The R-PSO algorithm solving multi-skill resource-constrained project scheduling problem

The Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP) is a combinational optimization problem with many applications in science and practical areas. This problem aims to find out the feasible schedule for the completion of projects and workflows that is minimal duration or cost (or both of them - multi objectives). The researches show that MS-RCPSP is classified into NP-Hard classification, which could not get the optimal solution in polynomial time. Therefore, we usually use approximate methods to carry out the feasible schedule. There are many publication results for that problem based on evolutionary methods such as GA, Greedy, Ant, etc. However, the evolutionary algorithms usually have a limitation that is fallen into local extremes after a number of generations. This paper will study a new method to solve the MS-RCPSP problem based on the Particle Swarm Optimization (PSO) algorithm that is called R-PSO. The new improvement of R-PSO is re-assigning the resource to execute solution tasks. To evaluate the new algorithm's effectiveness, the paper conducts experiments on iMOPSE datasets. Experimental results on simulated data show that the proposed algorithm finds a better schedule than related works.

Polynomial algorithms for some scheduling problems with one nonrenewable resource

This paper deals with the Extended Resource Constrained Project Scheduling Problem (ERCPSP) which is defined by events, nonrenewable resources and precedence constraints between pairs of events. The availability of a resource is depleted and replenished at the occurrence times of a set of events. The decision problem of ERCPSP consists of determining whether an instance has a feasible schedule or not. When there is only one nonrenewable resource, this problem is equivalent to find a feasible schedule that minimizes the number of resource units initially required. It generalizes the maximum cumulative cost problem and the two-machine maximum completion time flow-shop problem. In this paper, we consider this problem with some specific precedence constraints: parallel chains, series-parallel and interval order precedence constraints. For the first two cases, polynomial algorithms based on a linear decomposition of chains are proposed. For the third case, a polynomial algorithm is introduced to solve it. The priority between events is defined using the properties of interval orders.

Relocation Scheduling in a Two–Machine Flow Shop with Resource Recycling Operations

This paper considers a variant of the relocation problem, which is formulated from an urban renewal project. There is a set of jobs to be processed in a two-machine flow shop subject to a given initial resource level. Each job consumes some units of the resource to start its processing on machine 1 and will return some amount of the resource when it is completed on machine 2. The amount of resource released by a job is not necessarily equal to the amount of resource acquired by the job for starting the process. Subject to the resource constraint, the problem is to find a feasible schedule whose makespan is minimum. In this paper, we first prove the NP-hardness of two special cases. Two heuristic algorithms with different processing characteristics, permutation and non-permutation, are designed to construct feasible schedules. Ant colony optimization (ACO) algorithms are also proposed to produce approximate solutions. We design and conduct computational experiments to appraise the performances of the proposed algorithms.

A mathematical model for teamwork scheduling problem in available time-windows

This paper deals with teamwork scheduling problem in available time windows. This problem has been posed by combining the three constraints are the jobs can split into some sub-jobs which should not be less than a threshold called splitmin, the jobs are only assigned into available time windows and the jobs can be assigned into many people in the organization. Since then the four properties of this problem considered are everyone handles any jobs; a job can be handled by some person at the same time; jobs can be broken down into some sub-jobs; the size of the job/sub-job should not be less than splitmin. The goal aims to determine a feasible schedule that minimizes makespan. And a numerical example is presented to demonstrate the essential constraint with given input data to well define this scheduling problem. Besides the authors proposed a mathematical model to determine the optimal solution by using solvers to solve it and some simple heuristics with computing time less than one second to find the good solutions such as Assignment approach, SPT/LPT rules. All experiments were evaluated on two criteria are the maximum completion time for all jobs and runtime in seconds to determine the solution. These experiments were conducted by the comparison of the lower bound, the exact method based on using CPLEX solver to solve the MILP model, and proposed heuristics. The experimental results show it is very time consuming to determine the optimal solution by CPLEX solver, while the solution found by heuristic algorithms is only good enough.

The parallel approach to issue of operational management optimization problem on transport gateway system

The universal parallelization software shell for joint data processing, implemented in combination with a distributed computing system, is considered. The research purpose – to find the most effective solution for the navigable canal management information system organizing. One optimization option is to increase computer devices computing power by combining them into a single computing cluster. The management optimizing task of a locked shipping channel for execution to adapt in a multi-threaded environment is proposed with constraints on a technologically feasible schedule. In article shows algorithms and gives recommendations for their application in the subtasks formation in parallel processing case, as well as on a separate thread. The proposed approach to building a tree of options allows you to optimally distribute the load between all resources multi-threaded system any structure.

MILP models and valid inequalities for the two-machine permutation flowshop scheduling problem with minimal time lags

In this paper, we consider the problem of scheduling on two-machine permutation flowshop with minimal time lags between consecutive operations of each job. The aim is to find a feasible schedule that minimizes the total tardiness. This problem is known to be NP-hard in the strong sense. We propose two mixed-integer linear programming (MILP) models and two types of valid inequalities which aim to tighten the models’ representations. One of them is based on dominance rules from the literature. Then, we provide the results of extensive computational experiments used to measure the performance of the proposed MILP models. They are shown to be able to solve optimally instances until the size 40-job and even several larger problem classes, with up to 60 jobs. Furthermore, we can distinguish the effect of the minimal time lags and the inclusion of the valid inequalities in the basic MILP model on the results.

A Hybrid based Distributed Slot Scheduling Approach for WSN MAC

In Wireless Sensor Networks(WSNs), collision handling during transmission of data is an important challenge. MAC protocol plays a vital role in handling those collisions. Among different types of MAC protocols, schedule based MAC protocol is one where a valid schedule is prepared to handle the collision. The existing schedule based MAC protocols focus on preparing either a feasible schedule or an optimal schedule. In order to satisfy both feasibility as well as optimality feature, in this paper, we proposed a hybrid approach for slot scheduling that prepares a feasible schedule in a distributed manner and at the same time reduces the number of feasible schedule to achieve optimality. In this paper, we named this algorithm as Distributed hybrid Slot Scheduling(DHSS) algorithm. The proposed DHSS algorithm initially prepares a feasible schedule which is furthertuned in quick time to prepare a valid schedule with a reducednumber of slots. The reduction of the number of slots in theschedule improves the efficiency of data transmission in termsof latency. The simulation results show that the DHSS algorithmoutperforms RD-TDMA with respect to both the number of slotsallotted for a feasible schedule as well as the data transmissionlatency.