Production Planning

The vendor selection process has undergone significant changes during the past twenty years. These include increased quality guidelines, improved computer communications, and increased technical capabilities. Given radical changes in the purchasing selection process, it is appropriate to review the past research and to determine its relevance to the supplier selection decision. This paper reviews, annotates, and classifies 74 related articles which have appeared since 1966. Specific attention is given to the criteria and analytical methods used in the vendor selection process. In response to the increased interest in Just-In-Time (JIT) manufacturing strategies, and analysis of JIT's impact on vendor selection is also presented. Finally, conclusions and potential areas for future research are presented.

Lot sizing is one of the most important and also one of the most di cult problems in production planning. This subject has been studied extensively in the literature. In this article, we consider single-level lot sizing problems, their variants and solution approaches. After introducing factors a ecting formulation and the complexity of production planning problems, and introducing di erent variants of lot sizing and scheduling problems, we discuss single-level lot sizing problems, together with exact and heuristic approaches for their solution. We conclude with some suggestions for future research. ?

The consideration of uncertainty in manufacturing systems supposes a great advance. Models for production planning which do not recognize the uncertainty can be expected to generate inferior planning decisions as compared to models that explicitly account for the uncertainty. This paper reviews some of the existing literature of production planning under uncertainty. The research objective is to provide the reader with a starting point about uncertainty modelling in production planning problems aimed at production management researchers. The literature review that we compiled consists of 87 citations from 1983 to 2004. A classification scheme for models for production planning under uncertainty is defined. r (J. Mula). Analytical models [3] 3. Material requirement planning Conceptual models [9] Analytical models [6] Artificial intelligence models [4] Simulation models [10] 4. Capacity planning Analytical models [4] Simulation models [1] 5. Manufacturing resource planning Analytical models [7] Artificial intelligence models [5] Simulation models [2] 6. Inventory management Analytical models [10] Artificial intelligence models [5] 7. Supply chain planning Conceptual models [1] Analytical models [5] Artificial intelligence models [5] J. Mula et al. / Int. J. Production Economics 103 (2006) 271-285 272

This paper develops operational methods for minimization of energy consumption of manufacturing equipment. It is observed that there can be a significant amount of energy savings when non-bottleneck (i.e., underutilized) machines/equipment are turned off when they will be idle for a certain amount of time. Using this fact, several dispatching rules are proposed. A detailed performance analysis indicates that the proposed dispatching rules are effective in decreasing the energy consumption of especially underutilized manufacturing equipment. In addition, a multi-objective mathematical programming model is proposed to minimize the energy consumption and total completion time. Using this approach, a production manager will have a set of nondominated solutions (i.e., the set of efficient solutions) which he/she can use to determine the most efficient production sequence which will minimize the total energy consumption while optimizing the total completion time. 1

The management of food distribution networks is receiving more and more attention, both in practice and in the scientific literature. In this paper, we review quantitative operations management approaches to food distribution management, and relate this to challenges faced by the industry. Here, our main focus is on three aspects: food quality, food safety, and sustainability. We discuss the literature on three decision levels: strategic network design, tactical network planning, and operational transportation planning. For each of these, we survey the research contributions, discuss the state of the art, and identify challenges for future research.

This paper presents a review of mathematical programming models for supply chain production and transport planning. The purpose of this review is to identify current and future research in this field and to propose a taxonomy framework based on the following elements: supply chain structure, decision level, modeling approach, purpose, shared information, limitations, novelty and application. The research objective is to provide readers with a starting point for mathematical modeling problems in supply chain production and transport planning aimed at production management researchers.

A state of the art of a particular planning problem, the single item lot sizing problem (SILSP), is given for its uncapacitated and capacitated versions. First classes of lot sizing problems are briefly surveyed. Various solution methods for the Uncapacitated Single Item Lot Sizing Problem (USILSP) are reviewed. Four different mathematical programming formulations of the classical problem are presented. Different extensions for real-world applications of this problem are discussed. Complexity results of the capacitated Single Item Lot Sizing Problem (CSILSP) are given together with its different formulations and solution techniques.

