Stability and resource allocation in project planning

The majority of resource-constrained project scheduling efforts assume perfect information about the scheduling problem to be solved and a static deterministic environment within which the precomputed baseline schedule is executed. In reality, project activities are subject to considerable uncertainty, which generally leads to numerous schedule disruptions. In this paper, we present a resource allocation model that protects a given baseline schedule against activity duration variability. A branch-and-bound algorithm is developed that solves the proposed resource allocation problem. We report on computational results obtained on a set of benchmark problems.     

The integration of resource allocation and time buffering for bi-objective robust project scheduling

In the recent decades, the recognition that uncertainty lies at the heart of modern project management has induced considerable research efforts on robust project scheduling for dealing with uncertainty in a scheduling environment. The literature generally provides two main strategies for the development of a robust predictive project schedule, namely robust resource allocation and time buffering. Yet, the previous studies seem to have neglected the potential benefits of an integration between the two. Besides, few efforts have been made to protect simultaneously the project due date and the activity start times against disruptions during execution, which is desperately demanded in practice. In this paper, we aim at constructing a proactive schedule that is not only short in time but also less vulnerable to disruptions. Firstly, a bi-objective optimisation model with a proper normalisation of the two components is proposed in the presence of activity duration variability. Then a two-stage heuristic algorithm is developed which deals with a robust resource allocation problem in the first stage and optimally determines the position and the size of time buffers using a simulated annealing algorithm in the second stage. Finally, an extensive computational experiment on the PSPLIB network instances demonstrates the superiority of the combination between resource allocation and time buffering as well as the effectiveness of the proposed two-stage algorithm for generating proactive project schedules with composite robustness.     

Flexible job-shop scheduling/rescheduling in dynamic environment: a hybrid MAS/ACO approach

In real-world manufacturing, disruptions are often encountered during the execution of a predetermined schedule, leading to the degradation of its optimality and feasibility. This study presents a hybrid approach for flexible job-shop scheduling/rescheduling problems under dynamic environment. The approach, coined as ‘HMA’ is a combination of multi-agent system (MAS) negotiation and ant colony optimisation (ACO). A fully distributed MAS structure has been constructed to support the solution-finding process by negotiation among the agents. The features of ACO are introduced into the negotiation mechanism in order to improve the performance of the schedule. Experimental studies have been carried out to evaluate the performance of the approach for scheduling and rescheduling under different types of disruptions. Different rescheduling policies are compared and discussed. The results have shown that the proposed approach is a competitive method for flexible job-shop scheduling/rescheduling for both schedule optimality and computation efficiency.     

An effective genetic algorithm for the resource levelling problem with generalised precedence relations

Resource levelling aims to obtain a feasible schedule to minimise the resource usage fluctuations during project execution. It is of crucial importance in project scheduling to ensure the effective use of scarce and expensive renewable resources, and has been successfully applied to production environments, such as make-to-order and engineering-to-order systems. In real-life projects, general temporal relationships are often needed to model complex time-dependencies among activities. We develop a novel genetic algorithm (GA) for the resource levelling problem with generalised precedence relations. Our design and implementation of GA features an efficient schedule generation scheme, built upon a new encoding mechanism that combines the random key representation and the shift vector representation. A two-pass local search-based improvement procedure is devised and integrated into the GA to enhance the algorithmic performance. Our GA is able to obtain near optimal solutions with less than 2% optimality gap for small instances in fractions of a second. It outperforms or is competitive with the state-of-the-art algorithms for large benchmark instances with size up to 1000 activities.     

Predictable scheduling of a single machine with breakdowns and sensitive jobs

Production schedules released to the shop floor have two important functions: allocating shop resources to different jobs to optimize some measure of shop performance and serving as a basis for planning external activities such as material procurement, preventive maintenance and delivery of orders to customers. Schedule modification may delay or render infeasible the execution of external activities planned on the basis of the predictive schedule. Thus it is of interest to develop predictive schedules that can absorb disruptions without affecting planned external activities while maintaining high shop performance. We present a predictable scheduling approach, that inserts additional idle time into the schedule to absorb the impacts of breakdowns. The effects of disruptions on planned support activities are measured by the deviations of job completion times in the realized schedule from those in the predictive schedule. We apply our approach to minimizing total tardiness on a single machine with stochastic machine failures. We then extend the procedure to consider the case where job processing times are affected by machine breakdowns, and provide specialized rescheduling heuristics. Extensive computational experiments show that this approach provides high predictability with minor sacrifices in shop performance.     

