Two heuristics for scheduling multiple projects with resource constraints

Abstract

The purpose of this paper is to develop two ef® cient heuristic priority rules for the resource-constrained multiproject scheduling problem. The aptness of the two heuristic rules is analysed in terms of several dynamic characteristics of the scheduling problem. Fifteen heuristic rules presented in previous studies are used for comparison with the two heuristic rules on 4941 test problems which were generated by combining two, three or four projects from seven typical networks. The results indicate that the two proposed heuristics are superior to the other scheduling rules under the performance criteria of the minimum total project delay and the maximum number of times that a scheduling rule can obtain the best solution. Encouragingly, the two heuristic rules are proven to be adaptive and stable enough for scheduling under different problem sizes, network structures and degrees of resource tightness. As a result, the two proposed rules are the best representatives of the single priority rule method and the weighted combination search method, respectively. This study also includes a categorization process on which a project summary measure is based and then provides project schedulers with a convenient scheme to adopt appropriate scheduling rules.

An efficient search procedure for the resource-constrained multi-project scheduling problem with discounted cash flows

“…Only the smaller test problems were 60 Chiu and Tsai Resource-constrained multi-project scheduling problem 61 Figure 2 Solution process when applying the CRS method to the example included in this phase, since solving a very large-scale problem would require a very large amount of computational effort. This comparison was made by using 42 smaller test problems generated by combining two projects from seven small-scale networks (less than fteen activities) and two levels of the degree of resource availability (>0.5 and £ 0.5), which is a measure of the tightness of the resources available for a project network (see Tsai and Chiu (1996), equation (4)). The superiority and applicability of the CRS method for larger test problems were con rmed further by performing additional computations and comparisons in the next two phases.…”

“…Step 1: Initialization of scheduling First, each activity is scheduled by using the critical ratio rule, which was presented in Tsai and Chiu (1996). An activity with the smallest critical ratio value is granted top priority in scheduling.…”

A genetic algorithm-based optimal resource-constrained scheduling simulation model

“…This leads to computation inef ciency and it does not work in practical scheduling (Gen and Cheng, 1997). After a great deal of computational effort, to avoid the 'combinatorial explosion' problem, heuristic rules were introduced (David and Patterson, 1975;Whitehouse and Brown, 1979;Elsayed, 1982;Morse and Whitehouse, 1988;Boctor, 1990;Tsai and Chiu, 1996). Each heuristic model has its own philosophy, and they all try to increase the possibility of obtaining the best solution.…”

Effects of project size and resource constraints on project duration through priority rule-base heuristics

“…A great deal of computational effort is required to solve problems of this kind. Therefore, to avoid the problem of "combinatorial explosion" heuristic rules were also used to solve such problems (Morse and Whitehouse 1988;Tsai and Chiu 1996). To date, numerous heuristic scheduling rules have been proposed to solve deterministic resource-constrained scheduling problems, such as the MINSLCK model (David and Patterson 1975), the ROT model (Elsayed 1982), and the GENRES model by Whitehouse and Brown (1979).…”