To provide effective managerial support for decisions related to production planning and scheduling processes, it is useful to partition the set of decisions into a hierarchical framework. In the resulting system, higher level decisions impose constraints on lower level actions, and lower level decisions provide the necessary feedback to reevaluate higher level actions. The purpose of this paper is to suggest optimum procedures to deal with the resulting subproblems and to analyze the interaction mechanisms among the different hierarchical levels. Computational results are given.

Cross-functional integration offers numerous, well-documented benefits for newproduct development (NPD), but it also can carry significant costs. Joint involvement of R&D, manufacturing, and marketing personnel can increase the quality, the manufacturability, and the marketability of the final product. However, building consensus among these groups, with their differing perspectives and goals, may require time-consuming meetings as well as tremendous finesse from the managers who guide the NPD effort. Those managers require an approach to cross-functional integration that strikes a balance between efficiency and effectiveness. X. Michael Song, R. Jeffrey Thieme, and Jinhong Xie propose that the right mix of cross-functional involvement may differ depending on the stage in the NPD process. They also suggest that blindly promoting the involvement of all functional areas in all stages of the NPD process may actually decrease NPD performance. They test these propositions in a study that examines the relationships between new product performance and cross-functional joint involvement between R&D, manufacturing, and marketing in five major stages of the NPD process: market opportunity analysis, planning, development, pretesting, and launch. Their objective in this study is to identify patterns of effective crossfunctional involvement in different NPD stages. The study uses data collected from 236 managers working in the R&D, manufacturing, and marketing departments of 16 Fortune 500 firms. Their findings suggest that new-product success may be more likely when a firm employs function-specific and stage-specific patterns of cross-functional integration than it is when the firm attempts to integrate all functions during all NPD stages. For example, during the market opportunity analysis stage, the findings suggest that joint involvement between R&D and marketing may be productive, but joint involvement between R&D and manufacturing and among all three functions may be counterproductive. The results also indicate that joint involvement among all three functions either does not have a significant effect on new product success or may be counterproductive in all stages of the NPD process. For the firms in this study, the three functions seem to take turns playing the central role in cross-functional activities. During the product planning, development, and testing phases, the role of the focal function, or communication hub, shifts from manufacturing to R&D and then to marketing.

E$cient long-term capacity management is vital to any manufacturing "rm. It has implications on competitive performance in terms of cost, delivery speed, dependability and #exibility. In a manufacturing strategy, capacity is a structural decision category, dealing with dynamic capacity expansion and reduction relative to the long-term changes in demand levels. Sales and operations planning (S&OP) is the long-term planning of production levels relative to sales within the framework of a manufacturing planning and control system. Within the S&OP, resource planning is used for determining the appropriate capacity levels in order to support the production plan. Manufacturing strategy and sales and operations planning provide two perspectives on long-term capacity management, raising and treating di!erent issues. In this paper, we compare and link them in a framework for long-term capacity management.

T he purpose of this paper is to develop an approach to a resource-allocation problem that typically appears in organizations with a centralized decision-making environment, for example, supermarket chains, banks, and universities. The central unit is assumed to be interested in maximizing the total amount of outputs produced by the individual units by allocating available resources to them. We will develop an interactive formal approach based on data envelopment analysis (DEA) and multiple-objective linear programming (MOLP) to find the most preferred allocation plan. The units are assumed to be able to modify their production in the current production possibility set within certain assumptions. Various assumptions are considered concerning returns to scale and the ability of each unit to modify its production plan. Numerical examples are used to illustrate the approach.