Schedule robustness analysis with the help of attainable sets in continuous flow problem under capacity disruptions

Continuous flow scheduling problems have their place in many industries such as gas, oil, chemicals, glass and fluids production as well as production of granular goods and steel details. The disruptions in processing capacities may result in schedule performance decrease. In this paper, we develop a new method for robustness analysis of those schedules that are formulated in continuous time in the state-space domain. The developed method is based on attainable sets (ASs) that allow computing a form to represent the states and performance of schedules in regard to different capacity degradation levels. Having such a form, it becomes possible to estimate the schedule robustness. The technical development and approximation of ASs are presented. A robustness index is developed on the basis of the minimax regret approach, and it can be used for decision-makers regarding the trade-off ‘performance vs. robustness’. As such, it becomes possible to compare maximal possible profits in situations without disruptions and realistic profits subject to some robustness investments and costs of protection against disruptions. With the presented results, it becomes possible to obtain ASs for interval data with no a priori information about perturbation impacts, i.e. for non-stationary perturbations. ASs permit to consider perturbations and schedule performances as time functions. Perturbation functions may be set up for different uncertainty scenarios, including interval perturbations.     

An improved robust buffer allocation method for the project scheduling problem

Unpredictable uncertainties cause delays and additional costs for projects. Often, when using traditional approaches, the optimizing procedure of the baseline project plan fails and leads to delays. In this study, a two-stage multi-objective buffer allocation approach is applied for robust project scheduling. In the first stage, some decisions are made on buffer sizes and allocation to the project activities. A set of Pareto-optimal robust schedules is designed using the meta-heuristic non-dominated sorting genetic algorithm (NSGA-II) based on the decisions made in the buffer allocation step. In the second stage, the Pareto solutions are evaluated in terms of the deviation from the initial start time and due dates. The proposed approach was implemented on a real dam construction project. The outcomes indicated that the obtained buffered schedule reduces the cost of disruptions by 17.7% compared with the baseline plan, with an increase of about 0.3% in the project completion time.     

Integrated Project Scheduling and Material Planning with Variable Activity Duration and Rewards

Integrating project scheduling and material ordering adds more realism to project scheduling and considers additional trade-offs that could lead to reductions in the overall project cost. In this paper we review the evolution of the integrated problem, and investigate the impact of treating the activity duration as a decision variable on the activities schedule and materials plan. The effects of introducing rewards (penalties) for early (late) completion as well as materials quantity discounts on the project schedule and cost are also considered- These additions provide scheduling flexibility that might lead to further reduction in the project's total cost or makespan. Considering the various project costs, we found that there exists an optimal schedule that either starts as early as possible (ai time zero) or completes as late as possible. We also show that if the project starts at time zero, then its duration cannot be longer than that for the case where the schedule ends as late as possible. Ft is also shown that the material ordering policy does not follow the Wagner and Whitin model when activity duration is variable or in the presence of quantity discounts. These results have led to modeling and solving the problem in a more efficient manner. Extensive computational work shows the validity of the model and the solution approach.     

The discrete time/resource trade-off problem in project networks: a branch-and-bound approach

In many solution methods for resource-constrained project scheduling, it is assumed that both the duration of each activity and its resource requirements are known and fixed. In real-life projects, however, it often occurs that only one renewable bottleneck resource is available and that the activities have a total work content which indicates how much work (expressed in man-periods) has to be performed. The objective then is to schedule each activity in one of its possible execution modes, subject to the precedence and resource constraints, in order to minimize the project makespan. We present a branch-and-bound procedure and report computational results, obtained using a full factorial experiment on a randomly generated problem set.     

Robust makespan minimisation in identical parallel machine scheduling problem with interval data

Parallel machine scheduling problems are commonly encountered in a wide variety of manufacturing environments and have been extensively studied. This paper addresses a makespan minimisation scheduling problem on identical parallel machines, in which the specific processing time of each job is uncertain, and its probability distribution is unknown because of limited information. In this case, the deterministic or stochastic scheduling model may be unsuitable. We propose a robust (min–max regret) scheduling model for identifying a robust schedule with minimal maximal deviation from the corresponding optimal schedule across all possible job-processing times (called scenarios). These scenarios are specified as closed intervals. To solve the robust scheduling problem, which is NP-hard, we first prove that a regret-maximising scenario for any schedule belongs to a finite set of extreme point scenarios. We then derive two exact algorithms to optimise this problem using a general iterative relaxation procedure. Moreover, a good initial solution (optimal schedule under a mid-point scenario) for the aforementioned algorithms is discussed. Several heuristics are developed to solve large-scale problems. Finally, computational experiments are conducted to evaluate the performance of the proposed methods.     

