Analysis of the newsboy problem with fuzzy demands and incremental discounts

This paper proposes an analysis method for the single-period (newsboy) inventory problem with fuzzy demands and incremental quantity discounts. In fuzzy environments, the availability of the quantity discount makes the analysis of the associated model more complex. The proposed analysis method is based on ranking fuzzy number and optimization theory. By applying the Yager ranking method, the fuzzy total cost functions with different unit purchasing costs are transformed into convex piecewise nonlinear functions. To effectively and efficiently find the optimal inventory policy, the proofs of two properties regarding the relative position between the price break and minimums of these nonlinear functions are proposed. The closed-form solutions to the optimal order quantities are also derived. Four cases of a numerical example are solved to demonstrate the validity of the proposed analysis method. It is clear that the proposed methodology is applicable to further cases with different types of quantity discounts and other more complicated cases. More importantly, managerial implications are also provided for decision-makers’ references.     

The effect of advertising on the distribution-free newsboy problem

Advertising is very important for the newsboy problem because the shelf-life of the newsboy product is short and advertising may increase sales to avoid overstocking. In this paper, models to study the effect of advertising are developed for the distribution-free newsboy problem where only the mean and variance of the demand are known. As in Khouja and Robbins (2003), it is assumed that the mean demand is an increasing and concave function of advertising expenditure. Three cases are considered: (1) demand has constant variance, (2) demand has constant coefficient of variation, and (3) demand has an increasing coefficient of variation. This paper provides closed-form solutions or steps to solve the problem. Numerical results of the model are also compared with those from other papers. The effects of model parameters on optimal expenditure on advertising, optimal order quantity, and the lower bound on expected profit are derived or discussed.     

A heuristic algorithm for a multi-product dynamic lot-sizing and shipping problem

This paper analyzes a dynamic lot-sizing problem, in which the order size of multiple products and a single container type are simultaneously considered. In the problem, each order (product) placed in a period is immediately shipped by some containers in the period and the total freight cost is proportional to the number of containers used. It is also assumed that backlogging is not allowed. The objective of this study is to simultaneously determine the lot-sizes and the transportation policy that minimizes the total costs, which consist of production cost, inventory holding cost, and freight cost. Because this problem is NP-hard, a heuristic algorithm with an adjustment mechanism is proposed based on the optimal solution properties. The computational results from a set of simulation experiment are also presented.     

The distribution-free newsboy problem: Extensions to the shortage penalty case

In the classical newsboy problem, no cost is assumed if the ordered quantity is less than the demand. However, in reality failure to meet demand is always associated with a penalty. The aim of this work is to extend the analysis of the distribution-free newsboy problem to the case when shortage cost is taken into consideration. The analysis is based on the assumption that only the mean and variance of demand are known, but its particular probability distribution is not. A model is presented for determining both an optimal order quantity and a lower bound on the profit under the worst possible distribution of the demand. The following cases are considered: the single product case, the fixed ordering cost case, the random yield case, and the resource-constrained multi-product case.     

An efficient hybrid genetic algorithm for the multi-product multi-period inventory routing problem

The inventory routing problem (IRP) addressed in this study is a many-to-one distribution network consisting of an assembly plant and many distinct suppliers where each supplies a distinct product. We consider a finite horizon, multi-periods, multi-suppliers and multi-products where a fleet of capacitated homogeneous vehicles, housed at a depot, transport products from the suppliers to meet the demand specified by the assembly plant in each period. The demand for each product is deterministic and time varying. A mathematical formulation of the problem is given and CPLEX 9.1 is run for a finite amount of time to obtain lower and upper bounds. A hybrid genetic algorithm, which is based on the allocation first route second strategy and which considers both the inventory and the transportation costs, is proposed. In addition to a new set of crossover and mutation operators, we also introduce two new chromosome representations. Several medium and small sized problems are also constructed and added to the existing data sets to show the effectiveness of the proposed approach.     

A unified solution approach for the due date assignment problem with tardy jobs

We analyze a number of due date assignment problems with the weighted number of tardy jobs objective and show that these problems can be solved in O(n²) time by dynamic programming. We show that the effects of learning or the effects of past-sequence-dependent setup times can be incorporated into the problem formulation at no additional computational cost. We also show that some single-machine due date assignment problems can be extended to an identical parallel machine setting. Finally, we improve the complexity of the solution algorithms for two other due date assignment problems.     

Customer ownership of the local loop. A solution to the problem of interconnection

Interconnection is necessary for competition in telephony, but interconnection agreements are difficult to negotiate. With local service competition, entrants usually cannot duplicate the local loop, so interconnection also involves ‘renting’ the incumbent's local loops. An incumbent may object to being asked to permit use of its infrastructure by its competitors, in order to allow its competitors to compete with it. If customers own their local loops, however, the problems of interconnection virtually disappear: customers will determine whose traffic their loops carry, and incumbents will not have to supply competitors with the means of competing with them.     

A note on constructive heuristics for the flowshop problem with blocking

This paper analyzes the minimization of the makespan criterion for the flowshop problem with blocking. In this environment, there are no buffers between successive machines, and therefore intermediate queues of jobs waiting in the system for their next operations are not allowed. As the problem is NP-hard, a constructive heuristic that explores specific characteristics of the problem is developed. The small computational effort of such strategy, which is valuable in practical applications, is one of the reasons that motivated this study. The performance of a combination of the proposed method with existing ones is examined through a comparative study. The new methods outperform the NEH algorithm, currently the best constructive heuristic for this problem, in problems with up to 500 jobs and 20 machines.     

