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The time-cost tradeoff problem is one of the most important and applicable problems in project scheduling area. There are many factors that force the mangers to crash the time. This factor could be early utilization, early commissioning and operation, improving the project cash flow, avoiding unfavorable weather conditions, compensating the delays, and so on. Since there is a need to allocate extra resources to short the finishing time of project and the project managers are intended to spend the lowest possible amount of money and achieve the maximum crashing time, as a result, both direct and indirect costs will be influenced in the project, and here, we are facing into the time value of money. It means that when we crash the starting activities in a project, the extra investment will be tied in until the end date of the project; however, when we crash the final activities, the extra investment will be tied in for a much shorter period. This study is presenting a two-objective mathematical model for balancing compressing the project time with activities delay to prepare a suitable tool for decision makers caught in available facilities and due to the time of projects. Also drawing the scheduling problem to real world conditions by considering nonlinear objective function and the time value of money are considered. The presented problem was solved using NSGA-II, and the effect of time compressing reports on the non-dominant set.

Research Summary: To investigate time compression diseconomies (TCD), this study estimated time-cost elasticities using 459 oil and gas global investment projects (1997-2010). Results show that the average cost of accelerating investments is negative: a firm could cut $6.3 million in costs of a single project by accumulating asset stocks 1 month faster. About 88% of the projects exhibit negative time-cost elasticities with over 39% of unrealized economies of time compression. Only 12% of the projects are subject to TCD. These time inefficiencies or frictions do not negate the existence of TCD, but suggest they are less prevalent than assumed in the literature. Management experience, R&D investment, firm size, economic development, and political stability are shown to be associated with greater time compression efficiency. Managerial Summary: How fast should firms invest? The conventional view is that acceleration increases market revenues but also inflates costs. However, there is no recent empirical evidence of this tradeoff. Our article systematically investigates the costs of compressing time in investment projects. Results show that most firms in the oil and gas industry are significantly time inefficient in their operations. Specifically, by accelerating investments, firms would also substantially decrease costs. We estimate the magnitude of these time inefficiencies for specific oil and gas industries and firms and study which strategies might mitigate this problem. This fine-grained analysis should help firms assess their financial incentives to accelerate projects and prove informative to stock market analysts' valuations of firm investment timing.

Construction companies are required to employ effective methods of project planning and scheduling in today's competitive environment. Time and cost are critical factors in project success, and they can vary based on the type and amount of resources used for activities, such as labor, tools, and materials. In addition, resource leveling strategies that are used to limit fluctuations in a project's resource consumption also affect project time and cost. The multi-mode resource-constrained discrete-time–cost-resource optimization (MRC-DTCRO) is an optimization tool that is developed for scheduling of a set of activities involving multiple execution modes with the aim of minimizing time, cost, and resource moment. Moreover, uncertainty in cost should be accounted for in project planning because activities are exposed to risks that can cause delays and budget overruns. This paper presents a fuzzy-multi-mode resource-constrained discrete-time–cost-resource optimization (F-MRC-DTCRO) model for the time-cost-resource moment tradeoff in a fuzzy environment while satisfying all the project constraints. In the proposed model, fuzzy numbers are used to characterize the uncertainty of direct cost of activities. Using this model, different risk acceptance levels of the decision maker can be addressed in the optimization process. A newly developed multi-objective optimization algorithm called ENSCBO is used to search non-dominated solutions to the fuzzy multi-objective model. Finally, the developed model is applied to solve a benchmark test problem. The results indicate that incorporating the fuzzy structure of uncertainty in costs to previously developed MRC-DTCRO models facilitates the decision-making process and provides more realistic solutions.

The time-cost tradeoff problem is to find optimal combinations of construction options with the objective of minimizing project time and cost. In order to search for such a set of optimal solutions, the total time and cost of projects needs to be calculated properly. In repetitive construction projects (RCPs), due to division of work into several units and involvement of many resources for activities completion, scheduling is unique and more complicated. Scheduling method in this paper besides addressing common constraints such as precedence relationships, required lead time and lag distance between activities, enables project managers to consider all resources and their shortages in scheduling. To raise the practicality of model, in addition to direct and indirect costs, resource idle cost is considered as a cost element in estimating total cost of project. The time variant multi-objective particle swarm optimization is applied to find non-dominated solutions on the basis of minimizing time and cost of project. An application example is presented at the end to illustrate the performance of the model. This research presents a resource-constrained time-cost tradeoff model to find the optimal set of crew combinations for project activities in RCPs, considering time and cost simultaneously.

