Competitive two-agent scheduling with deteriorating jobs on a single parallel-batching machine

We consider a scheduling problem in which the jobs are generated by two agents and have time-dependent proportional-linear deteriorating processing times. The two agents compete for a common single batching machine to process their jobs, and each agent has its own criterion to optimize. The jobs may have identical or different release dates. The batching machine can process several jobs simultaneously as a batch and the processing time of a batch is equal to the longest of the job processing times in the batch. The problem is to determine a schedule for processing the jobs such that the objective of one agent is minimized, while the objective of the other agent is maintained under a fixed value. For the unbounded model, we consider various combinations of regular objectives on the basis of the compatibility of the two agents. For the bounded model, we consider two different objectives for incompatible and compatible agents: minimizing the makespan of one agent subject to an upper bound on the makespan of the other agent and minimizing the number of tardy jobs of one agent subject to an upper bound on the number of tardy jobs of the other agent. We analyze the computational complexity of various problems by either demonstrating that the problem is intractable or providing an efficient exact algorithm for the problem. Moreover, for certain problems that are shown to be intractable, we provide efficient algorithms for certain special cases

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 common framework and taxonomy for multicriteria scheduling problems with Interfering and competing Jobs: Multi-agent scheduling problems

Most classical scheduling research assumes that the objectives sought are common to all jobs to be scheduled. However, many real-life applications can be modeled by considering different sets of jobs, each one with its own objective(s), and an increasing number of papers addressing these problems has appeared over the last few years. Since so far the area lacks a uni ed view, the studied problems have received different names (such as interfering jobs, multi-agent scheduling, mixed-criteria, etc), some authors do not seem to be aware of important contributions in related problems, and solution procedures are often developed without taking into account existing ones. Therefore, the topic is in need of a common framework that allows for a systematic recollection of existing contributions, as well as a clear de nition of the main research avenues. In this paper we review multicriteria scheduling problems involving two or more sets of jobs and propose an uni ed framework providing a common de nition, name and notation for these problems. Moreover, we systematically review and classify the existing contributions in terms of the complexity of the problems and the proposed solution procedures, discuss the main advances, and point out future research lines in the topic

Scheduling Two Competing Agents When One Agent Has Significantly Fewer Jobs

We study a scheduling problem where two agents (each equipped with a private set of jobs) compete to perform their respective jobs on a common single machine. Each agent wants to keep the weighted sum of completion times of his jobs below a given (agent-dependent) bound. This problem is known to be NP-hard, even for quite restrictive settings of the problem parameters. We consider parameterized versions of the problem where one of the agents has a small number of jobs (and where this small number constitutes the parameter). The problem becomes much more tangible in this case, and we present three positive algorithmic results for it. Our study is complemented by showing that the general problem is NP-complete even when one agent only has a single job

Production Scheduling

Generally speaking, scheduling is the procedure of mapping a set of tasks or jobs (studied objects) to a set of target resources efficiently. More specifically, as a part of a larger planning and scheduling process, production scheduling is essential for the proper functioning of a manufacturing enterprise. This book presents ten chapters divided into five sections. Section 1 discusses rescheduling strategies, policies, and methods for production scheduling. Section 2 presents two chapters about flow shop scheduling. Section 3 describes heuristic and metaheuristic methods for treating the scheduling problem in an efficient manner. In addition, two test cases are presented in Section 4. The first uses simulation, while the second shows a real implementation of a production scheduling system. Finally, Section 5 presents some modeling strategies for building production scheduling systems. This book will be of interest to those working in the decision-making branches of production, in various operational research areas, as well as computational methods design. People from a diverse background ranging from academia and research to those working in industry, can take advantage of this volume

04231 Abstracts Collection -- Scheduling in Computer and Manufacturing Systems

During 31.05.-04.06.04, the Dagstuhl Seminar 04231 "Scheduling in Computer and Manufacturing Systems" was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Holistic, data-driven, service and supply chain optimisation: linked optimisation.

The intensity of competition and technological advancements in the business environment has made companies collaborate and cooperate together as a means of survival. This creates a chain of companies and business components with unified business objectives. However, managing the decision-making process (like scheduling, ordering, delivering and allocating) at the various business components and maintaining a holistic objective is a huge business challenge, as these operations are complex and dynamic. This is because the overall chain of business processes is widely distributed across all the supply chain participants; therefore, no individual collaborator has a complete overview of the processes. Increasingly, such decisions are automated and are strongly supported by optimisation algorithms - manufacturing optimisation, B2B ordering, financial trading, transportation scheduling and allocation. However, most of these algorithms do not incorporate the complexity associated with interacting decision-making systems like supply chains. It is well-known that decisions made at one point in supply chains can have significant consequences that ripple through linked production and transportation systems. Recently, global shocks to supply chains (COVID-19, climate change, blockage of the Suez Canal) have demonstrated the importance of these interdependencies, and the need to create supply chains that are more resilient and have significantly reduced impact on the environment. Such interacting decision-making systems need to be considered through an optimisation process. However, the interactions between such decision-making systems are not modelled. We therefore believe that modelling such interactions is an opportunity to provide computational extensions to current optimisation paradigms. This research study aims to develop a general framework for formulating and solving holistic, data-driven optimisation problems in service and supply chains. This research achieved this aim and contributes to scholarship by firstly considering the complexities of supply chain problems from a linked problem perspective. This leads to developing a formalism for characterising linked optimisation problems as a model for supply chains. Secondly, the research adopts a method for creating a linked optimisation problem benchmark by linking existing classical benchmark sets. This involves using a mix of classical optimisation problems, typically relating to supply chain decision problems, to describe different modes of linkages in linked optimisation problems. Thirdly, several techniques for linking supply chain fragmented data have been proposed in the literature to identify data relationships. Therefore, this thesis explores some of these techniques and combines them in specific ways to improve the data discovery process. Lastly, many state-of-the-art algorithms have been explored in the literature and these algorithms have been used to tackle problems relating to supply chain problems. This research therefore investigates the resilient state-of-the-art optimisation algorithms presented in the literature, and then designs suitable algorithmic approaches inspired by the existing algorithms and the nature of problem linkages to address different problem linkages in supply chains. Considering research findings and future perspectives, the study demonstrates the suitability of algorithms to different linked structures involving two sub-problems, which suggests further investigations on issues like the suitability of algorithms on more complex structures, benchmark methodologies, holistic goals and evaluation, processmining, game theory and dependency analysis