scholarly journals ASSESSMENT OF EFFICIENCY OF ASSIGNMENT OF VEHICLES TO TASKS IN SUPPLY CHAINS: A CASE STUDY OF A MUNICIPAL COMPANY

Transport ◽  
2017 ◽  
Vol 32 (3) ◽  
pp. 243-251 ◽  
Author(s):  
Ilona Jacyna Gołda ◽  
Mariusz Izdebski ◽  
Askoldas Podviezko
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Garbage Collection ◽  
Collection Efficiency ◽  
Optimal Assignment ◽  
Two Stage ◽  
Time Length ◽  
Second Stage ◽  
Collection Time

The main purpose of the paper is to present criteria of efficiency of assignment of vehicles to tasks at municipal companies, which collect garbage from city inhabitants. Three types of criteria are introduced in the paper: garbage collection time, length of route allocation, and utilization of resources. A two-stage method of optimization of taskroutes is proposed. It generates tasks at the first stage and assigns vehicles to the tasks at the second stage. At municipal companies that are responsible for garbage, collection tasks are not pre-defined, and consequently tasks must be designated before the workday. The proposed method is based on genetic algorithm, which is used for the purpose of optimization of the assignment problem. The obtained by the algorithm optimal assignment is compared with assignments obtained in the random way. Criteria of evaluation of efficiency of the obtained route of different mutually conflicting dimensions were introduced, such as is task realization time, distances travelled on particular routes, and number of vehicles involved in garbage collection. Efficiency of the obtained assignment appeared to be sufficiently good.

scholarly journals Urban Railway Transit Timetable Optimisation Based on Passenger-and-Trains Matching – A Case Study of Beijing Metro Line

2021 ◽  
Vol 33 (5) ◽  
pp. 671-687
Author(s):  
Junsheng Huang ◽  
Tong Zhang ◽  
Runbin Wei
Keyword(s):  
Genetic Algorithm ◽  
Waiting Time ◽  
Stage Model ◽  
Two Stage ◽  
Left Behind ◽  
Second Stage ◽  
Decision Variables ◽  
Proposed Model ◽  
Railway System

Due to the congested scenarios of the urban railway system during peak hours, passengers are often left behind on the platform. This paper firstly brings a proposal to capture passengers matching different trains. Secondly, to reduce passengers’ total waiting time, timetable optimisation is put forward based on passengers matching different trains. This is a two-stage model. In the first stage, the aim is to obtain a match between passengers and different trains from the Automatic Fare Collection (AFC) data as well as timetable parameters. In the second stage, the objective is to reduce passengers’ total waiting time, whereby the decision variables are headway and dwelling time. Due to the complexity of our proposed model, an MCMC-GASA (Markov Chain Monte Carlo-Genetic Algorithm Simulated Annealing) hybrid method is designed to solve it. A real-world case of Line 1 in Beijing metro is employed to verify the proposed two-stage model and algorithms. The results show that several improvements have been brought by the newly designed timetable. The number of unique matching passengers increased by 37.7%, and passengers’ total waiting time decreased by 15.5%.

scholarly journals A Two-Stage Mono- and Multi-Objective Method for the Optimization of General UPS Parallel Manipulators

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 543
Author(s):  
Alejandra Ríos ◽  
Eusebio E. Hernández ◽  
S. Ivvan Valdez
Keyword(s):  
Genetic Algorithm ◽  
Performance Metrics ◽  
Tracking Error ◽  
Parallel Manipulators ◽  
Optimization Method ◽  
Statistical Hypothesis ◽  
Two Stage ◽  
Multi Objective ◽  
Second Stage ◽  
Conflicting Objectives

This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests.

scholarly journals Two-Stage Robust Optimization for the Orienteering Problem with Stochastic Weights

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Ke Shang ◽  
Felix T. S. Chan ◽  
Stephen Karungaru ◽  
Kenji Terada ◽  
Zuren Feng ◽  
...  
Keyword(s):  
Mathematical Programming ◽  
Robust Optimization ◽  
Comparative Studies ◽  
Complex Model ◽  
Orienteering Problem ◽  
Two Stage ◽  
Second Stage ◽  
Robust Model ◽  
Uncertainty Set