The COVID-19 pandemic outbreak deeply impressed supply chains in different aspects. In response to this unexpected situation, supply chain managers have decided to recover and reinforce their supply chains. Considering the expanse of these decisions, project management principle and tools seems inevitable to successfully manage the transformation from before pandemic to post-pandemic supply chains (SCs). In this study, the problem of time–cost tradeoff is extended to time, cost, and risk tradeoff. The risk factor is considered to convey the uncertainty arising from the COVID-19 pandemic situation. Since projects are affected by the level of pandemic expansion and different countries ruled out various quarantine policies (isolation, quarantine, social distancing, and lock-down), the tradeoff problem is influenced accordingly. Therefore, a scenario-based robust optimization model is proposed to deal with time, cost, and risk tradeoff problems to reflect the effects of the global pandemic of COVID-19 on managing projects in supply chains. In addition, various quarantine policies (isolation, quarantine, social distancing, and lock-down) as a prevalent response to the pandemic have been investigated separately. To illustrate the model, a real-world case study in the emerging economy of Iran is examined using the proposed approach. The results indicated that supply chain managers can use the designed model and approach as a tool for a flexible and adaptable decision-making framework dealing with a global pandemic such as COVID-19.

We consider a stochastic model for the time–cost tradeoffs of an activity. In this model the activity can be undertaken by using several different resources, and the resource in use may change according to the way the activity is evolving. We present two algorithms for identifying strategies that are in a predefined neighborhood of the efficient set: one of them is based on a tree structure and the other is based on dynamic programming. Both algorithms take advantage of some mathematical properties of the model in order to reduce their running time and memory requirements. We present the results of some computational tests, as well as an application example. We conclude that the dynamic programming algorithm performs quite well, although it is sometimes necessary to adjust the parameters related to the neighborhood of the efficient set to be able to have reasonable running times.

United States State Highway Agencies (SHAs) use Incentive/Disincentives (I/D) to minimize negative impacts of construction on the traveling public through construction acceleration. Current I/D practices have the following short-comings: not standardized, over- or under-compensate contractors, lack of auditability result in disincentives that leave SHAs vulnerable to contractor claims and litigation and are based on agency costs/savings rather than contractor acceleration. Presented within this paper is an eleven-step I/D valuation process. The processes incorporate a US-nationwide RUC and agency cost calculation program, CA4PRS and a time-cost tradeoff I/D process. The incentive calculation used is the summation of the contractor acceleration and a reasonable contractor bonus (based on shared agency savings) with an optional reduction of contractor’s own saving from schedule compression (acceleration). The process has a capability to be used both within the US and internationally with minor modifications, relies on historical costs, is simple and is auditable and repeatable. As such, it is a practical tool for optimizing I/D amounts and bridges the gap in existing literature both by its industry applicability, integrating the solution into existing SHA practices and its foundation of contractor acceleration costs.

Time/cost trade-offs in project networks have been the subject of extensive research since the development of the critical path method (CPM) in the late 50s. Time/cost behaviour in a project activity basically describes the trade-off between the duration of the activity and its amount of non-renewable resources (e.g., money) committed to it. In the discrete version of the problem (the discrete time/cost trade-off problem), it is generally accepted that the trade-off follows a discrete non-increasing pattern, i.e., expediting an activity is possible by allocating more resources (i.e., at a larger cost) to it. However, due to its complexity, the problem has been solved for relatively small instances. In this paper we elaborate on three extensions of the well-known discrete time/cost trade-off problem in order to cope with more realistic settings: time/switch constraints, work continuity constraints, and net present value maximization. We give an extensive literature overview of existing procedures for these problem types and discuss a new meta-heuristic approach in order to provide near-optimal heuristic solutions for the different problems. We present computational results for the problems under study by comparing the results for both exact and heuristic procedures. We demonstrate that the heuristic algorithms produce consistently good results for two versions of the discrete time/cost trade-off problem. [PUBLICATION ABSTRACT]

Assume that a train reaches a station with delay. At the station there is a bus ready to depart. The question of whether the bus should wait for the delayed train or depart on time is called the delay management problem . Different single objective functions for this problem have been introduced and analyzed. In this paper, we present a bicriteria model for the delay management problem, taking into account both the delay of the vehicles and the number of passengers who miss a connection. Our model does not depend on detailed data about the passengers and hence can be easily implemented in practice. To analyze the problem, we present an integer programming formulation and a graph-theoretic approach that is based on discrete time/cost trade-off project networks. Using results of project planning, we develop an efficient solution method. We tested our procedure using real-world data. The results show the applicability of the approach.