Revenue management for make-to-order manufacturing systems with a real-life application

Abstract

Due to successful applications of revenue management in the airline industry, in recent years, there has been a growing interest to adopt revenue management in make-to-order (MTO) manufacturing systems. Several interrelated decision problems such as order acceptance/rejection, short-term capacity planning, due date assignment, and order scheduling need to be studied simultaneously in order to manage revenues effectively in MTO manufacturing systems. Both the producer’s and customer’s requirements need to be taken into account through some negotiation mechanisms that are sensitive to the service-level reputation of the manufacturing companies. In this article, we propose a new dynamic bid price–based revenue management model that considers all of the aforementioned decision problems simultaneously. A simulation optimization approach is utilized in order to determine the best possible values of control parameters for bid price, due date assignment, and price increment/reduction mechanisms. The performance of the proposed integrated revenue management model is tested on both a hypothetical example and a real problem of a bridal gown company. The computational results show that the proposed model provides significant improvements in total revenue compared to other static and dynamic bid price policies.     

Scheduling deteriorating jobs with CON/SLK due date assignment on a single machine

This paper considers the problem of scheduling deteriorating jobs and due date assignment on a single machine. The actual processing time of a job is a linear increasing function of its starting time. The problem is to determine the optimal due dates and the processing sequence simultaneously to minimize costs for earliness, due date assignment and weighted number of tardy jobs. We present polynomial-time algorithms to solve the problem in the case of two popular due date assignment methods: CON and SLK.     

Scheduling multiple robots in a no-wait re-entrant robotic flowshop

No-wait re-entrant robotic flowshops are widely used in the electronic industry, such as PCB and semiconductor manufacturing. In such an industry, cyclic production policy is often used due to large lot size and simplicity of implementation. This paper addresses cyclic scheduling of a no-wait re-entrant robotic flowshop with multiple robots for material handling. We formulate the problem and propose a polynomial algorithm to find the minimum number of robots for all feasible cycle times. Consequently, the minimum cycle time for any given number of robots can be obtained with the proposed algorithm. The algorithm runs in O(N⁵) time in the worst case, where N is the number of machines in the robotic flowshop.     

Outsourcing horizontally differentiated tasks under asymmetric information

We explore how asymmetric information affects task assignment between a manufacturer and its supplier when tasks are horizontally differentiated, and when the comparative advantage in terms of marginal costs differs during the production process. We show that the manufacturer over-outsources to a generalist supplier and under-outsources to a specialist supplier depending on its level of efficiency. The presence of countervailing incentives drives these results. When the manufacturer’s internal costs are sufficiently low, it can externalize some of its best tasks and internalize its worst tasks. These two distortions simultaneously affect the contract offered to the generalist supplier.     

An integrated product planning model for pricing and bundle selection using Markov decision processes and data envelope analysis

When bundling products during the product planning stage, there are a number of possible combinations that can be offered to customers. Consider a firm that offers N distinctive products, then there are 2^N−(N+1) possible bundling combinations. Now, if we wish to make pricing and bundle selection decisions, keeping in my mind limited inventory and a finite time horizon, then the size of the state space could be very large and finding an optimal solution could be impossible. To tackle this issue, we formulate an integrated model that utilizes a Markov decision process and data envelope analysis. Bundle selections are made using data envelope analysis in each decision epoch. Once the efficient bundles are selected they are priced by solving a Markov decision process using dynamic programming. Numerical examples are solved to exhibit the model's potential in solving real-world problems.     

A new lot-sizing heuristic for manufacturing systems with product recovery

We consider a deterministic model of the manufacturing system with product recovery. Two types of policies for the problem had been proposed in literature, namely the (1,R) policy, in which one manufacturing setup is followed by R remanufacturing setups and the (P,1) policy, which has P manufacturing setups, following every remanufacturing setup. Teunter (2004) developed heuristics to evaluate the cost for both policies and recommended choosing the better one among them. In this paper, we develop a new class of general (P,R) policies, where the long-run ratio of the number of manufacturing setups to the number of remanufacturing setups is P/R. Rather than have P manufacturing setups followed by R remanufacturing setups, we interleave (or intersperse) the setups of the manufacturing lots and the remanufacturing lots in such a way that the buildup of the recoverable inventory is minimized. We develop interleaving based (P,R) policy heuristics for the problem. Numerical results presented in the paper show that the proposed heuristic outperforms or performs as well as the best of the Teunter (2004) policies for all the problems tested.     