R&D project selection and scheduling with a filtered beam search approach

Models developed for selecting an optimal portfolio of R&D projects from among a pool of available projects do not generally include project scheduling as part of the selection criteria. Typically after a portfolio of projects is selected according to various selection criteria, they are subsequently scheduled. If it is not possible to schedule the selected projects through various research facilities and/or stages given the available resources, projects may be replaced with others requiring less time, or resources may be increased, which can result in a suboptimal portfolio. In this paper, project scheduling is included in the selection process, with a heuristic filtered beam search approach. Projects are selected on the basis of traditional selection criteria such as expected profits, as well as the makespan of the portfolio. The heuristic search procedure is demonstrated by an in-depth example and computational experimentation.     

Assessing scheduling policies in a permutation flowshop with common due dates

This paper focuses onto a situation arising in most real-life manufacturing environments when scheduling has to be performed periodically. In such a scenario, different scheduling policies can be adopted, being perhaps the most common to assume that, once a set of jobs has been scheduled, their schedule cannot be modified (‘frozen’ schedule). This implies that, when the next set of jobs is to be scheduled, the resources may not be fully available. Another option is assuming that the schedule of the previously scheduled jobs can be modified as long as it does not violate their due date, which has been already possibly committed to the customer. This policy leads to a so-called multi-agent scheduling problem. The goal of this paper is to discern when each policy is more suitable for the case of a permutation flowshop with common due dates. To do so, we carry out an extensive computational study in a test bed specifically designed to control the main factors affecting the policies, so we analyse the solution space of the underlying scheduling problems. The results indicate that, when the due date of the committed jobs is tight, the multi-agent approach does not pay off in view of the difficulty of finding feasible solutions. Moreover, in such cases, the policy of ‘freezing’ the schedule of the jobs leads to a very simple scheduling problem with many good/acceptable solutions. In contrast, when the due date has a medium/high slack, the multi-agent approach is substantially better. Nevertheless, in this latter case, in order to perceive the full advantage of this policy, powerful solution procedures have to be designed, as the structure of the solution space of the latter problem makes extremely hard to find optimal/good solutions.     

A hybrid genetic algorithm and tabu search for a multi-objective dynamic job shop scheduling problem

In most real manufacturing environments, schedules are usually inevitable with the presence of various unexpected disruptions. In this paper, a rescheduling method based on the hybrid genetic algorithm and tabu search is introduced to address the dynamic job shop scheduling problem with random job arrivals and machine breakdowns. Because the real-time events are difficult to express and take into account in the mathematical model, a simulator is proposed to tackle the complexity of the problem. A hybrid policy is selected to deal with the dynamic feature of the problem. Two objectives, which are the schedule efficiency and the schedule stability, are considered simultaneously to improve the robustness and the performance of the schedule system. Numerical experiments have been designed to test and evaluate the performance of the proposed method. This proposed method has been compared with some common dispatching rules and meta-heuristic algorithms that have been widely used in the literature. The experimental results illustrate that the proposed method is very effective in various shop-floor conditions.     

Conceptual modeling of an object-oriented scheduling architecture based on the shifting bottleneck procedure

The importance of finite-capacity schedulers is increasing, with respect to the widespread MRP packages, due to their ability to model the shop floor more accurately. However, this very advantage may turn into a disadvantage, since it is quite difficult to devise a high-quality general purpose scheduler able to cope with the technological peculiarities of different production environments. Furthermore, a detailed schedule is prone to disruptions due to the uncertainty affecting the shop floor. Hence, we need both a modular approach to devise and assemble local schedulers, and a way to link predictive and real time scheduling. To cope with both requirements, we propose a scheduling architecture inspired by the well-known Shifting Bottleneck method. The knowledge needed to cope with technological peculiarities is pushed down the decision hierarchy, to the level of local schedulers. A material coordinator negotiates a reference trajectory with the local schedulers, by deriving local due dates which can be used as targets to drive real time dispatching. The modularity of the architecture is illustrated through an object-oriented conceptual model based on the Unified Modeling Language.     

An efficient time-windowing procedure for scheduling projects under multiple resource constraints

This paper describes an efficient procedure for resource-constrained project scheduling problems. It starts with a simulated annealing technique to find a base schedule, and then improves the result by a time-windowing process. Every time-window, which is part of the base schedule, is a basis for a small subproject with two sets of specific constraints. Associated subprojects with time-windows are scheduled to optimality and based on their results the base schedule is updated. The overlapping feature of time-windows makes the displacement of an activity possible within the range of the entire project. The process of creating time-windows, scheduling their associated subprojects to optimality and improving the base schedule is controlled by a feed-back based mechanism that realises a trade-off between computational effort and the improvement made. The computational results indicate that the procedure is promising and yields better solutions than several heuristic algorithms presented in the literature.     

