Time Window Constraints Research Articles

Casualty rescue path optimization in cities in early post-earthquake period

After an earthquake, decision makers are required to make a rapid response. In order to minimize the damage caused after an earthquake disaster and improve the efficiency of casualty rescue, scientific and effective casualty transportation dispatching is required. Especially for small and medium-sized cities where materials are scarce and transportation is inconvenient, effective optimization of casualty dispatching can minimize the amount of casualties and improve rescue efficiency. Therefore, this paper analyzes and researches the post-earthquake casualty rescue problem against the background of the initial rescue stage after the earthquake disaster in small and medium-sized cities, so as to provide the government and other relevant post-earthquake emergency logistics management departments with decision-making references. By establishing a casualty rescue planning model for small and medium-sized cities pursuing the minimization of total rescue time, and applying genetic algorithm to solve the path optimization model with soft time window constraints. The empirical analysis substitutes the street city simulation data of Wuxi County, Chongqing, and generates the stochastic solution and the optimal solution by MATLAB software, and the study finds that the optimal solution has significantly better travel time than the stochastic solution.

Research on Cold Chain Logistics Transportation Scheme under Complex Conditional Constraints

A mathematical model is proposed to minimize the sum of vehicle fixed cost, fuel cost, carbon-emission cost, cooling cost, time-penalty cost and split-compensation cost, on the basis of considering the three-level cold-chain-logistics network of manufacturer, distribution center, and seller. The model is constructed based on the constraints of customer time window, vehicle load, demand-splitable, and semi-open driving of multiple distribution centers. We to divide the customer areas according to geographical locations and to carry out the transportation processes in stages. The target solution, which includes vehicle routing, service time and type, cargo details, etc., has been formulated. A two-stage hybrid-heuristic-path-scheme solution algorithm that combines a taboo table, a genetic algorithm, an optimal-path-generation algorithm, a load-capacity-constraint algorithm, and a time-window-constraint algorithm is designed in view of the complexity of the model and the uniqueness of the solution scheme. This paper aims to reasonably plan the resource allocation of cold chain logistics enterprises, reduce the comprehensive cost of cold chain transportation, improve customer satisfaction, and respond to the green logistics policy advocated by the state by reducing vehicle transit time and fuel consumption, and promote energy conservation and emission reduction.

Simulation of high-dimensional collaborative distribution of disaster materials with time window constraints

In recent years, with the continuous occurrence of natural disasters, people have gradually realized the importance of improving emergency response capability, and the weight of time constraints for rational allocation of emergency materials has gradually increased. Therefore, a high-dimensional collaborative allocation method of disaster materials with time window constraints is studied. A high-dimensional collaborative distribution model of disaster materials with time window constraints is constructed by combining four dimensional decision-making indexes: maximizing the satisfaction of material demand, fairness of material distribution and minimizing the total cost of expected emergency response; Build SPEA2 + SDE hybrid algorithm, solve the model and output the optimal solution set. The simulation results show that this method can have the ability of high-dimensional distribution of disaster materials, obtain the output of the optimal distribution scheme set of disaster materials, and the material satisfaction is more than 0.70. Under the condition of minimum distribution cost, the distribution of disaster materials can be completed.

Impact of soft ride time constraints on the complexity of scheduling in Dial-A-Ride Problems

The Dial-a-Ride problem can contain various constraints for pickup-delivery requests, such as time windows and ride time constraints. Given a tour as a sequence of pickups and deliveries, there exist polynomial time algorithms to find a schedule respecting these constraints (provided that there exists one). However, if no such schedule exists, it is natural to ask for a schedule minimising constraint violations. We model a generic fixed-sequence scheduling problem, which we call Min Pickup-Delivery Scheduling, that allows violation of time window constraints (by late visits) and ride time constraints, both penalised with linear penalty functions. Although our model only considers these two types of time constraints, we show that it can express other common time constraints such as earliness and waiting times. We prove that Min Pickup-Delivery Scheduling is APX-hard, even in the very restrictive case where only the ride time constraints can be violated and the time windows have the same length. Conversely, when only the time window constraints can be violated (by late visits), we present a polynomial time algorithm to solve the problem. Then we focus on instances with some specific structural properties and present two polynomial-time algorithms: one for the case where all the ride time constraints are bounded by a constant, and a second one for the case where all the pickups precede all the deliveries in the sequence of stops.