A bicriteria approach to maximize the weighted number of just-in-time jobs and to minimize the total resource consumption cost in a two-machine flow-shop scheduling system

We analyze a two-machine flow-shop scheduling problem in which the job processing times are controllable by the allocation of resources to the job operations and the resources can be used in discrete quantities. We provide a bicriteria analysis of the problem where the first criterion is to maximize the weighted number of just-in-time jobs and the second criterion is to minimize the total resource consumption cost. We prove that although the problem is known to be NP-hard even for constant processing times, a pseudo-polynomial time algorithm for its solution exists. In addition, we show how the pseudo-polynomial time algorithm can be converted into a two-dimensional fully polynomial approximation scheme for finding an approximate Pareto solution.     

Piracy and box office movie revenues: Evidence from Megaupload

In this paper we evaluate the heterogeneous effects of online copyright enforcement. We ask whether the unexpected shutdown of the popular file hosting platform Megaupload had a differential effect on box office revenues of wide-release vs. niche movies. Identification comes from a comparison of movies that were available on Megaupload to those that were not. We show that only movies that premiere in a relatively large number of theaters benefitted from the shutdown of Megaupload. The average effect, however, is negative. We provide suggestive evidence that this result is driven by information externalities. The idea is that online piracy acts as a mechanism to spread information about product characteristics across consumers with different valuations for the product. Our results question the effectiveness of blanket public anti-piracy policy, not only from a consumer perspective, but also from a producer perspective.     

Upward pricing pressure in two-sided markets: Incorporating rebalancing effects

In two-sided markets it is important to consider rebalancing effects following a merger, i.e. the impact of a change in margin on one side of the market, either due to a price change or to efficiency gains, on the pricing incentives on the other side. We propose modified versions for the indices of pricing pressure (UPP and GUPPI) that take this into account. We show that in two-sided markets where the cross-group externalities are positive the upward pricing pressure will typically be overstated if the rebalancing effect is ignored. Our approach explains why competition agencies should look at both sides of the market when assessing platform mergers.     

Analyzing firm behavior in restructured electricity markets: Empirical challenges with a residual demand analysis

Using data from Alberta’s wholesale electricity market, we demonstrate the challenges that can arise when characterizing a firm’s unilateral expected profit-maximizing offer curve. We illustrate that the residual demand curves faced by firms can be highly non-linear, resulting in non-monotonic, downward sloping, optimal best-response offer curves violating common restrictions imposed on bidding behavior. This can have important implications on the conclusions drawn from such empirical analyses. We identify features of residual demand curves that can lead to these problems, providing guidance to researchers utilizing these methods. We find that a simplified monotonic smoothing of the unconstrained ex-post optimal offer curve can achieve the majority of the expected profits, offering an alternative to calculating the ex-ante expected profit-maximizing offer curve that can be computationally burdensome.     

Minimizing mean absolute deviation of completion time about a common due window subject to maximum tardiness for a single machine

This study deals with the problem of scheduling jobs on a single machine to minimize the mean absolute deviation of the job completion time about a large common due window subject to the maximum tardiness constraint. Using the well-known three-field notation, the problem is identified as MAD/large DueWindow/T_max. The common due window is set to be large enough to allow idle time prior to the beginning of a schedule to investigate the effect of the T_max constraint. Penalties arise if a job is completed outside the due window. A branch and bound algorithm and a heuristic are proposed. Many properties of the solutions and precedence relationships are identified. Our computational results reveal that the branch and bound algorithm is capable of solving problems of up to 50 jobs and the heuristic algorithm yields approximate solutions that are very close to the exact solution.     

The deterministic EOQ and EPQ with partial backordering at a rate that is linearly dependent on the time to delivery

Our original models for the EOQ and EPQ with partial backordering assumed that the backordering rate, β, is a constant. In this paper we extend those models to allow β to increase linearly as the time until delivery decreases. We show how those previous models can be adapted to find the optimal decision variable values for this new assumption and develop, for each model type, a condition that the initial value of β must meet for partial backordering to be optimal.     

SAM — a baby with many faces

In the last issue of Food Policy, two articles appeared on the subject of the Mexican Food System, or the Sistema Alimenticio Mexicano (SAM). The first article by Frank Meissner¹ reviewed the background to SAM, the proposed strategies, intended targets, its funding and put forward a strong case for considering SAM as a constructive policy for alleviating Mexico's food and nutrition problems. However, Michael Redclift commented in his article that:² ‘There are three principal points to be made of Dr Meissner's article: its interpretation of Mexico's recent agrarian development is questionable; it does not distinguish between the analysis provided in the SAM documents and the policy measures to which they give rise; and, it glosses over serious problems of implementation, giving too little weight to the participatory element without which SAM is doomed to failure’. This rejoinder is Frank Meissner's response to Michael Redclift's comments.     

