Production planning and scheduling optimization model: a case of study for a glass container company

Based on a case study, this paper deals with the production planning and scheduling problem of the glass con-tainer industry. This is a facility production system that has a set of furnaces where the glass is produced in order to meet the demand, being afterwards distributed to a set of parallel molding machines. Due to huge setup times involved in a color changeover, manufacturers adopt their own mix of furnaces and machines to meet the needs of their customers as flexibly and efficiently as possible. In this paper we proposed an optimization model that maximizes the fulfillment of the demand considering typical constraints from the planning production formulation as well as real case production constraints such as the limited product changeovers and the minimum run length in a machine. The complexity of the proposed model is assessed by means of an industrial real life problem

Integrated approach to assignment, scheduling and routing problems

This research considers a real life case study that determines the minimum number of sellers required to attend a set of customers located in a certain region taking into account the weekly schedule plan of the visits, as well as the optimal route. The problem is formulated as a combination of assignment, scheduling and routing problems. In the new formulation, case studies of small size subset of customers of the above type can be solved optimally. However, this subset of customers is not representative within the business plan of the company. To overcome this limitation, the problem is divided into three phases. A greedy algorithm is used in Phase I in order to identify a set of cost-effective feasible clusters of customers assigned to a seller. Phase II and III are then used to solve the problem of a weekly program for visiting the customers as well as to determine the route plan using MILP formulation. Several real life instances of different sizes have been solved demonstrating the efficiency of the proposed approach

Integrated Approach to Assignment, Scheduling and Routing Problems in a Sales Territory Business Plan

AbstractThis paper considers a real life case study that determines the minimum number of sellers required to attend a set of customers located in a certain region taking into account the weekly schedule plan of the visits, as well as the optimal route. The problem is formulated as a combination of assignment, scheduling and routing problems. In the new formulation, case studies of small size subset of customers of the above type can be solved optimally. However, this subset of customers is not representative within the business plan of the company. To overcome this limitation, the problem is divided into three phases. A greedy algorithm is used in Phase I in order to identify a set of cost-effective feasible clusters of customers assigned to a seller. Phase II and III are then used to solve the problem of a weekly program for visiting the customers as well as to determine the route plan using MILP formulation. Several real life instances of different sizes have been solved demonstrating the efficiency of the proposed approach

Iterative Projection approach for solving the Territorial Business Sales optimization problem

A well designed territory enhances customer coverage, increases sales, fosters fair performance and rewards systems and lower travel cost. This paper considers a real life case study to design a sales territory for a business sales plan. The business plan consists in assigning the optimal quantity of sellers to a territory including the scheduling and routing plans for each seller. The problem is formulated as a combination of assignment, scheduling and routing optimization problems. The solution approach considers a meta-heuristic using stochastic iterative projection method for large systems. Several real life instances of different sizes were tested with stochastic data to represent raise/fall in the customers demand as well as the appearance/loss of customers. © 2017 The Authors. Published by Elsevier B.V

Optimal pricing model based on reduction dimension: A case of study for convenience stores

This paper proposes a methodology to define an optimal pricing strategy for convenience stores based on dimension reduction methods and uncertainty of data. The solution approach involves a multiple linear regression (MLR) as well as a linear programming optimization model. Two strategies Principal Component Analysis (PCA) and Best subset Regression (BSR) methods for the selection of a set of variables among a large number of predictors is presented. A linear optimization model then is solved using diverse business rules. To show the value of the proposed methodology optimal prices calculation results are compared with previous results obtained in a pilot performed for selected stores. This strategy provides an alternative solution that shows how a decision maker can include proper business rules of their particular environment in order to define a pricing strategy that meets business goals. © 2017 The Authors. Published by Elsevier B.V