Power-intensive processes can lower operating expenses when adjusting production planning according to time-dependent electricity pricing schemes. In this paper, we describe a deterministic MILP model that allows optimal production planning for continuous powerintensive processes. We emphasize the systematic modeling of operational transitions, that result from switching the operating modes of the plant equipment, with logic constraints. We prove properties on the tightness of several logic constraints. For the time horizon of one week and hourly changing electricity prices, we solve an industrial case study on air separation plants, where transitional modes help us capture ramping behavior. We also solve problem instances on cement plants where we show that the appropriate choice of operating modes allows us to obtain practical schedules, while limiting the number of changeovers. Despite the large size of the MILPs, the required solution times are small due to the explicit modeling of transitions.

the constraint knowledge utilized in actual factory environments. This led to the construction of the Intelligent Scheduling and Information System (ISIS), a series of experimental job shop scheduling systems (Fox, Allen, and Strohm 1982; Smith 1984a, 1984b). ISIS-2 was demonstrated in the context of the Westinghouse Turbine Components Plant (WTCP) in Winston-Salem, North Carolina. More recently, we initiated work on a new system called OPIS , which continues to generalize from the ISIS experience and provide greater system flexibility in approaching various scheduling tasks. We feel we have made significant progress in this work, but there are difficult issues that remain to be addressed. This article attempts to coalesce what we have learned thus far and to reflect on the potential of knowledge-based approaches to this difficult problem. Abstract To be useful in practice, a factory production schedule must reflect the influence of a large and conflicting set of requirements, objectives and preferences. Human schedulers are typically overburdened by the complexity of this task, and conventional computer-based scheduling systems consider only a small fraction of the relevant knowledge. This article describes research aimed at providing a framework in which all relevant scheduling knowledge can be given consideration during schedule generation and revision. Factory scheduling is cast as a complex constraint-directed activity, driven by a rich symbolic model of the factory environment in which various influencing factors are formalized as constraints. A variety of constraint-directed inference techniques are defined with respect to this model to provide a basis for intelligently compromising among conflicting concerns. Two Bnowledge-based factory scheduling systems that implement aspects of this approach are described.

We introduce a methodology for automated production planning of semiconductor manufacturing based on iterative linear programming (LP) optimization and discrete-event simulation calculations. The LP formulation incorporates epochdependent parameters for flow times from lot release up to each operation on each manufacturing route. LP-derived release schedules are used as input to the simulation model, from which statistics on flow times are collected and used to reformulate the LP model for a revised planning calculation. Iteration continues until satisfactory agreement between simulation and LP models is obtained. We demonstrate in experiments on an industry data set that a relatively small number of iterations is required to develop a production plan correctly characterizing future flow times as a function of factory load and product mix. The methodology makes possible automated production planning of semiconductor manufacturing on an engineering work station.

Information sharing practices such as vendor-managed inventory (VMI) give manufacturers access to more accurate demand information, e.g. customer sales data, than before. The value of this type of information sharing has been established in many studies. However, most of the research has focused on the ideal situation of the manufacturer having access to information from all downstream parties. In practice, this is rarely the case. In this paper, discrete-event simulation is used to examine how a manufacturer can combine traditional order data available from non-VMI customers with sales data available from VMI customers in its production and inventory control and what impact this has on the manufacturer's operational ef®ciency. The simulation model is based on a real-life VMI implementation and uses actual demand and product data. The key ®nding is that even for products with stable demand a partial improvement of demand visibility can improve production and inventory control ef®ciency, but that the value of visibility greatly depends on the target products' replenishment frequencies and the production planning cycle employed by the manufacturer. The Emerald Research Register for this journal is available at The current issue and full text archive of this journal is available at http://www.emeraldinsight.com/researchregister http://www.emeraldinsight.com/0960-0035.htm IJPDLM 33,4 336

An experimental study was designed to investigate the efficiency of decisions obtained from optimizing a finite, multiperiod model and implementing (structure is parallel with "optimizing" above) those decisions on a rolling basis. The results of the study suggest that rolling schedules are quite efficient and also that they point to some important design issues in model-building for production planning.

