A hybrid solution approach for fuzzy multiobjective dual supplier and material selection problem of carton box production systems

Abstract

A novel optimization problem of carton box manufacturing industries is introduced in this paper. A mixed integer linear formulation with multiple objective functions is developed in order to determine the value of some criteria of carton raw sheets such as size, amount, and supplier under simultaneous minimization of multiple goals such as purchasing cost of raw sheets under discount policy, wastage remained from raw sheets, and quantity of surplus of carton boxes. In order to cope with the unstable market of this sector, some parameters of the proposed formulation such as demand value of the products and price given for raw sheets are assumed to be fuzzy numbers. To tackle such fuzzy multiobjective problem, first, the fuzzy problem is converted to a crisp form using the concepts of necessity‐based chance‐constrained modelling approach. Then a new hybrid form of the fuzzy programming approach is proposed to solve the obtained crisp multiobjective problem effectively. Computational experiments on a real case given by a carton box factory show the superior result of the proposed solution approach compared with the well‐known multiobjective solution methods taken from the literature.

Tabu Search Based Hybrid Meta-Heuristic Approaches for Schedule-Based Production Cost Minimization Problem for the Case of Cable Manufacturing Systems

“…Such scheduling problems under the given goals are mathematically modelled as a linear programming model (LP), or a mixed integer linear programming model (MILP), or even as a mixed integer nonlinear programming model (MINLP). For such models of scheduling problems the studies of Pinedo (2008), Vakhania et al (2014), , Ku and Beck (2016), Ahmadi et al (2016), He et al (2016), Qu et al (2016), Mahmoodirad et al (2019), Niroomand et al (2019), Mahmoodirad and Niroomand (2020), etc. can be referred.…”

Fractional two-stage transshipment problem under uncertainty: application of the extension principle approach

“…Meaning that the values of parameters may vary in a range of values, may be a fuzzy number, may be determined stochastically, etc. (see [4,9,27,29,31,36]). This case may happen for transshipment problem where the time and cost to pass the routes are not certain values.…”

Automobile Components Procurement Using a Dea-Topsis-Fmip Approach With All-Unit Quantity Discount and Fuzzy Factors

“…; these uncertainties are often expressed by fuzzy variables in imprecise terms, e.g. "pessimistic", "most likely" and "optimistic" (Riedl et al, 2013;Zhang & Chen, 2013;Jin et al, 2014;Shadkam & Bijari, 2017;Niroomand et al, 2019). For converting the fuzzy information into accurate values to enable computational procedures, this paper integrates the fuzzy theory (Zadeh, 1965;Bellman & Zadeh, 1970;Tiwari et al, 1987;Sakawa, 1993;Kumar et al, 2004) into the optimization procedures and adopts triangular fuzzy values for the uncertain variables.…”