Capacity planning using linear programming

In deterministic models good decisions bring about good outcomes. You get that what you expect; therefore, the outcome is deterministic i.

Capacity planning using linear programming

Most organizations have a hard time minimizing the discrepancies between their capacity and the demands of their customers. As a result, most of them are under-utilizing resources or are unable to fulfill customer demand.

These inefficiencies can have a massive negative impact on an organization. Most organizations receive mediocre results from their capacity planning because they fail create a plan that is feasible — or doable given their constraints — and to align the plan with their strategic objectives.

12th Seminar Papers The objective is to maximise total profit, i. Each day of every working week is divided into three eight-hour shift periods

In other words, organizations fail to create a plan that is both feasible and optimal — the former being more important that the latter.

An organization will be able to meet financial and inventory objectives while being able to fulfill service level objectives, which most organizations believe is a trade-off.

Is the Capacity Plan Feasible? The biggest point of failure in most capacity plans is they are not feasible, as I mentioned earlier. Organizations have more problems ensuring their plans are feasible than ensuring they are optimal.

Feasible capacity plans can be executed within the realities of the business. They are not starry-eyed objectives an organization will never be able to execute given all of their important constraints. A feasible plan must account for the constraints, trade-offs, regulations, policies, and the financial requirements of a business.

A constraint is anything that hampers an organization from being able to produce more of what it strive for. Some constraint examples are labor, equipment, and third-party capacity limits. Many manufacturing companies, especially those in the chemical industry are heavily regulated when it comes to their production.

For example, factories are limited in production by the regulation of greenhouse gas emission the EPA enforces.

Aggregate planning is the process of developing, analyzing, and maintaining a preliminary, approximate schedule of the overall operations of an organization. The aggregate plan generally contains targeted sales forecasts, production levels, inventory levels, and customer backlogs. This schedule is. Computer Solutions of Linear Programs B29 Using Linear Programming Models for Decision Chapter 2 2. Capacity management concepts, Chapter 9 3. Aggregate planning, Chapter 13 4. Developing a master schedule, Chapter 14 Linear Programming. Linear Programming Linear programming is often a favorite topic for both professors and students. The ability to introduce LP using a graphical approach, the relative ease of the solution method, the widespread availability of LP software packages, and the wide range of applications make LP accessible even to students with relatively weak mathematical backgrounds.

So, while production may be possible from a physical standpoint, it is not possible from a regulation standpoint. It is possible for organizations to grow themselves out of business. Organizations are sometimes limited from a cashflow perspective.

If an organization fails to convert their cash fast enoughthey could find their survival being threatened. So, how does an organization know if their plan is feasible given their constraints? Typically, if a complex organization like CPG, chemicals or resource planning is using rules for their planning, then it is likely the plan is infeasible.

Their planning process would roughly follow this kind of structure Begin with demand and work backwards to find inventory needed, taking into account existing inventory. Adjust inventory requirements for lead time and use them to define production requirements.

Assign production requirements using some convoluted ruleset. For example, assign production of product 1 to line A, B if it overflows. Assign production of product 2 to line C, B if it overflows, etc.

At this point, the rules are so complex that organizations fail to properly consider their resource allocation and labor rules. An organization may be able to physically increase production by running over-time shifts, but this may not be practical from a financial standpoint.

There are thousands of examples how an organization could be limited by their constraints, but tools like Excel, fail to allow organizations to create feasible capacity plans. For a plan to be feasible, it must be able to meet the output given the constraints over a specified time period.

Is the Capacity Plan Optimal? The second factor in creating an effective capacity plan is optimization. Most organizations are able to create optimal capacity plans, they just fail to integrate it with feasibility.

An optimal plan allows organizations to maximize or minimize the objectives they are trying to meet — whatever they may be. For example, an organization may wish to achieve better results in terms of profitability or a higher service level, so their plan should be organized around achieving these objectives.Employing strategic capacity planning through formulation of mixed integer linear programming model, to meet the annual demand, the resulting optimal machine combination was 3: Two single chamber floor model vacuum sealers and one double chamber floor model vacuum sealer.

The vehicle routing problem (VRP) is a combinatorial optimization and integer programming problem which asks "What is the optimal set of routes for a fleet of vehicles to traverse in order to deliver to a given set of customers?". It generalises the well-known travelling salesman problem (TSP).

It first appeared in a paper by George Dantzig and John Ramser in , in which first algorithmic. Employing strategic capacity planning through formulation of mixed integer linear programming model, to meet the annual demand, the resulting optimal machine combination was 3: Two single chamber floor model vacuum sealers and one double chamber floor model vacuum sealer.

and discrete capacity options result the capacity planning models in non-linear integer programming formulations. We develop efiective solution algorithms to obtain high quality solutions particularly. ii modeling and analysis of production and capacity planning considering profits, throughputs, cycle times, and investment approved by: dr.

Capacity planning using linear programming

chen zhou, co-advisor. A Mixed Integer Programming Approach for Allocating Operating Room Capacity Bo Zhang, Pavankumar Murali, Maged Dessouky*, and David Belson by developing a mixed integer programming approach for allocating operating capacity planning or resource allocation in many complex systems, including healthcare;.

Chapter 11, Head 3