways to clusters may also do so for clouds, but for this they require new architectures and scheduling frameworks. In this paper, we present the design and implementation of KOALA-C, which is an extension of the KOALA multicluster scheduler to multicloud environments. KOALA-C enables uniform management across multicluster and multicloud environments by provisioning resources from both infrastructures and grouping them into clusters of resources called sites. KOALA-C incorporates a comprehensive list of policies for scheduling jobs across multiple (sets of) sites, including both traditional policies and two new policies inspired by the well-known TAGS task assignment policy in distributed-server systems. Finally, we evaluate KOALA-C through realistic simulations and real-world experiments, and show that the new architecture and in particular its new policies show promise in achieving good job slowdown with high resource utilization.

Process mining provides new ways to utilize the abundance of data in enterprises. Suddenly many organizations realize that survival is not possible without exploiting available data intelligently. A new profession is emerging: the data scientist. Just like computer science emerged as a new discipline from mathematics when computers became abundantly available, we now see the birth of data science as a new discipline driven by the torrents of data available today. Process mining will be an integral part of the data scientist's toolbox. Also enterprise computing will need to focus on process innovation through the intelligent use of event data. In his talk Wil van der Aalst will focus on challenges related to "process mining in the large", i.e., dealing with many processes, many actors, many data sources, and huge amounts of data at the same time. By adequately addressing these challenges (e.g., using process cubes) we get a new kind of superglue that will impact the future of enterprise computing.

In this work, we present an interactive system for visual analysis of urban traffic congestion based on GPS trajectories. For these trajectories we develop strategies to extract and derive traffic jam information. After cleaning the trajectories, they are matched to a road network. Subsequently, traffic speed on each road segment is computed and traffic jam events are automatically detected. Spatially and temporally related events are concatenated in, so-called, traffic jam propagation graphs. These graphs form a high-level description of a traffic jam and its propagation in time and space. Our system provides multiple views for visually exploring and analyzing the traffic condition of a large city as a whole, on the level of propagation graphs, and on road segment level. Case studies with 24 days of taxi GPS trajectories collected in Beijing demonstrate the effectiveness of our system.

This study covers the design of innovative product concepts based on a combination of PV and LED technology. The products were developed in a project that took place in 2008 and 2009 during a cooperation of the University of Twente with Philips Lighting. It is shown that surprisingly unpredictable - yet technically feasible - PV powered LED products can be designed by carefully selected industrial design methods and an open view towards potential applications. In this scope innovation is not just a matter of implementing advanced technology in existing products, but particularly a matter of sensing new opportunities which are created by the combination of new technologies such as LEOs and PV technology. The PV powered LED product concepts range from small products, like watches, remote controls and other electronic handhelds, to large-sized products such as tents. In this paper it is shown how the concepts have been developed and they will be visualized by high key renderings. Also attention will be paid to the prototyping a few product concepts to evaluate their functional requirements.

A recent trend in technological innovation is towards the development of increasingly multifunctional and complex products to be used within rich socio-cultural contexts such as the high-end office, the digital home, and professional or personal healthcare. One important consequence of the development of strongly innovative products is a growing market uncertainty regarding ‘if’, ‘how’, and ‘when’ users can and will adopt such products. Often, it is not even clear to what extent these products are understood and interacted with in the intended manner. The mentioned problems have already become an evident concern in the field, where there is a significant rise in the numbers of seemingly sound products being complained about, signaling a lack of soft reliability. In this paper, we position soft reliability as a growing and critical industrial problem, whose solution requires new academic expertise from various disciplines. We illustrate potential root causes for soft reliability problems, such as discrepancy between the perceptions of users and designers. We discuss the necessary approach to effectively capture subjective feedback data from actual users, e.g. when they contact call centers. Furthermore, we present a novel observation and analysis approach that enables insight into actual product usage, and outline opportunities for combining such objective data with the subjective feedback provided by users. Copyright © 2008 John Wiley & Sons, Ltd.

