Rationing Capacity Between Two Product Classes

Abstract

In this paper, we study the capacity allocation problem faced by make-to-order manufacturing f m s encountering expected total demand in excess of available capacity. Specifically, we focus on fms' manufacturing short-life-cycle or seasonal products such as high fashion apparel. Using a decision-theory-based approach, we develop a capacity allocation policy that allows such firms to discriminate between two classes of products (one yielding a higher profit contribution per unit of capacity allocated to it than the other), resulting in selective rejection of orders for the class with the lower unit contribution. The effectiveness of capacity rationing is investigated under a wide array of conditions characterized by variations in factors such as the ratio of unit profit contributions of the two product classes, the mtio of total available capacity to expected total demand, the ratio of expected demands between the two classes, and the variability in demand for each product class. The results indicate that capacity rationing is very effective in increasing the total profit, and could therefore serve as a valuable decision tool for managers in such fms.

Revenue Management in Make-To-Order Manufacturing: Case Study of Capacity Control at ThyssenKrupp VDM

“…Examples of the practical application of planning models are provided by Tang and Liu (2007) and Tang, Liu, Rong, and Yang (2000). Starting in the mid-90's, initial works of Balakrishnan, Sridharan, and Patterson (1996), Harris and Pinder (1995) and Patterson, Balakrishnan, and Sridharan (1997) investigate the application of RM methods to manufacturing environments in general. Models of capacity control in m-t-o environments, specifically, are later presented by Barut and Sridharan (2005), Defregger and Kuhn (2007), Gallien, Le Tallec, and Schoenmeyr (2004), Gupta and Wang (2004) and Jalora (2006), all considering the sequential order acceptance for multiple offered products with exogenous delivery dates with respect to expected demand.…”

A branch and price solution approach for order acceptance and capacity planning in make-to-order operations

“…Balakrishnan et al [18,19] decide which orders to accept and which to reject. They model the problem as a discrete…”

“…We report the time taken to solve the problem, and the absolute and relative gap. The relative gap is defined by CPLEX as given in Equation (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), where Z IP is the best known integer solution and the Bound is the best bound in the branch and bound tree.…”

An order acceptance using FAHP and TOPSIS methods: A case study of Iranian vehicle belt production industry

“…The literature on order acceptance has grown rapidly over the past decade (Slotnick & Morton, 2007). Thus, researchers have attempted to find an optimum system to either accept or reject orders using multitudes of techniques such as Dynamic Programming (Alidaee et al, 2001;Lewis & Slotnick, 2002;Herbots et al, 2007), Mathematics Programming (Guerrero & Kern, 1990;Slotnick & Morton, 1996;Slotnick & Morton, 2007), Simulation (Ten kate, 1994;Ten kate, 1995;Nandi & Rogers, 2003;Nandi & Rogers, 2004;Ebben et al, 2005), Decision theory (Balakrishnan et al,1996;Balakrishnan et al,1999), Heuristics (Ghosh, 1997;Defregger & Kuhn, 2004;Hing et al, 2007), Genetic algorithm (Roundy et al, 2005;Rom & Slotnick, 2009), Neural networks Hing et al, 2002), Neuro-genetics (Snoek, 2000), Markov decision (Kniker & Burman, 2001) etc. References Slotnick and Morton (2007), Ghosh (1997), Jalora (2006) and Ivanescu (2004) provide more detailed information.…”