scholarly journals Balanced Scheduling of School Bus Trips using a Perfect Matching Heuristic

2018 ◽  
Vol 2672 (48) ◽  
pp. 1-11 ◽  
Author(s):  
Ali Shafahi ◽  
Sanaz Aliari ◽  
Ali Haghani
Keyword(s):  
Heuristic Algorithm ◽  
School Setting ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
School Bus ◽  
Heuristic Solution ◽  
Mip Model ◽  
Bus Scheduling ◽  
Minimum Number ◽  
The Cost

In the school bus scheduling problem, the main contributing factor to the cost is the number of buses needed for the operations. However, when subcontracting the pupils’ transportation, unbalanced tours can increase the costs significantly as the lengths of some tours can exceed the daily fixed driving goal and will result in over-hour charges. This paper proposes a mixed integer programming (MIP) model and a matching-based heuristic algorithm to solve the “balanced” school bus scheduling problem with fixed start times in a multi-school setting. The heuristic solution always has the minimum number of buses as it starts with a minimal number of tours and does not alter the number of tours during its balancing stage. The effectiveness of the heuristic is tested by comparing its solutions with results from solving the MIP using commercial solvers whenever solvers could find a good solution. To illustrate the performance of the MIP and the heuristic, 11 problems were examined with different numbers of trips which are all based on two real-world problems: a California case study with 54 trips and the Howard County Public School System with 994 trips. Our numerical results indicate the proposed heuristic algorithm can find reasonable solutions in a significantly shorter time. The balanced solutions of our algorithm can save up to 16% of school bus operation costs compared with the best solution found by solvers from optimizing the MIP model after 40 hours. The balancing stage of the heuristic decreases the standard deviation of the tour durations by up to 47%.

A school bus scheduling problem

2012 ◽  
Vol 218 (2) ◽  
pp. 577-585 ◽  
Author(s):  
Byung-In Kim ◽  
Seongbae Kim ◽  
Junhyuk Park
Keyword(s):  
Scheduling Problem ◽  
School Bus ◽  
Bus Scheduling

An Efficient Heuristic Algorithm for Solving Crane Scheduling Problem

2013 ◽  
Vol 442 ◽  
pp. 443-449
Author(s):  
Xie Xie ◽  
Yan Ping Li ◽  
Yong Yue Zheng ◽  
Xiao Li Li
Keyword(s):  
Heuristic Algorithm ◽  
Mixed Integer Linear Programming ◽  
Numerical Experiments ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Crane Scheduling ◽  
Technological Requirement ◽  
Iron And Steel ◽  
Analytical Properties ◽  
Upper Level

This paper focuses on a single crane scheduling problem which is motivated by cooled-rolling material warehouse in the iron and steel enterprise. As storage technological requirement, coils have been stored on the pre-specified position in two levels. If a demanded coil is in the upper level, it can be picked up directly. If a demanded coil in the lower level is blocked by un-demanded coils, the coil can not be transported until all the blocking coils are shuffled to another position. Our problem combines transportation and shuffling simultaneously for crane to pick up all demanded coils as early as possible to designated place (makespan). We first propose a mixed integer linear programming (MILP) model. Some analytical properties are further provided. Based on these properties, we propose a polynomial-time heuristic algorithm. Numerical experiments are carried out to confirm our proposed methods can provide high quality solutions.

scholarly journals Modelling and Optimizing an Open-Pit Truck Scheduling Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yonggang Chang ◽  
Huizhi Ren ◽  
Shijie Wang
Keyword(s):  
Programming Model ◽  
Valid Inequalities ◽  
Upper Bounds ◽  
Open Pit ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Heuristic Solution ◽  
Solution Approach ◽  
Truck Scheduling ◽  
Improvement Strategies

