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.