This paper addresses the unit commitment in multi-period combined heat and power (CHP) production planning, considering the possibility to trade power on the spot market. In CHP plants (units), generation of heat and power follows joint characteristics, which means that production planning for both heat and power must be done in coordination. We present an improved unit decommitment (IUD) algorithm that starts with an improved initial solution with less heat surplus so that the relative cost-efficiency of the plants can be determined more accurately. Then the subsequent decommitment procedures can decommit (switch off) the least cost-efficient plants properly. The improved initial solution for the committed plants is generated by a heuristic procedure. The heuristic procedure utilizes both the Lagrangian relaxation principle that relaxes the system-wide (heat and power) demand constraints and a linear relaxation of the ON/OFF states of the plants. We compare the IUD algorithm with realistic test data against a generic unit decommitment (UD) algorithm. Numerical results show that IUD is an overall improvement of UD. The solution quality of IUD is better than that of UD for almost all of tested cases. The maximum improvement is 11.3% and the maximum degradation is only 0.04% (only two sub-cases out of 216 sub-cases) with an average improvement of 0.3-0.5% for different planning horizons. Moreover, IUD is more efficient (1.1-3 times faster on average) than UD.

Several Linear Programming (LP) and Mixed Integer Programming (MIP) models for the production and capacity planning problems with uncertainty in demand are proposed. In contrast to traditional mathematical programming approaches, we use scenarios to characterize the uncertainty in demand. Solutions are obtained for each scenario and then these individual scenario solutions are aggregated to yield a nonanticipative or implementable policy. Such an approach makes it possible to model nonstationarity in demand as well as a variety of recourse decision types. Two scenario-based models for formalizing implementable policies are presented. The first model is a LP model for multi-product, multi-period, single-level production planning to determine the production volume and product inventory for each period, such that the expected cost of holding inventory and lost demand is minimized. The second model is a MIP model for multiproduct, multi-period, single-level production planning to help in sourcing decisions for raw materials supply. Although these formulations lead to very large scale mathematical programming problems, our computational experience with LP models for real-life instances is very encouraging.

Most of the research in the production planning area has been aimed at the needs of Make-To-Stock (MTS) companies. The make-to-order sector, although constituting an important part of world industry, has received relatively little attention. This neglect may be based on an assumption that the systems developed for MTS firms can also be used by MTO companies. However, the requirements of the two sectors are quite different. This paper sets out to identify these differences and to determine whether the research to date can meet the needs of the MTO sector. Many important application areas are considered, these include production scheduling, capacity control and the setting of delivery dates. Popular production planning approaches, including MRP II, OPT and JIT, are also discussed. It is shown that there is a need for more research to provide production planning systems which have been designed specifically for the MTO sector.

I n this paper we study how the time-series structure of the demand process affects the value of information sharing in a supply chain. We consider a two-stage supply chain model in which a retailer serves autoregressive moving-average (ARMA) demand and a manufacturer fills the retailer's orders. We characterize three types of situations based on the parameters of the demand process: (i) the manufacturer benefits from inferring demand information from the retailer's orders; (ii) the manufacturer cannot infer demand, but benefits from sharing demand information; and (iii) the manufacturer is better off neither inferring nor sharing, but instead uses only the most recent orders in its production planning. Using the example of ARMA(1,1) demand, we find that sharing or inferring retail demand leads to a 16.0% average reduction in the manufacturer's safety-stock requirement in cases (i) and (ii), but leads to an increase in the manufacturer's safety-stock requirement in (iii). Our results apply not only to two-stage but also to multistage supply chains.

We do two things in this paper. First, we put forward some elements of a microeconomic theory of technological evolution. This involves adding nascent (essentially undiscovered) technologies to the existing technologies of neoclassical production theory, and, more importantly, expanding the notion of the production plan to include the recipe } the complete description of the underlying engineering process. Second, we use the recipes approach in constructing a simple microeconomic model of shop-#oor learning by doing. We simulate the dynamics of the model and report the e!ects of changes in the basic parameters on the resulting engineering experience curves. For correctly chosen values of these parameters, the predictions of the model match the observed experience curves.