Inventory management with random supply and imperfect information: A hidden Markov model

In most of the papers on inventory models operating in a random environment, the state of the environment in each period is assumed to be fully observed with perfect information. However, this assumption is not realistic in most real-life situations and we provide a remedy in this paper by assuming that the environment is only partially observed with imperfect information. We accomplish this by analyzing two formulations of single-item models with periodic-review and random supply in a random environment. In the first one, supply is random due to random capacity of production and random availability of transportation. We show that state-dependent base-stock policy is optimal if the capacity and all costs are observed, while demand and availability are unobserved. In the second model, we consider a model with random availability only with fixed-ordering cost. We show that state-dependent (s,S) policy is optimal if the availability process is observable.     

Green logistics at Eroski: A case study

In today’s highly competitive environment, green logistics issues are gaining interest. This paper analyses how logistics managers could lead the initiative in this area by incorporating environmental management principles into their daily decision-making process. A case study is given to show how they can turn practices into green while simultaneously meet the efficiency objectives. We have chosen one of the leader companies of the Spanish food distribution sector to check this hypothesis. The study covers the introduction of several changes into its fleet management and the implementation of a methodology to solve vehicle routing problems with environmental criteria minimisation.     

Optimal two-stage lot sizing and inventory batching policies

This paper extends previous studies of two-stage lot sizing problems with finite production rates. We develop various lot sizing and inventory batching (i.e., operation–unit batching (OUB) and unit–unit batching (UUB)) models under different system characteristics and lot sizing and inventory policies. The analysis of the optimality of the lot size ratio between the two stages reveals (1) that both non-increasing and non-decreasing lot sizing policies can be optimal in both OUB and UUB, (2) that a non-integer lot size ratio can be optimal in OUB, and (3) that an integer lot size ratio is always optimal in UUB. We present a simple, easy-to-implement, optimal solution approach to the two-stage lot sizing and inventory batching problem, along with examples.     

Derivation of confidence intervals of service measures in a base-stock inventory control system with low-frequent demand

We explore a base-stock system with backlogging where the demand process is a compound renewal process and the compound element is a delayed geometric distribution. For this setting it holds that the long-run average service measures order fill rate (OFR) and volume fill rate (VFR) are equal in values. However, though equal ex ante one will ex post observe differences as actual sample paths are different. By including a low-frequency assumption in the model, we are able to derive mathematical expressions of the confidence intervals one will get if OFR and VFR are estimated in a simulation using the regenerative method. Through numerical examples we show that of the two service measures it is OFR that in general can be estimated most accurately. However, simulation results show that the opposite conclusion holds if we instead consider finite-horizon service measures, namely per-cycle variants of OFR and VFR.     

Genetic and psychological underpinnings of motivation and satisfaction of industrial salespeople

We investigate the effects of genetic polymorphisms of salespeople on their motivation and job satisfaction. We hypothesize that psychological attachment styles (anxious, avoidance, and secure) and role conflict interact with variants of three genes (dopamine DRD4, COMT and OXTR) to influence motivation and satisfaction. Our approach follows the spirit of the emerging subfield of organizational neuroscience and biological foundations of social behavior recently introduced into the literature and is one of the first studies to uncover complex dependence of behavior on genes and to employ a large enough sample to reduce false positive inferences. Variants of the DRD4 and COMT genes are found to interact with avoidance and secure attachment style, respectively, to influence motivation, under conditions of felt role conflict. Variants of OXTR have main effects on job satisfaction, as do anxious attachment styles and felt role conflict. Hypotheses are tested on a sample of 334 salespersons selling goods and services to business customers. The role of genetic endowment represents untapped origins for research into the behavior of salespeople and has a number of managerial implications that we discuss.     

Quantifying the costs of a nationwide public safety wireless network

The existing US public safety wireless infrastructure consists of thousands of disparate systems built by separate local agencies. Problems with interoperability, cost, spectral efficiency, and limited functionality plague these systems but could be significantly reduced through the deployment of a single nationwide network that serves all public safety personnel. Two major efforts towards such a nationwide network are the federal-government-only Integrated Wireless Network (IWN) and an FCC-led effort to create a public-private partnership in the 700 MHz band; the future of both projects is uncertain due in part to concerns surrounding cost. This paper presents a model to estimate cost for two fundamental approaches to a nationwide network: a public-safety-only network and a public–private partnership which serves both public safety and commercial subscribers. We apply this general model to four network scenarios which differ in the amount and band of spectrum allocated as well as the number and type of subscribers (public-safety-only versus commercial and public safety) under three traffic scenarios: voice-only, data-only, and voice and data. We demonstrate that the nation's many small systems could be replaced with a single nationwide network with a small fraction of the tower sites and spectrum. The cost of building this new infrastructure is comparable to what is likely to be spent in just a few years on upgrading and maintaining the existing infrastructure. In addition, we show that these cost estimates are highly dependent on some key system design parameters including the public safety capacity required and signal coverage reliability, which must therefore be well-defined in advance.