Research on requirement fusion of imaging reconnaissance satellites

Aiming at the problem of how to improve the requirement satisfaction of multi-point target imaging reconnaissance tasks and the utilization efficiency of satellite resources, a multi-point target imaging reconnaissance requirement fusion method based on genetic optimization is proposed in this paper. Firstly, the basic constraints that can be fused between multi-point targets are analysed, and on this basis, a point-target synthetic observation model is constructed. Secondly, considering the constraints of observation time window, observation angle and transition time between synthetic tasks, a genetic algorithm-based multi-point target requirements fusion method is designed. Finally, through the simulation analysis, it is concluded that with the continuous increase of user requirements, the fusion method based on genetic optimization can still achieve better task satisfaction to the user’s imaging requirements, which greatly improves the use efficiency of imaging satellites.

Multiresource fair allocation with time window constraints

Multiresource fair allocation with time window constraints

Route efficiency implications of time windows and vehicle capacities in first- and last-mile logistics

In this paper, we analyze the route efficiency effects that emerge from combining realistically constrained first-mile pickup and last-mile delivery operations into joint vehicle routes. Specifically, we examine (i) the individual effect of discrete, non-overlapping time window constraints; (ii) the individual effect of vehicle capacity constraints; and (iii) the joint impact of both constraint types on local route efficiency gains due to the integration of pickup and delivery operations. Extending the existent literature on continuum approximation of route distances, we propose closed-form adjustment factors which accurately capture these non-trivial route efficiency effects. To derive the adjustment factors, we conduct extensive numerical experiments and apply a novel hybrid data analysis approach which combines exploratory data analysis with symbolic regression. Our analysis suggests that these real-world constraints affect the optimal stop sequence and diminish or even eliminate the expected efficiency gains from integrating first- and last-mile operations. The proposed adjustment factors are particularly relevant for the optimal strategic design and operational planning of modern, industrial-scale distribution networks in light of growing and increasingly fragmented e-commerce volumes and a high cost pressure on the logistics industry. They help researchers and practitioners to efficiently quantify the expected benefits from integrating pickup and delivery operations, and to assess under which circumstances such an integration is (not) desirable.

Group-Based Distributed Auction Algorithms for Multi-Robot Task Assignment

This paper studies the multi-robot task assignment problem in which a fleet of dispersed robots needs to efficiently transport a set of dynamically appearing packages from their initial locations to corresponding destinations within prescribed time-windows. Each robot can carry multiple packages simultaneously within its capacity. Given a sufficiently large robot fleet, the objective is to minimize the robots’ total travel time to transport the packages within their respective time-window constraints. The problem is shown to be NP-hard, and we design two group-based distributed auction algorithms to solve this task assignment problem. Guided by the auction algorithms, robots first distributively calculate feasible package groups that they can serve, and then communicate to find an assignment of package groups. We quantify the potential of the algorithms with respect to the number of employed robots and the capacity of the robots by considering the robots’ total travel time to transport all packages. Simulation results show that the designed algorithms are competitive compared with an exact centralized Integer Linear Program representation solved with the commercial solver Gurobi, and superior to popular greedy algorithms and a heuristic distributed task allocation method. Note to Practitioners—This work presents two group-based distributed auction algorithms for a sufficiently large fleet of robots to efficiently transport a set of dynamically appearing dispersed packages from their initial locations to corresponding destinations within prescribed time-windows. Each robot can carry multiple packages simultaneously within its capacity, and the objective is to minimize the robots’ total travel time to transport all the packages within the prescribed time-windows. The paper’s practical contributions are threefold: First, the multi-robot task assignment problem is formulated through a robot-group assignment strategy, which enables complex logistic scheduling for tasks grouped according to their distributions and time-windows. Second, we theoretically show that the multi-robot task assignment problem is an NP-hard problem, which implies the necessity for designing approximate task assignment algorithms. Third, the proposed group-based distributed auction algorithms are efficient and can be adapted for real scenarios.

