Gary E. Whitehouse

Meta-RaPs approach for the 0-1 multidimensional Knapsack problem

A promising solution approach called Meta-RaPS is presented for the 0-1 Multidimensional Knapsack Problem (0-1 MKP). Meta-RaPS constructs feasible solutions at each iteration through the utilization of a priority rule used in a randomized fashion. Four different greedy priority rules are implemented within Meta-RaPS and compared. These rules differ in the way the corresponding pseudo-utility ratios for ranking variables are computed. In addition, two simple local search techniques within Meta-RaPS improvement stage are implemented. The Meta-RaPS approach is tested on several established test sets, and the solution values are compared to both the optimal values and the results of other 0-1 MKP solution techniques. The Meta-RaPS approach outperforms many other solution methodologies in terms of differences from the optimal value and number of optimal solutions obtained. The advantage of the Meta-RaPS approach is that it is easy to understand and easy to implement, and it achieves good results.

GENRES: An extension of Brooks Algorithm for project scheduling with resource constraints

Abstract In project scheduling by network analysis, traditional critical path methods fail to include resource considerations. Other methods must be used to allow for resource considerations. This article explores one area of resource considerations: project scheduling under resource constraints. The specific case investigated is the single resource, single project schedule. A model, entitled the GENRES search model, is developed. The model utilizes Brooks Algorithm (BAG) to generate the project schedule. The criteria used are various weighted combinations of ACTIM and ACTRES (Bedworth, Industrial Systems ). The best project schedule is that which gives the least project duration. The GENRES model was found effective in finding project durations equal to or less than that of ACTIM, ACTRES or TIMRES (the combination of ACTIM and ACTRES with each given equal weight). The research also found that when the project completion time found by the algorithm approaches the critical path duration, resource leveling may be preferred.

A simple and effective heuristic for the resource constrained project scheduling problem

This paper investigates the development and application of a simple heuristic to the resource constrained project scheduling problem (RCPSP). This computer heuristic, which is based on the COMSOAL heuristic, constructs a feasible solution at each iteration and chooses the best solution of several iterations. Although COMSOAL was originally a solution approach for the assembly-line balancing problem, it can be extended to provide solutions to the resource allocation problem. The Modified COMSOAL technique presented in this paper uses priority schemes intermittently with a random selection technique. This hybrid of randomness and priority scheme allows a good solution to be found quickly while not being forced into the same solution at each iteration. Several different priority schemes are examined within this research. The COMSOAL heuristic modified with the priority schemes heuristic was tested on several established test sets and the solution values are compared with both known optimal values and the results of several other resource allocation heuristics. In the vast majority of cases, the Modified COMSOAL heuristic outperformed the other heuristics in terms of both average and maximum percentage difference from optimal. The Modified COMSOAL heuristic seems to have several advantages over other RCPSP heuristics in that it is easy to understand, easy to implement, and achieves good results.

A Meta-RaPS for the early/tardy single machine scheduling problem

This paper investigates a meta-heuristic solution approach to the early/tardy single machine scheduling problem with common due date and sequence-dependent setup times. The objective of this problem is to minimise the total amount of earliness and tardiness of jobs that are assigned to a single machine. The popularity of just-in-time (JIT) and lean manufacturing scheduling approaches makes the minimisation of earliness and tardiness important and relevant. In this research the early/tardy problem is solved by Meta-RaPS (meta-heuristic for randomised priority search). Meta-RaPS is an iterative meta-heuristic which is a generic, high level strategy used to modify greedy algorithms based on the insertion of a random element. In this case a greedy heuristic, the shortest adjusted processing time, is modified by Meta-RaPS and the good solutions are improved by a local search algorithm. A comparison with the existing ETP solution procedures using well-known test problems shows Meta-RaPS produces better solutions in terms of percent difference from optimal. The results provide high quality solutions in reasonable computation time, demonstrating the effectiveness of the simple and practical framework of Meta-RaPS.

A study of combining heuristics for scheduling projects with limited multiple resources

Abstract The main objective of this study was to find a simple and quick procedure on the microcomputer for scheduling activities of a constrained multiple resource single project network that would minimize project duration. From this research there are two different types of results presented. First, a combination of simple heuristics which find the average of the minimum project durations for the constrained resource problem is presented. This combination not only supports the previous research on successful simple heuristic methods which set the priorities for constrained resource problems, but also produces results which are significantly better than those obtained by single heuristics. Second, a procedure for determining this combination of heuristics is introduced. A computer algorithm, COMAL, was developed for this study with constrained resource problems, but in the future its use may be expanded into other fields.