The real world is a complex dynamic and stochastic environment. This is especially true for the traffic moving daily on our roads. As such, accurate modeling that correctly considers the real-world dynamics and the inherent stochasticity is very important, especially if government will base its road tax decisions on the outcomes of these models. The contemporary traffic prices, if any, however, do not reflect the external congestion costs. In order to induce road users to make the correct decision, marginal external costs should be internalized. To assess these costs, the public sector managers need accurate operational models. We show in this article that using a better representation and characterization of the road traffic, via stochastic queueing models, leads to a more adequate reflection of the congestion costs involved. Using extensive numerical experiments, we show the superiority of the stochastic traffic flow models.

We study inventory optimization for locally controlled, continuous-review distribution systems with stochastic customer demands. Each node follows a base-stock policy and a first-come, first-served allocation policy. We develop two heuristics, the recursive optimization (RO) heuristic and the decomposition-aggregation (DA) heuristic, to approximate the optimal base-stock levels of all the locations in the system. The RO heuristic applies a bottom-up approach that sequentially solves single-variable, convex problems for each location. The DA heuristic decomposes the distribution system into multiple serial systems, solves for the base-stock levels of these systems using the newsvendor heuristic of Shang and Song (2003), and then aggregates the serial systems back into the distribution system using a procedure we call “backorder matching.” A key advantage of the DA heuristic is that it does not require any evaluation of the cost function (a computationally costly operation that requires numerical convolution). We show that, for both RO and DA, changing some of the parameters, such as leadtime, unit backordering cost, and demand rate, of a location has an impact only on its own local base-stock level and its upstream locations’ local base-stock levels. An extensive numerical study shows that both heuristics perform well, with the RO heuristic providing more accurate results and the DA heuristic consuming less computation time. We show that both RO and DA are asymptotically optimal along multiple dimensions for two-echelon distribution systems. Finally, we show that, with minor changes, both RO and DA are applicable to the balanced allocation policy.

Assigning and scheduling vehicle routes in a dynamic environment is a crucial management problem. Despite numerous publications dealing with efficient scheduling methods for vehicle routing, very few addressed the inherent stochastic and dynamic nature of travel times. In this paper, a vehicle routing problem with time-dependent travel times due to potential traffic congestion is considered. The approach developed introduces the traffic congestion component based on queueing theory. This is an innovative modelling scheme to capture the stochastic behavior of travel times as it generates an analytical expression for the expected travel times as well as for the variance of the travel times. Routing solutions that perform well in the face of the extra complications due to congestion are developed. These more realistic solutions have the potential to reduce real operating costs for a broad range of industries which daily face routing problems. A number of datasets are used to illustrate the appropriateness of the novel approach. Moreover it is shown that static (or time-independent) solutions are often infeasible within a congested traffic environment which is generally the case on European road networks. Finally, the effect of travel time variability (obtained via the queueing approach) is quantified for the different datasets.

Navigation for and tracking of humans within a building usually implies significant infrastructure investment and devices are usually too high in weight and volume to be integrated into garments. We propose a system that relies on existing infrastructure (so requires little infrastructure investment) and is based on a sensor that is low cost, low weight, low volume and can be manufactured to have similar characteristics to everyday clothing (flexible, range of colours). This proposed solution is based on solar modules. This paper investigates their theoretical and practical characteristics in a simplified scenario. Two models based on theory and on experimental results (empirical model) are developed and validated. First distance estimations indicate that an empirical model for a particular scenario achieves an accuracy of 18cm with a confidence of 83%. © Springer-Verlag Berlin Heidelberg 2005.

This paper describes and illustrates different configurations of the interface between new product development and production processes, including both intra–firm and inter–firm interfaces. These configurations are partly based on a process view of product innovation and partly on a structural view of product innovation. In addition to this typology of interfaces some integration mechanisms are described. The typology will serve as a basis for further research aimed at identifying consistent configurations of the different types of integration mechanisms that are available for industry to increase the effectiveness and efficiency of product innovation processes.