Due to the strong competition that exists today, most manufacturing organizations are in a continuous effort for increasing their profits and reducing their costs. Accurate sales forecasting is certainly an inexpensive way to meet the aforementioned goals, since this leads to improved customer service, reduced lost sales and product returns and more efficient production planning. Especially for the food industry, successful sales forecasting systems can be very beneficial, due to the short shelf-life of many food products and the importance of the product quality which is closely related to human health. In this paper we present a complete framework that can be used for developing nonlinear time series sales forecasting models. The method is a combination of two artificial intelligence technologies, namely the radial basis function (RBF) neural network architecture and a specially designed genetic algorithm (GA). The methodology is applied successfully to sales data of fresh milk provided by a major manufacturing company of dairy products.

A rational approach toward decision-making should take into account human subjectivity, rather than employing only objective probability measures. This attitude towards the uncertainty of human behavior led to the study of a relatively new decision analysis field: Fuzzy decision-making. Fuzzy systems are suitable for uncertain or approximate reasoning, especially for the system with a mathematical model that is difficult to derive. Fuzzy logic allows decision-making with estimated values under incomplete or uncertain information. A major contribution of fuzzy set theory is its capability of representing vague data. Fuzzy set theory has been used to model systems that are hard to define precisely. As a methodology, fuzzy set theory incorporates imprecision and subjectivity into the model formulation and solution process. Fuzzy set theory represents an attractive tool to aid research in industrial engineering (IE) when the dynamics of the decision environment limit the specification of model objectives, constraints and the precise measurement of model parameters. This chapter provides a survey of the applications of fuzzy set theory in IE.

Since its introduction more than a decade ago, the CONstant Work In Process (CONWIP) production control system has received a great deal of attention from practitioners and researchers. In this paper, different contributions are reviewed according to three issues: operation, application, and comparison of Conwip. Referring to the operation of Conwip, some implementation decisions, such as lot sizing, have been overlooked, most studies being focused on card setting and job sequencing. Besides, no research has been conducted to show the relative importance of the different implementation decisions on the system performance. With respect to the application of Conwip, although it has been successfully implemented in many manufacturing environments, there are no studies to assess the impact of the manufacturing conditions on the performance of the system. Finally, most of the comparisons of Conwip with other production control systems suffer the lack of a unified framework for comparison where each system is tested under optimal parameter setting. The latter may explain partly the contradictory results found in the literature. It is believed that all these issues are relevant subjects for future research.

Much of the research on mass customization strategy implementation reflects a functional focus, considering product design, marketing, manufacturing or sourcing, individually. This paper takes a step towards integrating these perspectives into a more systemic view of mass customization strategy implementation. More precisely, the paper explores how a firm's supply chain -meant as the whole of its supply, manufacturing and distribution networks -should be configured when different degrees of customization are offered. The empirical research consists of a multiple case study including firms in the telecommunications, transportation vehicles and food processing equip-ment industries. Case analyses highlighted that the degrees of freedom customers have in specifying product features, heavily affects the supply-chain configuration, as well as product architecture and, ultimately, firm performances. Our findings further show that two peculiar supply-chain configurations can be identified, each one suggesting an isomorphism between market characteristics, product structure and supply-chain configuration.

Purpose -This paper aims to analyse expectation and reality in enterprise resource planning implementation from the consultants' and software vendors' point of view and process these further as requirements of future IT systems. Design/methodology/approach -A small-scale survey among Finnish enterprise resource planning system (ERP) software vendors and consultants on ERP implementation challenges is analysed (n ¼ 59). The results are connected to existing literature in the field of deploying ERP systems in the form of discussion. Findings -The consultants' opinions show similar results with studies conducted with companies using ERP systems. The implementer's point of view shows clearly the challenge of using standardized ERP packages for various requirements on different levels. Although the sales presentations tend to emphasize the general purpose and flexibility of software packages, the dilemma between customization and vanilla system remains. The implementer's viewpoint emphasises challenges in operations: production planning, materials management, sales and marketing.

