Research Paper On Job Shop Scheduling

The JIT approach to manufacturing involves timing the delivery of resources so that they arrive just when needed. Inventory optimization models help the firm determine how many of which items in which sizes should be delivered to each specific store during twice-weekly shipments, ensuring that each store is stocked with just what it needs. Trucks serve destinations that can be reached

The assembly line needs to produce 6 units per hour and there is room for only four workstations. The tasks and the order in which they must be performed are shown in the following table. Tasks cannot be split, and it would be too expensive to duplicate any task.

The JC Gear Company has decided to initiate a project aimed at automating its production planning and control system. Among the options, the company focuses on two alternatives: (1) purchasing the most suitable system off of the shelf and modifying it according to its individual production needs; or (2) developing a system

Akveld, M., & Bernhard, R. (2012). Job shop scheduling with unit length tasks. RAIRO -- Theoretical Informatics & Applications, 46(3), 329-342. Retreived from http://search.ebscohost.com.ezproxy.liberty.edu:2048/login.aspx?direct=true&db=iih&AN=85410818&site=ehost-live&scope=site

From this equation we get x approximately equal to 6. So Donner Company should use the CNC Drill for orders of more than or equal to 6 boards. The Manual Drill process should be used for all orders of less than 6 boards. This is because production time for CNC is less than manual for order size of

* Loads were picked up from location A and delivered to one of 5 warehouses, placed on another truck with optimized route for location B (software driven route optimization)

The shipping cost and/or unavailability of transportation between the plants and some locomotive locations will eliminate some of the routes due to cost inefficiency. These routes are the unacceptable routes and will not be considered for distribution from the specified plant. By removing unacceptable routes, Solutions Plus is able to build a linear programming solution to determine which plant/locomotive location combinations are optimal. Based on the shipping cost provided, the routes that are eliminated are as follows:

The process engineer suggests that you find the most labor-intensive operation that the plant performs. Reduce costs by automating the operation.

When offers of reduced pricing are accepted for equipment, meeting delivery expectations becomes an important part of enhancing the customer experience to maintain satisfied loyal customers. An inventory specialist in the current distribution center would be given the additional task of segregating and maintaining inventory levels to meet the needs of the customer loyalty department.

Other assumptions that we made for the case study were that we could only use the 7.5-hour workday and one line of production.  We also assumed that we were limited to the range of 8 to 12 people stated in the problem. Because we are making complex equipment, we assumed that we could not change the order of the operations themselves but that we could have a station do varying combinations of operations. The projections in the Excel spreadsheet also assume that the engineers’ specified times would be accurate once production begins.  Regarding the hardware testing operations, the activities are to be performed on three computers concurrently, so we divided the operation times by three to arrive at the true operation times.

The idea of applying exact optimisation approach on requirements selection and optimisation is similar with search-based requirements optimisation. The only difference is that, instead of using search-based optimisation algorithm, the search-based requirements selection and optimisation problem is tracked with exact optimisation algorithm. There are three main categories exact optimisations found in the literature. They are Integer linear programming [25], dynamic programming [26], and exhaustive search [27].

A brief description of a job shop scheduling problem has already been provided in Chapter 1. In this project, the size of the considered problem is J×M, where J represents the number of jobs (5) and M represents the number of machines (5). Four rules will be tested on a data set consists of twenty case

During the recent past period, academic researchers and practitioners devoted their efforts for improving the scheduling process and developing an effective approach. They were seeking to optimize scheduling problems through exploration and exploitation the solution space. The first section in this chapter presents two different optimization approaches that are able to solve and optimize job shop scheduling problem. The first type of optimization approaches is based on exact algorithms and they are usually used for simple and small size problem. Another type of approaches is based on more sophisticated algorithms that can be applied to more complex and larger problems. These approaches are denoted as approximate algorithms where the solutions may be optimal or near-optimal. Approximate algorithms can be classified into heuristic and meta-heuristic methods. The main purpose of this chapter is to present a comprehensive survey of different approaches available for solving the job-shop scheduling problem. It will be important also to identify the trend of research and the gaps that require further research in the second section of this chapter Jain and Meeran [12].

−−−Preliminary study of an automotive assembly plant for high volume production(1300cars/day with 2 type of cars, 4 doors and 2 doors respectivly, and flexible in volume, 3 shifts of workers)

This paper presents a Dynamic Programming (DP) method based an algorithm to solve the Unit Commitment (UC) scheduling of the thermal generation units in Yangon. Electricity demands are in its peak in Yangon, it has become very difficult for operators to fulfill the demand in the present. The main objective of Unit Commitment is to determine a minimum cost turn-on and turn-off schedule of a set of electrical power generating units to meet a load demand while satisfying a set of operational constraints. The total production costs include fuel, startup, shutdown, and no-load costs. There are many conventional and evolutionary programming methods used for solving the unit commitment problem. Dynamic programming method is one of the successful approaches to unit commitment problem. Dynamic Programming has many advantages over the enumeration scheme, the chief advantage being a reduction in the dimensionality of the problem. It is one of the refined algorithm design standards and is powerful tool which yields definitive algorithm for various types of optimization problems. To implement the unit commitment problem into an optimization program, the MATLAB® software is used.