An economic production quantity model with a positive resetup point under random demand

In this paper, an extended economic production quantity (EPQ) model is investigated, where demand follows a random process. This study is motivated by an industrial case for precision machine assembly in the machinery industry. Both a positive resetup point s and a fixed lot size Q are implemented in this production control policy. To cope with random demand, a resetup point, i.e., the lowest inventory level to start the production, is adapted to minimize stock shortage during the replenishment cycle. The considered cost includes setup cost, inventory carrying cost, and shortage cost, where shortage may occur at the production stage and/or at the end of one replenishment cycle. Under some mild conditions, the expected cost per unit time can be shown to be convex with respect to decision parameters s and Q. Further computational study has demonstrated that the proposed model outperforms the classical EPQ when demand is random. In particular, a positive resetup point contributes to a significant portion of this cost savings when compared with that in the classical lot sizing policy.     

Design rules of flexible assembly and inspection cells

Every leading institution and manufacturing company in the world is dealing with some aspects of flexible automation. This is because they have found that designing and making goods and products on order rather than for stock is the key issue in cutting manufacturing costs and lead time, and eventually staying in business in the future. This paper discusses the way in which this above outlined goal can be implemented in the assembly business, as well as giving design rules on how to design flexible assembly and inspection cells and systems. The rules are justified by solid model simulation and the author's industrial and laboratory experience.     

Mean-risk optimal decision of a steel company under emission control

We propose a mean-risk decision model for a steel company facing emission limits and trading with emission allowances. The model is calibrated using data of a real-life steel company and is subsequently solved for five different scenarios of demand and different levels of risk aversion. It is found that while the limits are never reached, permit trading influences the decision to a great extent, especially given extremely low or extremely high demand, i.e., when large amounts of permits need to be traded. We demonstrate that the risk caused by emission trading may increase not only with an increasing demand but also when the demand is low and a great amount of allowances must be sold.     

Robust supply chain network design with multi-products for a company in the food sector

The paper aims to solve a problem faced by a company competing in the snacks market in Turkey. In line with the growth in this market, the company needs to make important decisions over the next few years about the timing and location of a new plant, its initial capacity, the timing and amount of additional capacity to be installed at the new and existing plants, the assignment of demand points to plants and the amount of raw materials to be shipped from suppliers to the plants in each period. The objective is to minimize the total cost of various components. The problem is formulated as a multi-period supply chain network design model with multi products. The resulting mixed-integer linear programming model is solved by the commercial solver CPLEX. This model enables us to carry out all analyses requested by the company in an efficient way. After this deterministic model is solved on the basis of a 9% annual increase in demand, it is extended to a minimax regret model to deal with uncertainty in demand quantities. The results suggest that opening the new plant in the city of İzmir is indeed a robust solution that is unaffected in different scenarios that are based on three distinct demand increase rates. Even though the location of the new plant remains unchanged with respect to scenarios, the optimal robust solution differs from the optimal solution of each scenario in terms of the capacity expansion decisions. After all obtained results had been communicated to the company managers and executives, the new plant construction was started in 2016 very close to the city that the mathematical model had determined. The new plant is expected to start operating in 2018.     

Optimal dividend-financing strategies in a dual risk model with time-inconsistent preferences

In this paper, we consider an optimal dividend-financing problem for a company whose capital reserve is described by the dual of classical risk model. We assume that the manager of the company has time-inconsistent preferences, which are described by a quasi-hyperbolic discount function, and that financing is permitted to prevent the company from going bankrupt. The manager’s objective is to maximize the expected cumulative dividend payments minus financing costs. We solve the optimization problems for a naive manager and a sophisticated manager, and obtain explicit solutions for both managers. Our results show that the manager with time-inconsistent preferences tends to pay out dividends earlier. We also present some economic implications and sensitivity analysis for our results.     