This paper investigates the problem of designing cellular manufacturing systems incorporating several design features including multi-period production planning, sequence of operations, alternate process routings, intra-cell layout, dynamic system reconfiguration, duplicate machines, machine capacity, lot splitting, and material flow between machines in a dynamic environment. The problem is formulated through a comprehensive integer linear programming model. The objective is to minimise the total costs of inter-cell material handling, forward and backward intra-cell material handling, machine operating, machine maintenance and overhead, cell reconfiguration, outsourcing and inventory holding. The main constraints are demand satisfaction, cell size, machine availability, machine time-capacity, material flow conservation and machine location. Computational results are presented by solving some numerical examples to verify the model performance and illustrate its advantageous aspects.

Managing the order-delivery processes between organisations has been a key issue in supply chain management. Despite of the increasing application of JIT, lean and agile practices and new information systems that increase the visibility in supply chains, a lot of problems still remain. Surveys among European companies indicate that no significant improvements have taken place in delivery performance during last decade.

In this paper we consider production planning of remanufactured products when inputs have different and uncertain quality levels, and there are capacity constraints. This situation is typical of most remanufacturing environments, where inputs are product returns (also called cores). Production (remanufacturing) cost increases as the quality level decreases, and any unused cores may be salvaged at a value that increases with their quality level. Decision variables include, for each period and under a certain probabilistic scenario, the amount of cores to grade, the amount to remanufacture for each quality level and the amount of inventory to carry over for future periods for un-graded cores, graded cores, and finished remanufactured products. Our model is grounded with data collected at a major OEM that also remanufactures. We formulate the problem as a stochastic program, and illustrate how the deterministic version of the problem yields solutions that cannot be implemented in practice. The stochastic program, although a large linear program, can be solved easily using Cplex. We provide a numeric study to generate insights into the nature of the solution.

This survey describes some of the main optimization problems arising in the context of production planning for the assembly of printed circuit boards. The discussion is structured around a hierarchical decomposition of the planning process into distinct optimization subproblems, addressing issues such as the assignment of board types to machine groups, the allocation of component feeders to individual machines, the determination of optimal production sequences, etc. The paper reviews the literature on this topic with an emphasis on the most recent developments, on the fundamental structure of the mathematical models and on the relation between these models and some 'environmental' variables such as the layout of the shop or the product mix. ?

The paper deals with lot sizing and scheduling problem (LSSP) of a multi-site manufacturing system with capacity constraints and uncertain multi-product and multi-period demand. LSSP is solved by an hybrid model resulting from the integration of a mixed-integer linear programming model and a simulation model.

Manufacturing,and,logistics service provision,enterprises are currently moving,towards,open,virtual enterprise collaboration networks to meet the needs of the Global Economy. In such networks, manufacturing and logistics planning and scheduling is challenging,due,to the difficulties in integrating information,from,different partners,and,in exploring,a large and,dynamically changing,number,of planning,and scheduling,alternatives. Agent-based technology,is considered,suitable to support planning,and scheduling in such enterprises because agents can dynamically,adapt their behaviour,to changing,requirements,and they can

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This paper reports the results of a survey on the critical success factors (CSFs) of Web-based supply chain management systems (WSCMS). An empirical study was conducted and an exploratory factor analysis of the survey data revealed five major dimensions of the CSFs for WSCMS implementation, namely (1) communication, top management commitment, (3) data security, (4) training and education, and hardware and software reliability. The findings of the results provide insights for companies using or planning to use WSCMS.

