New developments in stochastic models of manufacturing and service operations

This issue originates from the 9th Conference on Stochastic Models of Manufacturing and Service Operations (SMMSO 2013) that took place in Kloster Seeon, Bavaria, Germany, in 25–30 May 2013.

The aim of that conference was to serve as a forum for researchers and practitioners to present and discuss their most recent findings in the development and analysis of stochastic models for the design, coordination, and control of manufacturing and service system operations. Although the title includes both manufacturing and service systems, the main emphasis was on manufacturing system operations. The term ‘service operations’ was intended to refer mainly to functions that are supportive of manufacturing system operations.

To accomplish a wider dissemination of the results that were reported at the conference along with those obtained by other researchers in the area of stochastic modelling of manufacturing and service systems operations, an open call for papers in the International Journal of Production Research (IJPR) was announced after the conference, leading to this special issue. The ten articles published herein were selected among over 40 submitted manuscripts, following the standard, rigorous review procedure of IJPR.

In the first article, ‘Setting optimal production lot sizes and planned lead times in a job shop,’ Rong Yuan and Stephen Graves propose a planning model for a job shop to determine the optimal tactical policies that minimise the relevant manufacturing costs subject to workload variability and capacity limits. For this model, they consider two tactical decisions, namely the production lot size for each part and the planned lead time for each work station.

In their article, ‘A segmentation approach for solving buffer allocation problems in large production systems,’ Chuan Shi and Stanley Gershwin consider the problem of finding the optimal buffer allocation for a production line subject to random disturbances. They propose a solution method that divides a long production line into several short segments that are optimised separately, and they numerically demonstrate the efficiency and accuracy of this method.

Qingkai Ji, Lijun Sun, Xiangpei Hu, and Jing Hou, in their article, ‘Optimal policies of a two-echelon serial inventory system with general limited capacities,’ characterise the structure of the optimal policy of capacitated, two-echelon, serial inventory systems with zero lead times, limited capacities, and random demand. They also provide a number of properties to underscore their research.

In ‘Dynamic admission control for two customer classes with stochastic demands and strict due dates,’ Tanja Mlinar and Philippe Chevalier analyse a dynamic capacity allocation problem with admission control decisions for a company that caters to two demand classes with random arrivals, capacity requirements, and strict due dates. They develop a Markov Decision Process for finding the optimal policy for capacity reservation over time with respect to two kinds of supplied services, and they construct efficient threshold-based approximate algorithms to numerically solve it.

Sonja Otten, Ruslan Krenzler, and Hans Daduna, in their article, ‘Models for integrated production-inventory systems: steady state and cost analysis,’ consider a supply chain network consisting of production systems (servers) in several locations, each with a local inventory and a supplier. They develop a Markov process model of the network and show that the stationary distribution of its global state is of a product form. Based on this result, they show a number of monotonicity properties of the network and present numerical results on its performance.

In ‘Two-stage stochastic master production scheduling under demand uncertainty in a rolling planning environment,’ Julian Englberger, Frank Herrmann, and Michael Manitz propose a scenario-based two-stage stochastic programming model for master production scheduling under demand uncertainty. The goal is to find a plan that minimises the average inventory and overtime costs and the deviations of the production quantities and overtime from this plan over all demand scenarios. Numerical experimentation shows that the application of this model results in near avoidance of customer order tardiness and more balanced capacity loads, at the expense of increased inventory levels.

Chunyan Gao, Edwin Cheng, Houcai Shen, and Liang Xu in their article, ‘Incentives for quality improvement efforts coordination in supply chains with partial cost allocation contract,’ consider the coordination of quality improvement efforts in a supplier–manufacturer decentralised supply chain with a partial cost allocation contract that allocates external failure cost based on information derived from unreliable failure root cause analysis. They explore the properties of the allocation contract under various assumptions regarding the observability of the quality levels of the two players.