A discrete bilevel brain storm algorithm for solving a sales territory design problem: a case study

A sales territory design problem faced by a manufacturing company that supplies products to a group of customers located in a service region is addressed in this paper. The planning process of designing the territories has the objective to minimizing the total dispersion of the customers without exceeding a limited budget assigned to each territory. Once territories have been determined, a salesperson has to define the day-by-day routes to satisfy the demand of customers. Currently, the company has established a service level policy that aims to minimize total waiting times during the distribution process. Also, each territory is served by a single salesperson. A novel discrete bilevel optimization model for the sales territory design problem is proposed. This problem can be seen as a bilevel problem with a single leader and multiple independent followers, in which the leader’s problem corresponds to the design of territories (manager of the company), and the routing decision for each territory corresponds to each follower. The hierarchical nature of the current company’s decision-making process triggers some particular characteristics of the bilevel model. A brain storm algorithm that exploits these characteristics is proposed to solve the discrete bilevel problem. The main features of the proposed algorithm are that the workload is used to verify the feasibility and to cluster the leader’s solutions. In addition, four discrete mechanisms are used to generate new solutions, and an elite set of solutions is considered to reduce computational cost. This algorithm is used to solve a real case study, and the results are compared against the current solution given by the company. Results show a reduction of more than 20% in the current costs with the solution obtained by the proposed algorithm. Furthermore, a sensitivity analysis is performed, providing interesting managerial insights to improve the current operations of the company.     

Furniture supply chain tactical planning optimization using a time decomposition approach

We study the supply chain tactical planning problem of an integrated furniture company located in the Province of Québec, Canada. The paper presents a mathematical model for tactical planning of a subset of the supply chain. The decisions concern procurement, inventory, outsourcing and demand allocation policies. The goal is to define manufacturing and logistics policies that will allow the furniture company to have a competitive level of service at minimum cost. We consider planning horizon of 1 year and the time periods are based on weeks. We assume that customer’s demand is known and dynamic over the planning horizon. Supply chain planning is formulated as a large mixed integer programming model. We developed a heuristic using a time decomposition approach in order to obtain good solutions within reasonable time limit for large size problems. Computational results of the heuristic are reported. We also present the quantitative and qualitative results of the application of the mathematical model to a real industrial case.     

An integrated material handling system for a truck assembly plant

The paper attempts to develop a more comprehensive analytical framework for examining the relative merits of alternative material handling and inventory strategies for a truck assembly plant. This model can be used to minimise handling in delivering requirements from the warehouse to the assembly line and from the unpacking area to the warehouse. The model is also designed to look at arrangements of the storage areas to increase the efficiency of material management. This model is based on extensions of the Vehicle Routing Problem. Solutions to the model have been obtained by an implementation of a Genetic Algorithm. The historical demand data of the plant are used to simulate and analyse the different strategies to statistically determine the best material handing and inventory strategies for the truck assembly plant. As a result of implementation, the efficiency of the current system has been increased by about 30%.     

The submartingale assumption in risk theory

The main consequences of the submartingale assumption are examined in a discrete time model. After we have shown how the submartingale decomposition theorem can be given an actuarial meaning, we formulate the ruin theory by classifying the gain processes according to the properties of the set of the safety indexes of their increments. Inequalities for ruin probabilities are derived for embeddable submartingales and for P-submartingales. As an example, we describe a rather general risk model with an adjustable gain process and we show how it can be modified to obtain examples of embeddable submartingales and P-submartingales. Optional gain processes, in which the number of policies in the portfolio is depending on the charged premiums, are also shown to satisfy the submartingale assumption; suitable demand functions can be introduced. We shortly discuss the construction of decision models to define the pricing policy of the insurance company and we provide a simple example of a pricing model with stochastic demand function.     

航空公司中的飞行员配置研究

随着新航线的开辟和新飞机的投入使用,航空公司的任务量急剧增长,飞行员需求的估计和机组的合理配置变得日益重要。本文基于实际工作需求,提出一类用于评估飞行员数量,并进行机组均衡配置的整数规划模型,为航空公司进行飞行员的数量评估和任务分配提供决策支持。本文在以下两个方面做出改进:1)加入国际长途航线,研究国内短途航线和国际长途航线混合搭配情况下飞行员的配置问题;2)鉴于大型整数规划求解的复杂性,通过评估航空公司所需要的飞行员数量的下界,为决策人员制定中长期规划提供依据。本文通过启发式算法进行飞行员的配置,计算结果接近理论问题的下界,证明了算法的有效性。     