Finite capacity scheduling using a predetermined placement sequence

This paper presents a new heuristic for the finite capacity scheduling of factories. It treats the task of scheduling a factory the same as scheduling a large project that has many delivery points. The job placement sequence on machines is used as a link between infinite capacity schedules and finite capacity schedules. An ideal placement sequence is proposed from the infinite capacity backward schedule and this is embedded into the project network for finite capacity scheduling. This allows a finite capacity algorithm whose boundary condition is the most tightly packed schedule possible, and is also ideal from a cash flow perspective. The paper proposes a preference list of machines as an integral part of the scheduling process and shows how to switch between machines using preferences. It also shows how to integrate infinite and finite capacity schedules within the same algorithm by using a parameter called the constraint horizon. The heuristic is explained using input-output matrices and by working through an example of a project network. The example includes a discussion of circuits and a detailed explanation of how to implement the heuristic in a computer program.     

Production rescheduling for machine breakdown at a job shop

Rescheduling is a procedure to repair a production plan affected by unexpected disruptions. It is important because a production schedule released to a shop floor is subject to unexpected disruptions. This paper develops two novel heuristic rescheduling procedures, AWI-J and AWI-O, to minimise mean tardiness of jobs in a job shop. The procedures are based on an active schedule-generation procedure and Wilkerson–Irwin algorithm, which is well known for minimising mean tardiness of a schedule. The performances of the new procedures are compared with those of the Affected Operations Rescheduling (AOR) procedure, which is popular in the rescheduling literature. Efficiency measures such as mean tardiness, mean flowtime, and makespan, and a stability measure, deviation of new operation start times from the original start times, are used for comparison. Test results show that AWI-J improves efficiency over AOR, and AWI-O improves efficiency and stability as well.     

Scheduling of deadlock and failure-prone automated manufacturing systems via hybrid heuristic search

This work focuses on the scheduling problem of deadlock and failure-prone automated manufacturing systems, and presents a new scheduling method by combining a robust supervisory control policy and hybrid heuristic search. It aims to minimise makespan, i.e. the completion time of the last part. Based on the extended reach ability graph of the system, it establishes a new heuristic function and two dispatching rules to guide the search process for a schedule. By embedding a robust supervisory control policy into the search process, it develops a polynomial robust dynamic window search algorithm. Failure and repair events of unreliable resources may occur during the execution of a schedule obtained by the proposed algorithm and may make the schedule infeasible. To reduce the influence caused by them and ensure all parts to be finished, this work proposes two event-driven strategies. The first one suspends the execution of the parts requiring failed resources and those to be started until all failed resources are repaired and permits only those parts that have already been processed on working machines to be completed. The second one invokes the proposed algorithm to obtain a new schedule at the vertex generated after a resource failure or repair event and executes the new schedule. Both strategies are effective while the latter performs better at the expense of more computation.     

Towards building an intelligent decision support system for project management

Management of large projects, especially the ones in which a major component of R&D is involved and those requiring knowledge from diverse specialised and sophisticated fields, may be classified as semi-structured problems. In these problems, there is some knowledge about the nature of the work involved, but there are also uncertainties associated with emerging technologies. In order to draw up a plan and schedule of activities of such a large and complex project, the project manager is faced with a host of complex decisions that he has to take, such as, when to start an activity, for how long the activity is likely to continue, etc. An Intelligent Decision Support System (IDSS) which aids the manager in decision making and drawing up a feasible schedule of activities while taking into consideration the constraints of resources and time, will have a considerable impact on the efficient management of the project. This report discusses the design of an IDSS that helps in project planning phase through the scheduling phase. The IDSS uses a new project scheduling tool, the Project Influence Graph (PIG).     

Comparing deterministic,robust and online scheduling using entropy

We conjecture that when the uncertainty of scheduling information increases, one should change from deterministic, through robust to, finally, online scheduling techniques. Previously, extensive mathematical investigations have been carried out on the stability of a deterministic schedule for uncertain operation processing times. In this paper, we will use an empirical approach and an entropy measure to justify the transition between deterministic, robust and online scheduling. The use of an entropy measure in our context can be perceived, in a broader sense, as a pro-active approach to deal with changes in the level of information uncertainty and relative importance of each term in the total schedule execution cost. The level of information uncertainty may change due to the performance deterioration of processors (machines or human) and the replacement of old machines with new ones; and the changes in relative importance of cost elements may be due to changes in shop floor priorities and pressure from partners in the supply chain network. One can decide upon the scheduling strategies to be employed based on the latest entropy value of the information considered and the relative importance of each cost term.