In ‘Balancing stochastic two-sided assembly lines,’ Wen-Chyuan Chiang, Timothy Urban, and Chunyong Luo consider a two-sided stochastic assembly line balancing problem whose main goal is to complete all tasks in a timely manner for a given confidence level. They provide lower bounds for the line length and the number of stations in the line and propose an approximation for the completion probability based on extreme value theory. They also present a Particle Swarm Optimization algorithm for solving deterministic and stochastic two-sided assembly line balancing problems.

Justus Arne Schwarz and Martin Epp, in their article, ‘Performance evaluation of a transportation-type bulk queue with generally distributed inter-arrival times,’ propose an analytical Markovian model for predicting the distribution of the queue length, waiting time and interdeparture time in bulk type transportation systems. They use this model to numerically investigate the impact of arrival stream variability and design alternatives on system performance.

Finally, in his article, ‘Optimal economic production quantity policy for a parallel system with repair, rework, free-repair warranty and maintenance,’ Gwo-Liang Liao investigates the interactions between production, quality and maintenance activities in a parallel system with units that are prone to failure and degradation. For this system, he derives the optimal economic production quantity, also considering the cost of warranties when delivering poorly performing items to customers. Numerical examples demonstrate the effect of the optimal policy as a function of the key parameters of the problems.

The publication of this issue coincides with the 55th anniversary of IJPR. Throughout its lifetime, IJPR has played a significant role in disseminating research results in the area of stochastic modelling of manufacturing systems. One of the prevailing themes in the SMMSO conferences is the role of intermediate storage buffers in increasing the efficiency and throughput of production flow lines by mitigating the effect of machine breakdowns and other sources of processing time variability. Two of the earliest papers in this area (Buzacott 1967, 1971) were published in IJPR. Since then, the stochastic modelling of flow lines has evolved into a thriving research area with significant practical implications and results that have been published in hundreds of papers – many of them in IJPR – and several books and monographs.

IJPR also published one of the earliest papers on the Toyota production and kanban system implementation of JIT (Sugimori et al. 1977). Today, this paper is listed as the most read paper in the website of IJPR. More generally, IJPR has been a major outlet for research on pull production control systems. The paper by Kimura and Terada (1981) on the design and analysis of pull systems and the paper by Spearman, Woodruff, and Hopp (1990) introducing CONWIP are two examples of works in this area that have had great impact, but there have been numerous other novel works on the analysis and comparison of pull control systems within a stochastic modelling framework (e.g. Gstettner and Kuhn 1996; Bonvik, Couch, and Gershwin 1997).

Queueing network models of manufacturing systems have also had an important place in IJPR. Shanthikumar and Buzacott (1981) presented the first open queueing network model of dynamic job-shops with general service times and FIFO or SPT service discipline, and Yao and Buzacott (1985) was one of the earliest works on modelling flexible manufacturing systems as open queueing networks with general service times and finite local buffers.

The above references are only indicative papers in the area of stochastic models of manufacturing and service operations that were published in IJPR before the turn of the millennium. Countless other papers with significant contributions in this area have filled the pages of IJPR.

We hope that the articles in this issue will add to the measured success of IJPR in publishing novel results in manufacturing, production and operations management research.

Special Issue Editors
George Liberopoulos
University of Thessaly, Greece
[email protected]
Chrissoleon T. Papadopoulos
Aristotle University of Thessaloniki, Greece
[email protected]
James MacGregor Smith
University of Massachusetts Amherst, USA
[email protected]
Horst Tempelmeier
University of Cologne, Germany
[email protected]
Tullio Tolio
Politecnico di Milano, Italy
[email protected]

Acknowledgements

We would like to thank the authors and the numerous anonymous referees who contributed to the review process of this issue. We would also like to thank Professor Alexandre Dolgui, Editor-in-Chief of IJPR, for supporting the special issue and Ms Tamara Bowler, Peer Review Coordinator, Taylor & Francis for her help and guidance in developing this issue.