Quantity discount is considered to encourage the producer to order more quantities.Interrelationship between lead time, transportation cost, and GHGs is considered.Demand uncertainty is assumed to follows a pre-specified distribution function.Shortages are penalized by a generic nonlinear multiple breakpoint function.The concepts of GHG emission and waste management are incorporated into the model.In this paper we develop a stochastic programming approach to solve a multi-period multi-product multi-site aggregate production planning problem in a green supply chain for a medium-term planning horizon under the assumption of demand uncertainty. The proposed model has the following features: (i) the majority of supply chain cost parameters are considered; (ii) quantity discounts to encourage the producer to order more from the suppliers in one period, instead of splitting the order into periodical small quantities, are considered; (iii) the interrelationship between lead time and transportation cost is considered, as well as that between lead time and greenhouse gas emission level; (iv) demand uncertainty is assumed to follow a pre-specified distribution function; (v) shortages are penalized by a general multiple breakpoint function, to persuade producers to reduce backorders as much as possible; (vi) some indicators of a green supply chain, such as greenhouse gas emissions and waste management are also incorporated into the model. The proposed model is first a nonlinear mixed integer programming which is converted into a linear one by applying some theoretical and numerical techniques. Due to the convexity of the model, the local solution obtained from linear programming solvers is also the global solution. Finally, a numerical example is presented to demonstrate the validity of the proposed model.

Nowadays companies operate in a difficult environment: the dynamics of innovations increase and product life cycles become shorter. Furthermore products and the corresponding manufacturing processes get more and more complex. Therefore, companies need new methods for the planning of manufacturing systems. One promising approach in this context is digital factory/virtual production-the modeling and analysis of computer models of the planned factory with the objective to reduce time and costs. For the modeling and analysis various simulation methods and programs have been developed. They are a highly valuable support for planning and visualizing the manufacturing system. But there is one major disadvantage: only experienced and long trained experts are able to operate with these programs. The graphical user interface is very complex and not intuitive to use. This results in an extensive and error-prone modeling of complex simulation models and a time-consuming interpretation of the simulation results. To overcome these weak points, intuitive and understandable man-machine interfaces like augmented and virtual reality can be used. This paper describes the architecture of a system which uses the technologies of augmented and virtual reality to support the planning process of complex manufacturing systems. The proposed system assists the user in modeling, the validation of the simulation model, and the subsequent optimization of the production system. A general application of the VR-and AR-technologies and of the simulation is realized by the development of appropriate linking and integration

Resource-constrained project scheduling with generalised precedence constraints is a very general scheduling model with applications in areas such as make-to-order production planning. We describe a time-oriented branch-and-bound algorithm that uses constraint-propagation techniques which actively exploit the temporal and resource constraints of the problem in order to reduce the search space. Extensive computational experiments with systematically generated test problems show that the algorithm solves more problems to optimality than other exact solution procedures which have recently been proposed, and that the truncated version of the algorithm is also a very good heuristic.

Transport energy modeling is a subject of current interest among transport engineers and scientists concerned with problems of sustainable transport. Transport energy planning is not possible without a reasonable knowledge of past and present energy consumption and likely future demands. In this study, three forms of the energy demand equations are developed in order to improve transport energy demand estimation efficiency for future projections based on genetic algorithm (GA) notion. The Genetic Algorithm Transport Energy Demand Estimation (GATEDE) model is developed using population, gross domestic product and vehicle-km. All equations proposed here are linear and non-linear, of which one is linear, second is exponential and third is quadratic. The quadratic form of the GATEDE model provided better-fit solution to the observed data and can be used with a high correlation coefficient for Turkey's future transport energy projections. It is expected that this study will be helpful in developing highly applicable and productive planning for transport energy policies. The GATEDE gives transport energy demand in comparison with the other transport energy demand projections. The GATEDE model plans the sectoral energy demand of Turkey until 2020.