In this paper, the two-stage orienteering problem with stochastic weights is studied, where the first-stage problem is to plan a path under the uncertain environment and the second-stage problem is a recourse action to make sure that the length constraint is satisfied after the uncertainty is realized. First, we explain the recourse model proposed by Evers et al. (2014) and point out that this model is very complex. Then, we introduce a new recourse model which is much simpler with less variables and less constraints. Based on these two recourse models, we introduce two different two-stage robust models for the orienteering problem with stochastic weights. We theoretically prove that the two-stage robust models are equivalent to their corresponding static robust models under the box uncertainty set, which indicates that the two-stage robust models can be solved by using common mathematical programming solvers (e.g., IBM CPLEX optimizer). Furthermore, we prove that the two two-stage robust models are equivalent to each other even though they are based on different recourse models, which indicates that we can use a much simpler model instead of a complex model for practical use. A case study is presented by comparing the two-stage robust models with a one-stage robust model for the orienteering problem with stochastic weights. The numerical results of the comparative studies show the effectiveness and superiority of the proposed two-stage robust models for dealing with the two-stage orienteering problem with stochastic weights.

A Two-Stage Approach for Project Reviewer Assignment Problem

2012 ◽  
Vol 452-453 ◽  
pp. 369-373 ◽  
Author(s):  
Rong Chang Chen ◽  
Pei Hsuan Shang ◽  
Mei Chun Chen
Keyword(s):  
Assignment Problem ◽  
Total Change ◽  
Two Stage ◽  
Second Stage ◽  
Stage Scheme ◽  
Total Movement

In this paper we employ a two-stage approach to solve the project reviewer assignment problem. The objective is to best satisfy the preferences of reviewers. In addition, the number of total movement times of reviewers is minimized. Reviewers are first invited to show their preferences to the projects with a number to indicate their priority. After aggregating the data, a two-stage approach is used to best match the reviewers and projects. At the first stage reviewers are assigned, while at the second stage review venues are arranged in a way that the total change times of venues for reviewers are minimized. The results show that the proposed two-stage scheme is very helpful in solving the project reviewer assignment problem.

scholarly journals A Bicycle Origin–Destination Matrix Estimation Based on a Two-Stage Procedure

2020 ◽  
Vol 12 (7) ◽  
pp. 2951
Author(s):  
Seungkyu Ryu
Keyword(s):  
Gravity Model ◽  
Transportation Network ◽  
Modal Shift ◽  
Two Stage ◽  
Matrix Estimation ◽  
Second Stage ◽  
Local Mobility ◽  
Proposed Model ◽  
Path Flow Estimator

As more people choose to travel by bicycle, transportation planners are beginning to recognize the need to rethink the way they evaluate and plan transportation facilities to meet local mobility needs. A modal shift towards bicycles motivates a change in transportation planning to accommodate more bicycles. However, the current methods to estimate bicycle volumes on a transportation network are limited. The purpose of this research is to address those limitations through the development of a two-stage bicycle origin–destination (O–D) matrix estimation process that would provide a different perspective on bicycle modeling. From the first stage, a primary O–D matrix is produced by a gravity model, and the second stage refines that primary matrix generated in the first stage using a Path Flow Estimator (PFE) to build the finalized O–D demand. After a detailed description of the methodology, the paper demonstrates the capability of the proposed model for a bicycle demand matrix estimation tool with a real network case study.

Two-Stage Design for Cellular Manufacturing System

2012 ◽  
Vol 220-223 ◽  
pp. 137-140
Author(s):  
Ling Gou ◽  
Cong Lu ◽  
Jun Feng Fei
Keyword(s):  
Genetic Algorithm ◽  
Manufacturing System ◽  
Cellular Manufacturing ◽  
Cell Formation ◽  
Layout Design ◽  
Cellular Manufacturing System ◽  
Two Stage ◽  
Stage Design ◽  
Second Stage ◽  
Handling Cost

Cell formation（CF）and cellular layout design are the two main steps in designing a cellular manufacturing system (CMS). Efficient grouping is the prerequisite of a successful Cellular Manufacturing, and it is too hard to obtain a perfect consequence of CF and layout simultaneously. In this paper, a two-stage strategy is used to solve CF problem and layout sequentially. At the first stage, the cells are formed based on the part-machine clustering results obtained through the genetic algorithm, to get the initial inter-cell layout. At the second stage, the genetic algorithm is used to improve the solution obtained, to minimize the handling cost and maximize the utility of the machines.

