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In the distribution field, distribution costs and customer service satisfaction are extremely important issues for enterprises. However, both the Vehicle Routing Problem with Soft Time Windows (VRPSTW) and the Vehicle Routing Problem with Fuzzy Time Windows (VRPFTW) have certain deficiencies in describing real-world scenarios. Therefore, this paper considers both soft time windows and fuzzy time windows, improving upon the traditional VRPSTW and VRPFTW models to establish a more comprehensive and realistic model called the Vehicle Routing Problem with Soft Time Windows and Fuzzy Time Windows (VRPSFTW). Secondly, to solve the relevant problems, this paper proposes a Directed Mutation Genetic Algorithm integrated with Large Neighborhood Search (LDGA), which fully utilizes the advantages of the Genetic Algorithm (GA) in the early stages and appropriately adopts removal and re-insertion operators from the Large Neighborhood Search (LNS). This approach not only makes efficient use of computational resources but also compensates for the weaknesses of crossover and mutation operators in the later stages of the genetic algorithm. Thereby, it improves the overall efficiency and accuracy of the algorithm and achieves better solution results. In addition, in order to solve multi-objective problems, this paper employs a two-stage solution approach and designs two sets of algorithms based on the principles of “cost priority” and “service-level priority”. Simulation experiments demonstrated that the algorithms designed in this study achieved a more competitive solving performance.

The pickup and delivery problem with time windows is the problem of serving a number of transportation requests using a limited amount of vehicles. Each request involves moving a number of goods from a pickup location to a delivery location. Our task is to construct routes that visit all locations such that corresponding pickups and deliveries are placed on the same route, and such that a pickup is performed before the corresponding delivery. The routes must also satisfy time window and capacity constraints. This paper presents a heuristic for the problem based on an extension of the large neighborhood search heuristic previously suggested for solving the vehicle routing problem with time windows. The proposed heuristic is composed of a number of competing subheuristics that are used with a frequency corresponding to their historic performance. This general framework is denoted adaptive large neighborhood search . The heuristic is tested on more than 350 benchmark instances with up to 500 requests. It is able to improve the best known solutions from the literature for more than 50% of the problems. The computational experiments indicate that it is advantageous to use several competing subheuristics instead of just one. We believe that the proposed heuristic is very robust and is able to adapt to various instance characteristics.

Vehicle routing problems with time windows (VRPTW) have gained a lot of attention due to their important role in real-life logistics and transport. As a result of the complexity of real-life situations, most problems are multi-constrained and multi-objective, which increases their difficulty. The aim of this paper is to contribute to the effective solution of VRPTW-related problems. Therefore, research questions and objectives are set in accordance with PRISMA guidelines, and data extraction and analysis of the relevant literature within the last five years (2018–2022) are compared to answer the set research questions. The results show that approximately 86% of the algorithms involved in the literature are approximate methods, with more meta-heuristics than heuristics, and nearly 40% of the literature uses hybrid methods combining two or more algorithms.

Commonly encountered in cold chain logistics, third-party distribution firms are required to deliver temperature-sensitive food products to various retailers with two kinds of time-window constraints: (1) the delivery service must begin within the time windows imposed by the retailers (called node time windows) and (2) each vehicle route is available only in a predefined time interval prescribed by the government (called arc time windows). We study the effects of the retailer time window type (i.e., density of the node time-window constraints) and other cost-related factors on a distribution firm’s legitimacy choice (i.e., the firm chooses to either comply with or violate the governmental time-window policy), food quality, and pollutant emissions in the urban environment. We model the problem as an intractable vehicle routing problem with node and arc time windows and develop a genetic algorithm to tackle it. We conduct a case study to generate the managerial insights on dealing with time windows. We find that the governmental time windows will increase the distribution cost. The governmental time windows has a negative effect on pollutant emissions while showing a positive effect on food safety. Given governmental time windows, a higher demand for node time windows will result in more governmental time-window violations or lower vehicle load factor, which depends on the vehicle fixed cost, fuel price, and government penalty.

