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This study aims to propose a novel method for the conflict detection and eradication of autonomous vehicles which has predetermined routes to establish multi pickup and delivery tasks according to task priorities and vehicle capacity status on each pickup and delivery nodes in assembly cells in the automotive production.

In the designed system, the routing of autonomous vehicles (AVs) and scheduling of pickup and delivery tasks are established in production logistics. Gantt chart is created according to vehicle routes, and conflicts are detected using the proposed conflict-sweep algorithm. The proposed conflict-solving algorithm eliminates conflicts on intersections and roads by considering vehicle routes and task priorities.

In many production systems, there is a need to obtain flexible routes in each pickup delivery task group that changes during day, week, etc. Proposed system provides remarkable advantages in obtaining conflict-free routes for pre-scheduled multi transport tasks of vehicles by considering efficiency in production systems.

A novel method is proposed for the conflict detection and eradication of AVs. Proposed system eliminates conflicts on intersections and roads by considering pre-planned vehicle routes for a fleet of heterogeneous AVs. Unlike most of the other conflict-free algorithms, in which conflicts are solved between two points, proposed system also considers multi pickup and delivery points for AVs. This is pioneering paper that addresses conflict-free route planning with backhauls and scheduling of multi pickup and delivery tasks for AVs.

This paper proposes an approach to solve the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) in the context of the Physical Internet (PI) supply chain. The main objective is to minimize the total distribution costs (transportation cost and holding cost) to supply retailers from PI hubs.

Mixed integer programming (MIP) is proposed to solve the problem in smaller instances. A random local search (RLS) algorithm and a simulated annealing (SA) metaheuristic are proposed to solve larger instances of the problem.

The results show that SA provides the best solution in terms of total distribution cost and provides a good result regarding holding cost and transportation cost compared to other heuristic methods. Moreover, in terms of total carbon emissions, the PI concept proposed a better solution than the classical supply chain.

The sustainability of the route construction applied to the PI is validated through carbon emissions.

This approach also relates to the main objectives of transportation in the PI context: reduce empty trips and share transportation resources between PI-hubs and retailers. The proposed approaches are then validated through a case study of agricultural products in Thailand.

This approach is also relevant with the reduction of driving hours on the road because of share transportation results and shorter distance than the classical route planning.

This paper addresses the VRPSPD problem in the PI context, which is based on sharing transportation and storage resources while considering sustainability.

In this paper, the author introduces a new variant of the pickup and delivery transportation problem, where one commodity is collected from many pickup locations to be delivered to many delivery locations within pre-specified time windows (one–to many–to many). The author denotes to this new variant as the 1-commodity pickup-and-delivery vehicle routing problem with soft time windows (1-PDVRPTW).

The author proposes a hybrid genetic algorithm and a scatter search to solve the 1-PDVRPTW. It proposes a new constructive heuristic to generate the initial population solution and a scatter search (SS) after the crossover and mutation operators as a local search. The hybrid genetic scatter search replaces two steps in SS with crossover and mutation, respectively.

So, the author proposes a greedy local search algorithm as a metaheuristic to solve the 1-PDVRPTW. Then, the author proposes to hybridize the metaheuristic to solve this variant and to make a good comparison with solutions presented in the literature.

The author considers that this is the first application in one commodity. The solution methodology based on scatter search method combines a set of diverse and high-quality candidate solutions by considering the weights and constraints of each solution.

In the Norwegian oil and gas industry the upstream logistics includes providing the offshore installations with needed supplies and return flow of used materials and equipment. This paper considers a real‐life routing problem for supply vessels serving offshore installations at Haltenbanken off the northwest coast of Norway from its onshore supply base. The purpose of the paper is to explore how the offshore installation's limited storage capacity affects the routing of the supply vessels aiming towards creating efficient routes.

A simplified version of the real‐life routing problem for one supply vessel is formulated as a mixed integer linear programming model that contains constraints reflecting the storage requirements problem. These constraints ensure that there is enough capacity at the platform decks and that it is possible to perform both pickup and delivery services.

The model has been tested on real‐life‐sized instances based on data provided by the Norwegian oil company Statoil ASA. The tests show that in order to obtain optimal solutions to the pickup and delivery problem with limited free storage capacities at installations, one has to include in the formulation the new sets of constraints, the storage feasibility and the service feasibility requirements. In addition, two visits to some platforms are necessary to obtain optimality.