Integrated scheduling optimization of production and transportation for precast component with delivery time window

PurposeProduction and transportation of precast components, as two continuous service stages of a precast plant, play an important role in meeting customer needs and controlling costs. However, there is still a lack of production and transportation scheduling methods that comprehensively consider delivery timeliness and transportation economy. This article aims to study the integrated scheduling optimization problem of in-plant flowshop production and off-plant transportation under the consideration of practical constraints of customer order delivery time window, and seek an optimal scheduling method that balances delivery timeliness and transportation economy.Design/methodology/approachIn this study, an integrated scheduling optimization model of flowshop production and transportation for precast components with delivery time windows is established, which describes the relationship between production and transportation and handles transportation constraints under the premise of balancing delivery timeliness and transportation economy. Then a genetic algorithm is designed to solve this model. It realizes the integrated scheduling of production and transportation through double-layer chromosome coding. A program is designed to realize the solution process. Finally, the validity of the model is proved by the calculation of actual enterprise data.FindingsThe optimized scheduling scheme can not only meet the on-time delivery, but also improve the truck loading rate and reduce the total cost, composed of early cost in plant, delivery penalty cost and transportation cost. In the model validation, the optimal scheduling scheme uses one less truck than the traditional EDD scheme (saving 20% of the transportation cost), and the total cost can be saved by 17.22%.Originality/valueThis study clarifies the relationship between the production and transportation of precast components and establishes the integrated scheduling optimization model and its solution algorithm. Different from previous studies, the proposed optimization model can balance the timeliness and economy of production and transportation for precast components.

Design of Fast Green Distribution Route Based on Greedy Algorithm

This study focused on the fast vehicle route optimization issue with carbon emission and time window constraints for on-time consumer demand based on the greedy approach. A greedy algorithm was established to rapidly plan the distribution route to obtain the lowest distribution cost and shortest distribution time to achieve a green distribution. The distribution cost model covering the costs of vehicle transportation time, time window deviation, fossil consumption, and PM2.5 emission was established based on on-time demand and green distribution characteristics. This study analyzed the milk distribution route for Guangxi A Diary Co., Ltd. based on the greedy algorithm. The results show that compared with the genetic algorithm, the algorithm running time is reduced by 2 s, although the greedy algorithm requires an additional cost of 21.73 CNY.

Customer-oriented multi-objective optimization on a novel collaborative multi-heterogeneous-depot electric vehicle routing problem with mixed time windows

With the increasing demand for express delivery and enhancement of sustainable logistics, the collaborative multi-depot delivery based on electric vehicles has gradually attracted the attention of logistics industry. However, most of the existing studies assumed that the products required by different customers could be delivered from any homogeneous depot, ignoring the limitations in facilities and environment of depots in reality. Thus, this study proposed a novel collaborative multi-heterogeneous-depot electric vehicle routing problem with mixed time windows and battery swapping, which not only involves the multi-heterogeneous-depot to meet different customer demands, but also considers the constraints of mixed time windows to ensure timely delivery. Furthermore, a customer-oriented multi-objective optimization model minimizing both travel costs and time window penalty costs is proposed to effectively improve both delivery efficiency and customer satisfaction. To solve this model, an extended non-dominated sorting genetic algorithm-II is proposed. This combines a new coding scheme, a new initial population generation method, three crossover operators, three mutation operators, and a particular local search strategy to improve the performance of the algorithm. Experiments were conducted to verify the effectiveness of the proposed algorithm in solving the proposed model.