Active-set sequential quadratic programming method with compact neighbourhood algorithm for the multi-polygon mass production cutting-stock problem with rotatable polygons

The cutting-stock problem, which considers how to arrange the component profiles on the material without overlaps, can increase the utility rate of the sheet stock, and is thus a standard constrained optimisation problem. In some applications the components should be placed with specific orientations, but in others the components may be placed with any orientation. This study presents an overlap index and it is much more suitable for the active-set SQP method which can reduce the time spend for constraint consideration. Using this method, various object orientations can be considered easily and the number of object on the sheet stock can be improved by up to eight percent.     

A coevolutionary algorithm for the flexible delivery and pickup problem with time windows

This paper addresses a flexible delivery and pickup problem with time windows (FDPPTW) and formulates the problem into a mixed binary integer programming model in order to minimize the number of vehicles and to minimize the total traveling distance. This problem is shown to be NP-hard. In this study, therefore, a coevolutionary algorithm incorporated with a variant of the cheapest insertion method is developed to speed up the solution procedure. The FDPPTW scheme overcomes the shortcomings of the existing schemes for the delivery and pickup problems. By testing with some revised Solomon's benchmark problems, the computational results have shown the efficiency and the effectiveness of the developed algorithm.     

A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty

Manufacturers need to satisfy consumer demands in order to compete in the real world. This requires the efficient operation of a supply chain planning. In this research we consider a supply chain including multiple suppliers, multiple manufacturers and multiple customers, addressing a multi-site, multi-period, multi-product aggregate production planning (APP) problem under uncertainty. First a new robust multi-objective mixed integer nonlinear programming model is proposed to deal with APP considering two conflicting objectives simultaneously, as well as the uncertain nature of the supply chain. Cost parameters of the supply chain and demand fluctuations are subject to uncertainty. Then the problem transformed into a multi-objective linear one. The first objective function aims to minimize total losses of supply chain including production cost, hiring, firing and training cost, raw material and end product inventory holding cost, transportation and shortage cost. The second objective function considers customer satisfaction through minimizing sum of the maximum amount of shortages among the customers’ zones in all periods. Working levels, workers productivity, overtime, subcontracting, storage capacity and lead time are also considered. Finally, the proposed model is solved as a single-objective mixed integer programming model applying the LP-metrics method. The practicability of the proposed model is demonstrated through its application in solving an APP problem in an industrial case study. The results indicate that the proposed model can provide a promising approach to fulfill an efficient production planning in a supply chain.     

A tabu search approach for cell scheduling problem with makespan criterion

Scheduling problem in a cellular manufacturing system is treated as the group scheduling problem, assuming that intercellular moves can be eliminated by duplicating machines. However, in a typical CMS, duplicating bottleneck machines may be costly and infeasible. This fact limits the applicability of group scheduling. Scheduling problem in the presence of bottleneck machines is termed as cell scheduling. A mixed-integer linear programming model is proposed for the attempted cell scheduling problem and a nested application of tabu search approach is investigated in this paper to solve the problem heuristically. The effectiveness of the proposed nested tabu search (NTS) algorithm is evaluated on 16 problems selected from the literature. Comparison of the results of NTS with SVS-algorithm reveals the effectiveness and efficiency of the proposed algorithm.     

中国煤炭成本核算框架现状及存在问题分析

通过描述我国煤炭成本核算框架现状,指出现行煤炭成本核算框架存在的煤炭成本总体水平低、煤炭成本反映不完全、煤炭职工收入水平低等六个方面不足,并在此基础上提出政策性建议,以期完善我国煤炭成本核算体系。     

论国有煤炭企业法人治理结构存在的问题与对策

党的十五届四中全会决定指出．公司法人治理结构是公司制的核心．而对于国有煤炭企业来说．明确股东会、董事会、监事会和经理层的职责，形成各负其责、协调运转、有效制衡的公司法人治理结构是改革攻坚战的关键所在。     

The component procurement problem for the loss-averse manufacturer with spot purchase

This paper studies the purchasing behaviour of a loss-averse engineer-to-order manufacturer, who purchases a key component for his final product from a supplier under a single-wholesale-price contract with spot purchase opportunities, where both the product demand and the component spot price are uncertain. Through newsvendor type of models, we analyze several key issues, including the effects of the manufacturer's loss aversion, and the effects of demand and spot price uncertainties on the manufacturer's decision behaviour. We find that the purchasing behaviour of the loss-averse manufacturer differs from those of the risk-neutral and risk-averse ones. Specifically, we identify some sufficient conditions under which the loss-averse manufacturer may purchase a larger order quantity in advance when demand becomes more uncertain or when the price becomes more uncertain. We also discuss the two-wholesale-price contract and show that fixing the emergency supply price may lead to a smaller order quantity.     

A hybrid artificial bee colony algorithm for the job shop scheduling problem

The job shop scheduling problem (JSSP) has attracted much attention in the field of both information sciences and operations research. In terms of the objective function, most existing research has been focused on the makespan criterion (i.e., minimizing the overall completion time). However, for contemporary manufacturing firms, the due date related performance is usually more important because it is crucial for maintaining a high service reputation. Therefore, in this study we aim at minimizing the total weighted tardiness in JSSP. Considering the high complexity, a novel artificial bee colony (ABC) algorithm is proposed for solving the problem. A neighborhood property of the problem is discovered, and then a tree search algorithm is devised to enhance the exploitation capability of ABC. According to extensive computational tests, the proposed approach is efficient in solving the job shop scheduling problem with total weighted tardiness criterion.