Goal programming approach to marketing/ production planning

This article focuses on the need for greater coordination and integration between production and marketing. The potential conflicts between the production and marketing areas are highlighted, and a goal-programming algorithm is developed for dealing with the complex trade-off decisions involved in marketing/production planning. The algorithm is applied to a case example to illustrate its use in managerial decision making.     

Cement transportation planning via fuzzy linear programming

The mining of more than ten million tons of limestone for cement production in western Taiwan will be prohibited by the end of 1997 and hence almost half of the cement production will move toward eastern Taiwan. Since the supply and demand will be separated by the Central Ridge, the planning of transportation networks and facilities for limestone and cement appears urgent. This research resolves the cement transportation planning problem using fuzzy linear programming methods. Three types of fuzzy linear programming models are used to determine the optimal transportation amount and the capacity of new facilities. The emphasis is also on how to formulate a transportation planning problem where the port capacity, demand fulfillment, trans-shipping operation capacity and traffic congestion constraints are included.     

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.     

Fuzzy goal programming based genetic algorithm approach to nutrient management for rice crop planning

The main purpose of this paper is to present a tolerance based fuzzy goal programming (FGP) and a FGP based genetic algorithm (GA) model for nutrient management decision-making for rice crop planning in India. In the proposed model, we have included fuzzy goals such as fertilizer cost and rice yield in the decision-making process. Fuzzy goals are converted to goal constraints using their corresponding membership function values and the deterministic equivalent of the fuzzy model is obtained using tolerance based FGP approach. This deterministic nutrient management model is also solved using a proposed real coded GA and the optimal combination of fertilizer is obtained to maximize the yield of rice within the available budget. A case example is solved under various scenarios to demonstrate the proposed approach.     

Applying quality function deployment in industrial design curriculum planning

Industrial design is a discipline that combines multiple professional fields. Enterprise demands for industrial design competencies also change over time; thus, the curriculum of industrial design education should be compatible with the current demands of the industry. However, scientific approaches have not been previously employed to plan industrial design curriculums. Therefore, a gap exists between the expectations of enterprises and the professional competencies of numerous graduates in the industrial design discipline. To solve this problem, we first identified the professional competencies required of industrial design professionals in industry and academia, and then ranked these competencies based on their degree of importance using the analytic hierarchy process (AHP). Subsequently, we planned an industrial design curriculum that meets practical workplace needs by applying quality function deployment (QFD), and proposed items that require special attention for the industrial design curriculum. The results of this study are as follows (1) we obtained a ranking of various items of the professional competencies in industrial design based on their degree of importance to determine which professional competencies should be specifically cultivated; (2) we planned an industrial design curriculum that addresses the needs of enterprises according to industrial design ability index (IDAI); and (3) we recommend the courses and subjects that should be emphasized and enhanced in the existing industrial design curriculum.     

An iterative approach to item-level tactical production and inventory planning

In this paper, we propose an iterative approach to jointly solve the problems of tactical safety stock placement and tactical production planning. These problems have traditionally been solved in isolation, even though both problems operate in the same decision making space and the outputs of one naturally serve as the inputs to the other. For simple supply chain network structures, two stages and one or many products, we provide sufficient conditions to guarantee the iteration algorithm’s termination. Through examples, we show how the algorithm works and prove its applicability on a realistic industrial-scale problem.     

A practical approach to sensitivity analysis in linear programming under degeneracy for management decision making

Linear programming (LP) is a widely used tool in management decision making. Theoretically, sensitivity analysis of LP problems provides useful information for the decision maker. In practice, however, most LP software provides misleading sensitivity information if the optimal solution is degenerate. The paper shows how sensitivity analysis of LP problems can be done correctly when the optimal solution is degenerate. A production planning example is presented to illustrate the incorrect sensitivity analysis results automatically provided by most LP solvers. The general characteristics of the misleading results and the possible effects of this incorrect information on management decisions are also discussed.     

深圳拟打造成我国服务机器人主要生产基地

《深圳商报讯》（记者彭琰） 报道，通过2012年第一期《市政府公报》发布的《深圳市科学技术发展“十二五”规划》提出要转化和产业化服务机器人技术成果。记者注意到，《规划》提出一个“有意思”的目标：深圳要成为我国服务机器人主要生产基地。     

A class of chance constrained multiobjective linear programming with birandom coefficients and its application to vendors selection

In this paper, a class of chance constrained multiobjective linear programming model with birandom coefficients is considered for vendor selection problem. Firstly we present a crisp equivalent model for a special case and give a traditional method for crisp model. Then, the technique of birandom simulation is applied to deal with general birandom objective functions and birandom constraints which are usually difficult to be converted into their crisp equivalents. Furthermore, a genetic algorithm based on birandom simulation is designed for solving a birandom multiobjective vendor selection problem. Finally, a real numbers example is given. The paper makes certain contribution in both theoretical and application research related to multiobjective chance constrained programming, as well as in the study of vendor selection problem under uncertain environment.     

Integrated production planning with sequence-dependent family setup times

This paper proposes an integrated optimization model of aggregate production planning (APP), family disaggregation planning, and family scheduling problems in hierarchical production planning (HPP) systems considering sequence-dependent family setup times. The model obtains the optimal production plan for each product type and product family in each period, together with the globally optimal production sequence of product families in all planning periods. The proposed model is tested with randomly generated experimental data consistent with what is prevalent in the manufacturing industry and its results are compared with those of the traditional HPP models. Our results show that the integrated model realizes greater cost savings.     