The ride-hailing sharing problem with parcel transportation

This paper proposes a ride-hailing sharing problem with parcel transportation. In the study, both passengers and parcels can be transported by ordering the requests through a ride-hailing platform, in which the passengers can only be delivered by ride-hailing vehicles (RHVs) while parcels can be delivered by both RHVs and electric motorcycles (EMs). Compared with RHVs, EMs have a lower speed, a smaller capacity for parcels, and a lower driving cost per kilometer. Parcels are divided into eight classes according to the size of parcels (i.e., extra small, small, middle, and large) and the urgency of parcels (i.e., urgent and non-urgent). Passengers and passengers, passengers and parcels, as well as multiple parcels, are allowed to share the same vehicles in this problem. A two-level lexicographic multi-objective function is proposed from the perspective of the ride-hailing platform, where the first-level objective is to maximize the profit of the platform and the second-level objective is to minimize the total driving cost of RHVs and EMs. The time-varying property of passenger requests is considered and the proposed problem is divided into a set of continuous and small-scale ride-hailing subproblems. Each subproblem is formulated to match both passenger and parcel requests to RHVs and EMs, and to determine vehicle routes. Time window constraints, RHV capacity constraints for passengers, RHV trunk capacity constraints for parcels and luggage, and EM delivery box capacity constraints are considered. The modified artificial bee colony (MABC) algorithm is proposed based on the framework developed by Zhan et al. (2022) to solve the ride-hailing sharing problem with parcel transportation. The results of numerical experiments show that allowing RHVs to transport parcels can significantly increase the profit of the platform, the number of matched requests, and the average profit of RHV drivers. If RHVs are allowed to pick up parcels, the government should encourage the replacement of all EMs with RHVs under the more-passengers-fewer-parcels scenario. However, the government should not encourage the replacement of all EMs with RHVs and allow some EMs in the ride-hailing market under the fewer-passengers-more-parcels scenario.

A Heuristic Approach to Support Route Planning for Delivery and Installation of Furniture: A Case Study

The number of variants of the vehicle routing problem (VRP) has grown rapidly in the last decades. Among these, VRPs with time window constraints are among the most studied ones. However, the literature regarding VRPs that concerns the delivery and installation of products is scarce. The main aim of this study was to propose a heuristic approach for the route planning process of a company whose focus is on furniture delivery and assembly and, thus, contributing to the research around the Delivery and Installation Routing Problem. The case study method was used, and two scenarios were compared: the current scenario (showing the routes created by the company worker); and the future scenario (representing the routes created by the heuristic). Results show that the proposed heuristic approach provided a feasible solution to the problem, allowing it to affect customers and teams without compromising the teams’ competencies and respecting all constraints.

Modelling and comparing two modes of sharing parking spots at residential area: Real‐time and fixed‐time allocation

AbstractThis research aims to improve the utilization efficiency of parking facilities in residential areas. The real‐time and fixed‐time shared parking spot allocation models based on a time window constraint are proposed, respectively. The real‐time model adopts the dynamic response service mechanism, introducing a multi‐objective decision weighting method to construct the weighted evaluation function. Then, the 0–1 planning model with user optimization is established, utilizing branch‐bound algorithm for a solution. The fixed‐time model adopts the periodic service mechanism, where a rejection penalty factor is introduced to add penalty cost. Then, the 0–1 programming model with system optimization is constructed, where genetic annealing algorithm solves the large‐scale calculation problem. The results from this case study illustrate the fixed‐time allocation mode has more balanced utilization of parking facilities, whereas over‐utilization of preferred parking lots occurs in the real‐time model; additionally, when supply and demand are in balance, the fixed‐time model can obtain higher system revenues, reduce effective rejection rate by 9.43%, and increase resource utilization efficiency by 5.28%. In conclusion, the real‐time allocation mode reflects the advantage of a user's optimal allocation mode when supply is greater than demand; conversely, the fixed‐time allocation mode has the advantage of optimum system resources utilization efficiency.