Applying the COMSOAL computer heuristic to the constrained resource allocation problem

Generating a project schedule that meets all activity precedence constraints while minimizing the overall project duration is often difficult. The complex project-scheduling problem is further complicated by the real-world constraint that often a limited number of resources must be allocated to competing activities. This paper investigates the application of the computer method COMSOAL to this resource allocation problem. COMSOAL (Computer Method of Sequencing Operations for Assembly Lines), originally a solution approach for the assembly line balancing problem, is a computer heuristic that can be used to generate a feasible solution to the resource allocation problem at each iteration of the heuristic. A solution methodology of repeatedly running COMSOAL will result in many feasible solutions from which the best is chosen. This solution approach now becomes viable given the increased speed of inexpensive computers. This paper discusses the adaptation of the COMSOAL approach to the resource allocation problem as well as a designed experiment used to investigate the appropriateness of COMSOAL for a known set of resource allocation test problems. Results from this experiment show COMSOAL is a viable method to solve these resource allocation problems when compared to the results from several well-known resource allocation algorithms and to the optimal solutions.

Meta-RaPS approach for solving the resource allocation problem

Project scheduling is an important planning function that involves scheduling activities of a project such that the total completion time for the entire project is minimized. In performing this function, one is often faced with the problem of limited resources in addition to considering the time element and precedence constraints of the project. The task of allocation of limited resources to competing activities further complicates the project scheduling procedure. Because of the fact that the Resource Allocation Problem is a very known and well-studied combinatorial problem, a number of heuristic rules can be found in literature. In this paper, authors present a new approach, Meta-RaPS (metaheuristic for randomized priority search), to address this combinatorial problem. This article will show experimental results using the Meta-RaPS approach on several well-known resource constrained project scheduling problem test sets.

Research needs and challenges in application of computer and information sciences for industrial engineering

Abstract By invitation from the Editor of IIE Transactions, a research forum was established in 1987 to develop and prepare this article. The objective: to write on the directions, needs and challenges for research by the IE com-munity in applying computer and information sciences. The motivation: realizing the major advancements in computer and information sciences in the recent decade and their significant impact on the IE profession, it is vital to examine how IE research activities can respond effectively to current and emerging needs. This article is viewed as a useful contribution to such an examination. Forum members were invited from academia, government and industry based on their experience in and commitment to research in this area. The forum was chaired by Shimon Y. Nof and the members are the co-authors of this article. Forum members communicated and deliberated throughout 1987 and met for a review and planning session dur-ing the IIE Conference in Washington, D.C. in May, 1987 (Forum, 1987)....

Solving Constrained Multiple Resource Networks Both Forward and Backward Using Brooks Algorithm

Many Brooks algorithm (BAG)-based heuristics have been proposed to solve the constrained multiple resource problem. BAG starts at the beginning of a project network, determines a list of available tasks, and then uses a heuristic to select a task to schedule. This research modifies BAG to solve the project network in both a forward and backward (F&B) direction. This modified BAG approach was used on Pattersons 110 test problems with several selection heuristics, such as ACTIM, latest finish time (LFT), resource over time (ROT), and ACTRES. Results of this research show that solving the networks in both the F&B directions led to improvements of more than 30% for the average error, the maximum error and the percentage of optimum solutions found when compared to the traditional BAG approach.

Application of SLAM in the design and performance analysis of multi-processor database system

Abstract The software-oriented, multi-processor database systems are characterized by a set of processing elements (PEs) which run identical software and operate on a partitioned database in parallel. Performance improvements and capacity growth can be achieved in this type of system by adding PEs to the configuration and replicating the existing software on the added PEs. Much work has been done applying graph theory, queuing theory, algorithmic approaches, and analytical techniques towards the design of multi-processor database systems. This paper describes a simulation approach and the application of a simulation language, SLAM, to the design and performance analysis of one such multi-processor database system. The system, relational replicated database system (RRDS), was developed using a five-phase design process. Simulation and analytical techniques were used throughout the development to determine critical elements, components, and design issues, and to evaluate proposed solutions. RRDS was modeled as an open queuing system with SLAM service times determined analytically. The model consisted of a workload scenario generator, a query processing module, and a statistics collection module. In phase one of the simulation study, different hardware organizations were evaluated. Results indicated that the RRDS approach performs approximately three times better than other approaches such as SIMD, MIMD, and functional specialization. In phase two, algorithms and mechanisms for data access were developed. Results favored the B + -tree approach over the clustering approach. A data placement strategy was determined in phase three. Results indicated that the value range partitioning (VRP) approach is more desirable than the round-robin (RR) approach. In phase four, a directory management strategy was selected. Results favored a partitioned and parallel-processed directory, as opposed to a rotating approach. Finally, phase five consisted reveal system strengths and weaknesses, and gain insight into optimal RRDS operating environments. This approach to database system design is both iterative and evolutionary, and can be applied regardless of the type of system being considered. Simulation can be a useful tool in all phases of database system design, from the actual physical hardware architecture to the resolution of software design questions. It plays an important role in predictive performance analysis to determine the extent to which original design goals are achieved.