This article first reviews the literature on the transition from the Fordist to Post -Fordist industrial system during the last three decades. Particular attention is paid to an important aspect of this transformation, the decline in the degree of the vertical integration of large firms and the rise of Subcontracting relations in industrial societies. The role of social institutions and norms in facilitating the formation of such relations is investigated. In the second part, the researcher studies the trends in Subcontracting relations among the Iranian manufacturing establishments during 1976-1997. Both the economic and social aspects are looked at in an attempt to explain the very limited nature of such relations.

Challenges such as advances in technology, demands of the global market, and limited warehouse spaces resort manufacturing industries to Subcontracting. Subcontracting has been a considerable alternative in the manufacturing industries and is utilized as a strategic tool to diminish operation costs primarily to address the problem of scarcity when the firm faces a large demand on the commodity it supplies. The present study employed a mathematical model among firms engaging in Subcontracting in search of an optimal schedule in the manufacture of the product and distribution of production time involved with an objective of obtaining a maximum profit. The constraints in the mathematical formulation included the total demand, processing capacity, available supply, processing rate, and time. The plausibility and the possible utility of the mathematical model has been explored employing sequential quadratic programming algorithm in the search of the optimal solutions.

While traditional production planning focused on optimizing supply-demand balance through make-to-stock/make-to-order strategies and capacity management, the new imperative of carbon neutrality introduces critical complexities. Regulatory emission caps now require manufacturers to strategically trade carbon allowances, fundamentally transforming the challenges of production optimization. This study developed an aggregate production planning model that incorporates carbon trading constraints into operational decision-making, providing industries with a systematic approach to address both economic and environmental objectives. The model optimized multi-period production plans across alternative technologies, each with distinct cost-emission profiles, while incorporating Subcontracting options. It simultaneously considered government-allocated emission permits, dynamic carbon market prices, technology-specific costs and emissions, and Subcontracting expenses. Through mathematical optimization of production quantities, Subcontracting levels, and carbon credit transactions, the model minimized total costs while ensuring compliance. Computational experiments with nonlinear programming solved via LINGO demonstrated the model's effectiveness in identifying optimal technology deployment strategies that achieve significant cost reductions while meeting environmental targets, offering manufacturers a powerful tool for sustainable operations in carbon-constrained markets.

Purpose: Outsourcing is a prevalent strategy in the industry and business, which can enhance the performance of a company and rectify its constraints. This strategy can be integrated into various managerial aspects of a company, particularly the scheduling subject. In this paper, a single machine scheduling problem is investigated in which a set of jobs can be outsourced in a batch to a single subcontractor. It is supposed that the outsourcing time and cost of a job are proportional to its in-house processing time. In addition, a fixed logistics time and cost are also considered for the outsourcing batch. Moreover, the problem objective is to minimize the sum of the total completion time of the jobs and the total outsourcing cost. Methodology: To solve the problem, a set of optimal properties of the problem solution are proven via a lemma and a theorem to determine the optimal solution of the problem. Moreover, some computational experiments are also conducted at the end of the paper to examine the effect of the outsourcing strategy in the addressed problem. Findings: By the developed solution approach, the structure of the optimal solution is determined, using which the optimal solution of the problem can be chosen among a limited set of candidate solutions via some simple computations. Moreover, the computational results indicate the remarkable possible role of outsourcing in decreasing the value of the objective function of the problem. Originality/Value: In this paper, a new practical problem in the research area of scheduling with outsourcing is defined and its optimal solution is determined via a detailed analysis. Furthermore, using computational experiments it is shown that the outsourcing strategy can have a great role to attain the problem objectives.

This paper presents a new cell formation and cell layout problem considering multiple process routings and Subcontracting using the principles of queuing theory. It is assumed that each machine operates as an M/M/1 queuing system and a queuing network is used to obtain in-process inventories and machine utilization. The problem is formulated as a mixed-integer nonlinear program with the objective of minimizing the total costs, including the production, Subcontracting, material handling, machine idleness, and holding costs. Due to the computational complexity of the problem, a heuristic method is suggested to effectively solve the problem. A numerical example is given to clarify the proposed approach, and finally, further instances are solved to verify the performance of the solution method and to accomplish comparisons. The computational results show that the proposed heuristic is both effective and efficient.

In this paper, we develop a new mathematical model that integrates layout configuration and production planning in the design of dynamic distributed layouts. The model incorporates a number of important manufacturing attributes such as demand fluctuation, system reconfiguration, lot splitting, work load balancing, alternative routings, machine capability and tooling requirements. In addition, the model allows several cost elements to be optimized in an integrated manner. These costs are associated with material handling, machine relocation, setup, inventory carrying, in-house production and Subcontracting needs. Numerical examples of different sizes are presented to illustrate the nature of the developed model and shed light on several managerial insights.

Optimization of the complete manufacturing and supply process has become a critical ingredient for gaining a competitive advantage. This article provides a unified mathematical framework for modeling manufacturing cell configuration and raw material supplier selection in a two-level supply chain network. The commonly used manufacturing design parameters along with supplier selection and a Subcontracting approach are incorporated into our mathematical model. To the authors’ knowledge, there is no single model which integrates all of these attributes simultaneously. A sensitivity analysis is also performed to study the effects of this integration. An efficient meta-heuristic based on Genetic Algorithm (GA) search procedure is employed to effectively solve the model in medium and large scales.We improve the GA search mechanism by proper combination of linear programming optimization technique and GA in a cooperative framework.Computational results show that our hybrid solution technique can find satisfactory solutions in a timely manner.

Cell Formation (CF) is the initial step in the configuration of cell assembling frameworks. This paper proposes a new mathematical model for the CF problem considering aspects of production planning, namely inventory, backorder, and Subcontracting. In this paper, for the first time, backorder is considered in cell formation problem. The main objective is to minimize the total fixed and variable costs, including the machine related costs, intercellular movements, deviation between the levels of cell utilizations, inventory, backorder, and sub-contracting costs. The presented mathematical model is validated using GAMS software, and various test problems are solved by Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) algorithm. The performance of the algorithms is compared with the results obtained by the GAMS. The results demonstrate, there is no significant difference between the results of algorithms. Finally, some sensitive analyses are carried out to analyze the effects of backorder and inventory holding costs.

In today's competitive world, companies need to effectively manage their supply chains in changing market conditions, and they are also obliged to compensate for their environmental damages. In this research, a two-echelon multi-product multi-period supply chain network with production and distribution centers has been modeled with three objectives: minimizing logistic costs, delivery time, and CO2 emission costs. Customer demand parameters, available levels of human and machinery resources are uncertain and considered as fuzzy numbers. Additionally, the possibility of using Subcontracting services for production and transportation operations at a higher cost exists. The main innovation of this research is modeling the possibility of using different transportation systems and considering their pollution, and using a novel fuzzy multi-criteria goal programming method (proposed in 2018) for solving the problem. Real data from "Daya Technology" company has also been used for case study and model evaluation.