Über ein Problem der Fließbandfertigung bei stochastischer Nachfrage

Zusammenfassung Bei der Fließbandfertigung mit stochastischer Nachfrage interessiert man sich für die Anpassung der Produktion an die Marktgegebenheiten. Es wird die Produktion eines Gutes betrachtet. Der Unternehmung stehen mehrere gleichwertige Fertigungsstraßen zur Verfügung. Es wird angenommen, daß alle in Betrieb befindlichen Fertigungsstraßen den gleichen und unveränderlichen Output pro Zeiteinheit haben. Gesucht ist ein optimales Verhalten, das die diskontierte Summe der zu erwartenden Kosten minimiert. Es ist naheliegend, Verfahren der dynamischen Programmierung zu verwenden. Für das Modell wird der Fall zyklischer Kontrolle betrachtet. Die Verteilung der Nachfrage während der Kontrollperioden sei bekannt, konstant, und es gelte stochastische Unabhängigkeit für verschiedene Perioden. Überschreitet die Nachfrage den Lagerbestand, dann erfolgen für den fehlenden Output Vormerkungen. Die Belieferung findet später statt.

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Summary In assembly line production with stochastic demand one is interested in the adaption of the output to the market situation. The production of one product is considered. The company has several assembly lines of the same type. It is assumed that the assembly lines in action have the same, and a constant output rate. An optimal policy, minimizing the expected value of the total discounted costs, has to be found. It is concievable that the method of dynamic programming should be used. In the model the case of cyclic control is considered. The distribution of demand during each control period is given, constant, and there is stochastic independence for different periods. If the demand exceeds the stock the deficient output is noted and delivery takes place later.

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Vorgel. v.:W. Wittmann, Heidelberg.     

Demand selection decisions for a multi-echelon inventory distribution system

In this paper, we study an integrated demand selection and multi-echelon inventory control problem that generalizes the classical deterministic single distribution centre (DC) multi-retailer model by incorporating demand selection decisions. In addition to the ordering and holding cost components, a concave operating cost of the DC and a capacity on the total market demand served are also considered. For given revenue and cost parameters, the problem is to determine which sets of demand to fulfill and which multi-echelon inventory control policy to implement so as to maximize the net profit. We show that the problem can be formulated as a nonlinear discrete optimization model. We analyse the structural properties of the model and, based on these, outline an approach to solve the model efficiently. We also present some interesting managerial insights obtained from the numerical experiments.     

Forecasting demand for single-period products: A case study in the apparel industry

The problem considered is that of forecasting demand for single-period products before the period starts. We study this problem for the case of a mail order apparel company that needs to order its products pre-season. The lack of historical demand data implies that other sources of data are needed. Advance order data can be obtained by allowing a selected group of customers to pre-order at a discount from a preview catalogue. Judgments can be obtained from purchase managers or other company experts. In this paper, we compare several existing and new forecasting methods for both sources of data. The methods are generic and can be used in any single-period problem in the apparel or fashion industries. Among the pre-order based methods, a novel ‘top-flop’ approach provides promising results. For a small group of products from the case company, expert judgment methods perform better than the methods based on advance demand information. The comparative results are obviously restricted to the specific case study, and additional testing is required to determine whether they are valid in general.     

A model of clinker capacity expansion

This paper describes a model which has been applied in practice to determine an optimal plan for clinker capacity expansion. The problem was formulated as an integer linear program aiming to determine the optimal number, size and location of kilns to be introduced each year during a given planning horizon. The optimal solution is defined as the one which maximises the net present value (NPV) of the cash flow generated by the introduction and operation of new kilns subject to capacity and demand constraints. Various demand scenarios and capital expenditure structures were tested by means of the model and a number of possible locations for new kilns were evaluated. Finally, the benefits derived by the company where the application took place are listed.     

Optimal proportional reinsurance and investment for stochastic factor models

In this work we investigate the optimal proportional reinsurance-investment strategy of an insurance company which wishes to maximize the expected exponential utility of its terminal wealth in a finite time horizon. Our goal is to extend the classical Cramér–Lundberg model introducing a stochastic factor which affects the intensity of the claims arrival process, described by a Cox process, as well as the insurance and reinsurance premia. The financial market is supposed not influenced by the stochastic factor, hence it is independent on the insurance market. Using the classical stochastic control approach based on the Hamilton–Jacobi–Bellman equation we characterize the optimal strategy and provide a verification result for the value function via classical solutions to two backward partial differential equations. Existence and uniqueness of these solutions are discussed. Results under various premium calculation principles are illustrated and a new premium calculation rule is proposed in order to get more realistic strategies and to better fit our stochastic factor model. Finally, numerical simulations are performed to obtain sensitivity analyses.     

