Benchmarking Permutation Flow Shop Problem: Adaptive and Enumerative Approaches Implementations via Novel Threading Techniques

A large number of real-world planning problems are combinatorial optimization problems which are easy to state and have a finite but usually very large number of feasible solutions. The minimum spanning tree problem and the shortest path problem are some which are solvable through polynomial algorithms. Even though there are other problems such as crew scheduling, vehicle routing, production planning, and hotel room operations which have no properties such as to solve the problem with polynomial algorithms. All these problems are NP-hard. The permutation flow shop problem is also NP-hard problem and they require high computation. These problems are solvable as in the form of the optimal and near-optimal solution. Some approach to get optimal are exhaustive search and branch and bound whereas near optimal are achieved annealing, Genetic algorithm, and other various methods. We here have used different approach exhaustive search, branch and bound and genetic algorithm. We optimize these algorithms to get performance in time as well as get the result closer to optimal. The exhaustive search and branch and bound gives all possible optimal solutions. We here have shown the comparative result of optimal calculation for 10 jobs with varying machine number up to 20. The genetic algorithm scales up and gives results to the instances with a larger number of jobs and machines

A survey of scheduling problems with setup times or costs

Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Design of a solution technique based on an integral approach for the Flexible Open-Flow Shop scheduling problem

In manufacturing industries, scheduling is a form of decision-making that plays a crucial role. The determination of the methods by which a set of jobs must be manufactured in order to seek specific goals leads to the development of different schedule techniques. However, scheduling depends on the type of workshop or manufacturing environment such as open shop, job shop and flow shop. There are cases that more than one environment for the same manufacturing process could coexist. This project deals with a specific scheduling problem in which each job is processed under the combination of two shop environments; the first one is related to an open shop while the second one corresponds to a flow shop; this problem is called the Flexible open-flow shop (FOFS). These types of scheduling problems present NP-hardness, meaning the neediness of sophisticated algorithms to find solutions in reasonable computational times. Additionally, are commonly solved separately or by approximating into another workshop, leaving the interaction of both environments irrelevant. Thus, the main objective of this project is to design solution techniques based on an integral approach to minimize the maximum completion time also known as makespan.Ingeniero (a) IndustrialPregrad

Optimal Ship Maintenance Scheduling Under Restricted Conditions and Constrained Resources

The research presented in this dissertation addresses the application of evolution algorithms, i.e. Genetic Algorithm (GA) and Differential Evolution algorithm (DE) to scheduling problems in the presence of restricted conditions and resource limitations. This research is motivated by the scheduling of engineering design tasks in shop scheduling problems and ship maintenance scheduling problems to minimize total completion time. The thesis consists of two major parts; the first corresponds to the first appended paper and deals with the computational complexity of mixed shop scheduling problems. A modified Genetic algorithm is proposed to solve the problem. Computational experiments, conducted to evaluate its performance against known optimal solutions for different sized problems, show its superiority in computation time and the high applicability in practical mixed shop scheduling problems. The second part considers the major theme in the second appended paper and is related to the ship maintenance scheduling problem and the extended research on the multi-mode resource-constrained ship scheduling problem. A heuristic Differential Evolution is developed and applied to solve these problems. A mathematical optimization model is also formulated for the multi-mode resource-constrained ship scheduling problem. Through the computed results, DE proves its effectiveness and efficiency in addressing both single and multi-objective ship maintenance scheduling problem

Four decades of research on the open-shop scheduling problem to minimize the makespan

One of the basic scheduling problems, the open-shop scheduling problem has a broad range of applications across different sectors. The problem concerns scheduling a set of jobs, each of which has a set of operations, on a set of different machines. Each machine can process at most one operation at a time and the job processing order on the machines is immaterial, i.e., it has no implication for the scheduling outcome. The aim is to determine a schedule, i.e., the completion times of the operations processed on the machines, such that a performance criterion is optimized. While research on the problem dates back to the 1970s, there have been reviving interests in the computational complexity of variants of the problem and solution methodologies in the past few years. Aiming to provide a complete road map for future research on the open-shop scheduling problem, we present an up-to-date and comprehensive review of studies on the problem that focuses on minimizing the makespan, and discuss potential research opportunities

Integrating Capacitated Lot-Sizing and Lot Streaming in Flowshop Schedules with Time Varying Demand

Any reasonable production planning contains three important decisions on lot size, lead time, and capacity. The common approach in the literature is to divide the planning problem into lot sizing, lot sequencing, and lot splitting sub-problems. Very few studies, to the best of our knowledge, have been conducted on the interdependencies and three- way interaction of lead-time, lot size, and actual capacity usage. A particular lot size calculated by the sub-problem method, however, will likely yield an infeasible solution or at least result in schedule instability (nervousness). This is just because in most capacitated lot sizing models, the capacity constraints in the model only take into consideration the available time on each work station, ignoring the sequencing of lots, sublot sizes, and their effect on makespan and lead times. In this thesis we bridge the gap between lot sizing and scheduling in flowshops, and examine the use of the lot splitting and sequencing techniques to reduce schedule instability. A mixed integer programming formulation is presented, which enables us to simultaneously find the optimal lot sizes as well as the corresponding sublot sizes and sequence of jobs. With this model, small size problems can be solved within a reasonable time. The computational results confirm that this model can be advantageous in dampening the scheduling nervousness. For larger size instances, a Genetic algorithm is proposed