The lot-sizing problems address the issue of determining the production lot-sizes of various items appearing in consecutive production stages over a given finite planning horizon. In general capacitated lot-sizing problems, the product structure can be a general acyclic network, the capacity constraints can be very sophisticated, and all the known parameters can be time-varying. This paper proposes heuristic genetic algorithms for these problems by designing a domain-specific encoding scheme for the lot-sizes and by providing a heuristic shifting procedure as the decoding schedule. The main contribution of these genetic algorithms is the presentation technique that encodes only the binary variables for the setup patterns but derives other decision variables by making use of the problem-specific knowledge. Some results from the computational experiments are also given.

A computer model is built to simulate master production scheduling activities in a capacitated multi-item production system under demand uncertainty and a rolling time horizon. The output from the simulation is analyzed through statistical software. The results of the study show that forecasting errors have significant impacts on total cost, schedule instability and system service level, and the performance of forecasting errors is significantly influenced by some operational factors, such as capacity tightness and cost structure. Furthermore, the selection of the master production schedule freezing parameters is also significantly influenced by forecasting errors. The findings from this study can help managers optimize their production plans by selecting more reasonable forecasting methods and scheduling parameters, thus improving the performance of roduction systems.

This paper investigates the impact of freezing the master production schedule (MPS) in multi-item single-level systems with a single resource constraint under demand uncertainty. It also examines the impact of environmental factors on the selection of MPS freezing parameters. A computer model is built to simulate master production scheduling activities in a multi-item system under a rolling time horizon. The result of the study shows that the parameters for freezing the MPS have a significant impact on total cost, schedule instability and the service level of the system. Furthermore, the selection of freezing parameters is also significantly influenced by some environmental factors such as capacity tightness and cost structure. While some findings concerning the performance of MPS freezing parameters without capacity constraints can be generalised to the case of limited capacity, other conclusions under capacity constraints are different from those without capacity constraints.

This note extends the results in the manufacturer-dominated game model of the paper by Li et al. (Omega 30 (2002) 347) to the case where the manufacturer's marginal profit is not large enough. In such situations, the profit of the entire supply chain under the co-op advertising mode is higher than the one under the Stackelberg game, which is consistent with the results of the original paper. However, the advertising expenditures of the manufacturer and the retailer under the co-op advertising model are not always larger than those under the Stackelberg game, which is different from the results of the original paper. Furthermore, the results are also compared with the simultaneous move game of the paper by Huang and Li (Eur. J. Oper. Res. 135 (2001) 527). The manufacturer always prefers the leader-follower structure rather than the simultaneous move structure, which is consistent with the results of the original paper. However, the retailer always prefers the simultaneous move structure rather than the leader–follower structure, which differs from the results of the original paper.

Aligning its quality initiatives in synchronization with the customer's perception of values is one of the key management strategies for improving the competitive edge of an organization. Therefore, it will be a distinct advantage if one can succeed in effectively capturing the genuine and major customer attributes (requirements), systematically analysing and duly transforming them into the appropriate product attributes (features). This paper puts forward a novel approach for analysing customer attributes and projecting them into the relevant design, engineering and product attributes in order to facilitate decision-making and to guide downstream manufacturing planning and control activities. The proposed hybrid system incorporates the principles of quality function deployment, analytic hierarchy process and fuzzy set theory to tackle the complex and often imprecise problem domain encountered in customer requirement management. It offers an analytical and intelligent tool for decoding, prioritizing and inferring the qualitative, sometimes vague and imprecise Voice of Customer. As a result, the appropriate product attributes can be mapped out and their relevant design targets can be determined quantitatively and consistently. The software supporting the hybrid system is constructed within a generic framework which can be easily customized and configured into specific enterprise models capable of offering more timely responses to the dynamic market demand.

This paper compares the performance of a new improved heuristic (STIL) proposed by Coleman and McKnew (1991) against those of the Wagner-Whitin (WW), cost-modified Wagner-Whitin (MWW) and some simple lot-sizing rules found to perform well by Zhao et al. (1995). It also investigates the impact of the lot-sizing rules on the selection of the parameters for freezing the master production schedule (MPS). The experimental settings include forecasting, master production scheduling and material requirements planning under a rolling time horizon. The result of the study shows that the STIL heuristic, the WW rule and the MWW rule do not really outperform the simple cost-modified Silver-Meal (MSM) and the Silver-Meal/Lot-for-Lot (SM/LFL) even though they require substantially higher computation time. The study also shows that the selection of the parameters for freezing the MPS is not significantly influenced by the selection of the lot-sizing rules.

One approach to supply chain coordination is early order commitment, whereby a retailer commits to purchase a fixed-order quantity at a fixed delivery time before demand uncertainty is resolved. In this paper, we develop an analytical model to quantify the cost savings of an early order commitment in a two-level supply chain where demand is serially correlated. A decision rule is derived to determine whether early order commitment will benefit the supply chain, and accordingly to determine the optimal timing for early commitment. Our results indicate that the supply chain would experience greater savings from early order commitment when-(a) the inventory item receives less value-added activities at the retailer site; (b) the manufacturing lead time is short; (c) demand correlation over time is positive but weak; or (d) the delivery lead time is long (if a condition exists). We also propose a rebate scheme for the supply chain partners to share the gains of practicing early order commitment.