Robust Two-Stage Location Allocation for Emergency Temporary Blood Supply in Postdisaster

Disaster medical rescue in China mainly adopts the “on-site rescue” model. Whether the location of emergency temporary blood supply sites is reasonable or not directly affects the rescue efficiency. The paper studies the robust location-allocation for emergency temporary blood supply after disaster. First, the factors of several candidate sites were quantified by the entropy-based TOPSIS method, and 12 candidate blood supply sites with higher priority were selected according to the evaluation indicators. At the same time, the uncertainty of blood demand at each disaster site increased the difficulty of decision-making, and then, a robust location model (MIRP) was constructed with minimum cost with time window constraints. It is also constrained by the uncertain demand for blood in three scenarios. Second, the survival probability function was introduced, and the time window limit was given at the minimum cost to maximize the survival probability of the suffered people. Finally, the numerical example experiments demonstrate that the increase in demand uncertainty and survival probability cause the MIRP model to generate more costs. Compared with the three MIRP models, the MIRP-ellipsoid set model gained better robustness. Also, given the necessary restrictions on the time window, the cost can be reduced by about 13% with the highest survival probability. Decision-makers can select different combinations of uncertainty levels and demand disturbance ratios and necessary time constraints to obtain the optimal location-allocation solution according to risk preference and actual conditions.

Matheuristics and Column Generation for a Basic Technician Routing Problem

This paper considers a variant of the Vehicle Routing Problem with Time Windows, with site dependencies, multiple depots and outsourcing costs. This problem is the basis for many technician routing problems. Having both site-dependency and time window constraints lresults in difficulties in finding feasible solutions and induces highly constrained instances. Matheuristics based on Mixed Integer Linear Programming compact formulations are firstly designed. Column Generation matheuristics are then described by using previous matheuristics and machine learning techniques to stabilize and speed up the convergence of the Column Generation algorithm. The computational experiments are analyzed on public instances with graduated difficulties in order to analyze the accuracy of algorithms for ensuring feasibility and the quality of solutions for weakly to highly constrained instances. The results emphasize the interest of the multiple types of hybridization between mathematical programming, machine learning and heuristics inside the Column Generation framework. This work offers perspectives for many extensions of technician routing problems.

On-Demanding Information Acquisition in Multi-UAV-Assisted Sensor Network: A Satisfaction-Driven Perspective

When multiple heterogeneous unmanned aerial vehicles (UAVs) provide service for multiple users in sensor networks, users’ diverse priorities and corresponding priority-related satisfaction are rarely concerned in traditional task assignment algorithms. A priority-driven user satisfaction model is proposed, in which a piecewise function considering soft time window and users’ different priority levels is designed to describe the relationship between user priority and user satisfaction. On this basis, the multi-UAV task assignment problem is formulated as a combinatorial optimization problem with multiple constraints, where the objective is maximizing the priority-weighted satisfaction of users while minimizing the total energy consumption of UAVs. A multipopulation-based cooperation genetic algorithm (MPCGA) by adapting the idea of “exploration-exploitation” into traditional genetic algorithms (GAs) is proposed, which can solve the task assignment problem in polynomial time. Simulation results show that compared with the algorithm without considering users’ priority-based satisfaction, users’ weighted satisfaction can be improved by about 47% based on our algorithm in situations where users’ information acquisition is tight time-window constraints. In comparison, UAVs’ energy consumption only increased by about 6%. Besides, compared with traditional GA, our proposed algorithm can also improve users’ weighted satisfaction by about 5% with almost the same energy consumption of UAVs.

Intuitionistic fuzzy multi-objective milk-run modelling under time window constraints

Milk-run is a delivery method allowing to move small quantities of a large number of different items with predictable lead times from various suppliers to a customer. The main goal is to minimize the transportation cost by minimizing the travel distance and by maximizing vehicle capacities. The effects of uncertainties in arrival times of vehicles and loading times of shipments should also be considered in modeling the milk-run problems. In this paper, a multi-objective linear programming model, an ordinary fuzzy multi-objective linear programming model and an intuitionistic fuzzy multi-objective linear programming model are proposed for the milk-run modeling under time window constraints. The proposed approaches are applied on the real-life data of Borusan Logistics which is the one of the largest logistics firms in Turkey and the results are presented.

A Set Covering Model for a Green Ship Routing and Scheduling Problem with Berth Time-Window Constraints for Use in the Bulk Cargo Industry

This paper presents a set covering model based on route representation to solve the green ship routing and scheduling problem (GSRSP) with berth time-window constraints for multiple bulk ports. A bi-objective set covering model is constructed with features based on the minimization of the total CO2 equivalent emissions and the total travel time subject to a limited number of berths in each port, berthing time windows, and the time window for each job. The solutions are obtained using the ε-constraint method, after which a Pareto frontier is plotted. This problem is motivated by the operations of feeder barges and terminals, where the logistics control tower is used to coordinate the routing and berthing time of its barges. We show that the proposed method outperforms the weighted sum method in terms of the number of Pareto solutions and the value of the hypervolume indicator.

An Asymptotic Cyclicity Analysis of Live Autonomous Timed Event Graphs

Designing a discrete event system converging to steady temporal patterns is an essential issue of a system with time window constraints. Until now, to analyze asymptotic stability, we have modeled a timed event graph’s dynamic behavior, transformed it into the matrix form of (max,+) algebra, and then constructed a precedence graph. This article’s aim is to provide a theoretical basis for analyzing the stability and cyclicity of timed event graphs without using (max,+) algebra. In this article, we propose converting one timed event graph to another with a dynamic behavior equivalent to that of the original without going through the conversion process. This paper also guarantees that the derived final timed event graph has the properties of a precedence graph. It then investigates the relationship between the properties of the derived precedence graph and that of the original timed event graph. Finally, we propose a method to analyze asymptotic cyclicity and stability for a given timed event graph by itself. The analysis this article provides makes it easy to analyze and improve asymptotic time patterns of tasks in a given discrete event system modeled with a live autonomous timed event graph such as repetitive production scheduling.

Randomized and Generated Instances Fitting with the Home Health Care Problem Subjected to Certain Constraints

Home Health Care (HHC) is a worldwide issue. It focuses on how medical and social organizations of different countries handle providing patients with health support at home. In most developed countries, reducing hospital cost constitutes a main objective. It is important to research the improvement of HHC logistics. This paper addressed the generation and development of a benchmark properly fitting different constraints of the HCC problem. Consequently, a generator was proposed dealing with all kinds of constraints such as time window constraints, workload constraints, synchronization, and precedence constraints. This generator allows researchers to validate and compare solving methods on a common dataset regardless of confidentiality issues. We validated our generator by firstly creating a common benchmark available for researchers and secondly by proposing a set of instances and a solving method based on an HHC problem found in the literature.

Model of Flexible Periodic Vehicle Routing Problem-Service Choice Considering Inventory Status

Vehicle routing problems and inventory problems need to be integrated in order to improve performance. This research discusses the determination of vehicle routes for product delivery with periodic delivery times that are released at any time depending on the inventory status. A mixed-integer linear programming model in determining periodic flexible visiting vehicles' route considering inventory is proposed to solve this problem. This model also accommodates time window constraints, retailer warehouse capacity. The search for solutions was carried out using the branch-and-bound method with the help of Lingo 18.0. The mathematical model testing result saves shipping costs and inventory costs. In addition, the developing mathematical model offers the flexibility of visiting depending on the inventory status of the consumer. The sensitivity analysis of the model results in the vehicle capacity influence the total cost and routes formed.