TL;DR: A framework to classify different types of non-emergency and emergency HCFs in terms of location management is presented, and the literature based on the framework is reviewed and future research possibilities are analyzed.

TL;DR: A comprehensive overview of current work in the field of HHC routing and scheduling with a focus on considered problem settings is given and single-period and multi-period problems are differentiated.

TL;DR: This review introduces the concept of emergency care pathway following the current trend in health care systems, i.e., shifting the central role from health care providers to patients.

TL;DR: This research provides a decision-making tool to solve a multi-period green supplier selection and order allocation problem and provides top management with flexibility in giving more or less importance weight to green or traditional criteria regardless of the number of criteria in each category through the use of AHP, which reduces the effect of thenumber of criteria on the preference weight of the suppliers.

TL;DR: A metaheuristic, called adaptive large neighborhood search (ALNS), is developed, thus creating a conflict-free observational timeline, and time slacks are introduced to confine the propagation of the time-dependent constraint of transition time.

TL;DR: A new real-time routing problem, in which different types of drones can collect and deliver packages, and seven different objective functions are considered and sought to be minimized using a Mixed-Integer Linear Programming (MILP) model solved by a matheuristic algorithm.

TL;DR: The production planning problem in additive manufacturing and 3D printing is introduced for the first time in the literature and a mathematical model to formulate it is developed and coded in CPLEX and two different heuristic procedures, namely best-fit and adapted best- fit rules, are developed in JavaScript.

TL;DR: A number of algorithmic improvements implemented in the AGLIBRARY optimization solver are presented in order to improve the possibility of finding good quality solutions quickly and often outperform a state-of-the-art tabu search algorithm and a commercial solver in terms of reduced computation times and/or train delays.

TL;DR: This paper presents an effective algorithm for the GTSP based on adaptive large neighborhood search that operates by repeatedly removing from, and inserting vertices in, the tour and proposes a general insertion mechanism that contains as special cases the well-known nearest, farthest and random insertion mechanisms.

TL;DR: It is shown that large sized problems possessing essential 3V's of big data, i.e., volume, variety and velocity consume non-polynomial time and cannot be solved optimally, so a heuristic (H-1) is also proposed to solve the largesized problems involving big data.

TL;DR: In this paper, an iterated local search (ILS) based heuristic is proposed to solve the static bike rebalancing problem, where only a single vehicle is available and the objective is to find a least-cost route that meets the demand of all stations and does not violate the minimum (zero) and maximum (vehicle capacity) load limits along the tour.

TL;DR: This research has sought to provide policymakers with a means to evaluate new and existing policies, whilst also offering a practical basis through which food chains can be made more resilient through the consideration of management practices and policy decisions.

TL;DR: A single-machine scheduling problem with power-down mechanism to minimize both total energy consumption and maximum tardiness and a basic e − constraint method is proposed to obtain the complete Pareto front of the problem.

TL;DR: This study was partially supported by Korea National Research Foundation through Global Research Network Program (Project no. 2016S1A2A2912265) and an EU Marie Sklodowska-Curie action funded project, MINI-CHIP.

TL;DR: An Adaptive Large Neighborhood Search (ALNS) algorithm is developed, which can simultaneously handle the network design and line planning problems considering also rolling stock and personnel planning aspects, and is compared with state-of-the-art commercial solvers on a small-size artificial instance.

TL;DR: A multi-objective integer linear programming model aiming at efficiently planning and managing hospital operating room suites and could represent a suitable engine for the development of advanced and effective health care management decision support systems.

TL;DR: This paper investigates the Heterogeneous Dial-A-Ride Problem (H-DARP) that consists of determining a vehicle route planning for heterogeneous users' transportation with a heterogeneous fleet of vehicles and proposes a hybrid Genetic Algorithm (GA) to solve the problem.

TL;DR: A matheuristic based method based on large neighborhood search search and periodically solving a set partitioning and matching problem with third-party solvers for the vehicle routing problem with cross-docking is proposed.

TL;DR: The effectiveness of the proposed model has been evaluated on two test networks and the results suggest a step forward in the TSO/DSO coordination field, although further investigations to study the effect of assets with discrete control nature on the occurrence of disjoint flexibility areas should be carried.

TL;DR: A refinement of the polynomial method based on sampling theory proposed by Castro et al. (2009) to estimate the Shapley value for cooperative games is proposed and stratified random sampling with optimum allocation is employed in order to reduce the variance.

TL;DR: A static strategy is introduced that minimizes the number of branches subject to the constraint that a static vertex ordering in G must be kept during the search, and a new algorithm is proposed, called MoMC, that combines the strengths of the two strategies into a single algorithm.

TL;DR: This paper studies hybrid flowshops where jobs, if completed inside a due window, are considered on time and presents methods based on the simple concepts of iterated greedy and iterated local search, which yield superior results which are also demonstrated to be statistically significant.

TL;DR: This paper addresses a sequence- and machine-dependent batch scheduling problem on a set of unrelated-parallel machines where the objective is to minimize a linear combination of total weighted completion time and total weighted tardiness.

TL;DR: Comprehensive review and evaluation of heuristics and meta-heuristics for the two-sided assembly line balancing problem and computational results demonstrate that the proper selection of encoding scheme, decoding procedure and objective function improves the performance of the algorithms by a significant margin.

TL;DR: A tabu search heuristic is proposed and embedded into a iterated local search to solve the multi-compartment vehicle routing problem (MCVRP), and it consistently produces solutions that are better than existing heuristic algorithms.

TL;DR: A hybrid method is proposed, combining a branch-price-and-cut (BPC) algorithm with two metaheuristic approaches for the VRPTWMD, showing that the proposed hybrid approach outperforms the BPC algorithm used as standalone method in terms of both solution quality and running time.

TL;DR: Experimental results demonstrate that the proposed directed search strategy (DSS) is effective in handling the dynamic scheduling problems under investigation, under the assumption that jobs can be rejected and job processing time is controllable.

TL;DR: A decision making tool that allows deciding, in an integrated way, the optimal energy retrofit plan in order to simultaneously reduce energy consumption, maintain comfort, protect the environment, and optimize the distribution of actions in subsystems is proposed, while ensuring an efficient use of public funds.

TL;DR: This paper implements a two-stage Variable Neighborhood Search to tackle the OBP, and develops several mechanisms that can be helpful in similar problem and performs computational experiments that show the superiority of this approach.