Estimating stability boundaries of distributed-delay oscillators via two-stage numerical integration

2016 ◽  
Vol 24 (9) ◽  
pp. 1728-1740 ◽  
Author(s):  
Chigbogu Godwin Ozoegwu
Keyword(s):  
Numerical Integration ◽  
Distributed Delay ◽  
Computationally Efficient ◽  
Two Stage ◽  
Second Stage ◽  
Discrete Delays ◽  
The Stability ◽  
Spectral Finite Elements ◽  
Wheel Shimmy

A procedure based on the concept of full-discretization and numerical integration is established in this work for the stability analysis of periodic distributed-delay oscillators governed by delay integro-differential equations (DIDEs). DIDEs can be found as models of mechanical systems suffering from distributed-delay feedback, such as regenerative machine tool vibrations modeled by distributed force and wheel shimmy. Unstable vibrations in such systems are systematically avoided/controlled if the boundaries between the stable and unstable subspaces are established. The presented method involves the two-stage application of numerical integration to the governing DIDE. While the first-stage application discretizes and converts the distributed delay to fine series of short discrete delays, the second-stage application results in discrete solutions paving the way for a new method of constructing a finite monodromy operator. The error and convergence of the method are studied. It is found that the presented method is of the same convergence as that of the well-accepted first-order semi-discretization method, but more computationally efficient in terms of time savings. A number of case study DIDEs that have already been studied in the literature using methods of semi-discretization and spectral finite elements are studied with the presented method. It is seen that the presented method is valid as it produces stability results that compare well with those of the earlier works.

An Optimizing Approach for Multi Constraints Reassignment Problem of Human Resources

2016 ◽  
Vol 6 (4) ◽  
pp. 1907 ◽  
Author(s):  
Tkatek Said ◽  
Abdoun Otman ◽  
Abouchabaka Jaafar ◽  
Rafalia Najat
Keyword(s):  
Genetic Algorithm ◽  
Human Resources ◽  
Assignment Problem ◽  
Complex Problem ◽  
Second Step ◽  
Hard Problem ◽  
Optimal Assignment ◽  
Generalized Assignment Problem ◽  
Generalized Assignment ◽  
Np Hard Problem

<span lang="EN-US">This paper presents an effective approach to optimize the reassignment of Human Resources in the enterprise that is formed by several units of productions to take into consideration the human characteristics. This approach consists of two steps; the first step is to formalize the studied problem that is practically take the form of the generalized assignment problem (GAP) known as NP-hard problem. Additionally, the variables in the formulation of our problem are interlinked by certain constraints. These two proprieties can to justify the important complexity of this problem. The second step is focused to solve this complex problem by using the genetic algorithm. We present the experimentally result for justifying the validity of the proposed approach. So, the solution obtained allowed us to get an optimal assignment of personnel that can lead to improve the average productivity or ratability or at least ensure its equilibration within sites of enterprise.</span>

scholarly journals An Optimizing Approach for Multi Constraints Reassignment Problem of Human Resources

2016 ◽  
Vol 6 (4) ◽  
pp. 1907
Author(s):  
Tkatek Said ◽  
Abdoun Otman ◽  
Abouchabaka Jaafar ◽  
Rafalia Najat
Keyword(s):  
Genetic Algorithm ◽  
Human Resources ◽  
Assignment Problem ◽  
Complex Problem ◽  
Second Step ◽  
Hard Problem ◽  
Optimal Assignment ◽  
Generalized Assignment Problem ◽  
Generalized Assignment ◽  
Np Hard Problem

<span lang="EN-US">This paper presents an effective approach to optimize the reassignment of Human Resources in the enterprise that is formed by several units of productions to take into consideration the human characteristics. This approach consists of two steps; the first step is to formalize the studied problem that is practically take the form of the generalized assignment problem (GAP) known as NP-hard problem. Additionally, the variables in the formulation of our problem are interlinked by certain constraints. These two proprieties can to justify the important complexity of this problem. The second step is focused to solve this complex problem by using the genetic algorithm. We present the experimentally result for justifying the validity of the proposed approach. So, the solution obtained allowed us to get an optimal assignment of personnel that can lead to improve the average productivity or ratability or at least ensure its equilibration within sites of enterprise.</span>

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