Abstract
This work proposes a mathematical programming model for jointly scheduling of production and transport in flexible manufacturing systems considering alternative job routing. Although production scheduling and transport scheduling have been vastly researched, most of the works address them independently. In addition, the few that consider their simultaneous scheduling assume job routes as an input, i.e., the machine -operation allocation is previously determined. However, in flexible manufacturing systems, this is an important source of flexibility that should not be ignored. The results show the model efficiency in solving small -sized instances.

Abstract
In the past few years, important supply chain decisions have captured managerial interest. One of these decisions is the design of the supply chain network incorporating financial considerations, based on the idea that establishment and operating costs have a direct effect on the company’s financial performance. However, works on supply chain network design (SCND) incorporating financial decisions are scarce. In this work, we address a SCND problem in which operational and investment decisions are made in order to maximize the company value, measured by the Economic Value Added, while respecting the usual operational constraints, as well as financial ratios and constraints. This work extends current research by considering debt repayments and new capital entries as decision variables, improving on the calculation of some financial values, as well as introducing infrastructure dynamics; which together lead to greater value creation. © 2019, Springer Nature Switzerland AG.

Abstract
This work proposes a multi-criteria decision making model to assist in the choice of a strategic plan for a world-class company. The Balanced Scorecard (BSC) is a support tool of Beyond Budgeting that translates a company’s vision and strategy into a coherent set of performance measures. However, it does not provide help in choosing a strategic plan. The selection of a strategic plan involves multiple goals and objectives that are often conflicting and incommensurable. This paper proposes an integrated Analytic Hierarchy Process-Goal Programming (AHP-GP) approach to select such a plan. This approach comprises two stages. In the first stage, the AHP is used to evaluate the relative importance of the initiatives with respect to financial indicators/KPIs; while in the second stage a GP model incorporating the AHP priority scores is developed. The GP model selects a set of initiatives that maximizes the earnings before interest and taxes (EBIT) and minimizes the Capital Employed (CE). The proposed method was evaluated through a case study. © 2019, Springer Nature Switzerland AG.

Abstract
This work proposes a biased random key genetic algorithm (BRKGA) for the integrated scheduling of manufacturing, transport, and storage/retrieval operations in flexible manufacturing systems (FMSs). Only recently, research on this problem has been reported; however, no heuristic approaches have yet been reported. The computational results show the BRKGA to be capable of finding good quality solutions quickly.

Abstract
This paper describes a Decision Support System (DSS) that aims to plan and maintain the weekly self-promotion space for an over the air TV station. The self-promotion plan requires the assignment of several self-promotion advertisements to a given set of available time slots over a pre-specified planning period. The DSS consists of a data base, a statistic module, an optimization module, and a user interface. The input data is provided by the TV station and by an external audiometry company, which collects daily audience information. The statistical module provides estimates based on the data received from the audiometry company. The optimization module uses a genetic algorithm that can find good solutions quickly. The interface reports the solution and corresponding metrics and can also be used by the decision makers to manually change solutions and input data. Here, we report mainly on the optimization module, which uses a genetic algorithm (GA) to obtain solutions of good quality for realistic sized problem instances in a reasonable amount of time. The GA solution quality is assessed using the optimal solutions obtained by using a branch-and-bound based algorithm to solve instances of small size, for which optimality gaps below 1% are obtained.

Abstract
This work proposes an integrated formulation for the joint production and transportation scheduling problem in flexible manufacturing environments. In this type of systems, parts (jobs) need to be moved around as the production operations required involve different machines. The transportation of the parts is typically done by a limited number of Automatic Guided Vehicles (AGVs). Therefore, machine scheduling and AGV scheduling are two interrelated problems that need to be addressed simultaneously. The joint production and transportation scheduling problem is formulated as a novel mixed integer linear programming model. The modeling approach proposed makes use of two sets of chained decisions, one for the machine and another for the AGVs, which are inter-connected through the completion time constraints both for machine operations and transportation tasks. The computational experiments on benchmark problem instances using a commercial software (Gurobi) show the efficiency of the modeling approach in finding optimal solutions.