Robust optimization for resource-constrained project scheduling with uncertain activity durations

The purpose of this paper is to propose models for project scheduling when there is considerable uncertainty in the activity durations, to the extent that the decision maker cannot with confidence associate probabilities with the possible outcomes of a decision. Our modeling techniques stem from robust discrete optimization, which is a theoretical framework that enables the decision maker to produce solutions that will have a reasonably good objective value under any likely input data scenario. We develop and implement a scenario-relaxation algorithm and a scenario-relaxation-based heuristic. The first algorithm produces optimal solutions but requires excessive running times even for medium-sized instances; the second algorithm produces high-quality solutions for medium-sized instances and outperforms two benchmark heuristics.     

Comparison of mixed integer linear programming models for the resource-constrained project scheduling problem with consumption and production of resources

This paper addresses an extension of the resource-constrained project scheduling problem that takes into account storage resources which may be produced or consumed by activities. To solve this problem, we propose the generalization of two existing mixed integer linear programming models for the classical resource-constrained project scheduling problem, as well as one novel formulation based on the concept of event. Computational results are reported to compare these formulations with each other, as well as with a reference method from the literature. Conclusions are drawn on the merits and drawbacks of each model according to the instance characteristics.     

An ant-based coordination mechanism for resource-constrained project scheduling with multiple agents and cash flow objectives

We consider a multi-agent extension of the non-preemptive single-mode resource-constrained project scheduling problem with discounted cash flow objectives. Such a problem setting is related to project scheduling problems which involve different autonomous firms where project activities are uniquely assigned to the project parties (agents). Taking into account opportunistic agents and the resulting information asymmetry we propose a general decentralized negotiation approach which uses ideas from ant colony optimization. In the course of the negotiation the agents iteratively vote on proposed project schedules without disclosing preference information regarding cash flow values. Computational experiments serve to analyze the agent-based coordination mechanism in comparison to other approaches from the literature. The proposed mechanism turns out as an effective method for coordinating self-interested agents with conflicting goals which collaborate in resource-constrained projects.     

A new formulation for the project scheduling problem under limited resources

In this paper we propose a new formulation for the Resource Constrained Project Scheduling Problem (RCPSP) and minimum makespan objective. The new formulation exploits three variables, one associated with the start time of an activity, one associated with the finish time of an activity, and the last one associated with the amount of an activity in progress at a given time. We provide an extensive experimentation, and a comparison with known mathematical formulations for the RCPSP in the literature.     

The resource-constrained project scheduling model of Bianco and Caramia: clarifications and an alternative model formulation

Recently, Bianco and Caramia (Flex Serv Manuf J 25(1–2), 6–24, 2013) proposed a new model for the resource-constrained project scheduling problem. Despite its potential, the presentation of the mixed-integer programming model contains some ambiguity which may create misunderstanding in the implementation phase. Here, we clarify the definitions of the decision variables and illustrate their corresponding values using a numerical example. Furthermore, we propose a different interpretation of two decision variables which gives rise to an alternative model formulation also presented using the same numerical example.     

Multi-criteria HPC task scheduling on IaaS cloud infrastructures using meta-heuristics

Cluster Computing - With the rapid increase in the use of cloud computing systems, an efficient task scheduling policy, which deals with the assignment of tasks to resources, is required to obtain...     

On the use of meta-heuristics to increase the efficiency of online grid workflow scheduling algorithms

The competitiveness of online algorithms is measured based on the correctness of the results produced and processing time efficiency. Traditionally evolutionary algorithms are not favored in online paradigms because of the large number of iterations involved in the algorithm which translates directly into processing time overhead. In this paper we describe MARS (Management Architecture for Resource Services) online scheduling algorithm which uses Simulated Annealing and concepts from Tabu Search to drastically decrease the processing time of the algorithm. The paper outlines the concepts behind MARS, the components involved and scheduling methodology used. In addition we also identify the time consuming bottlenecks in the performance of the system and how evolutionary algorithms help us soar past them.

Two game-based solution concepts for a two-agent scheduling problem

In the current research papers on multi-agent (multi-person) scheduling, a person’s objective function is always considered as a cost function on scheduling, whereas a cooperative profit function is defined to serve as his objective one in this paper. In the two-person scheduling problem addressed in this paper, the two persons jointly order a common operational time interval of a single machine. Each person needs to process a set of his own jobs in that time window. The same objective function of each person still relies on the sequence of all the jobs of both persons since each part of the function is determined by some given parameters except one part assumed to be a given multiple of the total completion time of his own jobs. The two persons have to negotiate a job sequence and determine the (related) final solution on cooperative profit allocation. Such a two-person scheduling problem is essentially a cooperative game. An algorithm is designed to yield the cooperative-profit-based Pareto efficient solution set acting as the first game-based solution concept in this paper. The parallelized version of the algorithm is also developed. The second game-based solution concept is the Shapley value appropriate for the above cooperative-game situation on two-person scheduling. Several instances are presented and analyzed to reveal the necessity to employ the two solution concepts together.     

New technologies for the human cytome project

Cytomes or cell systems are composed of various kinds of single-cells and constitute the elementary building units of organs and organisms. Their individualised (cytomic) analysis overcomes the problem of averaged results from cell and tissue homogenates where molecular changes in low frequency cell populations may be hidden and wrongly interpreted. Analysis of the cytome is of pivotal importance in basic research for the understanding of cells and their interrelations in complex environments like tissues and in predictive medicine where it is a prerequisite for individualised preventive therapy. Analysis of molecular phenotypes requires instrumentation that on the one hand provides high-throughput measurement of individual cells and is on the other hand highly multiplexed, enabling the simultaneous acquisition of many parameters on the single cell level. Upcoming technology suitable to this task, such as slide based cytometry is available or under development. The realisation of cytomic technology is important for the realisation of the human cytome project.     

