Serial-batch scheduling – the special case of laser-cutting machines

The dissertation deals with a problem in the field of short-term production planning, namely the scheduling of laser-cutting machines. The object of decision is the grouping of production orders (batching) and the sequencing of these order groups on one or more machines (scheduling). This problem is also known in the literature as "batch scheduling problem" and belongs to the class of combinatorial optimization problems due to the interdependencies between the batching and the scheduling decisions. The concepts and methods used are mainly from production planning, operations research and machine learning

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

Project portfolio management: capacity allocation, downsizing decisions and sequencing rules.

This paper aims to gain insight into capacity allocation, downsizing decisions and sequencing rules when managing a portfolio of projects. By downsizing, we mean reducing the scale or size of a project and thereby changing the project's content. In previous work, we have determined the amount of critical capacity that is optimally allocated to concurrently executed projects with deterministic or stochastic workloads when the impact of downsizing is known. In this paper, we extend this view with the possibility of sequential processing, which implies that a complete order is imposed on the projects. When projects are sequenced instead of executed in parallel, two effects come into play: firstly, unused capacity can be shifted to later projects in the same period; and secondly, reinvestment revenues gain importance because of the differences in realization time of the sequenced projects. When project workloads are known, only the second effect counts; when project workloads are stochastic, however, the project's capacity usage is uncertain so that unused capacity can be shifted to later projects in the same period. In this case, both effects need to be taken into account. In this paper, we determine optimal sequencing rules when the selection and capacity-allocation decisions for a set of projects have already been made. We also consider a combination of parallel and sequential planning and we perform simulation experiments that confirm the appropriateness of our capacity-allocation methods.Project portfolio management; Downsizing; Sequencing;

Order Acceptance and Scheduling: A Taxonomy and Review

Over the past 20 years, the topic of order acceptance has attracted considerable attention from those who study scheduling and those who practice it. In a firm that strives to align its functions so that profit is maximized, the coordination of capacity with demand may require that business sometimes be turned away. In particular, there is a trade-off between the revenue brought in by a particular order, and all of its associated costs of processing. The present study focuses on the body of research that approaches this trade-off by considering two decisions: which orders to accept for processing, and how to schedule them. This paper presents a taxonomy and a review of this literature, catalogs its contributions and suggests opportunities for future research in this area

Heuristic and Exact Algorithms for the Two-Machine Just in Time Job Shop Scheduling Problem

The problem addressed in this paper is the two-machine job shop scheduling problem when the objective is to minimize the total earliness and tardiness from a common due date (CDD) for a set of jobs when their weights equal 1 (unweighted problem). This objective became very significant after the introduction of the Just in Time manufacturing approach. A procedure to determine whether the CDD is restricted or unrestricted is developed and a semirestricted CDD is defined. Algorithms are introduced to find the optimal solution when the CDD is unrestricted and semirestricted. When the CDD is restricted, which is a much harder problem, a heuristic algorithms proposed to find approximate solutions. Through computational experiments, the heuristic algorithms\u27 performance is evaluated with problems up to 500 jobs

Order acceptance and scheduling in a single-machine environment: exact and heuristic algorithms.

In this paper, we develop exact and heuristic algorithms for the order acceptance and scheduling problem in a single-machine environment. We consider the case where a pool consisting of firm planned orders as well as potential orders is available from which an over-demanded company can select. The capacity available for processing the accepted orders is limited and orders are characterized by known processing times, delivery dates, revenues and the weight representing a penalty per unit-time delay beyond the delivery date promised to the customer. We prove the non-approximability of the problem and give two linear formulations that we solve with CPLEX. We devise two exact branch-and-bound procedures able to solve problem instances of practical dimensions. For the solution of large instances, we propose six heuristics. We provide a comparison and comments on the efficiency and quality of the results obtained using both the exact and heuristic algorithms, including the solution of the linear formulations using CPLEX.Order acceptance; Scheduling; Single machine; Branch-and-bound; Heuristics; Firm planned orders;

Heuristics for periodical batch job scheduling in a MapReduce computing framework

Task scheduling has a significant impact on the performance of the MapReduce computing framework. In this paper, a scheduling problem of periodical batch jobs with makespan minimization is considered. The problem is modeled as a general two-stage hybrid flow shop scheduling problem with schedule-dependent setup times. The new model incorporates the data locality of tasks and is formulated as an integer program. Three heuristics are developed to solve the problem and an improvement policy based on data locality is presented to enhance the methods. A lower bound of the makespan is derived. 150 instances are randomly generated from data distributions drawn from a real cluster. The parameters involved in the methods are set according to different cluster setups. The proposed heuristics are compared over different numbers of jobs and cluster setups. Computational results show that the performance of the methods is highly dependent on both the number of jobs and the cluster setups. The proposed improvement policy is effective and the impact of the input data distribution on the policy is analyzed and tested.This work is supported by the National Natural Science Foundation of China (No. 61272377) and the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20120092110027). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "RESULT - Realistic Extended Scheduling Using Light Techniques" (No. DPI2012-36243-C02-01) partially financed with FEDER funds.Xiaoping Li; Tianze Jiang; Ruiz García, R. (2016). Heuristics for periodical batch job scheduling in a MapReduce computing framework. Information Sciences. 326:119-133. https://doi.org/10.1016/j.ins.2015.07.040S11913332