Optimal dividend-equity issuance strategy in a dual model with fixed and proportional transaction costs

In this paper, we consider the problem of optimal dividend payout and equity issuance for a company whose liquid asset is modeled by the dual of classical risk model with diffusion. We assume that there exist both proportional and fixed transaction costs when issuing new equity. Our objective is to maximize the expected cumulative present value of the dividend payout minus the equity issuance until the time of bankruptcy,which is defined as the first time when the company’s capital reserve falls below zero. The solution to the mixed impulse-singular control problem relies on two auxiliary subproblems: one is the classical dividend problem without equity issuance, and the other one assumes that the company never goes bankrupt by equity issuance.We first provide closed-form expressions of the value functions and the optimal strategies for both auxiliary subproblems. We then identify the solution to the original problem with either of the auxiliary problems. Our results show that the optimal strategy should either allow for bankruptcy or keep the company’s reserve above zero by issuing new equity, depending on the model’s parameters. We also present some economic interpretations and sensitivity analysis for our results by theoretical analysis and numerical examples.     

A two-level evolutionary algorithm for solving the facility location and design (1|1)-centroid problem on the plane with variable demand

In this work, the problem of a company or chain (the leader) that considers the reaction of a competitor chain (the follower) is studied. In particular, the leader wants to set up a single new facility in a planar market where similar facilities of the follower, and possibly of its own chain, are already present. The follower will react by locating another single facility after the leader locates its own facility. Both the location and the quality (representing design, quality of products, prices, etc.) of the new leader’s facility have to be found. The aim is to maximize the profit obtained by the leader considering the future follower’s entry. The demand is supposed to be concentrated at n demand points. Each demand point splits its buying power among the facilities proportionally to the attraction it feels for them. The attraction of a demand point for a facility depends on both the location and the quality of the facility. Usually, the demand is considered in the literature to be fixed or constant regardless the conditions of the market. In this paper, the demand varies depending on the attraction for the facilities. Taking variable demand into consideration makes the model more realistic. However, it increases the complexity of the problem and, therefore, the computational effort needed to solve it. Three heuristic methods are proposed to cope with this hard-to-solve global optimization problem, namely, a grid search procedure, a multistart algorithm and a two-level evolutionary algorithm. The computational studies show that the evolutionary algorithm is both the most robust algorithm and the one that provides the best results.     

马氏风险过程

研究了一般马氏风险过程,它是经典风险过程的拓广．具有大额索赔的风险过程用此马氏风险模型来描述是适合的．在此模型中,索赔到达过程由一点过程来描述,该点过程是一马氏跳过程从0到t时间段内的跳跃次数．主要研究了此风险模型的破产概率,得到了破产概率满足的积分方程,并应用本文引入的广更新方法,得到了破产概率的收敛速度上界．     

延期支付条件下零售商的最优售价和订货策略

在供应商给予零售商延期支付和现金折扣的优惠政策下,进一步假设产品的年需求量依赖于零售商产品售价的基础上,建立了由一个零售商和一个供应商所构成的库存决策模型,扩展了经典的经济生产批量(EPQ)模型。通过模型的分析求解,可以得出零售商在上述情况下的最优订货周期、最优售价及最优付款时间的简单判定方法。最后,通过算例,验证了模型的可行性,得出了与实际相符的结论。     

3.1. OR in banking

In this paper, we describe a deterministic multiperiod capacity expansion model in which a single facility serves the demand for many products. Potential applications for the model can be found in the capacity expansion planning of communication systems as well as in the production planning of heavy process industries. The model assumes that each capacity unit simultaneously serves a prespecified (though not necessarily integer) number of demand units of each product. Costs considered include capacity expansion costs, idle capacity holding costs, and capacity shortage costs. All cost functions are assumed to be nondecreasing and concave. Given the demand for each product over the planning horizon, the objective is to find the capacity expansion policy that minimizes the total cost incurred. We develop a dynamic programming algorithm that finds optimal policies. The required computational effort is a polynomial function of the number of products and the number of time periods. When the number of products equals one, the algorithm reduces to the well-known algorithm for the classical dynamic lot size problem.