Optimal algorithms for the online time series search problem

AbstractIn the problem of online time series search introduced by El-Yaniv et al. (2001) [1], a player observes prices one by one over time and shall select exactly one of the prices on its arrival without the knowledge of future prices, aiming to maximize the selected price. In this paper, we extend the problem by introducing profit function. Considering two cases where the search duration is either known or unknown beforehand, we propose two optimal deterministic algorithms respectively. The models and results in this paper generalize those of El-Yaniv et al. (2001) [1]

A tight lower bound for job scheduling with cancellation

Abstract The Job Scheduling with Cancellation problem is a variation of classical scheduling problems in which jobs can be cancelled while waiting for execution. In this paper we prove a tight lower bound of 5 for the competitive ratio of any deterministic online algorithm for this problem, for the case where all jobs have the same processing time

Multitasking scheduling problems with a rate-modifying activity

International audienc

The proceedings of the International Conference on Industrial Engineering and Systems Management (IESM 2019)

International audienc

Online Scheduling of Ordered Flow Shops

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Location-Routing Optimization with Renting Social Vehicles in a Two-Stage E-Waste Recycling Network

E-waste recycling has been a hot topic in recent years. The low efficiency and high-operation cost of recycling make it more important to build perfect e-waste recycling networks. To hedge against the limitation of vehicle resources being often neglected in existing research, we propose a mixed integer linear programming model of e-waste recycling by renting idle social vehicles. In the model, both decisions made on the location selection of recycling sites and vehicle routings satisfying all of the demand nodes over the network within time windows are required to minimize the total operating cost. An improved genetic algorithm and heuristic algorithm are designed to solve the model, and numerical experiments are produced to demonstrate the effectiveness of the proposed model and algorithms

Integration of Timetabling, Multi-type Vehicle Scheduling and User Routing in Public Transit Network considering Fuel Consumption

International audienceThis paper focuses on the integration of timetabling, multi-type vehicle scheduling and user routing in transit network considering fuel consumption. For the integrated problem, we consider the users' preferences for departure and arrival times and the capacity limits of vehicles. Additionally, the multi-type vehicle and fuel consumption factors are also the key contributions, which are firstly added into the integrated problem. The objective is to minimize the inconvenience of the users (i.e. in-vehicle times, line-transfer penalties and deviation between desired departure and arrival times), penalties of users not served and the cost of line runs and fuel consumption for operators. Finally, we establish a mixed-integer programming model for the integrated problem

Stochastic Drone Fleet Deployment and Planning Problem Considering Multiple-Type Delivery Service

Drone delivery has a great potential to change the traditional parcel delivery service in consideration of cost reduction, resource conservation, and environmental protection. This paper introduces a novel drone fleet deployment and planning problem with uncertain delivery demand, where the delivery routes are fixed and couriers work in collaboration with drones to deliver surplus parcels with a relatively higher labor cost. The problem involves the following two-stage decision process: (i) The first stage determines the drone fleet deployment (i.e., the numbers and types of drones) and the drone delivery service module (i.e., the time segment between two consecutive departures) on a tactical level, and (ii) the second stage decides the numbers of parcels delivered by drones and couriers on an operational level. The purpose is to minimize the total cost, including (i) drone deployment and operating cost and (ii) expected labor cost. For the problem, a two-stage stochastic programming formulation is proposed. A classic sample average approximation method is first applied. To achieve computational efficiency, a hybrid genetic algorithm is further developed. The computational results show the efficiency of the proposed approaches