New computational results on the discrete time/cost trade-off problem in project networks

We describe a new exact procedure for the discrete time/cost trade-off problem in deterministic activity-on-the-arc networks of the CPM type, where the duration of each activity is a discrete, nonincreasing function of the amount of a single resource (money) committed to it. The objective is to cons...

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Bibliographic Details
Main Authors: DEMEULEMEESTER, Erik, DE REYCK, Bert, FOUBERT, Bram, HERROELEN, Willy
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 1998
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Online Access:https://ink.library.smu.edu.sg/lkcsb_research/6739
https://ink.library.smu.edu.sg/context/lkcsb_research/article/7766/viewcontent/New_computational_results_on_the_discrete_time_cost_trade_off_problem_in_project_networks.pdf
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Institution: Singapore Management University
Language: English
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Summary:We describe a new exact procedure for the discrete time/cost trade-off problem in deterministic activity-on-the-arc networks of the CPM type, where the duration of each activity is a discrete, nonincreasing function of the amount of a single resource (money) committed to it. The objective is to construct the complete and efficient time/cost profile over the set of feasible project durations. The procedure uses a horizon-varying approach based on the iterative optimal solution of the problem of minimising the sum of the resource use over all activities subject to the activity precedence constraints and a project deadline. This optimal solution is derived using a branch-and-bound procedure which computes lower bounds by making convex piecewise linear underestimations of the discrete time/cost trade-off curves of the activities to be used as input for an adapted version of the Fulkerson labelling algorithm for the linear time/cost trade-off problem. Branching involves the selection of an activity in order to partition its set of execution modes into two subsets which are used to derive improved convex piecewise linear underestimations. The procedure has been programmed in Visual C ++ under Windows NT and has been validated using a factorial experiment on a large set of randomly generated problem instances.