Scheduling problems with two competing agents

We consider the scheduling problems arising when two agents, each with a set of nonpreemptive jobs, compete to perform their respective jobs on a common processing resource. Each agent wants to minimize a certain objective function, which depends on the completion times of its jobs only. The objective functions we consider in this paper are maximum of regular functions (associated with each job), number of late jobs, and total weighted completion times. We obtain different scenarios, depending on the objective function of each agent, and on the structure of the processing system (single machine or shop). For each scenario, we address the complexity of various problems, namely, finding the optimal solution for one agent with a constraint on the other agent's cost function, finding single nondominated schedules (i.e., such that a better schedule for one of the two agents necessarily results in a worse schedule for the other agent), and generating all nondominated schedules

Performance Bounds for Scheduling Queueing Networks

The goal of this paper is to assess the improvement in performance that might' be achieved by optimally scheduling a multiclass open queueing network. A stochastic process is defined whose steady-state mean value is less than or equal to the mean number of customers in a queueing network under any arbitrary scheduling policy. Thus, this process offers a lower bound on performance when the objective of the queueing network scheduling problem is to minimize the mean number of customers in the network. Since this bound is easily obtained from a computer simulation model of a queueing network, its main use is to aid job-shop schedulers in determining how much further improvement (relative to their proposed policies) might be achievable from scheduling. Through computational examples, we identify some factors that affect the tightness of the bound

Overview on: sequencing in mixed model flowshop production line with static and dynamic context

In the present work a literature overview was given on solution techniques considering basic as well as more advanced and consequently more complex arrangements of mixed model flowshops. We first analyzed the occurrence of setup time/cost; existing solution techniques are mainly focused on permutation sequences. Thereafter we discussed objectives resulting in the introduction of variety of methods allowing resequencing of jobs within the line. The possibility of resequencing within the line ranges from 1) offline or intermittent buffers, 2) parallel stations, namely flexible, hybrid or compound flowshops, 3) merging and splitting of parallel lines, 4) re-entrant flowshops, to 5) change job attributes without physically interchanging the position. In continuation the differences in the consideration of static and dynamic demand was studied. Also intermittent setups are possible, depending on the horizon and including the possibility of resequencing, four problem cases were highlighted: static, semi dynamic, nearly dynamic and dynamic case. Finally a general overview was given on existing solution methods, including exact and approximation methods. The approximation methods are furthermore divided in two cases, know as heuristics and methaheuristic