This paper addresses a special truck scheduling problem in the open-pit mine with different transport revenue consideration. A mixed integer programming model is formulated to define the problem clearly and a few valid inequalities are deduced to strengthen the model. Some properties and two upper bounds of the problem are proposed. Based on these inequalities, properties, and upper bounds, a heuristic solution approach with two improvement strategies is proposed to resolve the problem and the numerical experiment demonstrates that the proposed solution approach is effective and efficient.

scholarly journals Exact and Metaheuristic Approaches for a Bi-Objective School Bus Scheduling Problem

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0132600 ◽  
Author(s):  
Xiaopan Chen ◽  
Yunfeng Kong ◽  
Lanxue Dang ◽  
Yane Hou ◽  
Xinyue Ye
Keyword(s):  
Scheduling Problem ◽  
School Bus ◽  
Bus Scheduling

scholarly journals MILP of multitask scheduling of geographically distributed maintenance tasks

2022 ◽  
Vol 13 (1) ◽  
pp. 119-134 ◽  
Author(s):  
Hamed Allaham ◽  
Doraid Dalalah
Keyword(s):  
Preventive Maintenance ◽  
Mixed Integer Linear Programming ◽  
Programming Model ◽  
Commercial Buildings ◽  
Mixed Integer ◽  
Service Levels ◽  
Minimum Number ◽  
Geographically Distributed ◽  
Time Utilization ◽  
The Cost

Due to its proactive impact on the serviceability of components in a system, preventive maintenance plays an important role particularly in systems of geographically spread infrastructure such as utilities networks in commercial buildings. What makes such systems differ from the classical schemes is the routing and technicians' travel times. Besides, maintenance in commercial buildings is characterized by its short tasks’ durations and spatial distribution within and between different buildings, a class of problems that has not been suitably investigated. Although it is not trivial to assign particular duties solely to multi-skilled teams under limited time and capacity constraints, the problem becomes more challenging when travel routes, durations and service levels are considered during the execution of the daily maintenance tasks. To address this problem, we propose a Mixed Integer Linear Programming Model that considers the above settings. The model exact solution recommends collaborative choices that include the number of maintenance teams, the selected tasks, routes, tasks schedules, all detailed to days and teams. The model will reduce the cost of labor, replacement parts, penalties on service levels and travel time. The optimization model has been tested using different maintenance scenarios taken from a real maintenance provider in the UAE. Using CPLEX solver, the findings demonstrate an inspiring time utilization, schedules of minimal routing and high service levels using a minimum number of teams. Different travel speeds of diverse assortment of tasks, durations and cost settings have been tested for further sensitivity analysis.

scholarly journals Multi-objective multi-factory scheduling

2020 ◽  
Author(s):  
J. Behnamian ◽  
S.M.T. Fatemi Ghomi
Keyword(s):  
Heuristic Algorithm ◽  
Parallel Machines ◽  
Mixed Integer ◽  
Test Problems ◽  
Scheduling Problem ◽  
Production Network ◽  
Mixed Integer Linear Program ◽  
Multi Objective ◽  
Transportation Times ◽  
Factory Scheduling

This paper introducesa multi-factory scheduling problem with heterogeneous factories and parallel machines. This problem, as a major part of supply chain planning, includes the finding of suitable factory for each job and the scheduling of the assigned jobs at each factory, simultaneously. For the first time, this paper studies multi-objective scheduling in the production network in which each factory has its customers and demands can be satisfied by itself or other factories. In other words, this paper assumes that jobs can transfer from the overloaded machine in the origin factory to the factory which has fewer workloads by imposing some transportation times. For simultaneous minimization of the sum of the earliness and tardiness of jobs and total completion time, after modeling the scheduling problem as a mixed-integer linear program, the existing multi-objective techniques are analyzed and a new one is applied to our problem. Since this problem is NP-hard, a heuristic algorithm is also proposed to generate a set of Pareto optimal solutions. Also, the algorithms are proposed to improve and cover the Pareto front. Computational experiences of the heuristic algorithm and the output of the model implemented by CPLEX over a set of randomly generated test problems are reported.

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