Natural computation meta-heuristics for the in silico optimization of microbial strains

Background  

One of the greatest challenges in Metabolic Engineering is to develop quantitative models and algorithms to identify a set of genetic manipulations that will result in a microbial strain with a desirable metabolic phenotype which typically means having a high yield/productivity. This challenge is not only due to the inherent complexity of the metabolic and regulatory networks, but also to the lack of appropriate modelling and optimization tools. To this end, Evolutionary Algorithms (EAs) have been proposed for in silico metabolic engineering, for example, to identify sets of gene deletions towards maximization of a desired physiological objective function. In this approach, each mutant strain is evaluated by resorting to the simulation of its phenotype using the Flux-Balance Analysis (FBA) approach, together with the premise that microorganisms have maximized their growth along natural evolution.     

MIP formulations for an application of project scheduling in human resource management

In the literature, various discrete-time and continuous-time mixed-integer linear programming (MIP) formulations for project scheduling problems have been proposed. The performance of these formulations has been analyzed based on generic test instances. The objective of this study is to analyze the performance of discrete-time and continuous-time MIP formulations for a real-life application of project scheduling in human resource management. We consider the problem of scheduling assessment centers. In an assessment center, candidates for job positions perform different tasks while being observed and evaluated by assessors. Because these assessors are highly qualified and expensive personnel, the duration of the assessment center should be minimized. Complex rules for assigning assessors to candidates distinguish this problem from other scheduling problems discussed in the literature. We develop two discrete-time and three continuous-time MIP formulations, and we present problem-specific lower bounds. In a comparative study, we analyze the performance of the five MIP formulations on four real-life instances and a set of 240 instances derived from real-life data. The results indicate that good or optimal solutions are obtained for all instances within short computational time. In particular, one of the real-life instances is solved to optimality. Surprisingly, the continuous-time formulations outperform the discrete-time formulations in terms of solution quality.     

Flower pollination algorithm with runway balance strategy for the aircraft landing scheduling problem

Aircraft landing scheduling is a challenging problem in the field of air traffic, whose objective is to determine the best combination of assigning the sequence and corresponding landing time for a given set of aircraft to a runway, and then minimize the sum of the deviations of the actual and target landing times under the condition of safe landing. In this paper, a flower pollination algorithm embedded with runway balance is proposed to solve it. Context cognitive learning and runway balance strategy are devised here to enhance its searching ability. 36 scheduling instances are selected from OR-Library to validate its performance. The experimental results show that the proposed algorithm can get the optimal solutions for instances up to 100 aircrafts, and is also capable of obtaining better solutions compared with SS, BA and FCFS for instances up to 500 aircrafts in a shorter time.     

An efficient hybridized genetic algorithm architecture for the flexible job shop scheduling problem

The work presented in this paper proposes hybridized genetic algorithm architecture for the Flexible Job Shop Scheduling Problem (FJSP). The efficiency of the genetic algorithm is enhanced by integrating it with an initial population generation algorithm and a local search method. The usefulness of the proposed methodology is illustrated with the aid of an extensive computational study on 184 benchmark problems with the objective of minimizing the makespan. Results highlight the ability of the proposed algorithm to first obtain optimal or near-optimal solutions, and second to outperform or produce comparable results with these obtained by other best-known approaches in literature.     

A new method for a class of parallel batch machine scheduling problem

Flexible Services and Manufacturing Journal - This paper studies the scheduling problem of jobs with release times, non-identical sizes, and incompatible job families on unrelated parallel batch...     

A new approach to the job scheduling problem in computational grids

Job scheduling is one of the most challenging issues in Grid resource management that strongly affects the performance of the whole Grid environment. The major drawback of the existing Grid scheduling algorithms is that they are unable to adapt with the dynamicity of the resources and the network conditions. Furthermore, the network model that is used for resource information aggregation in most scheduling methods is centralized or semi-centralized. Therefore, these methods do not scale well as Grid size grows and do not perform well as the environmental conditions change with time. This paper proposes a learning automata-based job scheduling algorithm for Grids. In this method, the workload that is placed on each Grid node is proportional to its computational capacity and varies with time according to the Grid constraints. The performance of the proposed algorithm is evaluated through conducting several simulation experiments under different Grid scenarios. The obtained results are compared with those of several existing methods. Numerical results confirm the superiority of the proposed algorithm over the others in terms of makespan, flowtime, and load balancing.     

Experimental investigation of an FMS due-date scheduling problem: Evaluation of machine and AGV scheduling rules

Although extensive research has been conducted to solve design and operational problems of automated manufacturing systems, many of the problems still remain unsolved. This article investigates the scheduling problems of flexible manufacturing systems (FMSs). Specifically, the relative performances of machine and automated guided vehicle (AGV) scheduling rules are analyzed against various due-date criteria. First, the relevant literature is briefly reviewed, and then the rules are tested under different experimental conditions by using a simulation model of an FMS. The sensitivity to AGV workload, buffer capacity, and processing-time distribution is also investigated to assess the robustness of the scheduling rules.     

A heuristic method towards deadline-aware energy-efficient mapreduce scheduling problem in Hadoop YARN

Cluster Computing - The MapReduce (MR) scheduling is a prominent area of research to minimize energy consumption in the Hadoop framework in the era of green computing. Very few scheduling...