The main limitation is the present inability to solve large cases.

The contribution of this paper is to provide a better insight into a real‐life routing problem which has a unique feature arising from the limited deck capacity at the offshore installations that complicates the performance of service. This feature has neither been discussed nor modeled in the vehicle routing literature before, hence the formulation of the problem is original and reveals some interesting results.

Although buy-online-and-pick-up-in-store (BOPS) has been widely implemented by companies, scant attention has been paid to its effect on consumer experience and the concomitant outcomes. Using the psychological ownership theory, this study aims to examine whether and how the BOPS experience (vs online experience) can enhance consumer loyalty.

Study 1 investigated the consumer loyalty of shopping experience (self-pickup vs delivery) on actual consumer behavior through secondary data. Studies 2, 3 and 4 were controlled experiments to further investigate the mediating effect of product psychological ownership, and the moderating effects of product type and postdecision experience valence.

The authors found that BOPS shopping led to higher consumer loyalty (i.e. repeat purchase and repeat purchase frequency) compared with online shopping. Furthermore, the authors examined that this effect was mediated by product psychological ownership and moderated by product type and postdecision experience valence.

Theoretical speculations about how BOPS shopping affects consumer experience should be probed in future research.

Retailers with physical stores can offer in-store pickup options for their online consumers to increase their product psychological ownership and consumer loyalty. And the positive effects of the BOPS strategy relied on product type and postdecision experience valence.

This research offers theoretical contributions to research on the BOPS strategy, psychological ownership theory and consumer loyalty.

The objective is to determine if consumer preferences for grocery purchasing are impacted by the severity of the COVID-19 pandemic and whether these preferences differ by demographic and psychographic characteristics.

The authors conduct an online discrete choice experiment (DCE) with 900 U.S. consumers to assess grocery shopping preferences under various scenarios of the COVID-19 pandemic (i.e. decreasing, constant and increasing cases). The attribute of interest is the purchasing method (i.e. in-store purchase, in-store pickup, curbside pickup and home delivery) with minimum order requirements, time windows and fees as secondary attributes. Heterogeneity in individual-level willingness-to-pay (WTP) estimates for the main attribute is analyzed by means of mixed logit and quantile regression techniques.

The mixed logit model reveals heterogeneity in WTP estimates for grocery purchasing methods across participants. According to estimates from quantile regressions, the heterogeneity is partly explained by the severity of the COVID-19 pandemic. For example, the home delivery purchasing method is less preferred when the number of cases is decreasing. The results also show that consumer preferences for grocery shopping methods are affected more by psychographic characteristics than demographic characteristics. Consumers who comply with COVID-19 directives (e.g. wear face coverings) have stronger preferences for curbside pickup and home delivery, particularly at the tails of the WTP distributions.

Although there is much data on food consumer behavior during the COVID-19 pandemic at the aggregate level, there are few analyses of grocery shopping preferences at the individual level. The study represents a first attempt to relate individuals' demographic and psychographic characteristics to their grocery shopping preferences during the COVID-19 pandemic, thus yielding numerous recommendations in terms of consumer segmentation.

The purpose of this paper is to explore a real world vehicle routing problem (VRP) that has multi-depot subcontractors with a heterogeneous fleet of vehicles that are available to pickup/deliver jobs with varying time windows and locations. Both the overall job completion time and number of drivers utilized are analyzed for the automated job allocations and manual job assignments from transportation field experts.

A nested genetic algorithm (GA) is used to automate the job allocation process and minimize the overall time to deliver all jobs, while utilizing the fewest number of drivers – as a secondary objective.

Three different real world data sets were used to compare the results of the GA vs transportation field experts’ manual assignments. The job assignments from the GA improved the overall job completion time in 100 percent (30/30) of the cases and maintained the same or fewer drivers as BS Logistics (BSL) in 47 percent (14/30) of the cases.

This paper provides a novel approach to solving a real world VRP that has multiple variants. While there have been numerous models to capture a select number of these variants, the value of this nested GA lies in its ability to incorporate multiple depots, a heterogeneous fleet of vehicles as well as varying pickup times, pickup locations, delivery times and delivery locations for each job into a single model. Existing research does not provide models to collectively address all of these variants.

