Production planning under dynamic product environment: a multi-objective goal programming approach

Production planning is a complicated task that requires cooperation among multiple functional units in any organization. In order to design an efficient production planning system, a good understanding of the environment in terms of customers, products and manufacturing processes is a must. Although such planning exists in the company, it is often incorrectly structured due to the presence of multiple conflicting objectives. The primary difficulty in modern decision analysis is the treatment of multiple conflicting objectives. A formal decision analysis that is capable of handling multiple conflicting goals through the use of priorities may be a new frontier of management science. The objective of this study is to develop a multi objective goal programming (MOGP) model to a real-life manufacturing situation to show the trade-off between different some times conflicting goals concerning customer, product and manufacturing of production planning environment. For illustration, two independent goal priority structures have been considered. The insights gained from the experimentation with the two goal priority structures will guide and assist the decision maker for achieving the organizational goals for optimum utilization of resources in improving companies competitiveness. The MOGP results of the study are of very useful to various functional areas of the selected case organization for routine planning and scheduling. Some of the specific decision making situations in this context are: (i). the expected quality costs and production costs under identified product scenarios, (ii).under and over utilization of crucial machine at different combinations of production volumes, and (iii). the achievement of sales revenue goal at different production volume combinations. The ease of use and interpretation make the proposed MOGP model a powerful communication tool between top and bottom level managers while converting the strategic level objectives into concrete tactical and operational level plans.

Global supply chains of high value low volume products

Imperial Users onl

A multilevel integrative approach to hospital case mix and capacity planning.

Hospital case mix and capacity planning involves the decision making both on patient volumes that can be taken care of at a hospital and on resource requirements and capacity management. In this research, to advance both the hospital resource efficiency and the health care service level, a multilevel integrative approach to the planning problem is proposed on the basis of mathematical programming modeling and simulation analysis. It consists of three stages, namely the case mix planning phase, the master surgery scheduling phase and the operational performance evaluation phase. At the case mix planning phase, a hospital is assumed to choose the optimal patient mix and volume that can bring the maximum overall financial contribution under the given resource capacity. Then, in order to improve the patient service level potentially, the total expected bed shortage due to the variable length of stay of patients is minimized through reallocating the bed capacity and building balanced master surgery schedules at the master surgery scheduling phase. After that, the performance evaluation is carried out at the operational stage through simulation analysis, and a few effective operational policies are suggested and analyzed to enhance the trade-offs between resource efficiency and service level. The three stages are interacting and are combined in an iterative way to make sound decisions both on the patient case mix and on the resource allocation.Health care; Case mix and capacity planning; Master surgery schedule; Multilevel; Resource efficiency; Service level;

Optimal Capacity Utilization and Reallocation in a German Bank Branch Network: Exploring Some Strategic Scenarios

Quite a few studies have considered efficiency at the bank branch level by comparing mostly a single branch network, while an abundance of studies have focused on comparing banking institutions. However, to the best of our knowledge no study has ever assessed performance at the level of the branch bank network by looking for ways to reallocate resources such that overall performance improves. Here, we introduce the Johansen-Färe measure of plant capacity of the firm into a multi-output, frontier-based version of the short-run Johansen industry model. The first stage capacity model carefully checks for the impact of the convexity assumption on the estimated capacity utilization results. Policy scenarios considered for the short-run Johansen industry model vary in terms of their tolerance with respect to existing bank branch inefficiencies, the formulation of closure policies, the reallocation of labor in terms of integer units, etc. The application to a network of 142 bank branches of a German savings bank in the year 1998 measures their efficiency and capacity utilization and demonstrate that by this industry model approach one can improve the performance of the whole branch network.Bank Branch Network, Efficiency, Capacity, Reallocation

Possibilistic Aggregate Production Planning Considering Dynamic Workforce with Fuzzy Demand

This Aggregate Production Planning (APP) is a combination of Possibilistic Linear Programming (PLP), Fuzzy Goal Programming (FGP), and the Perfume Accounting System (PAS) to maximize profit. APP involves strategic decisions on Production levels, Inventory management, and Resource allocation to meet client demand while maximizing profit. Traditional planning models face significant challenges due to the uncertainties and complexities inherent in real world production environments. In this paper, there is an integration of PLP, Fuzzy Goal Programing, and throughput accounting system to overcome these challenges. At the very end of the paper, based on the data received from the company, the derived findings were by using Lingo Version 18 software. The model includes possibility distributions of the input parameters. Decision-makers can take into account the uncertainty and imprecision in demand forecasts and dynamic workforce in maximizing profit while taking into account risk tolerance

Operational Research in Education

Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions

The management of water resources: a synthesis of goal programming and input-output analysis with application to the Iowa economy

The study focused on the development of a multi-objective model combining input-output analysis and goal programming for providing guidance in the allocation of water resources. The model was applied in the allocation of water between the economic sectors of Iowa and within the eight Iowa water supply areas. The specific objective was to find out whether the state and its eight water supply areas have enough water resources to meet demands of economic and demographic projections to the year 2020;The input-output analysis possesses the capability of capturing the interdependence between the sectors of the economy while the goal programming model provides the analyst an opportunity to isolate certain sectors of the economy as highest priority sectors and to allocate water resources among sectors on the basis of these priorities. The closed system input-output model was utilized in order to capture the induced effects of income on consumption, production, and resource utilization;The eight water supply areas which were considered in the application of the model within the state of Iowa are the Western Southern, Des Moines, Iowa-Cedar, Northeastern, Skunk, Missouri, and Mississippi water supply areas. Four sources of water supply were identified for each area. These sources were alluvial aquifers, other shallow and bedrock aquifers, stream flow, and reservoir storage. Indicators of economic activities in each water supply area were estimated and it was found that the bulk of economic activities are expected to occur in the Des Moines and Iowa-Cedar water supply areas through 2020;Based on projected growth rates of various sectors of the Iowa economy, output projections required to support specified levels of final demands were estimated for the year 2020. These output projections at the state level were scaled down for each water supply area and the model was used to investigate whether each water supply area could accommodate the growth projections under the constraint of its water endowments. Two irrigation scenarios were considered. Irrigation scenario I represented the irrigation of 3.19 million crop acres of irrigable land while scenario II expanded irrigation to over 8 million crop acres of land throughout the state of Iowa;Under irrigation scenario I, the aggregate water consumption in Iowa in the year 2020 is 1.31 trillion gallons, and 1 trillion gallons will be consumed in crop irrigation. Irrigation scenario II consumes 3.12 trillion gallons of water for all economic activities, but the state has enough water resources to supply all consumptive water requirements for all projected economic activities to the year 2020

Imprecise WareHouse Space in Aggregate Production Planning Using Fuzzy Goal Programming

Considering the fluctuating market demands with variable storage capacity and available production capacity, this study examines a number of workable techniques for modeling multiproduct aggregate production planning problems with fuzzy numbers. The suggested method makes use of factors including; inventory levels, labor levels, overtime, backordering levels, workforce capacity, machine capacity, and fuzzy warehouse capacity in an effort to reduce operating costs, reduce production waste, and increase capacity utilization rate. With the aid of this formulation and interpretation, a fuzzy multiproduct aggregate production planning model is developed. Finally, the study's conclusions were arrived at using information provided by Rich Pharmaceuticals Ltd. using Lingo version 18 software (RPL).and it uses parametric programming, best balancing, and interactive techniques to give solutions that can be adjusted to fit a variety of decision-making circumstances