A hierarchical planning system for energy intensive production environments

This paper describes a hierarchical production planning approach with decision support features for energy intensive industries with particular reference to a tile manufacturing factory. In the tiling industry, the facilities which contribute most to the consumption of energy (and, hence, to the production costs) are usually the kilns where the curing operation is carried out. Frequently, the kilns are also the bottleneck in terms of capacity utilization. Thus, in order to save on energy costs, a planning approach which aims at minimizing the number of active kilns throughout the year is needed besides optimizing the process design in the curing department. To achieve the latter goal, it is necessary to take into account demand fluctuations as well as detailed capacity restrictions while deciding on the lot sizes of the products and the kilns on which the products are loaded. Rather than adopting a monolithic mathematical model for developing a desirable production plan, a hierarchical approach which decomposes the problem into two sub-problems is preferred. In the first level, products and capacity are aggregated over the planning horizon to achieve an overall consideration of demand fluctuations over time. Then, the solution provided by the aggregate solution for the current planning period is disaggregated into a detailed lot sizing and loading solution. The disaggregated problem is difficult to solve and hence, a heuristic is proposed here. This planning approach is sustained by a Decision Support System which enables the elimination of possible inconsistencies in the production plan by providing an effective interaction with the decision maker.     

An efficient heuristic for medium-term planning in shampoo production

This paper presents a single machine problem which occurs in shampoo production at medium-term planning phase. The considered production plant is linked to subsidiary companies which are themselves linked to final customers. The aim is to answer subsidiary companies requests by keeping their stocks in a window defined by their safety stock and maximum inventory levels. After an introduction, we present a formal definition of the problem. Next, we present a two-phase heuristic algorithm: the first phase is based on a greedy algorithm and the second phase on the Goldberg and Tarjan algorithm for the minimum cost flow problem. Experimental testings close to industrial instances show that the heuristic performs very efficiently.     

Determining safety stocks for production planning in uncertain manufacturing

When computing production plans and available-to-promise statements, the randomness of flow times and yield rates are the two major sources of uncertainty. Considering both of these uncertainties, this study employs a simple formula for computing the standard deviation for cumulative good product output of a particular product type at a particular time. Using this standard deviation, the necessary safety stock for that product type at that time can be obtained. This study was motivated by semiconductor wafer manufacturing, which requires many process steps performed by highly unreliable equipment. Such an environment causes the means and the variances in flow times to be generally larger than those for other industries. It also makes yield rates uncertain. Thus, when making order quotations and calculating production plans, safety stocks must be used to guard against the uncertainties of the manufacturing processes to ensure on-time-delivery ratios and to provide better customer service.     

Reengineering the production planning process in the food industry

This paper addresses managerial process reengineering and in particular the reengineering of the production planning process. The reengineered process highlights planning options to avoid the process imbalance and loss of production potential that can follow innovation in a facility that is fully committed to JIT production.The study was motivated by production problems following product innovation within the food industry but the reengineering conclusions and procedures are applicable to all similarly structured industries. An example based on data from a snack food manufacturing company illustrates the reengineered procedure for a plant that is typical of the food processing industry.     

An integrated production planning model for molds and end items

This study develops a mathematical modelling framework for simultaneously generating production plans for molds and the end items that are made with them. The inputs considered are the item demand (assumed constant over an infinite planning horizon), holding costs and shortage costs, together with the molds’ statistical lifetime distribution (in terms of number of uses) and costs pertaining to amortization, preventive replacements and corrective replacements.     

Applying Capital Budgeting Techniques to Mergers

The most important capital budgeting decision a firm may make is a merger or acquisition. Yet the literature in finance provides little coverage of the topic. Mergers are frequently analyzed on the basis of the exchange rate determination and the impact on earnings per share. Though this topic is covered in the latter part of the article, the main emphasis is on the capital budgeting aspects of a merger analysis. Thus, the key metric to be considered is free cash flow and not earnings per share. Also, the article considers the importance of residual value or the value of the target firm after the traditional 5 or 10 years of cash flow or earnings analysis. By ignoring residual value, as over 50% of Fortune 500 companies do, it is implicitly being assigned a value of zero. This neglect may substantially understate the true value of the target. The article also points out the fallacy of considering relative price/earnings (P/E) ratios on postmerger EPS.     

Modeling and simulation of order-driven planning policies in build-to-order automobile production

In adopting build-to-order order fulfillment systems, automotive companies strive to better synchronize their production output with market demand. This essentially gives rise to a new paradigm in production planning. Since all business is linked to customer orders, the operational performance is substantially determined by order-driven planning. Therefore, a clear understanding of the associated planning tasks, order promising and master production scheduling, as well as their dynamic interaction is essential. Based on the analysis of the decision situation of order-driven planning in build-to-order settings, we provide a framework comprising separate interlinked quantitative models for order promising and master production scheduling. The focus of the contribution is on the modeling and evaluation of both models in a dynamic setting. The approach is evaluated by means of a simulative analysis using empirical data from the automotive industry. Conclusions regarding the potentials of such systems with respect to customer service, the leveling of resource utilization, and holding are presented.