## 2 Literature Review  
The study of PMS in which additional resources are required for the changeover is a significant area of research. [PERSON] et al. [4] surveyed the related literature and classified it into an efficient framework. The present study deals with additional renewable resources, also called servers, which can only be deployed within certain constraints. The recent related literature is reviewed in the following. [PERSON] et al. [5] proposed a multi-objective model for a PMS problem with unrelated machines in which the setup times are sequence and machine dependent, processing times and due dates of jobs are uncertain, and jobs may have different ready times. Assignment of a job to a machine is permitted if the required resources (e.g., tool and die) are available, and the number of required resources is limited. An effective multi-objective particle swarm optimization algorithm was proposed to find a good approximation of the Pareto frontier, for which the total weighted flow time, total weighted tardiness, and total machine load variation are to be minimized simultaneously. [PERSON] and [PERSON] [6] addressed a PMS problem for unrelated machines with resource constrains, sequence-dependent setup times, different release dates, machine eligibility, and precedence constraints. Integer mathematical modeling was proposed to first minimize the makespan. Since the problem is strongly NP-hard, two new meta-heuristic algorithms, genetic algorithm and artificial immune system, were then deployed to find the optimal or near-optimal solutions. [PERSON] et al. [7] analyzed a PMS problem for unrelated machines in which the processing of jobs on the machines required a number of limited resources, which were fixed throughout the production horizon and depend both on the job and on the machine. Two integer linear programs were used to minimize the makespan. [PERSON] and [PERSON] [8] dealt with the PMS problem for identical machines with a common server and sequence-dependent setup times. They proposed a mixed-integer linear programming (MILP) model to minimize the makespan. In order to solve the proposed problem efficiently, they used simulated annealing and genetic algorithm approaches. [PERSON] and [PERSON] [9] proposed a PMS problem for unrelated machines in which a common shared renewable resource is considered, and the speed of a machine depends on the energy the machine consumes. A collaborative multi-objective fruit fly optimization algorithm was proposed to minimize the makespan and the total carbon emission. [PERSON] et al. [10] dealt with a PMS problem with a single server. A branch-and-bound algorithm was used to minimize the total weighted job completion time. [PERSON] and [PERSON] [11] dealt with a PMS problem for identical machines with sequence-dependent setup times, machine eligibility restrictions and multiple copies of shared resources. Mixed-integer programming models and a model-based genetic algorithm were proposed to minimize the total weighted completion time. [PERSON] et al. [12] dealt with a PMS  
Figure 2: An example of an inventory status with a delay in delivery. problem in which jobs can be split into multiple sections and the number of setups that can be performed simultaneously is restricted because of the limited setup operators. A mathematical programming model was presented first and then an iterative job splitting algorithm was proposed to minimize the makespan. [PERSON] et al. (2013) dealt with a PMS problem for unrelated machines with the need of additional resources during the processing of the jobs, as well as during the setups. Several heuristics and metaheuristics were proposed to minimize the makespan. Regarding the objectives of PMS in the literature, the makespan is the most widely used ([PERSON], 2016). A minimum makespan usually implies high utilization of the machines ([PERSON] and [PERSON], 2016). However, tardiness is another important criterion when the due dates of the orders are considered.