Mixed Integer Linear Programming Scheduling Research Articles

Co-optimization of multi-energy system operation, district heating/cooling network and thermal comfort management for buildings

The ongoing decarbonization of the energy sector spurs the employment of distributed generation and efficient load control approaches (demand side management). This work tackles the optimal operation of a Multi Energy System and thermal comfort management for buildings with an integrated approach. The dynamic thermal energy balance of the buildings is included in the Mixed Integer Linear Programming scheduling problem to exploit the heat capacity of buildings and increase the operational flexibility of the generators. The method is firstly applied to a single building served by different energy systems, comprising renewable energy sources, cogeneration units and heat pumps. Then, the methodology is further extended by integrating in the formulation the model of the district heating/cooling network. This method is tested in a group of 12 buildings of the Campus of University of Parma, featuring different thermal properties. By enabling a variation within ± 2 °C around the indoor temperature setpoint and by optimizing water delivery temperature, it is possible to achieve savings on operating costs over the baseline up to 80%. Results show that the load shift capability of buildings plays a major role when thermal demand mismatches renewable energy availability or low electricity price periods. Moreover, district heating network can be exploited as an additional short-term heat storage by varying water delivery temperature profile.

PATH: Performance-Aware Task Scheduling for Energy-Harvesting Nonvolatile Processors

Nonvolatile processors (NVPs) have strong vitality in battery-less energy-harvesting sensor nodes (EHSNs) due to their characteristics of zero standby power, resilience to power failures, and fast read/write operations. However, I/O and sensing operations cannot store their system states after power OFF; hence, they are sensitive to power failures and high power switching overhead is induced during power oscillation, which significantly degrades the system performance. In this paper, we propose a novel performance-aware task scheduling technique considering power switching overhead for energy-harvesting NVPs. We first present the analysis of the power switching overhead on EHSNs. Then, the scheduling problem is formulated by mixed-integer linear programming (MILP). Furthermore, offline and online performance-aware heuristic scheduling algorithms with the task splitting (TS) strategy are proposed to solve the scheduling problem efficiently. Experimental results show that comparing with the state-of-the-art energy-harvesting oblivious scheduling strategy, the proposed MILP scheduling approach can improve the performance by 16% on average, and the proposed scheduling algorithm with the TS strategy can reduce the average execution time by 24.8% and 22.5%.

Scheduling to minimize the makespan in large-piece one-of-a-kind production with machine availability constraints

Focus on scheduling of an LP-OKP model with machines availability constraints.Propose slack-based mixed integer linear programming scheduling algorithm.Propose slot-based search scheduling algorithm.Evaluate the proposed algorithms through a real case of power plant building industry. This paper addresses a job shop scheduling problem, in which the jobs belong to the large-piece one-of-a-kind production (LP-OKP) model, and the machines are subject to availability constraints (Jm(LP-OKP), hjk |r-a |Cmax). The objective is considered to minimize the makespan. The scheduling model is derived from real industries, such as ship building industry, power plant building industry, steel-structure building industry and so on, and represents a more realistic and complex situation. In this study, two different scheduling algorithms of a slack-based mixed integer linear programming (MILP) model and a time slot-based search method are proposed to solve the scheduling problem. The experiments are implemented on CPLEX and search scheduling platform, respectively. The results reveal that the slack-based MILP model and time slot-based search method give better solutions than the rule-based algorithm. The deviation of objective optimal value results show that the slack-based MILP model and time slot-based search method can improve the makespan solution by 0.7% and 5.6%, respectively. And the percentage improvement of CPU time results show that they can shorten the CPU time by 74.5% and 12.9%, respectively. We found that the performance of the time slot-based search method is significantly competitive as compared to the slack-based MILP model but its CPU time is significantly higher than that of the slack-based MILP model.

Novel MILP Models for Scheduling Permutation Flowshops

Flowshop scheduling via mixed integer linear programming (MILP) has received considerable attention in the past four decades. However, alternate models are limited; most numerical studies have used small problem sizes. A need for good model evaluation methodology exists; and limited work exists on flowshops with no intermediate storage. This paper presents a classification of flowshops and MILP scheduling models, and addresses some of these issues. It develops a host of new MILP formulations for minimizing makespan in a permutation flowshop with no storage and with or without unit setups. It presents some useful insights into model building by employing a variety of new and old binary variables and coupling them creatively. In contrast to previous work, it evaluates a range of new and existing MILP models using many larger test problems with no or unlimited intermediate storage, and presents a reliable procedure to rank various models based on problems with varying data and sizes. It shows that the top models for the two flowshops indeed show slightly different computational performance.

MILP scheduling model for multipurpose batch processes considering various intermediate storage policies

MILP (Mixed Integer Linear Programming) scheduling models for non-sequential multipurpose batch processes are presented. Operation sequences of products have to be made in each unit differently by considering production route of each product under a given intermediate storage policy to reduce idle time of units and to raise the efficiency of the process. We represent the starting and finishing time of a task in each unit with two coordinates for a given storage policy. One is based on products, and the other is based on operation sequences. Then, using binary variables and logical constraints, we match the variables used in the two coordinates into one. We suggest MILP models considering sequence dependent setup times to guarantee the optimality of the solutions. Two examples are presented to show the effectiveness of the suggested models.