Showing papers in "Computers & Operations Research in 2019"

TL;DR: This study maps the research landscape to provide a clear taxonomy of fuzzy multi-criteria decision making (FMCDM) and searches for articles related to technique for order of preference by similarity to ideal solution (TOPSIS), development, development and fuzzy sets in four primary databases.

TL;DR: Optimize machine learning algorithms have been applied to predict the accident outcomes such as injury, near miss, and property damage using occupational accident data and PSO-based SVM outperforms the other algorithms with the highest level of accuracy and robustness.

TL;DR: A new model, a heuristic, and an exact labeling algorithm for the problem of finding the optimal charging decisions for a given route, and introduces a path-based model which outperforms the classical models in experiments.

TL;DR: An extension of the VRPD that is called VRPDERO, where drones may not only be launched and retrieved at vertices but also on some discrete points that are located on each arc, is proposed and some valid inequalities that enhance the performance of the MILP solvers are introduced.

TL;DR: The results show that waiting at the stations may increase the total cost by 1%–26%, depending on the problem type and queue length, and that recharges tend to shift to less crowded mid-day hours due to the time-dependent waiting times.

TL;DR: An iterative local search heuristic to optimize the routing of a mixed vehicle fleet, composed of electric and conventional (internal combustion engine) vehicles, that considers the possibility of recharging partially at any of the available stations.

TL;DR: A large neighborhood search (LNS) metaheuristic as well as an exact mathematical programming algorithm, which uses decomposition techniques to enumerate promising first-level solutions in conjunction with bounding functions and route enumeration for the second-level routes, are proposed.

TL;DR: A two-stage stochastic programming problem for red blood cells that simultaneously considers production, inventory and location decisions and is solved using CPLEX for a real case study from The Hashemite Kingdom of Jordan.

TL;DR: An efficient Adaptive Large Neighborhood Search heuristic is proposed to solve a special vehicle routing problem, which extends the classical problem by considering the time window and synchronized-services constraints.

TL;DR: This paper models a distribution network in which the triple bottom lines of sustainability are captured, and three hybrid swarm intelligence techniques are proposed, and each is hybridized with variable neighborhood search (VNS).

TL;DR: A mixed integer linear programming (MILP) model is proposed to solve the unrelated parallel machine scheduling problem with sequence-dependent setup times and machine eligibility restrictions, and tabu search and simulated annealing algorithms are proposed.

TL;DR: The main contribution of this survey is providing an update of the literature review on blood supply chain according to a new taxonomy as well as a critical analysis of the state of the art in this topic.

TL;DR: A mixed integer programming (MIP) model is presented that comprehensively characterizes all relevant aspects of the business scenario, and an optimization-driven, progressive algorithm for online fleet dispatch operations is proposed.

TL;DR: A novel multi-stage, multi-method,multi-criteria approach is developed to help further incorporate sustainability into 3PRLPs selection modeling and is the first time that neighborhood rough set theory is integrated with TOPSIS and VIKOR techniques.

TL;DR: New mixed integer linear programs and a mathematical programming based algorithm are proposed for parallel machine scheduling problem with machine and job sequence setup times (UPMS) with makespan minimization criterion.

TL;DR: To solve the load-balancing problem in the cloud environments, the advantages and disadvantage of the nature-inspired meta-heuristic algorithms have been analyzed and their significant challenges are considered for proposing the techniques that are more effective in the future.

TL;DR: SA is described as an ensemble of algorithmic components, and variants from the literature within these components are described, and the advantages of this proposal are shown.

TL;DR: This work reviews the various model formulations, exact algorithms, and heuristic algorithms for the static and dynamic WTA, and places the formulations into a comparable form to provide insight into the evolution of the defense-related WTA problem.

TL;DR: This paper focuses on the scheduling problem of single and multiple AM machines and proposes mathematical models for optimisation and mixed-integer linear programming models allocate parts into jobs to be produced on AM machines to minimise makespan.

TL;DR: This paper analyzes the potential of combining parcel pickup stations and autonomous vehicles for same-day delivery and presents an intuitive policy function approximation (PFA), which allows real-time decision making while balancing the tradeoff between fast delivery and consolidation.

TL;DR: A dynamic lookahead policy (DLA) to anticipate potential future demands in the current inventory decisions, which simulates future demand over a predefined horizon that is time-dependent and autonomously parametrized by means of value function approximation, a method of approximate dynamic programming.

TL;DR: Crowdshipping is considered, a setting in which a company not only has its own fleet of vehicles to service requests, but may also use the services of occasional drivers who are willing to take a detour to serve one or more transportation requests for a small compensation.

TL;DR: This paper explores how to design a local search heuristic that computes good solutions for very large-scale instances of the capacitated vehicle routing problem in a reasonable computational time and introduces a new set of benchmark instances that contain up to 30,000 customers and use the heuristic to solve them.

TL;DR: This paper aims to demonstrate that a well-implemented local search on its own suffices to create a heuristic that computes high-quality solutions in a short time, and combines three powerful local search techniques in an efficient way that minimizes computational effort.

TL;DR: Results on the urban water demand data in Milan prove that forecasting error is significantly low and that preliminary clustering allows for further reducing error while also improving computational performances.

TL;DR: A genetic algorithm based scheduling application has been developed to serve distributed manufacturing lines in form of cloud manufacturing to provide service to geographically distributed manufacturers centralized services.

TL;DR: This study reveals that the proactive heuristic is effective at improving system performance (over an entirely myopic heuristic) according to several customer experience-based metrics (e.g. freshness, earliness, etc.).

TL;DR: A mathematical model, four variants of iterated greedy algorithms and a variable block insertion heuristic for the HFSP with total flow time minimization based on the well-known NEH heuristic, an efficient constructive heuristic is also proposed, and compared with NEH.

TL;DR: A two-phase solution approach in which a route is seen as the composition of paths, each one handling a subset of customers without intermediate stops at AFSs, which outperforms the existing exact methods and can also address larger- sized instances outperforming, in terms of solution quality, the best heuristic approach in the literature.

TL;DR: This study provides a mathematical model for minimizing the total costs of the aerial delivery system concerned with refuel stations, warehouses, drone procurement, and transportation.