This study aims to assess green food packages’ role in sustaining a restaurant’s curbside pickup service on three stages of consumer experiences: choosing a restaurant, evaluating their experiences of a recent purchase and weighing their post-consumption behavioral intentions after the recent purchase.

The service encounters framework and relevant literature guided the development of the questionnaire. A Qualtrics panel data of 314 valid questionnaires were collected and analyzed with choice experience, ordinary least squares regression and PROCESS modeling.

First, word-of-mouth (WOM) and function encounters significantly influence consumers’ first-time curbside pickup purchasing decisions. Then, service results encounter (besides distributor encounter) most significantly affects consumers’ overall curbside pickup experience. Finally, green food packages increase consumers’ shares of future purchases through their positive WOM intentions and extra efforts of revisiting the restaurant. Consumers’ perceived importance of green restaurant practices strengthens green food packages’ positive impact on extra efforts.

This study provides operational and marketing insights for restaurants to use food packages and sustain their curbside pickup service.

Besides assessing consumers’ evaluations and behavioral intentions for an off-premises restaurant service expected to stay beyond the pandemic, this research uniquely focuses on green food packages, a sustainability issue lacking research attention. The findings add new empirical insights to studies about sustainability and restaurant/food–retail operations.

In order to reduce logistics transportation costs and respond to low-carbon economy, the purpose of this paper is to study the more practical and common simultaneous pickup and delivery vehicle routing problem, which considers the carbon tax policy. A low-carbon simultaneous pickup and delivery vehicle routing problem model is constructed with the minimum total costs as the objective function.

This study develops a mathematical optimization model with the minimum total costs, including the carbon emissions costs as the objective function. An adaptive genetic hill-climbing algorithm is designed to solve the model.

First, the effectiveness of the algorithm is verified by numerical experiments. Second, the research results prove that carbon tax mechanism can effectively reduce carbon emissions within effective carbon tax interval. Finally, the research results also show that, under the carbon tax mechanism, the effect of vehicle speed on total costs will become more obvious with the increase of carbon tax.

This paper only considers the weight of the cargo, but it does not consider the volume of the cargo.

Few studies focus on environmental issues in the simultaneous pickup and delivery problem. Thus, this paper constructs a green path optimization model, combining the carbon tax mechanism for the problem. This paper further analyzes the impact of carbon tax value on total costs and carbon emission; at the same time, the effect of vehicle speed on total cost is also analyzed.

With the increasing awareness of global warming and the important role of last mile distribution in logistics activities, the purpose of this paper is to build an environmental and effective last mile distribution model considering fuel consumption and greenhouse gas emission, vehicle capacity and two practical delivery service options: home delivery (HD) and pickup site service (PS). This paper calls the problem as the capacitated pollution-routing problem with pickup and delivery (CPRPPD). The goal is to find an optimal route to minimize operational and environmental costs, as well as a set of optimal speeds over each arc, while respecting capacity constraints of vehicles and pickup sites.

To solve this problem, this research proposes a two-phase heuristic algorithm by combining a hybrid ant colony optimization (HACO) in the first stage and a multiple population genetic algorithm in the second stage. First, the HACO is presented to find the minimal route solution and reduce distribution cost based on optimizing the speed over each arc.

To verify the proposed CPRPPD model and algorithm, a real-world instance is conducted. Comparing with the scenario including HD service only, the scenario including both HD and PS option is more economical, which indicates that the CPRPPD model is more efficient. Besides, the results of speed optimization are significantly better than before.

The developed CPRPPD model not only minimizes delivery time and reduces the total emission cost, but also helps logistics enterprises to establish a more complete distribution system and increases customer satisfaction. The model and algorithm of this paper provide optimal support for the actual distribution activities of logistics enterprises in low-carbon environment, and also provide reference for the government to formulate energy-saving and emission reduction policies.

This paper provides a great space for the improvement of carbon emissions in the last mile distribution. The results show that the distribution arrangement including HD and PS services in the last mile adopting speed optimization can significantly reduce the carbon emission. Additionally, an integrated real-world instance is applied in this paper to illustrate the validity of the model and the effectiveness of this method.