This paper addresses a multi-period, multi-product sawmill production planning problem where the yields of processes are random variables due to non-homogeneous quality of raw materials (logs). In order to determine the production plans with robust customer service level, robust optimization approach is applied. Two robust optimization models with different variability measures are proposed, which can be selected based on the tradeoff between the expected backorder/inventory cost and the decision maker risk aversion level about the variability of customer service level. The implementation results of the proposed approach for a realistic scale sawmill example highlights the significance of using robust optimization in generating more robust production plans in the uncertain environments compared with stochastic programming.

This research compares seven approaches from the literature to the selection of part types for simultaneous production over the next time horizon. A flexible approach to the selection of part types and the simultaneous determination of their mix ratios so as to balance aggregate machine workloads is presented. Constraints on tool magazine capacity are considered. Simulation studies are conducted on realistic, detailed models of flexible flow systems (FFSs) configured as pooled machines of equal sizes. The simulated settings are constructed to evaluate the impact of such factors as blocking, transportation, buffer utilizations, and fixture requirements and limitations of various types.

We study a production planning problem with remanufacturing. We provide the problem's general formulation and assess its computational complexity under various cost structures. We prove that the problem is NP-complete for general concave-cost structures. When costs are linear, we obtain an OT 3 algorithm based on transforming the problem into the transportation problem in a special way. Finally, we suggest linearizing costs as an alternative for solving the problem in the real world.

A power generation system comprising thermal and pumped-storage hydro plants is considered. Two kinds of models for the cost-optimal generation of electric power under uncertain load are introduced: (i) a dynamic model for the short-term operation and (ii) a power production planning model. In both cases, the presence of stochastic data in the optimization model leads to multi-stage and two-stage stochastic programs, respectively. Both stochastic programming problems involve a l a r g e n umber of mixed-integer (stochastic) decisions, but their constraints are loosely coupled across operating power units. This is used to design Lagrangian relaxation methods for both models, which lead to a decomposition into stochastic single unit subproblems. For the dynamic model a Lagrangian decomposition based algorithm is described in more detail. Special emphasis is put on a discussion of the duality gap, the e cient solution of the multi-stage single unit subproblems and on solving the dual problem by bundle methods for convex nondi erentiable optimization.

In this paper the Discrete Lotsizing and Scheduling Problem (DLSP) is considered. DLSP relates to capacitated lotsizing as well as to job scheduling problems and is concerned with determining a feasible production schedule with minimal total costs in a single-stage manufactunng process. This involves the sequencing and sizing of production lots for a number of different items over a discrete and finite planning horizon. Feasibility of production schedules is subject to production quantities being within bounds set by capacity.

Production planning of a flexible manufacturing system (FMS) is plagued by two interrelated problems, namely 1) part-type selection and 2) operation allocation on machines. The combination of these problems is termed a machine loading problem, which is treated as a strongly NP-hard problem. In this paper, the machine loading problem has been modeled by taking into account objective functions and several constraints related to the flexibility of machines, availability of machining time, tool slots, etc. Minimization of system unbalance (SU), maximization of system throughput (TH), and the combination of SU and TH are the three objectives of this paper, whereas two main constraints to be satisfied are related to time and tool slots available on machines. Solutions for such problems even for a moderate number of part types and machines are marked by excessive computational complexities and thus entail the application of some random search optimization techniques to resolve the same. In this paper, a new algorithm termed as constraints-based fast simulated annealing (SA) is proposed to address a well-known machine loading problem available in the literature. The proposed algorithm enjoys the merits of simple SA and simple genetic algorithm and is designed to be free from some of their drawbacks. The enticing feature of the algorithm is that it provides more opportunity to escape from the local minimum. The application of the algorithm is tested on standard data sets, and superiority of the same is witnessed. Intensive experimentations were carried out to evaluate the effectiveness of the proposed algorithm, and the efficacy of the same is authenticated by efficiently testing the performance of algorithm over well-known functions. His research interests include algorithm development, statistical analysis, and modeling of manufacturing systems.