Abstract
Managing perishability may represent a remarkable problem in supply chain management of a varied set of industries. In fact, perishability can influence, for example, productivity or customer service and it may happen to occur in one or more processes throughout the supply chain. In this paper a review on planning models that handle perishability issues in production and distribution is conducted. The contribution of this paper is three-fold. First, a new framework for classifying perishability models based on multiple process features is presented. Second, it draws the community attention to the importance of managing perishability in many different industries' supply chains by showing its relevance and by reviewing the literature related to production and distribution planning. Finally, it points towards research opportunities so far not addressed by the research community in this challenging field.

Abstract
Several production environments require simultaneous planing of sizing and scheduling of sequences of production lots. Integration of sequencing decisions in lotsizing and scheduling problems has received an increased attention from the research community due to its inherent applicability to real world problems. A two-dimensional classification framework is proposed to survey and classify the main modeling approaches to integrate sequencing decisions in discrete time lotsizing and scheduling models. The Asymmetric Traveling Salesman Problem can be an important source of ideas to develop more efficient models and methods to this problem. Following this research line, we also present a new formulation for the problem using commodity flow based subtour elimination constraints. Computational experiments are conducted to assess the performance of the various models, in terms of running times and upper bounds, when solving real-word size instances.

Abstract
Disturbance Management is a major issue in process industries like the pulp and paper (P&P) case and is mostly performed in the execution/control level. That approach is confined to the amendment of plans sent by upper levels and can thus be problematic. This paper moves towards the integration of planning and control, starting from the planning's point of view. The application of Simulation-Optimization (S-O) allows considering uncertainty, but keeping a deterministic tractable optimization model. Indeed, it is the simulation model that incorporates more complex elements such as stochastic variables, as well as integrates (with more or less detail) the execution/control behaviour. In this work, we present a case study of a P&P mill, focusing on the two most critical production resources (the digester and the paper machine). The feedback obtained by simulating their interaction is used to adjust the slacks introduced in the intermediate tank. In this way, we are able to generate plans that are not only optimized concerning company's indicators, but also robust against disturbances.

Abstract
From long to short term planning, decision processes inherent to operating theater organization are often subject of empiricism, leading to far from optimal results. Waiting lists for surgery have always been a societal problem, which governments have been fighting with different management and operational stimulus plans. The current hospital information systems available in Portuguese public hospitals, lack a decision support system component that could help achieve better planning solutions. Thus, an intelligent decision support system has been developed, allowing the centralization and standardization of planning processes, improving the efficiency of the operating theater and tackling the waiting lists for surgery fragile situation. The intelligence of the system derives from data mining and optimization techniques, which enhance surgery duration predictions and operating rooms surgery schedules. Experimental results show significant gains, reducing overtime, undertime, and better resource utilization. Note to Practitioners-The Operating Theater (OT) is often considered hospitals' biggest budget consumer and revenue center in a hospital. This paper was motivated by a project that aims to reduce expenses and surgery waiting lists in Portuguese public hospitals, by developing an Intelligent Decision Support System (DSS) to support surgery scheduling. Prior to this research, decision makers (Surgeons, Department managers, Operating theatre managers) used their experience to make allocation, scheduling and estimation decisions. Since many of these decisions are made without analyzing past results, mistakes occur frequently, affecting the OT performance. With the help of business intelligence, data mining and optimization algorithms, surgeons' estimations can be more precise and the operating room schedule can be optimized. Preliminary experiments on the usage of DSS reveal a remarkable increase of the efficiency of the whole OT. In future research, we will extend the DSS and the techniques used to address the tactical master surgery scheduling problem, which aims to perform a better allocation of the different specialties to the operating rooms along the week. In addition, upstream and downstream resources shall be considered in the optimization module, as well as a simulation component to better evaluate generated solutions.

Abstract
The operating theater is the biggest hospital budget expenditure. The usage of surgery related resources and its intrinsic planning must be carefully devised in order to achieve better operational performance. However, from long to short term planning, the decision processes inherent to the operating theater are often the subject of empiricism. Moreover, the current hospital information systems available in Portuguese public hospitals lack a decision support system component, which could assist in achieving better planning solutions. This work reports the development of a centralized system for the operating theater planning to support decision-making tasks of surgeons, chief specialty managers, and hospital administration. Its main components concern surgery scheduling, operating theater's resource allocation and performance measurement. The enhancement of the planning processes, the increase of policy compliance, and the overall performance of the operating theater compared to the former methodologies are also discussed. © 2013, IGI Global.

Abstract
Allocation of jobs to machines and subsequent sequencing each machine is known as job scheduling problem. Classically, both operations are done in a centralized and static/offline structure, considering some assumptions about the jobs and machining environment. Today, with the advent of Industry 4.0, the need to incorporate real-time data in the scheduling decision process is clear and facilitated. Recently, several studies have been conducted on the collection and application of distributed data in real-time of operations, e.g., job scheduling and control. In practice, pure distribution and decentralization is not yet fully realizable because of e.g., transformation complexity and classical resistance to change. This paper studies a combination of decentralized sequencing and central optimum allocation in a lithography job-shop problem. It compares the level of applicability of two decentralized algorithms against the central scheduling. The results show better relative performance of sequencing in stochastic cases. © IFIP International Federation for Information Processing 2017.