Stochastic cyclic scheduling problem in synchronous assembly and production lines

This paper considers two types of stochastic group scheduling problems, one involves a single machine, and the other involves an m -machine flow shop. In both problems, jobs are classified into groups. The jobs of the same group are to be processed one after another, and a sequence independent setup time is incurred between each two consecutively scheduled groups. The job processing times are independent random variables and group setup times are functions of the job processing times. The objective is to find both a group sequence and a job sequence that minimizes the expected total flow time. An optimal group sequence and an optimal job sequence are obtained by processing jobs in increasing stochastic order of their mean processing times and group setup times for a single machine problem. A precedence relationship is derived for the m -machine flow shop problem.

No framework or classification scheme has ever been introduced for stochastic scheduling problems. It is more difficult to develop such a scheme for stochastic scheduling problems than for deterministic scheduling problems. In order to characterize a stochastic scheduling problem more information is required. For example, the distributions of the processing times have to be specified as well as the distributions of the due dates (which may be different). It has to be specified whether the processing times of the n jobs are independent or correlated (e.g., equal to the same random variable) and also which class of policies is considered. For these reasons no framework has been introduced in this book either

Efficient scheduling of a stochastic no-wait job shop with controllable processing times

With the insignificant incremental cost involved in hydrogeneration, the problem of minimizing the operation cost of a hydro-thermal system essentially reduces to that of minimizing the fuel cost for thermal plants under the constraints of the water available for hydro generation in a given period of time. The problem thus belongs to the realm of dynamic optimization.In the past, many papers pertaining to optimal hydro-thermal scheduling assume water inflows to reservoirs and load demand to be deterministic. In practice, however, this is not true. To be realistic, the scheduling problem must be tackled through stochastic optimization. This paper presents a new effective approach to the optimal stochastic hydro-thermal scheduling problem. A computational algorithm based on first- order gradient technique is developed to solve the scheduling problem.

A stochastic scheduling, allocation, and inventory replenishment problem for battery swap stations

The D.A.I. theorem of Gittins and Jones has proved a powerful tool in solving sequential statistical problems. A generalisation of this theorem is presented. This generalisation enables us to solve certain stochastic scheduling problems where the items or jobs to be scheduled have random times to completion, the random times having distributions dependent upon parameters to which prior distributions are allocated. Such problems are of interest in many areas where scheduling is important.

A two-stage assignment strategy for the robust scheduling of dual-resource constrained stochastic job shop scheduling problems

A simulation-based modified backtracking search algorithm for multi-objective stochastic flexible job shop scheduling problem with worker flexibility

Optimising stochastic task allocation and scheduling plans for mission workers subject to learning-forgetting, fatigue-recovery, and stress-recovery effects

Evolutionary algorithm for stochastic job shop scheduling with random processing time

Enumerations and stability analysis of feasible and optimal line balances for simple assembly lines

The issues of cycle time management and cost reduction in the service industry are the concerns of the empirical study presented in this paper. In specific, we study the claims handling process which is one of the most essential and critical functions of an insurance company. The objective of our study is to gain an understanding of the impact of various operational decisions, such as assignment of claims to claims analysts, choice of claims handling method and involvement of an attorney on the cycle time and cost performance of claims operation. The field study presented in this paper was conducted at a major insurance company where we collected and analysed a large dataset on claims characteristics, cost, cycle time and operational decisions of claims handling process. The paper identifies the important claim characteristics that drive the cost and cycle time performance in settling a claim and concludes with a discussion on the managerial implications of the field study.

Rearrangement inequalities, such as the classical Hardy-Littlewood-Polya inequality and the more general Day's inequality, and related majorization results are often useful in solving scheduling problems. Among other things, they are essential for pairwise interchange arguments. Motivated by solving stochastic scheduling problems, we develop stochastic versions of Day's Inequality, over both unrestricted and restricted (specifically, one-cycle) permutations. These lead to a general and unified approach, which we apply to solve the stochastic versions of several classical deterministic scheduling problems. In most cases, the approach leads to new or stronger results; in other cases it recovers known results with new insight. The approach is built upon recent developments in stochastic majorization and multivariate characterization of stochastic order relations.

We study the preemptive stochastic online scheduling problem on uniformly related machines where jobs are arriving over time. The objective is to minimize total weighted expected completion time. We consider the class of machines sets where the speed ratios have a lower bound. For each set of machines in this class, we design an algorithm with competitive is 2. More specially, we show that the competitive of our algorithm can't be improved. This generalizes the currently best known result for identical machines and includes the result for two uniform machines obtained by Gu et al.

Stochastic customer order scheduling on heterogeneous parallel machines with resource allocation consideration