The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling

Over the past few years growing global competition has forced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies in performance enhancement of the industries, in terms of their reduced makespan. The authors have also proposed a new hybrid Enhanced Swift Converging Simulated Annealing (ESCSA) algorithm, to solve the complex real-time scheduling problems. The proposed algorithm inherits the prominent features of the Genetic Algorithm (GA), Simulated Annealing (SA), and the Fuzzy Logic Controller (FLC). The ESCSA algorithm reduces the makespan significantly in less computational time and number of iterations. The efficacy of the proposed algorithm has been shown by comparing the results with GA, SA, Tabu, and hybrid Tabu-SA optimization methods

Pre-emptive resource-constrained multimode project scheduling using genetic algorithm: a dynamic forward approach

Purpose: The issue resource over-allocating is a big concern for project engineers in the process of scheduling project activities. Resource over-allocating drawback is frequently seen after scheduling of a project in practice which causes a schedule to be useless. Modifying an over-allocated schedule is very complicated and needs a lot of efforts and time. In this paper, a new and fast tracking method is proposed to schedule large scale projects which can help project engineers to schedule the project rapidly and with more confidence. Design/methodology/approach: In this article, a forward approach for maximizing net present value (NPV) in multi-mode resource constrained project scheduling problem while assuming discounted positive cash flows (MRCPSP-DCF) is proposed. The progress payment method is used and all resources are considered as pre-emptible. The proposed approach maximizes NPV using unscheduled resources through resource calendar in forward mode. For this purpose, a Genetic Algorithm is applied to solve. Findings: The findings show that the proposed method is an effective way to maximize NPV in MRCPSP-DCF problems while activity splitting is allowed. The proposed algorithm is very fast and can schedule experimental cases with 1000 variables and 100 resources in few seconds. The results are then compared with branch and bound method and simulated annealing algorithm and it is found the proposed genetic algorithm can provide results with better quality. Then algorithm is then applied for scheduling a hospital in practice. Originality/value: The method can be used alone or as a macro in Microsoft Office Project® Software to schedule MRCPSP-DCF problems or to modify resource over-allocated activities after scheduling a project. This can help project engineers to schedule project activities rapidly with more accuracy in practice.Peer Reviewe

Railway scheduling reduces the expected project makespan.

The Critical Chain Scheduling and Buffer Management (CC/BM) methodology, proposed by Goldratt (1997), introduced the concepts of feeding buffers, project buffers and resource buffers as well as the roadrunner mentality. This last concept, in which activities are started as soon as possible, was introduced in order to speed up projects by taking advantage of predecessors finishing early. Later on, the railway scheduling concept of never starting activities earlier than planned was introduced as a way to increase the stability of the project, typically at the cost of an increase in the expected project makespan. In this paper, we will indicate a realistic situation in which railway scheduling improves both the stability and the expected project makespan over roadrunner scheduling.Railway scheduling; Roadrunner scheduling; Feeding buffer; Priority list; Resource availability;

A hybrid shifting bottleneck-tabu search heuristic for the job shop total weighted tardiness problem

In this paper, we study the job shop scheduling problem with the objective of minimizing the total weighted tardiness. We propose a hybrid shifting bottleneck - tabu search (SB-TS) algorithm by replacing the reoptimization step in the shifting bottleneck (SB) algorithm by a tabu search (TS). In terms of the shifting bottleneck heuristic, the proposed tabu search optimizes the total weighted tardiness for partial schedules in which some machines are currently assumed to have infinite capacity. In the context of tabu search, the shifting bottleneck heuristic features a long-term memory which helps to diversify the local search. We exploit this synergy to develop a state-of-the-art algorithm for the job shop total weighted tardiness problem (JS-TWT). The computational effectiveness of the algorithm is demonstrated on standard benchmark instances from the literature

Optimization Algorithms in Project Scheduling

Scheduling, or planning in a general perspective, is the backbone of project management; thus, the successful implementation of project scheduling is a key factor to projects’ success. Due to its complexity and challenging nature, scheduling has become one of the most famous research topics within the operational research context, and it has been widely researched in practical applications within various industries, especially manufacturing, construction, and computer engineering. Accordingly, the literature is rich with many implementations of different optimization algorithms and their extensions within the project scheduling problem (PSP) analysis field. This study is intended to exhibit the general modelling of the PSP, and to survey the implementations of various optimization algorithms adopted for solving the different types of the PSP

Multi-Mode Resource-Constrained Project-Scheduling Problem With Renewable Resources: New Solution Approaches

We consider the multi-mode resource-constrained project scheduling problem (MRCPSP) with renewable resources.  In MRCPSP, an activity can be executed in one of many possible modes; each mode having different resource requirements and accordingly different activity durations.  We assume that all resources are renewable from period to period, such as labor and machines.  A solution to this problem basically involves two decisions – (i) The start time for each activity and (ii) the mode for each activity.  Given the NP-Hard nature of the problem, heuristics and metaheuristics are used to solve larger instances of this problem.  A heuristic for this type of problem involves a combination of two priority rules - one for each of the two decisions.  Heuristics generally tend to be greedy in nature.  In this study we propose two non-greedy heuristics for mode selection which perform better than greedy heuristics.  In addition, we study the effect of double justification and backward/forward scheduling for the MRCPS.  We also study the effect of serial vs. parallel scheduling.  We found that all these elements improved the solution quality.  Finally we propose an adaptive metaheuristic procedure based on neural networks which further improves the solution quality.  The effectiveness of these proposed approaches, compared to existing approaches in the literature, is demonstrated through empirical testing on two well-known sets of benchmark problems

Resource Schedule of Concrete Fish Pond Construction Using Network Analysis

In the construction of building, preparation of bid, maintenance and planning of oil refinery and preparation for agricultural activities, there is a need to know the completion days of the project without delay and the earliest time and the latest time for which each activity will take. It was based on this that we decide to analyze the construction of concrete fish pond using Network Analysis through the use of Critical Path Method (CPM) and Program Evaluation Review Technique (PERT). Sixty-four days was arrived at for the completion of the construction using CPM while sixty-eight days with 99% probability was arrived at using PERT method. In deciding which of the method is best suitable for the construction of the fish pond, PERT serve as the best method due to the fact that it considers the Pessimistic Time (longest time possible and can be seen as usual delay) and Optimistic Time (shortest time possible if things go perfectly) as well as the probability [which is 99%] of completing the task within a specific time. The result established some useful facts for researchers in this area as well as managers of industry in carrying out their study from the feasibility stage to the other stages so as to have a good practical target towards the completion of the project as planned. Keyword: Network Analysis, Critical Path Method, Program Evaluation Review Technique, Pessimistic Time, Optimistic Time and Probability DOI: 10.7176/JMCR/57-04 Publication date:June 30th 201

Parallel machine scheduling with release dates, due dates and family setup times

In manufacturing, there is a fundamental conflict between efficient production and delivery performance. Maximizing machine utilization by batching similar jobs may lead to poor delivery performance. Minimizing customers' dissatisfaction may lead to an inefficient use of the machines. In this paper, we consider the problem of scheduling n independent jobs with release dates, due dates, and family setup times on m parallel machines. The objective is to minimize the maximum lateness of any job. We present a branch-and-bound algorithm to solve this problem. This algorithm exploits the fact that an optimal schedule is contained in a specific subset of all feasible schedules. For lower bounding purposes, we see setup times as setup jobs with release dates, due dates and processing times. We present two lower bounds for the problem with setup jobs, one of which proceeds by allowing preemption