Efficient feasibility checks and an adaptive large neighborhood search algorithm for the time-dependent green vehicle routing problem with time windows

In recent years, reducing emissions has been important for mitigating global warming and the effects of traffic congestion. As a variant of the green vehicle routing problem (GVRP), the time-dependent GVRP with time windows (TDGVRPTW) accounts for both time-dependent travel times and time window constraints and integrates the minimization of carbon emissions. Therefore, the TDGVRPTW is of great practical interest.In this paper, we design an effective adaptive large neighborhood search (ALNS) algorithm for solving the TDGVRPTW. The proposed algorithm uses a time discretization search (TDS) method to determine the departure time from each customer node, together with efficient feasibility checking procedures.The ALNS algorithm has been extensively tested on benchmark instances derived from the literature. The results show that, for small-size instances for which optimal solutions are known, the proposed algorithm can solve several instances to optimality and that high-quality solutions can be obtained for the instances that are not solved to optimality. For large-size instances involving up to 1000 customers, ALNS is particularly effective in computing solutions using a very limited amount of computing time.

A Location Routing Problem with Time Windows Consideration: A Metaheuristics Approach

A substantial distribution system is achievable to attain through the simultaneous design of the location facility and route generation. This problem is generally called a location routing problem (LRP), where both strategic decision and operational decisions are considered during the decision initial phase. Nevertheless, for some real applications, a consideration of time restriction due to several reasons should be included in the model. Some examples include the distribution system of perishable goods, a company’s objective towards a high level of customer service, or emergency/disaster situations. Consequently, this research incorporates the location routing problem (LRP) with time window constraints, including the working hour and also time restriction in each customer. The location routing problem with time windows (LRPTW) could not be solved easily, especially for a large-sized problem and or one which is considered an NP-hard problem. In consideration with its large solution pools, this paper suggests an integration of variable neighborhood search (VNS) with path relinking (PR) to solve LRPTW. The method is first applied in the LRP benchmark to validate the effectiveness and the robustness of the proposed method. Computational results clarify that VNSPR can achieve some of the best and most competitive solutions for the LRP when compared to existing approaches. Further, we developed LRPTW instances from the best solution found in LRP datasets. The proposed VNSPR method is also able to yield nearly optimal solutions for LRPTW instances.

Switched max-plus linear-dual inequalities for makespan minimization: the case study of an industrial bakery shop1

In this paper, an industrial bakery shop is modeled by switched max-plus linear-dual inequalities (SLDIs). SLDIs are timed discrete event systems suitable for describing flow shops with time-window constraints and switching operating modes, where each mode corresponds to a job type. We consider the scheduling problem of minimizing the makespan of the shop, and we show that the application of methods based on the max-plus algebra leads to a faster solution compared to standard techniques. The results of the paper are general, in the sense that they can be applied to any permutation flow shop with time-window constraints.

An auction mechanism for capacity allocation in identical parallel machines with time window constraints

An auction mechanism for capacity allocation in identical parallel machines with time window constraints

An auction mechanism for capacity allocation in identical parallel machines with time window constraints

An auction mechanism for capacity allocation in identical parallel machines with time window constraints

An adaptive large neighborhood search algorithm for Vehicle Routing Problem with Multiple Time Windows constraints

<p style='text-indent:20px;'>The Vehicle Routing Problem with Multiple Time Windows (VRPMTW) is a generalization of problems in real life logistics distribution, which has a wide range of applications and research values. Several neighborhood search based methods have been used to solve this kind of problem, but it still has drawbacks of generating numbers of infeasible solutions and falling into local optimum easily. In order to solve the problem of arbitrary selection for neighborhoods, a series of neighborhoods are designed and an adaptive strategy is used to select the neighborhood, which constitute the Adaptive Large Neighborhood Search(ALNS) algorithm framework. For escaping from the local optimum effectively in the search process, a local search based on destroy and repair operators is applied to shake the solution by adjusting the number of customers. The proposed method allows infeasible solutions to participate in the iterative process to expand the search space. At the same time, an archive is set to save the high-quality feasible solutions during the search process, and the infeasible solutions are periodically replaced. Computational experimental results on VRPMTW benchmark instances show that the proposed algorithm is effective and has obtained better solutions.</p>