Purpose: To ensure customer satisfaction, e-retailers have focused on providing a last-mile service that includes time windows. Due to the sustainable challenge this posed, the purpose of this article is to define a method that can be used to evaluate the impact of time windows on sustainability and to apply this method to a particular case study.Design/methodology/approach: Desk research allows us to identify and define this method (based on a scoreboard). Then, a case study is conducted to evaluate the applicability of the scoreboard and analyse the impact of time windows on sustainability.Findings: A method to evaluate the impact of time windows on the three pillars of sustainability is defined and implemented. Through this implementation, the negative impact time windows have on the last-mile sustainability is identified and defined. Thus, the use of time windows leads to a greater impact on the environmental and social pillars. Regarding the economic pillar, the impact is ambiguous. Time windows have a negative impact on delivery costs and vehicle utilisation, but a positive impact on service levels and customer satisfaction. In this sense, intermediate alternatives can largely maintain the benefits of time windows elimination without significantly affecting the service level.Originality/value: Retailers can use the findings as a guide to evaluate and set up sustainable last-mile strategies, deciding whether the use of time windows is necessary and sustainable. In contrast to previous research, this study integrates the three pillars of sustainability. With this integration, it is concluded that intermediate alternatives, such as offering a limited time window system based on historical data, could be the most sustainable solution.

A basic understanding of delayed packet loss is key to successfully applying it to multi-node hopping networks. Given the problem of delayed data loss due to network delay in a hop network environment, we review early time windowing approaches, for which most contributions focus on end-to-end hopping networks. However, they do not apply to the general hopping network environment, where data transmission from the sending host to the receiving host usually requires forwarding at multiple intermediate nodes due to network latency and network cache overflow, which may result in delayed packet loss. To overcome this challenge, we propose a delay time window and a method for estimating the delay time window. By examining the network delays of different data tasks, we obtain network delay estimates for these data tasks, use them as estimates of the delay time window, and validate the estimated results to verify that the results satisfy the delay distribution law. In addition, simulation tests and a discussion of the results were conducted to demonstrate how to maximize the reception of delay groupings. The analysis shows that the method is more general and applicable to multi-node hopping networks than existing time windowing methods.

This work aims to compare two distinct approaches for solving a Travelling Salesman Problem with time window constraints. Given an environment with a fixed number of cities (points of interest), a robot must determine a route such that each city is visited in an imposed time interval. Both of the examined techniques have the objective of identifying the path with the lowest cost in terms of the distance traveled.The initial approach employs an exact method by defining the requirements as a mixed integer linear programming (MILP) optimization problem.The second method involves a meta-heuristic approach, using an ant colony procedure to solve the optimization problem.Besides qualitative information, the performed quantitative comparison relies on multiple numerical simulations performed in a MATLAB environment. We thus highlight the advantages and disadvantages of both methods, by taking into consideration criteria as the simulation time and the relative difference between the obtained costs versus the number of cities.

In many real-life routing problems there is more uncertainty with respect to the required timing of the service than with respect to the service locations. We focus on a pickup and delivery problem with time windows in which the pickup and drop-off locations of the service requests are fully known in advance, but the time at which these jobs will require service is only fully revealed during operations. We develop a sample-scenario routing strategy to accommodate a variety of potential time realizations while designing and updating the routes. Our experiments on a breadth of instances show that advance time related information, if used intelligently, can yield benefits. Furthermore, we show that it is beneficial to tailor the consensus function that is used in the sample-scenario approach to the specifics of the problem setting. By doing so, our strategy performs well on instances with both short time windows and limited advance confirmation.

The Vehicle Routing Problem with Time windows (VRPTW) is an extension of the capacity constrained Vehicle Routing Problem (VRP). The VRPTW is NP-Complete and instances with 100 customers or more are very hard to solve optimally. We represent the VRPTW as a multi-objective problem and present a genetic algorithm solution using the Pareto ranking technique. We use a direct interpretation of the VRPTW as a multi-objective problem, in which the two objective dimensions are number of vehicles and total cost (distance). An advantage of this approach is that it is unnecessary to derive weights for a weighted sum scoring formula. This prevents the introduction of solution bias towards either of the problem dimensions. We argue that the VRPTW is most naturally viewed as a multi-objective problem, in which both vehicles and cost are of equal value, depending on the needs of the user. A result of our research is that the multi-objective optimization genetic algorithm returns a set of solutions that fairly consider both of these dimensions. Our approach is quite effective, as it provides solutions competitive with the best known in the literature, as well as new solutions that are not biased toward the number of vehicles. A set of well-known benchmark data are used to compare the effectiveness of the proposed method for solving the VRPTW.[PUBLICATION ABSTRACT]

In this paper, we study the split delivery vehicle routing problem with time windows (SDVRPTW) that is a variant of the well-known vehicle routing problem with time windows (VRPTW), where each customer can be served by more than one vehicle. We propose enhancement procedures for the exact branch-and-price-and-cut algorithm that was recently developed for the SDVRPTW. In particular, we introduce a tabu search algorithm to solve the column-generation subproblem, extensions of several classes of valid inequalities, and a new separation algorithm for the k -path inequalities. Computational results show the effectiveness of the proposed enhancements.